Proceedings of the 2nd International Conference on Business and Policy Studies [1 ed.] 9789819964406, 9789819964413
This proceedings volume contains papers accepted by the 2nd International Conference on Business and Policy Studies (CON
128 28 98MB
English Pages xix; 1854 [1874] Year 2023
Contents
Crew Scheduling Problem: Integer Optimization Using Set-Covering Model
1 Introduction
2 Background and Problem Set-up
3 Constraints
3.1 Regulatory Constraints
3.2 Temporal Constraints
4 Methodology
5 Formulation
5.1 Decision Variables
5.2 Set-partitioning Problem (SPP)
5.3 Set-Covering Problem (SCP)
5.4 Equations
6 Solve the Integer Program
7 Discussion and Conclusion
Appendix A:
Appendix B:
Appendix C:
References
Research on Business Management Based on New Retail Model: The Case of Yonghui Supermarket
1 Introduction
2 Analysis on Yonghui Supermarket’s Management
2.1 Supply Chain Management of Yonghui Supermarket
2.2 Cost Management of Yonghui Supermarket
2.3 Issues on Supply Chain Management and Cost Management
3 Recommendations
3.1 Reduce Inventory Backlog
3.2 Improve Enterprise Information Management
3.3 Improve Store Operation Efficiency
4 Conclusion
References
Supply Chain Management—A Case Study of Huawei’s Supply-Chain Chip Shortage
1 Introduction
2 Definition of Concepts
2.1 Automation
2.2 Blockchain
3 Role of Huawei’s Global Supply Chain in Supporting its Competitive Advantage
3.1 Reverse Logistics
3.2 Distribution Channels of Huawei
4 Future Challenges of Huawei’s Global Supply Chain and their Implications
5 Conclusion
References
The Effect of Manufacturing Employment Changes on County-Level Partisan Voting Shares in the US Presidential Elections: An IV Analysis
1 Introduction
1.1 Research Background
1.2 Research Motivation and Aim
2 Employment Change and Political Elections in the U.S.
3 Methodology
3.1 Empirical Approach
3.2 Data and Measurement
3.3 Data Analysis Method and Classification
4 Findings and Discussions
4.1 Findings
4.2 Discussions
5 Conclusion
Appendix
References
Research on the Regulation of Internet Finance in Shanghai
1 Introduction
2 Internet Finance Regulation in Shanghai
2.1 The Development of Internet Finance in Shanghai
2.2 Regulation of Internet Finance in Shanghai
3 Shanghai Internet Finance Regulatory Model
3.1 Implementing Sectoral Regulatory Responsibility
3.2 Creating a Unified Standard Data Regulatory Repository and Building an Orderly Network of Local Financial Institutions Platform
3.3 Implementing the “Double Random, One Public” Supervision Model
3.4 Promoting the Credit System of Market Players
3.5 Relying on the Free Trade Zone to Promote the Development of Internet Finance
4 Problems with the Regulation of Internet Finance in Shanghai
4.1 Regulatory Efficiency Needs to be Improved
4.2 Inadequate Regulatory System
4.3 Inadequate Risk Control System
4.4 Information Security Cannot be Guaranteed
5 Suggestions for Optimising the Regulation of Internet Finance in Shanghai
5.1 Strengthening Network Information Security Protection
5.2 Enhancing Information Disclosure
5.3 Improving Laws and Regulations Related to Internet Finance
5.4 Establishing a Sound Risk Assessment System
6 Conclusion
References
The Empirical Analysis of ESG Index and Enterprise Investment Value Based on Different Regions
1 Introduction
2 Literature Review
3 The Impact of ESG on the Business Operation within the Company
3.1 Data Sources and Processing
3.2 Variable Formulation
3.3 Model Building
3.4 Empirical Results
4 Design of Panel Data Based on Different Regions
4.1 Data Sources and Processing
4.2 Model Building for Panel Data
4.3 Empirical Results of the Relationship Between Firm Value and ESG Score in Different Regions
5 Conclusion
References
Inflation-Related Factors Enhanced LSTM-Based Multivariate Time Series for Stock Market Forecasting
1 Introduction and Motivation
2 Related Work
3 Data Analysis
4 Methodology
4.1 LSTM
4.2 LSTM Multivariate Time Series Stock Price Forecasting Model
5 Analysis of Results
5.1 Data Construction
5.2 Evaluation Methods
5.3 Experimental Setting
5.4 Experimental Results
5.5 SSE and Single Inflation Factor
5.6 SSE and Double Inflation Factor
5.7 SSE and Triple Inflation Factor
5.8 SSE and All Inflation Factors Combination
6 Conclusion
References
How does the Reputation of Venture Capital Impact on Financial Constraints?
1 Introduction
2 Hypothesis
2.1 Sustainable Investment Model
2.2 Venture Capital Reputation and Certification
2.3 Venture Capital Reputation and Agency Cost
3 Variables Selection and Model Design
3.1 Model and Variable Description
3.2 Financing Constraints (Dependent Variable)
3.3 High Reputation Venture Capital (Independent Variable)
4 Regression and Analysis
4.1 Basic Model
4.2 Robustness: Cash-Cashflow Model
4.3 Endogeneity
4.4 Mechanism
4.5 Further Research: Venture Capital Reputation and Capital Availability
5 Conclusions and Policy Recommendations
References
Sustainability in Chinese Investment: How Chinese Investors Perceive the Benefit and Liabilities of ESG Rating of New Ventures
1 Introduction
2 Theoretical Background
2.1 Sustainable Theory
2.2 Stakeholder Theory
2.3 Category Theory
2.4 ESG Facilitate Responsible Investing Development
3 Uncertainty Within the Early-Stage Entrepreneurial Investment Process
4 Examine how ESG has Begun to Shape Investment Decisions in the Separate Context of China and SMEs
5 Research Methodology
5.1 Sampling
5.2 Interview Questionnaire
6 Findings and Analysis
6.1 Data Coding
6.2 Findings Analysis
7 Discussion
7.1 From Benefits
7.2 From Liabilities
8 Limitation and Conclusion
References
Analysis and Research on Sri Lanka’s National Bankruptcy under the Superposition of Triple Backgrounds
1 Introduction
2 Literature Review
3 First Background: Unresolved Domestic Economic Problems
3.1 Civil War and Recession
3.2 A Tale of Two Deficits
4 Second Background: Three Policies Wrongly Implemented by the Government
4.1 A Series of Tax Cuts
4.2 Speed up Money Printing
4.3 A Bold Policy: Organic Farming
5 The Third Background: COVID-19 and Russian-Ukrainian War
6 Conclusion
References
Disputes Along the Belt and Road and How to Improve the Dispute Resolution System with Diversified Mechanisms
1 Introduction
2 The Existing Dispute Resolution Mechanisms in the Belt and Road and International Law
2.1 How Disputes are Resolved in the Belt and Road
2.2 Dispute Resolution Mechanisms in International Law: Not all are Available in the BRI
3 Inadequacies in the Existing Dispute Resolution System in the Belt and Road: Two Demonstrative Case Studies
3.1 Case Studies
4 A Possible Step Forward: A “United” Dispute Resolution System
5 Conclusion
References
The Impact of the US Sanctions Against Huawei's Mobile Phone Business Development
1 Introduction
2 Historical Development of Huawei
3 Methodology
4 Results
5 Huawei Corporate Structure
6 Huawei’s Market Match Policy
7 Discussion
8 Conclusion
References
Local Government Financing in China: Fiscal Fatigue and Debt Sustainability
1 Introduction
2 Preliminary Assessment of Local Government debt Risk – Index Method
3 An Empirical Analysis of Local Government Debt Sustainability in China
3.1 Local Government Debt Ceiling Estimates
3.2 Standard Calculation of Debt Ceiling and “Warning Line”
3.3 Prospective Analysis
4 Policy Suggestion
4.1 Fiscal Revenue Dimension
4.2 Fiscal Expenditure Dimension
4.3 Debt Risk Warning
5 Conclusion
References
The Impact of Health Education on the Health Status of Migrant Workers—An Empirical Analysis Based on CMDS (2018) Data
1 Introduction
2 Literature Review
3 Data and Methods
3.1 Data Sources
3.2 Variable Selection
3.3 Model Setting
4 Empirical Analysis
5 Conclusion
References
Correlation Between GDP and Related Indicators: Comparison Between the U.S. and Germany
1 Introduction
2 Literature Review
3 Data and Methodology
3.1 Data
3.2 Methodology
4 Results
5 Discussion
6 Conclusion
References
Old Prices, Marginal Costs, Capital and Dynamic Pricing Models
1 Introduction
2 Literature Review
3 Model
3.1 Dynamic Optimization Problem
3.2 Bellman Equation
3.3 A Firm’s Dynamic Price Adjustment Problem
4 Result
5 Conclusion
References
Time Series Momentum Trading Strategy for Cryptocurrencies
1 Introduction
2 Data and Method
2.1 Cryptocurrency Data
2.2 Linear Regression Model for Prediction of Returns
2.3 Mean-variance Optimization
2.4 Backtesting
3 Results and Discussion
3.1 Regression Analysis
3.2 Strategy Performances
3.3 Limitations and Prospects
4 Conclusion
References
The Effect of ESG Disclosure on Stock Performance: Empirical Evidence from China
1 Introduction
2 Hypothesis Development
3 Data, Variables and Method
3.1 Data
3.2 Variable
4 Results
4.1 Descriptive Statistics
4.2 Empirical Results
5 Conclusion
References
A Study of Asset Allocation Based on the Markowitz Model
1 Introduction
2 Literature Review
3 Model
3.1 Expected Rate of Return on Investment
3.2 Expected Rate of Return on Investment
3.3 Expected Rate of Return on Investment
4 Data
5 Analysis of Model Results
6 Conclusion
References
CSR Communication on Social Media: Feminist Marketing and Advertising
1 Introduction
1.1 Corporate Social Responsibly on Social Media
1.2 Feminism-Related CSR
2 Hypothesis
3 Methods
3.1 Descriptive Statistics
4 Results
4.1 Hypothesis 1
4.2 Hypothesis 2
5 Conclusion
5.1 Discussion and Implications
5.2 Limits and Future Directions
References
The Research on Factors Influencing Stock Returns in the Travel Industry Under the Shock of COVID-19
1 Introduction
2 Methodology
2.1 Date Sources
2.2 Group of Representative Stocks
2.3 Variable Description
2.4 Model Building
3 Results and Discussion
3.1 Descriptive Statistics
3.2 Model Test
3.3 Results of Empirical Evidence
4 Conclusion
References
Comparison of Employment Status of Financial Service Sector Between Mainland and Hong Kong
1 Introduction
2 Methodology
2.1 Metrics
2.2 Formula Application
2.3 Data Source and Processing
3 Results and Discussion
3.1 GDP and Inflation Rate
3.2 Proportion of Different Academic Degree
3.3 Investor in Security Market
3.4 Summary Analysis
4 Conclusion
References
How Life Changes During COVID Pandemic are Mitigated Through Next Generation Economic Modalities that Leverage the Power of Platforms?
1 Introduction
1.1 Background Information
1.2 Research Question
2 Literature Review
2.1 The Platform Economy
2.2 The Sharing Economy
2.3 The Circular Economy
2.4 The Attention Economy
3 Analysis
3.1 Monetizing People’s Attention by Leveraging Online Platforms – Webcasting
3.2 Digital Platforms Enhancing Access to Public Goods – Healthcare Platforms
3.3 Sharing Platforms Mitigating Enterprises’ Financial Pressure During the Pandemic - Coworking
3.4 Circular Platforms Reconfiguring the Supply Chain - Blockchain
4 Discussion
5 Conclusion
References
Innovative Tools for Food Waste Management that Enable Higher Value Circular Economy Outputs
1 Introduction
2 Literature Review
3 Recycling Food Waste by Creating a Platform
3.1 Case Study
3.2 Functions
3.3 Purpose of Building a Platform
4 Conclusion
References
What are the Benefits of Influencer Marketing, and How can Brands Benefit from Them?
1 Introduction
2 Literature Review
2.1 Brief History of Influencer Marketing
2.2 Benefits
3 Case Studies
3.1 Adidas Review
3.2 Net Sales Growth of Adidas from 2010–2021
3.3 Adobe Review
3.4 Adobe’s Annual Revenue (2004–2021)
4 Gatorade Review
4.1 Cumulative Royalties the University of Florida Has Made from Gatorade
5 Colorkey Review
6 Discussion
7 Limitations
8 Conclusion
References
The Time-Varying Impact of the Federal Reserve Rate Hike on Bitcoin
1 Introduction
2 Research Design
2.1 Data Source
2.2 Unit Root Test
2.3 VAR Model Specification
2.4 ARMA-GARCH Model Specification
3 Empirical Results and Analysis
3.1 VAR Model Identification
3.2 Impulse and Response
3.3 ARMA Model Identification
3.4 ARMA-GARCHX Estimation Results
4 Discussion
5 Conclusion
References
The Impact of Changes in Currency Value on Technology Companies’ Yield and Volatility: A Long-Term Perspective
1 Introduction
1.1 Background
1.2 Literature Review
2 Research Design
2.1 Data Sources
2.2 Unit Root Test
2.3 VAR Model Setting
2.4 ARMA-GARCHX Model Setting
3 Results and Analysis
3.1 Decision on the Order of the VAR Models
3.2 Impulse Response
3.3 ARMA Identification
3.4 ARMA-GARCHX Estimation Results
4 Discussion
5 Conclusion
References
The Behaviour of Advertisers Within Video Platform, Along with Optimal Strategy for Platform
1 Introduction
2 Model Setting
2.1 The Behaviour of Advertisers
2.2 The Behaviour of the Platform
3 Conclusion
References
The Analysis of CHANEL’s Marketing Strategy Affects Consumer Behavior in the Pandemic Situation
1 Introduction
2 CHANEL Company
2.1 Overview
2.2 Finance Situation
3 Marketing Strategy Analysis
3.1 Social Media Values-Based Perspective
3.2 Consumer Psychology and Behavior
4 SWOT Analysis
4.1 Strengths
4.2 Weaknesses
4.3 Opportunities
4.4 Threats
5 Conclusion
References
The Impact of Real Estate Tax on Residents’ Housing Consumption —— An Empirical Study Based on British Data
1 Introduction
1.1 Background
1.2 Relevant Research
2 Construction of the Model
2.1 Different Theories on Housing Consumption
2.2 Theoretical Determination
2.3 Model Construction
3 Explore the Impact of the Real Estate Tax on Residents’ Housing Investment Decisions Through Empirical Research
3.1 Data Selection
3.2 Data Description
3.3 Empirical Research
4 Discussion
4.1 Discussion About Real Property Tax
4.2 Other Discussions
5 Conclusion
References
Study on the Influence of Stock Trading Volume of Traditional Chinese Medicine by Multiple Linear Regression
1 Introduction
2 Methodology
2.1 Data
2.2 Model Establishment and Analysis
2.3 Model Modification
3 Results and Discussion
4 Conclusion
References
Market Analysis and Strategy of Pet Industry Under Epidemic Situation Market Strategy Based on Case Analysis
1 Pet Industry Prospect and Market Analysis
1.1 Analysis of Consumer Groups
1.2 Industry Segmentation of the Pet Industry
1.3 Analysis of the Market Environment of the Epidemic
2 Case Analysis of New Market Model
3 Market Strategy
3.1 Online Business
3.2 Quality Consumption
References
Research on the Influencing Factors of Second-Hand Housing Price in Shanghai
1 Introduction
2 Methodology
2.1 Data Resources and Indicator Selection
2.2 Selected Model
3 Results and Discussion
3.1 Descriptive Statistics
3.2 Test for Multicollinearity
3.3 Goodness-of-Fit and Autocorrelation Tests
3.4 Model Data Analysis and Interpretation
4 Results and Discussion
References
The Impact of Social Media Marketing Strategy on Behavior Online Shopping: Case of TikTok Study
1 Introduction
2 Method
2.1 Study Population
2.2 Questionnaire Design
3 Analyze the Influence of TikTok on Consumers from the Gender Level
4 Analyze the Influence of TikTok on Consumers from the Age Level
5 Analysis of the Advantages and Disadvantages of TikTok Live for Consumer
6 Conclusion
References
Did China’s Stock Market Benefit from USD Appreciation: An Empirical Evidence
1 Introduction
2 Literature Review
3 Research Design
3.1 Data Source
3.2 ADF Test
3.3 VAR Model
3.4 ARMA-GARCHX Model
4 Empirical Result and Analysis
4.1 Order Estimation for VAR Model
4.2 Impulse Responses
4.3 Estimation Results for ARMA-GARCHX
5 Discussion
6 Conclusion
References
Yield and Volatility of PinDuoDuo in a Long-Term Uncertain Situation: The Covid-19 Pandemic
1 Introduction
2 Literature Review
2.1 The Impact of Covid-19 on the Stock Market
2.2 E-commerce and Covid-19
2.3 The Development of Pinduoduo Under the Covid-19
2.4 Review
3 Research Design
3.1 Data Source
3.2 Unit Root Test
3.3 VAR Model
3.4 ARMA-GARCH Model
4 Empirical Result
4.1 VAR Model Result
4.2 ARMA-GARCH Model Result
5 Discussion
6 Conclusion
References
Research on the Influencing Factors Affecting Beijing House Prices Using Linear Regression Model
1 Introduction
2 Method
2.1 Data Resource Description
2.2 Variable Selection and Data Correlation Description
2.3 Model Description
3 Results and Discussion
3.1 The Selected Model
3.2 Comparison of the Selected Model and Full Model
3.3 Comparison of the Selected Model with the Log Transformation Model
3.4 Model Fitness Checking
3.5 Assumptions and Checking
4 Conclusion
References
Research on the Investment Value of Stocks of the CITIC Securities Company
1 Introduction
2 Methodology
2.1 Data Source and Indicators
2.2 Method Description
2.3 Data Pre-processing
3 Results and Discussion
3.1 Descriptive Analysis
3.2 Factor Analysis
3.3 Analysis of Correlation
4 Conclusion
References
Research on the China’s Banking Industry Based on the CAPM Model – Take Ping an Bank of China as an Example
1 Introduction
2 Methodology
2.1 Theoretical Basis
2.2 Build Statistical Models
3 Results and Discussion
3.1 Sample Selection of Ping An Bank of China
3.2 Construction of CAPM Model of Ping an Bank of China
3.3 Parameter Estimation and Regression Analysis.
4 Conclusion
References
In the Sharing Economy Modality: Airbnb’s Failure in China
1 Introduction
2 Literature Review
2.1 Sharing Economy and Airbnb
2.2 AIrbnb’s Impact on Society
2.3 Airbnb in China
3 Methodology
3.1 Research Questions
3.2 Research Design
4 Analyzing Reasons for Airbnb’s Failure in China
4.1 Cultural Difference
4.2 Advertisement
4.3 Price
4.4 Limited Services
5 Conclusion
References
The Time-Varying Impact of the Fed’s Rate Hike on Japan’s Airline Industry
1 Instruction
2 Data Structure and Model Selection
2.1 Data Structure
2.2 VAR Model
2.3 ARMA Model
2.4 ADF Unit Root Test
2.5 The Establishment of the VAR Model
2.6 The Establishment of the ARMA-GARCHX Model
2.7 The GARCHX Model
3 Empirical Results and Analysis
3.1 VAR Order
3.2 ARMA Model Ordering
3.3 Impulse and Response
3.4 ARMA-GARCH Estimation Results
4 Conclusion
References
Large Supermarket Chain Under This Turn’s Interest Rate Policy: Gain or Lose
1 Introduction
2 Literature Review
2.1 The Reasons of Fed Raising Interest Rates
2.2 Market Focus on Fed Rate Hike
2.3 The Impact of Exchange Rate Changes on the Economy
2.4 Review
3 Methodology
3.1 The Source of Data
3.2 Unit Root Test
3.3 VAR Model Setting
3.4 ARMA-GARCHX Model Setting
4 Empirical Results and Analysis
4.1 Order Determination of VAR Model
4.2 Impulse Response
4.3 Order Determination of ARMA Model
4.4 Estimation Results of ARMA-GARCHX Model
5 Discussion
6 Conclusion
References
The Time-Varying Impact of Normalized Covid-19 Pandemic on BYD Stock Price
1 Introduction
1.1 General Content of the Article
2 Literature Review
2.1 The Influence of COVID-19 that Brought to China’s Automobile Industry
2.2 The State of BYD Cars in Recent Years
2.3 Literature Summary
3 Research Design
3.1 Data Source
3.2 Unit Root Test
3.3 VAR Model Specification
3.4 ARMA-GARCH Model Specification
4 Empirical Results and Analysis
4.1 VAR Model Order Determination
4.2 Stability Test of VAR Model
4.3 VAR Model Impulse Response Diagram
4.4 PACF and ACF
4.5 ARMA-GARCH Model Specification
5 Discussion
6 Conclusion
References
A Comparative Study of Shadow Banking Financial Supervision Between China and the United States
1 Introduction
2 The Concept of Shadow Banking
2.1 Causes of Shadow Banking
2.2 The Development of Shadow Banking
3 Financial Supervision of Shadow Banks in China and America
3.1 The Supervision Process of Shadow Banking in China
3.2 The Supervision Status of Shadow Banking in China
3.3 The Supervision Process of American Shadow Banks
3.4 The Supervision Status of Shadow Banking in the United States
4 Comparison of Financial Supervision on Shadow Banking System between China and America
4.1 Regulatory Commonalities
4.2 Differences in Regulation
4.3 Problems in China’s Shadow Banking Supervision
4.4 Suggestions for Improving China’s Shadow Banking Supervision System
5 Conclusion
References
Marketing Channel Innovation in the Beauty Industry in the Post-Epidemic Era - Estee Lauder Brand as an Example
1 Introduction
2 The Challenger of the COVID-19 for the Beauty Industry
2.1 Lower Consumer Appetite and Shifting Demand for Beauty
2.2 Live Streaming Becomes Popular as Offline Goes Online
3 Adaptation of Marketing Tools Under the Epidemic: An Analysis of Estee Lauder as an Example
4 Upgrading Marketing Channels in the Post-Epidemic Era
4.1 Building the Online Marketing Matrix
4.2 Online Channels are Linked at Multiple Points to Form a Closed Loop
4.3 Channel Model
5 Conclusion
References
Analysis on Siemens Online Marketing Strategies - Taking Siemens Advertisement of Dishwasher as an Example
1 Introduction
2 Introduction to the Siemens Advertisement of Dishwasher
3 Marketing Strategies Used in the Siemens Advertisement of Dishwasher
3.1 Source
3.2 Message Content
3.3 Inoculation
3.4 Middle-Aged Recipient
3.5 Channel - TVC
3.6 The Superimposed Voice of Celebrity Celebrities
4 Discussion of the Effect of Siemens Advertisement of Dishwasher
5 Conclusion
References
Gain or Loss from Fed’s Interest Rate Policy: Evidence from Disney
1 Introduction
2 Literature Review
3 Research Design
3.1 Data Source
3.2 ADF Test
3.3 VAR Model
3.4 ARMA-GARCH-X Model
4 Empirical Results and Analysis
4.1 VAR
4.2 Analysis
4.3 ARMA
4.4 ARMA-GARCH
5 Discussion
6 Conclusion
References
Research on the Influence Mechanism of Consumer Habits and Psychological Price Level on Advertising Effects: Based on Close-Up and Group Images
1 Introduction
2 Literature Review
3 Study Design
4 Data Analysis
4.1 Data Analysis of Experimental Group A and Control Group B
4.2 Data Analysis of Experimental Group C and Control Group D
5 Empirical Analysis
6 Meanings and Recommendations
7 Conclusion
References
Ownership Structure and Corporate Performance Evidence from China's Stock Market
1 Introduction
2 Literature Review
3 Data and Methods
3.1 Sample
3.2 Variables and Model Specification
4 Examining the Relation of Ownership Structure and Performance
4.1 Correlations
4.2 Regression Results and Analysis
5 Conclusions
References
Factor Analysis and PEST Analysis on Health Care Industry with 15 Stock Samples’ Evaluation
1 Introduction
2 Data and Methodology
2.1 Data
2.2 Methodology
3 Results
3.1 Results of KMO and BArtlett’s Test
3.2 Results of Factor Analysis
4 Discussion
4.1 Suggestion
4.2 Limitation
5 Conclusion
References
The Research of Three Parties’ Game Influenced by IWOM in Evolutionary Game Theory-Taking “Ice Cream Assassin” as an Example
1 Introduction
2 Evolutionary Model Assumptions
3 Results and Discussion
3.1 Constructing the Return Expectation Function
3.2 Replicate Dynamic Equation Solution
3.3 Constructing the Jacobian Matrix
3.4 Stability Analysis of Equilibrium Point
4 Numerical Simulation Analysis
4.1 Effect of the Initial Probability of the Three Parties’ Analysis
4.2 The Influence of Online Word-of-Mouth Factors Analysis
5 Conclusion
References
Empirical Analysis of Liquidity Risk of Chinese Listed Commercial Banks
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
Analysis on the Influence of Various Elements on the Net Profit of a Steel Group in Southwest China and the Prediction Based on Arima Model
1 Introduction
2 Research Methods
2.1 Data Sample
2.2 Variable Description
3 Results
3.1 Coefficient of Variation Method
3.2 Arima Time Series Forecasting
4 Discussion
5 Conclusions
References
Are There Any Winners on Both Sides of the Trade Conflict: Evidence from China and U.S.
1 Introduction
2 Research Design
2.1 Data Sources
2.2 Unit Root Test
2.3 VAR Model Identification
2.4 ARMA-GARCH-X Model Specification
3 Empirical Results
3.1 VAR Model Estimation Results
3.2 ARMA-GARCH Model Result
4 Discussion
5 Conclusion
References
Investment Strategy in a Down Market: Application of Market Neutral Strategy in Energy, Utilities and Technology Sector
1 Introduction
2 Method
2.1 Market Neutral Strategy
2.2 Stock Selection
2.3 Benchmarks
3 Results
4 Conclusion
References
Factors Analysis on Affecting the Sales Volume of K-Pack in Smartfood
1 Introduction
2 SWOT Analysis of Smartfood Company
2.1 Overview of Smartfood Company
2.2 SWOT Analysis
3 Regression and Results
3.1 Data and Variables
3.2 Results of OLS Model
4 Discussion
5 Conclusions
References
Resignation of Board of Directors Secretaries in Their Tenures and Business Violations
1 Introduction
2 Methodology
2.1 Sample Selection and Data Sources
2.2 The Number of Violations of Listed Companies
2.3 Trend Chart of the Number of Board Secretaries Leaving
3 Results and Discussion
3.1 Specification of Model
3.2 Further Analysis
4 Conclusion
References
Research on the Influencing Factors of Marriage Rate Among Young People in China
1 Introduction
2 Methodology
2.1 Data Sources
2.2 Variable Description
3 Results and Discussion
4 Conclusion
References
Constrained Portfolio Optimization: A Comparison of Markowitz Model and Single Index Model
1 Introduction
2 Problem Formulation
3 Data Collection and Analysis
4 Analysis on Different Constraints in MM and IM
5 Result Analysis
6 Conclusion
References
Tesla Stock Price Timeseries Analysis and Forecasting
1 Introduction
2 Data Preprocessing
3 Arima Model Introduction and Modeling Process
3.1 ARMA Model
4 ARMA Model Identification and Estimation
4.1 Introduction of Modeling Process
5 Empirical Analysis of Historical Closing Price of Tesla
5.1 Data Sources
5.2 Empirical Analysis
6 Model Test
6.1 Model Forecast
7 Conclusion
References
The Application of CAPM and Fama-French Three-Factor Model to the Investment Choice in Individual Stocks in China’s A-share Market and the Explanatory Power of Returns
1 Introduction
2 Literature Review
3 Model
3.1 CAPM
3.2 Fama-French Three-Factor Model
4 Empirical Result
4.1 Data Selection
4.2 Analysis of Regression Results
5 Conclusion
References
Optimal Capital Structure of China’s Small and Medium Listed Companies: A Case of RC Ltd.
1 Introduction
2 Capital Structure Theory
2.1 Studies in the West
2.2 Studies in China
3 A Case Study on the RC Ltd.
3.1 Company Profile and Financial Status
3.2 Status of Capital Structure
3.3 Costs of Capital of RC Ltd. and Peer Companies
3.4 Qualitative Analysis of Capital Structure
3.5 Discussion on Debt Financing Ratio in Optimal Capital Structure
4 Conclusion
References
The Main Challenge Faced by the Migrant Population and Recommended Policies: A Case of Hangzhou
1 Introduction
2 Literature Review
3 Methodology
4 Results
5 Conclusion
References
Research on United States Core CPI Forecast Based on Exponential Smoothing and ARIMA Model
1 Introduction
1.1 Research Background
1.2 Literature Review
1.3 Research Motivation
2 Methodology
2.1 Data Sources
2.2 ARIMA
3 Empirical Results Analysis
4 Conclusion
References
The Differences Between Longping High-Tech’s Company Value and Stock Prices of Agricultural and Forestry Industry Based on Factor Analysis and Structural Equation Model
1 Introduction
2 Literature Review
2.1 The Value of a Company
2.2 Stock Price of the Industry
3 Methodology
3.1 Research Hypotheses
3.2 Research Model
3.3 Research Design
3.4 Selection of Variables
3.5 Data Collection
3.6 Factor Analysis
4 Empirical Analysis and Result Analysis
4.1 Factor Analysis Results
4.2 AMOS Analysis
4.3 Fitting Degree Analysis (AMOS)
5 Conclusion
References
Analysis of the Impact of Corporate Responsibility on the Economic Interests of Educational Institutions Under the Impact of COVID-19
1 Introduction
2 Literature Review
3 Model Building
3.1 Basic Assumptions
3.2 Brief Introduction of the Model
3.3 Data Sources
4 Empirical Analysis
4.1 Variable Declaration
4.2 Descriptive Statistics
4.3 Correlation Analysis
4.4 Regression Analysis
4.5 Robustness Test
5 Conclusions
References
International Capital Flows and Dynamic Changes in Cryptocurrency Market
1 Introduction
2 Literature Review
3 Research Design
3.1 Data Sources
3.2 Unit Root Test
3.3 Vector-Autoregression
3.4 ARMA-GARCHX
4 Empirical Findings and Analysis
4.1 VAR Model Identification
4.2 Impulse Response
4.3 ARMA-GARCHX
5 Conclusion
References
Research on the Influencing Factors of Stock Return Based on Factor Analysis and OLS Regression Analysis
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Description
3.2 Pre-Data Analysis
4 Results and Discussion
4.1 Factor Analysis
4.2 OLS Regression Analysis
5 Conclusion
References
Social Media Peer Communication and Impacts on Purchase Intentions
1 Introduction
2 Literature Review
3 Methods
3.1 Research Design and Method
3.2 Study Participants
3.3 Data Collection Methods
3.4 Data Analysis
4 Results
5 Discussion
6 Conclusion
References
Research on Transparent and Opaque Packaging of Nearly Expired Food
1 Introduction
2 Literature Review
3 Method
4 Experiment Design
5 Experiment Result
6 Conclusion
Appendix
References
Research on the Impact of Investor Sentiment on the Cost of Equity Financing
1 Introduction
2 Methodology
2.1 Sample
2.2 Model Setting
2.3 Variables
2.4 Size
2.5 Leverage
2.6 ROE
2.7 Tobin Q
3 Results and Analysis
3.1 Summary Statistics
3.2 Correlation Coefficient
3.3 Test for Multicollinearity
3.4 The Model Results
3.5 Test of Model 1
3.6 Interpretation of the Results of Model 1
3.7 Test of Endogeneity Model and Results
3.8 Test for Robustness
4 Conclusion and Suggestions
4.1 Conclusion
4.2 Suggestion
References
Examining the Correlation for the US S&P 500 and Its Corresponding Futures from a Data-Driven Perspective
1 Introduction
2 Data Collection
3 Methods
4 Results
4.1 ADF Test
4.2 VAR Model Construction
4.3 Variance Decomposition Analysis
5 Discussion
6 Conclusion
References
Analysis of the Current Situation, Problems, and Countermeasures in the Development of China’s Agricultural Futures Market
1 Introduction
2 Current Status
3 Brief Review on the Agricultural Market
4 Problems
4.1 Lack of Multi-level Risk Management System
4.2 The Spot Market Is Not Without Flaws
4.3 The Overall Trading Size of Agricultural Futures is Stagnating
4.4 Few Species of Agricultural Products Futures Trading
5 Solution
5.1 “Order + Insurance + Futures” Model
5.2 Traffic Will Open up Markets with Imperfect Infrastructure
5.3 Increase Promotional Efforts to Make the Agricultural Futures Market More Appealing
5.4 Improve the Listing and Delisting System
6 Conclusion
References
BSM Model Application for the Southern Copper Corporation
1 Introduction
2 Data
3 Method
4 Result
5 Discussion
6 Conclusion
References
Value Investment: A Case Study for Technology Companies (Meta vs Microsoft)
1 Introduction
2 Data
3 Method
4 Results
5 Discussion
6 Conclusion
References
Comparing the S&P 500 Index’s and the NASDAQ Index’s Influence on Pfizer’s Stock Returns
1 Introduction
2 Firm Description
3 Regression Analysis
3.1 Introduction of CAPM
3.2 Regression Analysis
4 Discussion
5 Conclusion
References
Compare Nasdaq Index and S&P 500 Index on Exxonmobil Stock Returns
1 Introduction
2 Firm Description
2.1 EBIT Margin (the Data from 6/29/2021 to Now, by Quarterly)
2.2 Profit Margin (the Data from 6/29/2021 to Now, by Quarterly)
2.3 Operating Margin (the Data from 6/29/2021 to Now, by Quarterly)
2.4 Total Debt and Net Debt (the Data from 6/29/2021 to Now, by Quarterly)
3 Analysis of Regression
3.1 About CAPM
3.2 Analysis of Regression
4 Discussion
4.1 CAPM’s Advantage
4.2 CAPM’s Disadvantage
5 Conclusion
References
Applicable Conditions and Criteria for Material Adverse Effect Clause in International M&A Transactions
1 Introduction
2 Vagueness in the Criteria for Judging the Extent of Decline in Performance
2.1 Jurisprudence
2.2 Unsolved Problems in Determining the Decline of Corporate Income
2.3 Potential Solutions
3 Vagueness in the Criteria for Determining the Decline Duration
3.1 Jurisprudence
3.2 Unsolved Problems in Determining the Decline Duration
3.3 Potential Solution
4 Vagueness in the Scope of the Acquirer’s Knowledge
4.1 Jurisprudence
4.2 Unsolved Problems in Clarifying the Scope of the Acquirer’s Knowledge
4.3 Potential Solutions
5 Conclusion
References
Stock Price Prediction in the Context of Time-Series and Multiple Factorial Regression for Medical Industry
1 Introduction and Literature Review
1.1 Introduction
1.2 Literature Review
2 Data and Method
2.1 Data
2.2 Model
2.3 Procedure
3 Results and Discussion
3.1 Time-Series Model
3.2 Multifactorial Model Correlation and Regression Analysis
3.3 Comparison and Application
4 Limitation and Prospects
5 Conclusion
References
A Empirical Research on the Relationship Between Appearance and Income
1 Introduction
2 Literature Review
3 Data Structure and Methodology
3.1 Data Source
3.2 Summary Statistics for Key Variables
4 The Empirical Strategies and Results
4.1 Methodology and the Empirical Strategy
4.2 Does a Relationship Exist Between Wage or Income and Appearance
4.3 Are Physically Attractive Employees Concentrated in Higher-Paying Positions?
4.4 Would Physically Attractive Employees Have Higher Likelihood in Promotion?
4.5 Would Physically Attractive Employees Have Higher Human Capital?
4.6 Does the Special Treatment of Attractive Employees Still Exist After Controlling These Variables?
5 Robustness Test
6 Conclusion
References
Option Mispricing and Maturity Date: Evidence from China
1 Introduction
2 Data and Variables
3 Empirical Test
4 Conclusion
References
Applicability of Fama-French Six-Factor Improved Model to Explain Stock Returns in Chinese Rare Metal Industry
1 Introduction
2 Methodology
2.1 Data Source
2.2 Stocks Grouping
2.3 Factors Construction
3 Results and Discussion
3.1 Factor Analysis
3.2 Factors Analysis
4 Conclusion
References
Impact of COVID-19 on China’s Real Estate Industry - An Empirical Study Based on the Stock Market
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
Prediction and Analysis of Commodity House Price Based on ARIMA Model
1 Introduction
2 Method
3 Results and Discussion
3.1 Test of Stationarity
3.2 Perform Differential Operation
3.3 Test of the White Noise
3.4 Determine the Orders P and Q
3.5 Evaluation of the Model and Prediction of the Time Series
4 Conclusion
References
The Prospect of Chinese Internet Companies’ Strategy Investment
1 Introduction
1.1 Background
1.2 Related Research
1.3 Objective
2 Description of Statistics
2.1 Domestic Industry Circumstance
2.2 Features of Chinese Internet Companies’ Strategy Investment
3 Investment Analysis
3.1 Investment Numbers
3.2 Investment Fields
3.3 Investment Changes
4 Discussion
4.1 Policy Intervention by Government
4.2 Main Products and Strategic Expansion
4.3 Prospects and Suggestions
5 Conclusion
References
Rough Volatility
1 Introduction
2 Volatility Modeling
3 The Hurst Index
4 Option Price Dealing and Microstructure Related to the Roughness of Volatility
5 Smoothness of the Log-Volatility Process
5.1 Volatility Smoothness
5.2 Specification of the RFSV Model
5.3 Spurious Long-Term Volatility
5.4 Forecasting Log-Volatility Using the RFSV Model
6 Conclusion
References
Export Effect or International Capital Flows: Evidence From US Chip Industry
1 Introduction
2 Research Design
2.1 Data Source
2.2 Stock Return
2.3 Unit Root Test
2.4 VAR Model Specification
2.5 ARMA-GARCHX Model Specification
3 Empirical Results
3.1 VAR Order Selection
3.2 Impulse Response Result
3.3 ARMA-GARCHX Order Selection
3.4 ARMA-GARCHX Model Result
4 Conclusion
References
Analysis on the Influencing Factors of Profitability in China Minsheng Bank's
1 Introduction
2 Theoretical Analysis on the Profitability of Commercial Banks
2.1 The Concept of Commercial Bank Profitability
2.2 Theoretical Analysis of Factors Affecting the Profitability of Commercial Banks
3 Data, Variables and Method
3.1 Data Sources
3.2 Variables
3.3 Model
4 Results
4.1 Descriptive Statistics
4.2 Empirical Results
5 Discussion
5.1 Asset Quality and Profitability of Minsheng Bank
5.2 Analysis of the Relationship Between Capital Adequacy Ratio and Minsheng Bank’s Profitability
5.3 Analysis of the Relationship Between Liquidity and Profitability of Minsheng Bank
5.4 Analysis of the Relationship Between Operational Efficiency and Minsheng Bank’s Profitability
5.5 Analysis of the Relationship Between GDP Growth Rate and Minsheng Bank’s Profitability
6 Conclusion
6.1 A Subsection Sample
References
Stock Market Forecasting Using the ARIMA, GARCH and Random Forest Model During The Russia–Ukraine War
1 Introduction
2 Data and Research Method
2.1 Data
2.2 Methodology
3 Results
3.1 ARIMA Analysis
3.2 Results for GARCH Model
3.3 Results for Random Forest Model
4 Conclusions
References
Forecasts and Relationships for German and Russian Stock Indices
1 Introduction
2 Data and Methodology
2.1 Data Processing
2.2 Stationary of the Time Series
2.3 Weighted Moving Average Method (WMA)
2.4 Exponential Smoothing Model (ESM)
2.5 Short-Term Relationship
2.6 Long-Term Relationship
3 Empirical Results
3.1 Forecast Results
3.2 Comparison of Forecast Results
4 Long-Term and Short-term Relationship Between DAX and RTSI
4.1 Short Term Relationship
4.2 Long-Term Relationship
5 Conclusion
References
Comparison of Time-Series Forecasting Models in Predicting Stock Prices of Insurance Company
1 Introduction
2 Methodology and Data
2.1 Data
2.2 Methodology
3 Empirical Result and Analysis
3.1 Preliminary Test
3.2 Comparison of Forecast Error of Different Methods
4 Conclusion
References
Research on the Birth Inhibition Effect of House Prices in China-A Tianjin-Beijing-Hebei Regional Case Study
1 Introduction
2 Methodology
2.1 Data Sources
2.2 Model Setup
3 Results and Discussion
3.1 Data Analysis
3.2 Stationarity Test of Time Series
3.3 Parameter Estimation of Model
3.4 Summary Analysis
4 Conclusion
References
Game Theoretical Analysis of the Behavioral Strategies of the Chinese Government and Producers Under a Policy of Reward and Punishment Mechanism
1 Introduction
2 Method
2.1 Model 1
2.2 Model 2
3 Discussion
3.1 Model 1
3.2 Model 2
4 Conclusion
References
Anchoring Effect in the Market: Perspective from Market Interaction and Stock Investment
1 Introduction
1.1 Research Background and Significance
1.2 Research Content
2 Literature Review
2.1 Making Forecasts or Perceiving the Present Prices
2.2 Making Estimations or Computations
3 Discussion
3.1 Market Interactions
3.2 Stock Investment
4 Conclusion
References
The Relationship between Real E-commerce Platform Reviews and Stocks Price Change Based on Panel Regression Model
1 Introduction
2 Literature Review
3 Hypothesis Development
4 Research Design
4.1 Data Description
4.2 Variables
4.3 Regression Model
5 Results
5.1 Correlation Coefficient Test
5.2 Ordinary Least Square (OLS) Model
5.3 Fixed Effect Model (FE)
5.4 Random Effect Model (RE)
5.5 Hausman Test
5.6 Lagrange Multiplier Test
6 Conclusion
References
Basic Analysis of Hog Futures Market in China
1 Introduction
2 Chinese Hog Futures Market
2.1 Definition
2.2 Current Situation
2.3 Key Features
3 Factors that Affect the Price of Live Hog Futures
3.1 Growth and Supply Cycle of Pigs
3.2 Feed of Live Hogs
3.3 Disease
4 The Impact of Hog Futures
4.1 How the Hog Future Stabilizes the Hog Price
4.2 Why We Need the Hog Future to Stabilize the Hog Price
5 Prospect
5.1 Advantages and Challenges
5.2 The Development of Government Policy
6 Conclusion
References
Bank Customer Churn Prediction Based on Correlation Analysis and Multiple Linear Regression
1 Introduction
2 Data and Method
3 Results and Discussion
4 Limitation and Prospects
5 Conclusion
References
Impact of the Russian-Ukrainian War on the Global Non-ferrous Metals Market
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
A Statistical Study of Diabetes Prevalence and Poverty Rates in the United States using Linear Regression Methods
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
The Role of Derivatives in Risk Management After the Financial Crisis of 2008
1 Introduction
2 The Development of the Financial Derivative Market and Instruments
2.1 The Development of the Financial Derivative Market
2.2 The Development of Financial Derivate Instruments
3 Excessive Issuance of Financial Derivatives Would Lead to an Economic Crisis
4 The Role of Credit Derivatives in the Financial Crisis
5 Seven Frictions of the Securitization Process
6 Technical and Empirical Analysis
7 Conclusion
References
The Impact of the Covid-19 Pandemic on the Technology Sector of US Stock Market Using Time Series Model
1 Introduction
2 Methodology
2.1 Time Series Models
2.2 Accuracy Test
2.3 Data
2.4 Research Question and Hypothesis
3 Result
3.1 Descriptive Statistics
3.2 Raw Data Visualization
3.3 Time Series Forecasting
3.4 Accuracy Tests
3.5 Forecasting Using ARIMA Model
4 Discussion
5 Conclusion
References
The Impact of Fintech on Stock Price Crash: Evidence from China
1 Introduction
2 Literature Review
2.1 The Factors Influencing the Stock Price Crash Risk
2.2 The Meaning of the Digital Inclusive Finance Index
2.3 Prior Studies Show the Possible Conduction Mechanism
3 Empirical Chapter
3.1 Methodology
3.2 Data
3.3 Empirical Results
4 Conclusion
References
Apple’s Strategic Analysis and Cash Flow Forecast
1 The Profile of Apple and the Analysis of It
1.1 A Brief Introduction to Apple and Its Main Business
1.2 The Framework of Analysis
1.3 The Purpose and Significance of Research
2 Strategic Analysis
2.1 Analysis of the Data of Apple Annual Report in the Past and the Reason for Speculation
2.2 Strategy Speculation and Analysis
3 The Development Analysis of Apple
3.1 Analysis and Forecast in Apple’s Prospective Value
3.2 The Summary of the Influencing Factors
4 The Conclusion
References
When Companies Do Good: The Relationship Between Firm Size and Corporate Social Responsibility—An Empirical Study in Chinese Market
1 Introduction
2 Literature Review and the Hypotheses
2.1 Corporate Life Cycle
2.2 The Theory of Legality
3 Data and Methods
4 Results
5 Discussion
5.1 Crowding-Out Effects
6 Practical Implications, Limitations and Suggestions for Further Research
References
The Financial World After the Continuous Raise of the Federal Fund Rate-Subsequent Influence of the Increase of Interest Rate on the Financial Industry
1 Introduction
2 Literature Review
3 Methodology
3.1 S&P 500 Market Performance
3.2 The Change of the Interest Rate and Risk-Free Rate
3.3 The Influence of the Raise of Interest Rate on the Stocks of Five Outstanding Financial Companies
4 Discussion
5 Conclusion
References
Analysis of the Impact of the Trade War Between China and the United States on the Economy of Shandong Province and Countermeasures
1 Introduction
2 Literature Review
2.1 Impact on National Economic Development
2.2 Impact on Regional Economic Development
3 Analysis of the Economic Development of Shandong Province
3.1 Total GDP and Growth Rate, Industrial Structure
3.2 Open Economy in Shandong Province
4 The Impact of the China-US Trade War on the Economic Development of Shandong Province
4.1 The Impact on the Import and Export of Shandong Province
4.2 Impact on Industrial Development in Shandong Province
4.3 Impact on the Lives of People in Shandong Province
5 Policy Recommendations
6 Conclusion
References
Planning and Development of Smart Cities Based on the Concept of Sustainable Development - The Case of Shanghai
1 Introduction
1.1 Research Background
1.2 Research Significance
2 Literature Review
3 The Importance of Building Smart Cities in a Sustainable Development Perspective
3.1 Smart Cities Inspire Urban Innovation
3.2 Integrating Sustainable Smart Cities
4 The Present State of Smart City Construction in Shanghai and the Challenges Encountered
4.1 Shanghai Smart City Construction Effectiveness
4.2 Problems Facing the Construction of a Smart City in Shanghai
5 Suggestions Regarding the Development of a Smart City in Shanghai
5.1 Strengthening Smart Services and Advocacy
5.2 Optimizing the Distribution of Talents by Merging Technological Innovation and Industrial Development
5.3 Enhancing Legal Protection for Information Resource Sharing and Information Security
5.4 Establishing a Regulatory Body and Evaluation Mechanism for Smart City Construction
6 Conclusion
6.1 Research Findings
6.2 Future Outlook
References
The Road of Transformation of Resource-Based Cities
1 Introduction
2 Current Situation
2.1 City Overview
2.2 Background
3 Transformation
3.1 Improve Environmental Quality
3.2 Optimize Industrial Structure
3.3 Continuously Improve People’s Livelihood
4 Experience
4.1 Transform at the Right Time
4.2 Developing Economy According to Local Conditions
4.3 In Line with the Overall Trend of Development
5 Conclusion
References
Measuring COVID-19’s Shock to Economy: Evidence from Japan’s Inflation Rate
1 Introduction
2 Background
3 Data and Summary Statistics
3.1 Data Availability
3.2 Summary Statistics
4 Empirical Strategy
4.1 Event Study Approach
4.2 Heterogeneity
5 Empirical Findings
5.1 Main Results
5.2 Results of Heterogeneous Test
6 Conclusion
References
A Test of EMH Using Euro-Dollar Exchange Rate Fluctuations
1 Introduction
2 Literature Review
2.1 Random Walk
2.2 Cointegration Test
2.3 Market Volatility
3 Methodology
3.1 Three Forms of Efficient Market
3.2 Application of EMH in Foreign Exchange Market
4 Empirical Analysis
4.1 Data Sources
4.2 Test Process
4.3 Empirical Analysis
5 Conclusion
References
Wealth Management Product in China
1 Introduction
2 Literature Review
3 What Are WMPs?
3.1 Categories of WMPs
4 Why Are WMPs Important?
4.1 SMEs, the Supplier of WMPs
4.2 Asset Management in China
4.3 Consumer Base for Financial Products is Big
4.4 Financial Literacy of Chinese Investors
5 Risks
5.1 Regulatory Arbitrage
5.2 Maturity Mismatch
5.3 Default Risk
6 Government Guidance and Policy
7 Future Outlook
8 Conclusion
References
Forecasting the China Stock Prices During the Covid-19 Pandemic
1 Introduction
2 Literature Review
2.1 Stock Price Prediction Models
2.2 Sentiment Analysis Models
3 Data
4 Methodology
4.1 Sentiment Analysis of Covid-19
4.2 Random Forest Prediction Model
4.3 Result
5 Conclusion
References
Barrier Option Pricing for Discrete Market Hours
1 Introduction
2 Background
3 Methodology
3.1 Research Purpose
3.2 Hypothesis
3.3 Parameters
3.4 Program Setting
4 Result
4.1 Simulated Price
4.2 Risk Premium
5 Discussion
5.1 Major Findings
5.2 Contribution
5.3 Limitations and Suggestions
6 Conclusion
References
The Determining Factors of Inequality Across Space in China Cities, Rural Areas, and Suburbs (From the 1980s Till Now)
1 Introduction
2 Literature Review
3 Hypotheses and Factors
3.1 Factors that May Contribute to Economic Inequality
3.2 Urban-Rural Inequality
4 Methodology and Data Description
4.1 Methodology
4.2 Data Description
4.3 Results
5 Conclusion
References
The Economic Linkage Between China and US: An Empirical Evidence from CPI
1 Introduction
2 Literature Review
2.1 Study of US-China Trade, and Global Supply Chains
2.2 Studies on the Interaction of Inflation Between Countries Before the Financial Crisis in 2008
2.3 Review
3 Research Design
3.1 Data Source
3.2 Unit Root Test
3.3 Vector Autoregression (VAR) Model
3.4 ARMA-GARCH Model
4 Empirical Results and Analysis
4.1 Order of VAR Model
4.2 Impulse Response
4.3 PACF and ACF
4.4 ARMA-GARCH Estimation Results and Variance Equation
5 Discussion
6 Conclusion
References
Study on the Impact of Agricultural Mechanization on the Income of Rural Residents in China Based on Provincial Panel Data
1 Introduction
1.1 Background
1.2 Literature Review
2 Data Selection
2.1 Region Selection
2.2 Time Selection
3 Variable Selection
3.1 Explained Variable: Per Capita Disposable Income of Rural Residents (PCDI of Rural Residents)
3.2 Independent Variable: Per Person Agricultural Machinery Power
4 Preliminary Analysis
5 Model Establishment
5.1 Main Symbols and Explanations
5.2 Three Types of Panel Data Model
5.3 F-test
5.4 Hausman Test
5.5 Results and Analysis
6 Model Improvement
6.1 Improvement Direction
6.2 Improved Model Results
6.3 Analysis of Improved Model
7 Conclusion
References
The Impact of Financial Crisis on Real Estate Enterprises: An Empirical Analysis
1 Introduction
2 Data and Method
2.1 Data Source
2.2 Variable Selection
2.3 Descriptive Statistics
3 Empirical Analysis
3.1 Model Building
3.2 Basic Regression
3.3 Parallel Trend Test
3.4 ADF
4 Limitations and Prospects
5 Conclusion
References
The Debt Crisis in China’s Real Estate Industry: Evidence from Evergrande
1 Introduction
2 The Process of Evergrande Debt Crisis
3 Cause Analysis of Evergrande Group Debt Crisis
3.1 External Causes
3.2 Internal Causes
4 Suggestions
4.1 Invest Rationally and Control Financial Risks
4.2 Establish a Risk Early Warning System
4.3 Optimize the Capital Structure and Expand Financing Channels
5 Deficiencies and Prospects
6 Conclusion
References
Analysis on Value Investment in REIT Industry
1 Introduction
2 Data
2.1 Data Selection
2.2 Sector and Company
3 Method
3.1 Value
3.2 Profitability
3.3 Payout
3.4 Growth
4 Results
4.1 Value
4.2 Profitability
4.3 Payout
4.4 Growth
5 Discussion
6 Conclusion
References
Value Investment in Real Estate Industry
1 Introduction
2 Data Description
3 Method
3.1 Valuation
3.2 Growth
3.3 Profitability
3.4 Payout
4 Results
4.1 Valuation
4.2 Growth
5 Discussion
6 Conclusion
References
Analysis of Shared Bicycle’s Business Model and Development
1 Introduction
2 Problems in the Development of Shared Bicycle
3 Analysis of Shared Bicycle Market Structure in the Context of Sharing Economy
3.1 Industry Status
3.2 Oligopolistic Game
3.3 Industry Consolidation
4 Future Development Direction and Suggestions
5 Conclusion
References
A Brief Analysis of Countermeasures of Price cap of Masks during the Epidemic Period from the Perspective of Game Theory
1 Introduction
2 Literature Review
2.1 Game Theory
2.2 Price Ceiling
3 Discussion
3.1 Analysis Base on Model and Market
3.2 Discussion from the Perspective of Game Theory
4 Conclusion and Limitation
References
The Impact of Economic Policy Uncertainty on Bank Stability
1 Introduction
2 Literature Review
2.1 Economic Policy Uncertainty
2.2 Influence of Different Factors on Bank Stability
2.3 Mediating Factors
2.4 Bank Competition
2.5 Economic Policy Uncertainty (EPU)
2.6 Bank Size
2.7 Bank Capital
3 Theoretical Mechanism and Research Hypothesis
3.1 EPU and Bank Stability
3.2 EPU, Bank Competition, and Bank Stability
3.3 EPU, Loan Size, and Bank Stability
4 Research Design
4.1 Bank Stability
4.2 Economic Uncertainty (EPU)
4.3 Control Variables
4.4 Mediating Variables
4.5 Model
4.6 Descriptive Statistical Analysis
4.7 Correlation Analysis
5 Analysis of Empirical Results
5.1 EPU and Bank Stability
5.2 Mechanism Testing
5.3 Robust Test
6 Conclusion
References
Examining the Impact of Human Capital on China’s Income Disparity Between Rural and Urban Areas
1 Introduction
2 Analysis of the Effect of Educational Factors in Human Capital on the Rural-Urban Income Gap in China
2.1 China-Returns to Education and the Urban-Rural Income Gap
2.2 China-Returns to Education and the Urban-Rural Income Gap
3 Analysis of the Effect of Training Factors in Human Capital on the Rural-Urban Income Gap in China
4 Analysis of the Effect of Health Factors in Human Capital on the Rural-Urban Income Gap in China
5 Suggestion
6 Conclusion
References
Exploring the Development Path of Traditional Culture Handicraft Industry Under the Background of Digital Economy – A Case Study of Jingdezhen
1 Introduction
2 Related Concepts and Theoretical Basis
3 Exploring the Current Situation and Problems of Jingdezhen Ceramic Industry
3.1 Hightech Enterprises are Increasing Rapidly, but Their Management Experience is Insufficient
3.2 Slow Technology Update in the Process of Building “Internet + Ceramics” Industry
3.3 Lack of Brand Awareness and Insufficient Publicity
3.4 Affected by COVID-19, the Survival of the Industry Faces Great Risks
4 Analysis of the Influencing Factors of Traditional Cultural Handicrafts
4.1 Talents Play an Important Role in Traditional Cultural Handicrafts
4.2 Whether It can Build a Distinctive Digital Culture Platform is an Important Driving Force for Development
4.3 The Government Plays an Important Guiding Role
5 Policy Suggestions on the Development of Traditional Handicrafts
5.1 Improve the Connection Between Upstream and Downstream Industries and Create a Unique Cultural Industrial Chain
5.2 Using Digital Economy to Build Local and Characteristic Cultural Industry Community
5.3 Transform into a New Mode of Upstream and Downstream Operation
5.4 Accumulate Capital and Prepare for Profit Risks
6 Conclusion
References
Backtracking and Analysis of Stocks in Different Intervals Using Supertrend
1 Introduction
2 Basic Model Setting
2.1 Supertrend Models
2.2 Ten Stocks
2.3 Code Modification
3 Basic Model Setting
3.1 Backtesting at Different Intervals for Different Stocks
3.2 Optimal Parameter
4 Comparison of Multiple Strategies
4.1 Supertrend vs Buy and Hold and Volatility
4.2 Moving Average
4.3 SPY&TSLA Comparison
5 Conclusion
References
Empirical Evidence Analysis for Portfolio Selection Based on the CAPM Model and the Fama-French 3-Factor & 5-Factor Model
1 Introduction
2 Methodology
2.1 Overview
2.2 Data Progression
2.3 Regression
2.4 Portfolio Selection
3 Results
4 Conclusion
References
An Analysis of Significant Factors that Affect Branford Houses’ Appraised Value
1 Introduction
2 Data Processing
2.1 Extraction of Variables and Selection of Houses
2.2 Calculation of Distance to Coastline and I-95
3 Basic Explorations
3.1 Categorization of Variables
3.2 Plot Explorations
4 Results - Linear Models
4.1 Exploration to Linear Model
4.2 Analysis of Linear Regression Model
5 Conclusion
References
The Impact of Controlling Variate Technique for Calls in the Black-Scholes Model
1 Introduction
2 Data
3 Method
4 Results
5 Discussion
6 Conclusion
References
The Application of Control Variate Technique on the Monte Carlo Simulation Option pricing and Accuracy Check
1 Introduction
2 Data
3 Method
4 Result
5 Conclusion
References
Quantamental Trading: Fundamental and Quantitative Analysis with Multi-factor Regression Model Strategy
1 Introduction
2 Economic Intuition
3 Quantitative Analysis
3.1 Return: Cumulative Return, Annualized Return, Sharpe Ratio, IR, Transaction Cost
3.2 Risk: Volatility, Skewness, Kurtosis, Maximum Drawdown, VaR (95%), VaR (90%), Calmar Ratio, Efficacy
3.3 Correlation: Correlation with Shanghai Composite Index
3.4 Data
3.5 Strategy Detail
4 Development
4.1 Quantitative
4.2 Difference from Expectation
5 Refinement
5.1 Introduction to the Strategy
5.2 Summary of Statistics
5.3 Analysis
6 Conclusion
7 Additional Concern
8 Conclusion
Appendix
References
Applying Trend Following Strategy in Chinese Commodity Futures Market: The Case of the Moving Average Converge Divergence Indicator
1 Introduction
1.1 Strategy Overview
1.2 Performance Estimation
2 Specification
2.1 Qualitative Analysis
2.2 Quantitative Analysis
2.3 Data
2.4 Strategy Detail
3 Implementation
3.1 Results
3.2 Difference from Expectation
4 Refinements
4.1 Implemented
4.2 Results
5 Conclusion
5.1 Final Selection
5.2 Results
5.3 Additional Consideration
6 Trading Recommendation
References
Economic Transformation of Resource-Dependent Cities in Heilongjiang Province from 2013 to 2020
1 Introduction
2 Literature Review
2.1 Study on Resource-Based Cities outside China
2.2 Study on Resource-Based Cities in China
3 Characteristics of the Local Economy
3.1 Nine Resource Cities
3.2 Challenges
4 Progress of Transition
4.1 Data
4.2 Progress
5 Further Issues and Challenges
5.1 Financial Constraints
5.2 Business Environment
5.3 Human Capital Shortage
5.4 Suggestions
6 Conclusion
References
Research on the Applicability of an Improved SIRS Model to Disruption Risk Propagation of Healthcare Supply Chain
1 Introduction
2 Disruption Risk of Supply Chain
3 Analysis of Healthcare Supply Chain Disruption Risk
3.1 Healthcare Supply Chain
3.2 Disruption of Node Companies
3.3 Disruption of the Supply Chain Network
4 SIRS Model
4.1 Introduction
4.2 Basic Schematic Diagram of the SIRS Model
4.3 An Improved SIRS Model
5 Conclusion of Model Applicability
References
Comparison of Investment Risks and Differences in Autonomous Driving in Different Regions Based on EPU
1 Introduction
2 Literature Review
3 The Connotation and Impact of the Global Economic Policy Uncertainty Index
4 Comparison of the Investment Environment of Autonomous Cars in Different Regions
4.1 An Overview of the Current State of the Global Autonomous Car Industry
4.2 Representatives of the Development of the Autonomous Car Industry and Their Performance – Take Tesla as an Example
4.3 The Positive Impact of Policy Support on the Investment Environment of the Autonomous Car Industry
4.4 The Negative Impact of COVID-19 on the Development of the Autonomous Car Industry
5 Conclusion
References
Research on Change in Relationship of American Biotechnology Industry and Pharmaceuticals Industry Due to Covid-19
1 Introduction
2 Data Collection
3 Methods
3.1 Vector Autoregressive Model
4 Results
4.1 Stability Tests for data before Covid-19
4.2 Impulse Response Functions Before Covid-19
4.3 Variance Decomposition before Covid-19
4.4 Stability Tests for Data After Covid-19
4.5 Impulse Response Functions Before Covid-19
4.6 Variance Decomposition After Covid-19
5 Conclusion
References
Research on Prices of Listed Companies in China’s Biochemical Industry
1 Introduction
2 Data and Methods
2.1 Data
2.2 Method
3 Results and Discussion
3.1 VAR Model Stability Test for Certain Stocks
3.2 Impulse Response Function for Certain Stocks
3.3 Variance Decomposition Analysis for Certain Stocks
3.4 VAR Model Stability Test for Industry Index
3.5 Impulse Response Function for Industry Index
4 Conclusion
References
A Comparative Study of Fama-French Five Factor Model and Three Factor Model – A Case Study of CSI 1000
1 Introduction
2 Factor Model
2.1 Fama-French Three Factor Model
2.2 Fama-French Five Factor Model
3 Selection and Processing of Stock Data
4 Factor Construction
5 Empirical Results Analysis
5.1 B/M Grouping Regression Result Analysis
5.2 ROE Grouping Regression Result Analysis
5.3 I/A Grouping Regression Result Analysis
6 Conclusion and Deficiencies
References
The Impact of Russia-Ukraine Conflict and Shanghai Lockdown on Chinese Stock Market
1 Introduction
2 Sample and Methodology
2.1 Data Source
2.2 Methods
3 Results
3.1 Impact of Russia-Ukraine Conflict
3.2 Impact of Shanghai lockdown
3.3 Total Effect of Two Events
3.4 Brief Summary
3.5 Relationship Between CAR and Industry Characteristics
3.6 Trading Strategy
4 Conclusion
Appendix 1
References
Grey System Theory: A Case Study of Analyzing the Economic Growth in Anhui Province, China
1 Introduction
2 Methodology
2.1 Grey Relational Analysis (GRA)
2.2 Grey Modelling (1, 1)
2.3 Rolling-GM (1, 1)
3 Case Study
3.1 Grey Relational Analysis
3.2 Rolling-GM (1,1) Forecast
3.3 Discussion
4 Conclusion
References
Legislative Research on Personal Information Protection: Taking Face Recognition Information as an Example
1 Introduction
2 Current Situation of Personal Biological Information Protection
2.1 The Legal Attribute of Personal Biological Information is not Clear
2.2 The Classification Boundary of Personal Biological Information is not Clear
2.3 Excessive Misuse of Personal Biological Information
3 The Imperfection of China's Current Legal System
3.1 The Concept of Personal Information in China's Present Laws is Unclear
3.2 China's Current Laws have Limited Effect on the Criminal Liability of Infringing Personal Biological Information
3.3 China's Current Laws have Relatively Light Legal Consequences for the Violate of Personal Biological Information
4 Perfecting the Legal System of Personal Information Protection
4.1 Strengthening the Criminal Responsibility of Violating Personal Biological Information
4.2 Properly Improve the Punishment for Infringement of Personal Biological Information
5 Conclusion
References
Portfolio Optimization with Markowitz and Index Model
1 Introduction
2 Stocks
2.1 Industry Outlook
2.2 Stocks
3 Method
3.1 Markowitz Mean-Variance Model
3.2 Index Model
4 Data and Constraints
4.1 Data
4.2 Constraints
5 Data Analysis
5.1 MM Model
5.2 IM Model
5.3 Comparison
6 Conclusion
Appendix
References
Impact of Covid-19 and Its Variants on Chinese Aviation Market—An Event Study
1 Introduction
2 Methodology
2.1 Data Source
2.2 Estimation Window Selection
2.3 Event Window Selection
3 Result and Discussion
4 Conclusion
References
The Impact of China’s New Rural Social Endowment Insurance on China’s Elderly Labor Market
1 Introduction
2 Literature Review
3 Data
4 Analyze
5 Conclusion
References
Analysis of Online Travel Agency Ctrip Financial Risk
1 Introduction
2 Methodology
3 Results and Discussion
3.1 Descriptive Analysis
3.2 Prediction Analysis
4 Conclusion
References
Sentiment Index Construction and the Influence of Sentiments on Returns
1 Introduction
2 Method
2.1 Variables and Data
2.2 Principal Components Analysis
2.3 Linear Regression
2.4 GARCH-M Model
3 Results and Discussion
3.1 Sentiment Index
3.2 The Relationship Between Sentiment Index and Market Return
3.3 The Effect of Sentiment
3.4 Discussion
4 Conclusion
References
Research on the Impact of Macroeconomic Events on the Chinese Stock Market Through the Abnormal Investment Returns
1 Introduction
2 Literature Review
3 Methodology
4 Meeting Effects and Research
5 Conclusion
References
A Comparative Study Between Traditional Algorithms and Machine Learning Algorithms in Predicting Recidivism
1 Introduction
2 Literature Review
3 Methodology
3.1 Procedure
4 Exploratory Data Analysis
4.1 Data Description
4.2 Data Analysis
4.3 Model Building
4.4 Model Checking
5 Result in Discussion
6 Conclusion
6.1 Summary
6.2 Future Work
References
The Effects of Outsourcing: Winners and Losers
1 Introduction
2 Literature Review
3 Methodology
4 Results
4.1 Outsourcing and Income Inequality
4.2 Outsourcing and Wealth Inequality
4.3 Inequality in Job Quality Arising from Outsourcing
4.4 The Inequality from Outsourcing and Its Political Implications
5 Conclusion
References
Analysis of the Effect of Network Media Attention on the Stock Price of ChiNext Market
1 Introduction
2 Research Status at Home and Abroad
3 Theoretical Analysis and Research Assumptions
4 Data Sources and Sample Selection
5 Variable Design
5.1 Explained Variable
5.2 Explanatory Variables
5.3 Control Variable
5.4 Test Model
5.5 Empirical Analysis
5.6 Regression Analysis
6 Conclusion
References
Research on the Impact of Venture Capital on Technological Innovation of New Energy Enterprises in China
1 Introduction
2 Research Method
2.1 Literature Research Method
2.2 Empirical Analysis Method
3 Methodology
3.1 Data Collection
3.2 Research Hypothesis
3.3 Explained Variable
3.4 Explanatory Variable
3.5 Model Setting
4 Results
5 Conclusion
References
Prediction of the Exchange Rate Between USD and CNY Using ARIMA Model
1 Introduction
1.1 A Subsection Sample
2 Methods
2.1 Model
2.2 Databases
2.3 Data Analysis
3 Results Analysis
3.1 White Noise Examination
4 Conclusion
References
The Impact of Different Phases of COVID-19 on the Airline, Financial Services, and Healthcare Industry
1 Introduction
2 Data and Method
3 Results and Discussion
3.1 Airline Industry
3.2 Financial Services
3.3 Healthcare
3.4 Comparison
4 Limitations and Prospects
5 Conclusion
References
Influence of the Real Exchange Rate on Economic Growth
1 Introduction
2 Literature Review
3 Methodologies
4 Building the Model
4.1 Selection of Variables
4.2 Model Settings
4.3 The Dataset
4.4 Final Model and Conclusions
4.5 Checking the Model
5 Discussion
5.1 The Discrepancy Between Rich and Poor Countries
5.2 Lagging Effect
6 Policy Implications
7 Conclusion
References
A Cross-National Examination of the Determinants for Covid 19 Vaccination Rates
1 Introduction
2 Literature Review and Conceptual Frameworks
3 Hypothesis
4 Methodological Choices
5 Discussion
6 Conclusion
References
An Early Warning Analysis of Pharmaceutical Listed Companies: Based on PCA and Factor Analysis Method
1 Introduction
2 Methodology
2.1 Sample and Data
2.2 Index Selection
2.3 KMO Sample Detection and Bartlett Sphere Test
3 Results and Discussion
3.1 Principal Component Analysis and Factor Analysis
3.2 Risk Level Result
3.3 Discussion
4 Conclusion
References
Risk Analysis with Monte Carlo Simulation on the Price of Carbon Market in China
1 Introduction
2 Methodology
2.1 Data Source
2.2 Methodology
3 Results and Discussion
4 Conclusion
References
Identifying Macroeconomic Shocks on House Sales: Evidence from China
1 Introduction
2 Data
2.1 Monthly Data
2.2 Descriptive Statistics and Visualization
3 Methodology
3.1 Model Selection
3.2 Model Specification
4 Empirical Results
4.1 Test for Stationarity
4.2 Lag Selection
4.3 Granger Causality
4.4 VAR Estimations
4.5 Impulse Response
4.6 Variance Decomposition
5 Discussion
6 Conclusion
References
Analysis on Factors Affecting Financial Industry Annual Profit Based on Linear Regression Model
1 Introduction
2 Method
2.1 Sample and Data
2.2 Research Approach
3 Result and Discussion
3.1 Result
3.2 Discussion
4 Conclusion
References
How Did COVID Affect Regional GDP Growth?
1 Introduction
2 Literature Review
3 Data
4 Data Analysis
4.1 Pattern 1: In Most Sample Countries, the More Developed the Economy with Low COVID Deaths in 2020, and Takes up the Higher the Percentage of Total Mortality in 2019
4.2 Pattern 2: Negative Correlation Between COVID Death and GDP Growth
4.3 Pattern 3: The Countries in Real GDP, PPP GDP, Nominal GDP Show an Irregular Growth Fluctuation, and with Large Gap in GDP Growth Rates from 2020 to 2021 are Mainly in Mid and High COVID Death Group
4.4 Summary
4.5 COVID Plunges Asian Region into Inflation Crisis
5 Conclusion
References
Portfolio with Asset-Allocation Strategies in the Electric Vehicle Industry
1 Introduction
2 Industry Performance
3 Methodology
4 Model Construction and Algorithm Explanation
4.1 Equal Weight Model
4.2 Market Capitalization Model
4.3 Mean-Variance Model
4.4 Maximum Sharpe Ratio Model
5 Results Presentation and Interpretation
5.1 Assumptions
5.2 Results Chart
5.3 Performance Comparison
6 Investigating Stocks with Positive Weights in the Maximum Sharpe Ratio Model
7 Limitations of the Strategies
8 Conclusion
References
Tripartite Evolutionary Game Analysis of Green Financing for SMEs: A Case Study of China
1 Introduction
1.1 Research Background and Significance
1.2 Literature Review
2 Construction and Analysis of the Evolutionary Game Model
2.1 Model Description
2.2 Model Building
3 Results
3.1 Matrix Analysis of the Game
3.2 Suggestions
4 Conclusions
References
Analysis of Dual Moving Average Strategy
1 Introduction
2 Literature Review
3 Data
3.1 Description
3.2 Preprocessing
4 Data Analysis and Result
4.1 Model
4.2 Analysis
5 Conclusion
References
Pricing Strategy of Russian and European Gas Supply Based on Game Theory
1 Introduction
2 Review of Russian-European Supply Chain
2.1 A Review of the Russian-European Supply Chain
2.2 Influence of Russia-Ukraine War on European Natural Gas
2.3 Study Natural Gas Pricing Mechanism with Game Model
3 Methodology
3.1 Problem Description
3.2 Conceptual Model
3.3 Hypothesis
3.4 Model Description
3.5 Game Description
4 Decision-Making Model for Each Stakeholder in the Natural Gas Market
4.1 The Situation When the Russia Natural Gas Company is Dominant
4.2 The Situation when Ukrainian Pipeline Company is Dominant
4.3 The Situation when the European Market is Dominant
4.4 In the Common Game Situation
5 Results
6 Conclusion
References
Author Index
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Applied Economics and Policy Studies
Canh Thien Dang Javier Cifuentes-Faura Xiaolong Li Editors
Proceedings of the 2nd International Conference on Business and Policy Studies
Applied Economics and Policy Studies Series Editors Xuezheng Qin , School of Economics, Peking University, Beijing, China Chunhui Yuan, School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China Xiaolong Li, Department of Postal Management, Beijing University of Posts and Telecommunications, Beijing, China
The Applied Economics and Policy Studies present latest theoretical and methodological discussions to bear on the scholarly works covering economic theories, econometric analyses, as well as multifaceted issues arising out of emerging concerns from different industries and debates surrounding latest policies. Situated at the forefront of the interdisciplinary fields of applied economics and policy studies, this book series seeks to bring together the scholarly insights centering on economic development, infrastructure development, macroeconomic policy, governance of welfare policy, policies and governance of emerging markets, and relevant subfields that trace to the discipline of applied economics, public policy, policy studies, and combined fields of the aforementioned. The book series of Applied Economics and Policy Studies is dedicated to the gathering of intellectual views by scholars and poli-cymakers. The publications included are relevant for scholars, policymakers, and students of economics, policy studies, and otherwise interdisciplinary programs.
Canh Thien Dang · Javier Cifuentes-Faura · Xiaolong Li Editors
Proceedings of the 2nd International Conference on Business and Policy Studies
Editors Canh Thien Dang King’s Business School King’s College London London, UK
Javier Cifuentes-Faura Department of Financial Economics and Accounting University of Murcia Murcia, Spain
Xiaolong Li Department of Postal Management Beijing University of Posts and Telecommunications Beijing, China
ISSN 2731-4006 ISSN 2731-4014 (electronic) Applied Economics and Policy Studies ISBN 978-981-99-6440-6 ISBN 978-981-99-6441-3 (eBook) https://doi.org/10.1007/978-981-99-6441-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.
Contents
Crew Scheduling Problem: Integer Optimization Using Set-Covering Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yiqiao Wang, Hanfei Shi, and Liuzhouyu Shi
1
Research on Business Management Based on New Retail Model: The Case of Yonghui Supermarket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen Jing
12
Supply Chain Management—A Case Study of Huawei’s Supply-Chain Chip Shortage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiangke Chen
21
The Effect of Manufacturing Employment Changes on County-Level Partisan Voting Shares in the US Presidential Elections: An IV Analysis . . . . . . Yifan Gong
29
Research on the Regulation of Internet Finance in Shanghai . . . . . . . . . . . . . . . . Hanwen Xu
53
The Empirical Analysis of ESG Index and Enterprise Investment Value Based on Different Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yixin Chen
63
Inflation-Related Factors Enhanced LSTM-Based Multivariate Time Series for Stock Market Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wanshan Du, Kaixin Lei, Jiawen Lin, and Yifu Liang
72
How does the Reputation of Venture Capital Impact on Financial Constraints? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuxiao Duan
83
Sustainability in Chinese Investment: How Chinese Investors Perceive the Benefit and Liabilities of ESG Rating of New Ventures . . . . . . . . . . . . . . . . . Ruiying Li, Yufei Zhao, Haozheng Yang, Chenyi Liu, and Hongyi Liu
99
Analysis and Research on Sri Lanka’s National Bankruptcy under the Superposition of Triple Backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . Zihao Wen
115
vi
Contents
Disputes Along the Belt and Road and How to Improve the Dispute Resolution System with Diversified Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . Tianle Tan
125
The Impact of the US Sanctions Against Huawei’s Mobile Phone Business Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruiming Luo, Bojia Zhang, and Runfeng Guo
141
Local Government Financing in China: Fiscal Fatigue and Debt Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chenlin Wang
155
The Impact of Health Education on the Health Status of Migrant Workers—An Empirical Analysis Based on CMDS (2018) Data . . . . . . . . . . . . . Xiaohan Pu
166
Correlation Between GDP and Related Indicators: Comparison Between the U.S. and Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Tang
176
Old Prices, Marginal Costs, Capital and Dynamic Pricing Models . . . . . . . . . . . Mingyang Hu
186
Time Series Momentum Trading Strategy for Cryptocurrencies . . . . . . . . . . . . . Xiaolu Li and Xinyin Zhang
201
The Effect of ESG Disclosure on Stock Performance: Empirical Evidence from China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lin Lin A Study of Asset Allocation Based on the Markowitz Model . . . . . . . . . . . . . . . . Zhongyu Chen
213
222
CSR Communication on Social Media: Feminist Marketing and Advertising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yixian Zhang, Yuqian Zhang, and Tianxin Yang
230
The Research on Factors Influencing Stock Returns in the Travel Industry Under the Shock of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quan Chen, Wenting Kong, and Junlin Wei
241
Comparison of Employment Status of Financial Service Sector Between Mainland and Hong Kong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junge Ma
252
Contents
How Life Changes During COVID Pandemic are Mitigated Through Next Generation Economic Modalities that Leverage the Power of Platforms? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruiqi Huang Innovative Tools for Food Waste Management that Enable Higher Value Circular Economy Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huifeng Yin, Yiding Lu, Weikang Peng, Chutong Wang, and Di Jiang What are the Benefits of Influencer Marketing, and How can Brands Benefit from Them? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tianze Xiong, Linyi Jia, Han Zheng, Ca Ming Pi, Xuan Zhou, and Yimu Gu The Time-Varying Impact of the Federal Reserve Rate Hike on Bitcoin . . . . . . . Zixuan Cheng
vii
261
273
287
297
The Impact of Changes in Currency Value on Technology Companies’ Yield and Volatility: A Long-Term Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Du
307
The Behaviour of Advertisers Within Video Platform, Along with Optimal Strategy for Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daqin Zhao
319
The Analysis of CHANEL’s Marketing Strategy Affects Consumer Behavior in the Pandemic Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Canran Xiao
327
The Impact of Real Estate Tax on Residents’ Housing Consumption —— An Empirical Study Based on British Data . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Cong
337
Study on the Influence of Stock Trading Volume of Traditional Chinese Medicine by Multiple Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yue Chen
346
Market Analysis and Strategy of Pet Industry Under Epidemic Situation Market Strategy Based on Case Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bairu Wang, Jiawei Li, Liu Zhe, and Wan Ziyui
357
Research on the Influencing Factors of Second-Hand Housing Price in Shanghai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xizheng Chen, Yifan Mi, and Shubin Quan
366
viii
Contents
The Impact of Social Media Marketing Strategy on Behavior Online Shopping: Case of TikTok Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaixin Fu
375
Did China’s Stock Market Benefit from USD Appreciation: An Empirical Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yalong Sun
385
Yield and Volatility of PinDuoDuo in a Long-Term Uncertain Situation: The Covid-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caixuan Kong
398
Research on the Influencing Factors Affecting Beijing House Prices Using Linear Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingwei Xu and Zhaojing Yang
411
Research on the Investment Value of Stocks of the CITIC Securities Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhuyin Li and Wen Peng
425
Research on the China’s Banking Industry Based on the CAPM Model – Take Ping an Bank of China as an Example . . . . . . . . . . . . . . . . . . . . . . . Zijian Li
436
In the Sharing Economy Modality: Airbnb’s Failure in China . . . . . . . . . . . . . . . Gege Zhang, Wenyun Zeng, Ziyan Tao, Houbin Xiao, and Yutao Jiang
445
The Time-Varying Impact of the Fed’s Rate Hike on Japan’s Airline Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinyi Wang
456
Large Supermarket Chain Under This Turn’s Interest Rate Policy: Gain or Lose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dufan Chen
466
The Time-Varying Impact of Normalized Covid-19 Pandemic on BYD Stock Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yexuan Jiang
478
A Comparative Study of Shadow Banking Financial Supervision Between China and the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sichen Meng
490
Contents
ix
Marketing Channel Innovation in the Beauty Industry in the Post-Epidemic Era - Estee Lauder Brand as an Example . . . . . . . . . . . . . . Weixin Huang, Siyuan Lin, and Jianing Wang
499
Analysis on Siemens Online Marketing Strategies - Taking Siemens Advertisement of Dishwasher as an Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuyang Sun
509
Gain or Loss from Fed’s Interest Rate Policy: Evidence from Disney . . . . . . . . . Jingwen Sun Research on the Influence Mechanism of Consumer Habits and Psychological Price Level on Advertising Effects: Based on Close-Up and Group Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenxuan Li, Yuwei Liu, and Haoqin Jin
516
527
Ownership Structure and Corporate Performance Evidence from China’s Stock Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuxuan Cao and Kaifan Zhang
539
Factor Analysis and PEST Analysis on Health Care Industry with 15 Stock Samples’ Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yufei Li, Bingyue Zhang, and Daiming Zhou
550
The Research of Three Parties’ Game Influenced by IWOM in Evolutionary Game Theory-Taking “Ice Cream Assassin” as an Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jue Hu, Zhenhao Hu, and Ying Wang Empirical Analysis of Liquidity Risk of Chinese Listed Commercial Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ce Shen Analysis on the Influence of Various Elements on the Net Profit of a Steel Group in Southwest China and the Prediction Based on Arima Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhaoxin He
559
571
582
Are There Any Winners on Both Sides of the Trade Conflict: Evidence from China and U.S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lingyun Zhao
594
Investment Strategy in a Down Market: Application of Market Neutral Strategy in Energy, Utilities and Technology Sector . . . . . . . . . . . . . . . . . . . . . . . Wenjun Yang
606
x
Contents
Factors Analysis on Affecting the Sales Volume of K-Pack in Smartfood . . . . . Kewei Yang
616
Resignation of Board of Directors Secretaries in Their Tenures and Business Violations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongji Gao, Yangbo Xing, and Fulin Yu
623
Research on the Influencing Factors of Marriage Rate Among Young People in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuxuan Chen and Junji Yang
634
Constrained Portfolio Optimization: A Comparison of Markowitz Model and Single Index Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiucen Lin
644
Tesla Stock Price Timeseries Analysis and Forecasting . . . . . . . . . . . . . . . . . . . . . Chen Yang The Application of CAPM and Fama-French Three-Factor Model to the Investment Choice in Individual Stocks in China’s A-share Market and the Explanatory Power of Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wang Henan
655
664
Optimal Capital Structure of China’s Small and Medium Listed Companies: A Case of RC Ltd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mianlun Zhang
679
The Main Challenge Faced by the Migrant Population and Recommended Policies: A Case of Hangzhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yilin Jiang
692
Research on United States Core CPI Forecast Based on Exponential Smoothing and ARIMA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li Eric
702
The Differences Between Longping High-Tech’s Company Value and Stock Prices of Agricultural and Forestry Industry Based on Factor Analysis and Structural Equation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heying Xu and Weizhe Feng Analysis of the Impact of Corporate Responsibility on the Economic Interests of Educational Institutions Under the Impact of COVID-19 . . . . . . . . . Rundong Xi
715
728
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International Capital Flows and Dynamic Changes in Cryptocurrency Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuchen Yang
737
Research on the Influencing Factors of Stock Return Based on Factor Analysis and OLS Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingda Yang
747
Social Media Peer Communication and Impacts on Purchase Intentions . . . . . . Leran Li
756
Research on Transparent and Opaque Packaging of Nearly Expired Food . . . . . Simin Ma
766
Research on the Impact of Investor Sentiment on the Cost of Equity Financing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yue Zhang
777
Examining the Correlation for the US S&P 500 and Its Corresponding Futures from a Data-Driven Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hezi Ji
792
Analysis of the Current Situation, Problems, and Countermeasures in the Development of China’s Agricultural Futures Market . . . . . . . . . . . . . . . . . Xiang Li and Wenyao Ma
804
BSM Model Application for the Southern Copper Corporation . . . . . . . . . . . . . . Jilong Jia
815
Value Investment: A Case Study for Technology Companies (Meta vs Microsoft) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xu Huang
827
Comparing the S&P 500 Index’s and the NASDAQ Index’s Influence on Pfizer’s Stock Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinglun Wu
838
Compare Nasdaq Index and S&P 500 Index on Exxonmobil Stock Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liheng Tian
846
Applicable Conditions and Criteria for Material Adverse Effect Clause in International M&A Transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhihe Kang, Qijia Liu, Yiwen Wang, Jiayi Yu, and Zhewen Zhang
854
xii
Contents
Stock Price Prediction in the Context of Time-Series and Multiple Factorial Regression for Medical Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lan Xu
863
A Empirical Research on the Relationship Between Appearance and Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinyi Zhang
874
Option Mispricing and Maturity Date: Evidence from China . . . . . . . . . . . . . . . . Yaqi Tu, Yidi Zhai, and Moan Lu
892
Applicability of Fama-French Six-Factor Improved Model to Explain Stock Returns in Chinese Rare Metal Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fenyu Chen, Jiuan Jiang, and Yuting Jiang
899
Impact of COVID-19 on China’s Real Estate Industry - An Empirical Study Based on the Stock Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zilun Chen, Pengyao Gao, and Kaiqing Liang
909
Prediction and Analysis of Commodity House Price Based on ARIMA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaohu Liang
918
The Prospect of Chinese Internet Companies’ Strategy Investment . . . . . . . . . . . Lin Zicheng
930
Rough Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lida Zeng
941
Export Effect or International Capital Flows: Evidence From US Chip Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuxiang Jiang
951
Analysis on the Influencing Factors of Profitability in China Minsheng Bank’s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruizhe Luo
963
Stock Market Forecasting Using the ARIMA, GARCH and Random Forest Model During The Russia–Ukraine War . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianzhou Liu, Ruoqi Yan, and Yiyang Zhang
972
Forecasts and Relationships for German and Russian Stock Indices . . . . . . . . . . Xiaoyizhuo Zhao
985
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Comparison of Time-Series Forecasting Models in Predicting Stock Prices of Insurance Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 Shihua Xiao Research on the Birth Inhibition Effect of House Prices in China-A Tianjin-Beijing-Hebei Regional Case Study . . . . . . . . . . . . . . . . . . . . 1010 Mihan Zheng Game Theoretical Analysis of the Behavioral Strategies of the Chinese Government and Producers Under a Policy of Reward and Punishment Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1019 Zixin Wang Anchoring Effect in the Market: Perspective from Market Interaction and Stock Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1028 Ruoshi Ding The Relationship between Real E-commerce Platform Reviews and Stocks Price Change Based on Panel Regression Model . . . . . . . . . . . . . . . . 1036 Mingbei Dai, Peihan Wang, and Kun Zhang Basic Analysis of Hog Futures Market in China . . . . . . . . . . . . . . . . . . . . . . . . . . . 1048 Chen Xu, Lingyu Zhou, Anshi Lou, Manqi Qiu, and Mengyu Cao Bank Customer Churn Prediction Based on Correlation Analysis and Multiple Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065 Yanxuan Li Impact of the Russian-Ukrainian War on the Global Non-ferrous Metals Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073 Zipei Chen, Yuxi Geng, and Bolin Zhang A Statistical Study of Diabetes Prevalence and Poverty Rates in the United States using Linear Regression Methods . . . . . . . . . . . . . . . . . . . . . 1084 Shuwei Yang The Role of Derivatives in Risk Management After the Financial Crisis of 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094 Xinyu Wu The Impact of the Covid-19 Pandemic on the Technology Sector of US Stock Market Using Time Series Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1105 Yilun Wu
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The Impact of Fintech on Stock Price Crash: Evidence from China . . . . . . . . . . 1117 Yihong Zheng and Yuning Zhang Apple’s Strategic Analysis and Cash Flow Forecast . . . . . . . . . . . . . . . . . . . . . . . . 1134 Shitong Wang, Zixuan Chen, Haoyue Zhang, and Yining Xu When Companies Do Good: The Relationship Between Firm Size and Corporate Social Responsibility—An Empirical Study in Chinese Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147 Yisheng Liu The Financial World After the Continuous Raise of the Federal Fund Rate-Subsequent Influence of the Increase of Interest Rate on the Financial Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155 Xinze Chen Analysis of the Impact of the Trade War Between China and the United States on the Economy of Shandong Province and Countermeasures . . . . . . . . . 1169 Zhou YuJie Planning and Development of Smart Cities Based on the Concept of Sustainable Development - The Case of Shanghai . . . . . . . . . . . . . . . . . . . . . . . 1183 Shiyin Tang The Road of Transformation of Resource-Based Cities . . . . . . . . . . . . . . . . . . . . . 1196 Zicheng Yang Measuring COVID-19’s Shock to Economy: Evidence from Japan’s Inflation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1206 Keyu Chen A Test of EMH Using Euro-Dollar Exchange Rate Fluctuations . . . . . . . . . . . . . 1218 Yingyi Li Wealth Management Product in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1225 Xin Chen, Suyue Sun, Chenyi Zhu, Dongcheng Liang, and Kejie Zhang Forecasting the China Stock Prices During the Covid-19 Pandemic . . . . . . . . . . 1239 Zhiyuan Zhang, Yibo Sun, Hanzhi Li, and Xiaoxiao Xiong Barrier Option Pricing for Discrete Market Hours . . . . . . . . . . . . . . . . . . . . . . . . . 1250 Xiaolan Wu
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The Determining Factors of Inequality Across Space in China Cities, Rural Areas, and Suburbs (From the 1980s Till Now) . . . . . . . . . . . . . . . . . . . . . . 1260 Chenyu Wang, Sujin Lyu, Jiayue Dai, and Zhijian Zhang The Economic Linkage Between China and US: An Empirical Evidence from CPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1275 Yihui Zhang Study on the Impact of Agricultural Mechanization on the Income of Rural Residents in China Based on Provincial Panel Data . . . . . . . . . . . . . . . . 1287 Sa Ge The Impact of Financial Crisis on Real Estate Enterprises: An Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1298 Yiyun Hu, Huizhong Xia, and Quan Zhang The Debt Crisis in China’s Real Estate Industry: Evidence from Evergrande . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1306 Keyi Yan Analysis on Value Investment in REIT Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . 1315 Liuqing Wang Value Investment in Real Estate Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1328 Hanmo Zhang Analysis of Shared Bicycle’s Business Model and Development . . . . . . . . . . . . . 1338 Yuanheng Yu A Brief Analysis of Countermeasures of Price cap of Masks during the Epidemic Period from the Perspective of Game Theory . . . . . . . . . . . 1347 Ziyi Jia The Impact of Economic Policy Uncertainty on Bank Stability . . . . . . . . . . . . . . 1355 Jialing Li Examining the Impact of Human Capital on China’s Income Disparity Between Rural and Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1372 Yunlong Chang, Aochen Huang, Yuandong Liu, and Zhiyi Meng Exploring the Development Path of Traditional Culture Handicraft Industry Under the Background of Digital Economy – A Case Study of Jingdezhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1383 Ninghao Bao, Jia Liu, Yuchen Liu, and Jianuo Yang
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Backtracking and Analysis of Stocks in Different Intervals Using Supertrend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1398 Xiaomeng Ren Empirical Evidence Analysis for Portfolio Selection Based on the CAPM Model and the Fama-French 3-Factor & 5-Factor Model . . . . . . . . . . . . . . . . . . . 1409 Weixi Hao An Analysis of Significant Factors that Affect Branford Houses’ Appraised Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1422 Chenyue Li, Linchen Xie, Xingzhi Xie, and Huibin Cao The Impact of Controlling Variate Technique for Calls in the Black-Scholes Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1439 Yang Rui The Application of Control Variate Technique on the Monte Carlo Simulation Option pricing and Accuracy Check . . . . . . . . . . . . . . . . . . . . . . . . . . . 1447 Song Chen Quantamental Trading: Fundamental and Quantitative Analysis with Multi-factor Regression Model Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1455 Chenling Xie, Yufan Zhang, Meng Wang, and Ziyue Liu Applying Trend Following Strategy in Chinese Commodity Futures Market: The Case of the Moving Average Converge Divergence Indicator . . . . 1471 Nan Jiang and Shumeng Shi Economic Transformation of Resource-Dependent Cities in Heilongjiang Province from 2013 to 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1484 Yao Jiang Research on the Applicability of an Improved SIRS Model to Disruption Risk Propagation of Healthcare Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1504 Yiying Yang Comparison of Investment Risks and Differences in Autonomous Driving in Different Regions Based on EPU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1512 Kejia Chen Research on Change in Relationship of American Biotechnology Industry and Pharmaceuticals Industry Due to Covid-19 . . . . . . . . . . . . . . . . . . . . 1519 Yuwei Shen
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Research on Prices of Listed Companies in China’s Biochemical Industry . . . . 1532 Ke Xu, Jingyu Cheng, and Ying Xie A Comparative Study of Fama-French Five Factor Model and Three Factor Model – A Case Study of CSI 1000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1546 Weizhi Zhou The Impact of Russia-Ukraine Conflict and Shanghai Lockdown on Chinese Stock Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1555 Ruosen Yang, Yanwen Zhang, Xiao Teng, and Zhanpeng Sun Grey System Theory: A Case Study of Analyzing the Economic Growth in Anhui Province, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1569 Ziyi Wang Legislative Research on Personal Information Protection: Taking Face Recognition Information as an Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1579 Wang Lijia Portfolio Optimization with Markowitz and Index Model . . . . . . . . . . . . . . . . . . . 1588 Zeyong Zhang, Shanshan Liu, and Xingcong Liu Impact of Covid-19 and Its Variants on Chinese Aviation Market—An Event Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1600 Bochong Yuan The Impact of China’s New Rural Social Endowment Insurance on China’s Elderly Labor Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1609 Xinyan Liu Analysis of Online Travel Agency Ctrip Financial Risk . . . . . . . . . . . . . . . . . . . . 1616 Jiancheng Chen, Xi Chen, and Yue Wang Sentiment Index Construction and the Influence of Sentiments on Returns . . . . 1631 Jiarong Zhou Research on the Impact of Macroeconomic Events on the Chinese Stock Market Through the Abnormal Investment Returns . . . . . . . . . . . . . . . . . . . . . . . . 1643 Jingqiu Pan, Jiaqi Lei, and Yu Zhou A Comparative Study Between Traditional Algorithms and Machine Learning Algorithms in Predicting Recidivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1653 Enxian Fu, Feng Zhou, Yunfei Gao, and Yuhan Lu
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The Effects of Outsourcing: Winners and Losers . . . . . . . . . . . . . . . . . . . . . . . . . . 1667 Qinghe Liu, Jiarui Zhan, and Douglas Yuangeng Su Analysis of the Effect of Network Media Attention on the Stock Price of ChiNext Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1683 Jiacheng Ma Research on the Impact of Venture Capital on Technological Innovation of New Energy Enterprises in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1693 Letong Wang Prediction of the Exchange Rate Between USD and CNY Using ARIMA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1705 Li Yijin The Impact of Different Phases of COVID-19 on the Airline, Financial Services, and Healthcare Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1713 Hui Qi Zhang Influence of the Real Exchange Rate on Economic Growth . . . . . . . . . . . . . . . . . 1722 Wenjia Deng, Nick Ruiyu Lin, Xinren Guo, and Peter Pan A Cross-National Examination of the Determinants for Covid 19 Vaccination Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1739 Liao Hu, Runshi Gu, Xiwen Jin, and Xintong Yu An Early Warning Analysis of Pharmaceutical Listed Companies: Based on PCA and Factor Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1749 Letian Yang, Mingbo Yang, and Yuhui Xu Risk Analysis with Monte Carlo Simulation on the Price of Carbon Market in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1760 Xin Zhou Identifying Macroeconomic Shocks on House Sales: Evidence from China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1770 Fan Lei and Langfeng Zhou Analysis on Factors Affecting Financial Industry Annual Profit Based on Linear Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1782 Liwen Li, Yanbo Wang, and Kexin Wei How Did COVID Affect Regional GDP Growth? . . . . . . . . . . . . . . . . . . . . . . . . . 1792 Xu Shi, Jiayi Zou, Yufeng Dai, Yuhan Li, and Zicheng Wang
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Portfolio with Asset-Allocation Strategies in the Electric Vehicle Industry . . . . 1806 Longfu Xu Tripartite Evolutionary Game Analysis of Green Financing for SMEs: A Case Study of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1817 Tong Niu Analysis of Dual Moving Average Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1828 Yushu Wang and Xiaoya Deng Pricing Strategy of Russian and European Gas Supply Based on Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1837 Jiashan Chen, Weijia Hou, Yifan Liu, and Chenyu Zong Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1851
Crew Scheduling Problem: Integer Optimization Using Set-Covering Model Yiqiao Wang1 , Hanfei Shi2 , and Liuzhouyu Shi3(B) 1 Department of Math, Macalester College, St.Paul, MN 55105, USA
[email protected]
2 The Eberly College of Science Department of Mathematics, Pennsylvania State University,
State College 16802, USA [email protected] 3 Faculty of Applied Science and Engineering, University of Toronto, 35 St. George Street, Toronto, ON M5S 1A4, Canada [email protected]
Abstract. As the accessibility of airline transportation increases, the crew scheduling problem has received increasing attention. Moreover, especially with the limited flow of human resources and the economic stagnation during the pandemic stage, arranging the working hours of the attendants and the flight routes reasonably and effectively to reduce operational costs has become a crucial concern for airline companies. In this paper, we formulate a crew scheduling problem model with four international airports in the US with reasonable constraints to set up a set covering the problem and solve it using integer linear programming. We present computational results for our problem using MATLAB and discuss the concerns for further research. Keywords: Set-Covering Model · Crew Scheduling Problem · MATLAB
1 Introduction The net income of the airline from the pre–COVID-19 reference period to the COVID-19 period decreased by 289% on average [1]. Since the limited flow of human resources and the economic stagnation during the pandemic stage, arranging the working hours of the attendants and the flight routes reasonably and effectively to reduce operational costs is now the priority among priorities. This research paper focuses on United Airlines airline schedules problems to offer a feasible manner of maximizing profits during Covid-19. The airline scheduling problems are separated into two stages [2]: the first stage is to identify reasonable routes that meet regulatory and temporal constraints. For example, the crew cannot take off before arriving at the destination, and the time for everyone to get off the plane and passengers to get on the plane, as well as the maintenance time of the plane should be reserved. Since this route identification is quite significant, we assume that a collection of good routes has already been identified in this paper. Considering the potential routes, the second stage is to select a subset to ensure that each © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 1–11, 2023. https://doi.org/10.1007/978-981-99-6441-3_1
2
Y. Wang et al.
leg is covered by precisely one route. The airline crew pairing problem aims to generate a set of minimal-cost crew pairings covering all flight legs. We define pairing as a series of connectable flight legs in the same fleet, usually lasting one to five days [3]. In this module, we define its period as a one-day duration. Hence, we clarify the crew scheduling problem as follows. Here we pick four international airports in the East and Midwest of the US— BOS (Boston), ORD (Chicago), MSP (Minneapolis), and IAD (Washington D.C.) (Appendix A). We chose the date of August 1st , 2022, and the Airlines of United Airlines as the model [4]. Note that all flights are direct flights without any stop and the times shown below are estimated and not accurate per minute. We input a flight schedule table and a directed graph, which illustrate the detailed information of each flight, including departure station and time, arrival station and time, and flight time. Our objective is to assign flight crews, which minimizes a given cost function.
2 Background and Problem Set-up In Table 1, we show a flight schedule, assuming eight fixed flights per day per month [4]. Each line in the flight schedule corresponds to a route. These eight routes form a closed loop. Sometimes, two or more flights flown by the same crew are displayed as one leg. Each flight leg is uniquely identified by flight leg number, departure station and time, and arrival station and time. Since there is a time difference between some cities, we uniformly define the time as Eastern Standard Time denoted by EST, in order to facilitate us in counting the flight time. Note that the symbol of “(-1)” behind “ORD” and “MSP” in Table 1 means that both cities Chicago and Minneapolis are one hour later than the EST. Thus, the local time of Chicago and Minneapolis should be minus one hour for departure and arrival times. Table 1. Flight schedule. Leg number
Departure station
Departure time (EST)
Arrival station
Arrival time (EST)
Flight time
1
BOS
09:40
IAD
11:20
1h40 m
2
IAD
13:00
BOS
14:30
1h30 m
3
IAD
12:50
ORD (-1)
14:50
2h
4
ORD (-1)
09:50
IAD
11:50
2h
5
BOS
18:40
ORD (-1)
21:10
2 h 30 m
6
ORD (-1)
15:00
BOS
17:30
2 h 30 m
7
ORD (-1)
16:40
MSP (-1)
18:10
1 h 30 m
8
MSP (-1)
07:00
ORD (-1)
08:30
1 h 30 m
In Fig. 1, each node (ellipse) represents an airport, and each edge represents a flight leg, labeled with its departure and arrival time. Note that Fm denotes the number m flight leg for m ∈ {1, 2, . . . , 8}, for example, the number 1 flight leg is denoted by F1 .
Crew Scheduling Problem: Integer Optimization Using Set
3
Fig. 1. Flights schematic diagram.
3 Constraints 3.1 Regulatory Constraints 1. We define flight pairing as a series of flights that a single crew can serve. This means that the departure time of a flight must not be earlier than the arrival time of the previous flight. 2. In some cases, the crew can fly as passengers in a pair. This type of flight is called a deadhead [5]. In our case, assigning multiple crews to one flight is possible. 3. As shown in Fig. 1, there are eight flights in total. We are supposed to cover all the flights in one day. 4. For each pairing, there is no redundant flight. 3.2 Temporal Constraints 5. There are time costs to prepare in advance before flying (3 h) and to clean up after landing (2 h) [6]. We define the cost of a pairing as the time interval between the first departure time and the last arrival time adding 5 h to the latency cost. 6. According to the Federal Aviation Administration [7], each crew is limited to 8 h of flight time per day. 7. If the flight did not return to the original place, one extra hour will be added to the latency cost. 8. The total time of the pairing that exceeds 24 h will not be considered.
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4 Methodology A possible method is to construct all pairing by preprocessing. More specifically, construct the flight connection in a network form to figure out the possible pairings of each flight. As shown in Fig. 2 and Table 2, those are the potential connections of all the flights.
Fig. 2. The flight connection graph for Figure 1.
We can find there is a relationship between duration(D) and total time cost(C). We define the function of total time cost in terms of the variable D. The total time cost is equal to the duration plus the latency cost(L), which are 5 h + 1E (extra 1 h if the flight does not return to the take-off point), which are: C = D + 5 + 1E 1E : L → {0, 1}, the indicator function of a subset E of a set L 1E (L) :=
1if the flight does not return to the take − off point [8] 0 if the flight returns to thetake − off point
Crew Scheduling Problem: Integer Optimization Using Set
5
Table 2. Pairing obtained from Fig. 2. Pairing No
Pairing (Flight No.)
Time interval
Duration (hours)
Flight time
Latency cost
Total time cost (hours)
1
12
09:40–14:30
4.83
3 h 10 mins
+5
9.83
2
125
09:40–21:10
11.50
5 h 40 mins
+5+1
17.50
3
13
09:40–14:50
5.17
3 h 40 mins
+5+1
11.17
4
137
09:40–18:10
8.50
5 h 10 mins
+5+1
14.50
5
25
13:00–21:10
8.17
4h
+5+1
14.17
6
37
12:50–18:10
5.33
3 h 30 mins
+5+1
11.33
7
42
09:50–14:30
4.67
3 h 30 mins
+5+1
10.67
8
425
09:50–21:10
11.33
6h
+5
16.33
9
43
09:50–14:50
5.00
4h
+5
10.00
10
437
09:50–18:10
8.33
5 h 30 mins
+5+1
14.33
11
65
15:00–21:10
6.17
5h
+5
11.17
12
84
07:00–11:50
4.83
3 h 30 mins
+5+1
10.83
13
843
07:00–14:50
7.83
5 h 30 mins
+5+1
13.83
14
8437
07:00–18:10
11.17
7h
+5
16.17
15
86
07:00–17:30
10.50
4h
+5+1
16.50
16
865
07:00–21:10
14.17
6 h 30 mins
+5+1
20.17
17
87
07:00–18:10
11.17
3h
+5
16.17
18
1
09:40–11:20
1.67
1 h 40 mins
+5+1
7.67
19
2
13:00–14:30
1.50
1 h 30 mins
+5+1
7.50
20
3
12:50–14:50
2.00
2h
+5+1
8.00
21
4
09:50–11:50
2.00
2h
+5+1
8.00 (continued)
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Y. Wang et al. Table 2. (continued)
Pairing No
Pairing (Flight No.)
Time interval
Duration (hours)
Flight time
Latency cost
Total time cost (hours)
22
5
18:40–21:10
2.50
2 h 30 mins
+5+1
8.50
23
6
15:00–17:30
2.50
2 h 30 mins
+5+1
8.50
24
7
16:40–18:10
1.50
1 h 30 mins
+5+1
7.50
25
8
07:00–8:30
1.50
1 h 30 mins
+5+1
7.50
Notice: In constraint number 8, we will not take the total time of the pairing that exceeds 24 h into consideration. Since the maximum total time is 20.17 h for Pairing No. 16, which is less than 24 h, this limitation has no effect on this model.
5 Formulation 5.1 Decision Variables Before we formulate the ILP problem into equations, the definition of mathematical notations and decision variables are needed: • • • • •
m = the number of flight legs(m = 8) n = the number of routes(pairing)(n = 25) i = leg number(ifrom1to8) j = route number(jfrom1to25) cj = time cost of route numberj 1 if leg i is part of routej, • aij = [9] otherwise, 0 1 if leg i is part of routej, • xj = [9] otherwise, 0
5.2 Set-partitioning Problem (SPP) After all the potential flight routes are stipulated in Table 2, we need to select the possible combination of those 25 pairings. More specifically, in order to make sure that each flight has to fly once a day, all the pairings that contain the flight number, from 1 to 8, have to add up to one, respectively. For the optimal equation, we ensure the total time cost of the picked combinations of the pairing is the lowest. This type of ILP is called the set-partitioning problem (SPP): minimize
n j=1
cj xj
Crew Scheduling Problem: Integer Optimization Using Set
subject to
n
7
aij xj = 1i = 1, 2, . . . , m,
j=1
xj {0, 1}j = 1, 2, . . . , n.(∗)[9] 5.3 Set-Covering Problem (SCP) Since the crew may not fly along the same route, the constraints applicable to the crew are different from those of the aircraft (for example, the time the crew needs to rest and adjust). Therefore, the problem has a different set of potential paths. In addition, it is sometimes reasonable to allow the crew to ride on some flight legs as passengers to prepare for subsequent flights. With these changes, the crew scheduling problem becomes a set-covering problem (SCP): minimize
n
cj xj
j=1
subject to
n
aij xj ≥ 1i = 1, 2, . . . , m,
j=1
xj {0, 1}j = 1, 2, . . . , n.(∗∗)[9] 5.4 Equations We use the set-covering problem (**) model for our following calculation. The integer programming formulation is, therefore: Minimize z = 9.83x1 + 17.50x2 + 11.17x3 + 14.50x4 + 14.17x5 + 11.33x6 + 10.67x7 + 16.33x8 + 10.00x9 + 14.33x10 + 11.17x11 + 10.83x12 + 13.83x13 + 16.17x14 + 16.50x15 + 20.17x16 + 16.17x17 + 7.67x18 + 7.50x19 + 8.00x20 + 8.00x21 + 8.50x22 + 8.50x23 + 7.50x24 + 7.50x25 Subject to x1 + x2 + x3 + x4 + x18 ≥ 1(for flight 1) x1 + x2 + x5 + x7 + x8 + x19 ≥ 1(for flight 2) x3 + x4 + x6 + x9 + x10 + x13 + x14 + x20 ≥ 1(for flight 3) x7 + x8 + x9 + x10 + x12 + x13 + x14 + x21 ≥ 1(for flight 4) x2 +x5 + x8 + x11 + x16 + x22 ≥ 1(for flight 5)
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x11 + x15 + x16 + x23 ≥ 1(for flight 6) x4 + x6 + x10 + x14 + x17 + x24 ≥ 1(for flight 7) x12 + x13 + x14 + x15 + x16 + x17 + x25 ≥ 1(for flight 8) xj =
1 if leg i is part of route j, [9] 0 otherwise,
6 Solve the Integer Program To solve the linear integer program, the matrix formulation is essential (Appendix B). We reform the matrix A and b by multiplying “-1” by both of them in order to get a “less than” form for the constraints inequations. And we use “intcon” to make all the x variables integers and by using “lb” (lower bound) and “up” (upper bound), we restricted them as binary. Then, we use the “inlinprog” to solve the ILP problem. We obtained the solution that x1 = x11 = x14 = 1 and the rest are all 0 from MATLAB (Appendix C). Thus, the minimum total time cost subject to the given constraints is z = 37.17. This implies that the minimal time cost reached for the combination of pairing numbers 1, 11, and 14 for the crew- the pairing of flight numbers 1 and 2, the pairing of flight numbers 6 and 5, and the pairing of flight numbers 8 and 4 and 3 and 7. And the solution has no double-covering for each flight.
7 Discussion and Conclusion In summary, we could optimize the given crew scheduling problem by forming it into an integer linear programming problem. We find the time cost for all possible pairings and select a subset of them that meets our constraints. Then we reform this problem as an LP problem by building objective functions and constraints and using MATLAB to solve it. Previously, we discussed the method of the chew and airplane scheduling assignments for 4 cities that are next to each other and got a reasonable solution. For a larger system, such as the United States, we can also follow that same train of thought. But for a larger system, instead of listing all the combinations of flights and calculating the time cost for each, a more efficient algorithm of going over every pairing and their time cost is needed. In addition, only considering the minimization of the time cost is out of touch with reality. The chews’ working hours distribution and the flights’ pairing combination also need to be taken into account. Specifically, we cannot let one crew fly four flights a day and others only fly two. Hence, working balance is also one consideration in real-life situations. Moreover, the schedule of the crews and flights was arranged within one day, which has nothing to do with the overnight rest. However, in reality, the duration of each flight
Crew Scheduling Problem: Integer Optimization Using Set
9
is usually longer, so under normal circumstances, flight companies have to place their employees at places other than their departure station to sleep overnight. According to the Code of Federal Regulations Title 46 (Shipping Parts 90–139, Revised as of October 1, 2009) [10], overnight for the crews is necessary. This will lead to a more complex scenario, where the overnight rest time and the cost of the hotels associated need to be put into the objective time cost function that we want to minimize (Fig. 3). Acknowledgement. Yiqiao Wang, Hanfei Shi, and Liuzhouyu Shi, contributed equally to this work and should be considered co-first authors.
Appendix A:
Fig. 3. Map of the USA and the selected regions.
Appendix B: Minimizez = cT x Subject to Ax ≥ b(forflighti, i = 1, 2, . . . , 8)
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Appendix C:
References 1. Fontanet-Pérez, P., Vázquez, X.H., Carou, D.: The impact of the COVID-19 crisis on the US airline market: Are current business models equipped for upcoming changes in the Air Transport Sector? Case Studies on Transport Policy (2022). https://www.sciencedirect.com/ science/article/pii/S2213624X22000256#s0030 2. Kasirzadeh, A., Saddoune, M., Soumis, F.: Airline crew scheduling: models, algorithms, and data sets. EURO J. Trans. Logistics 6(2), 111–137 (2017). https://doi.org/10.1007/s13676015-0080-x
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3. Aydemir-Karadag, A., Dengiz, B., Bolat, A.: Crew pairing optimization based on hybrid approaches. Comput. Ind. Eng. 65(1), 87–96 (2013). https://doi.org/10.1016/j.cie.2011. 12.005 4. Cheap student flights, hotels and Tours. StudentUniverse. (n.d.). 18 July 2022. https://www. studentuniverse.com 5. Gopalakrishnan, B., Johnson, E.L.: Airline crew scheduling: state-of-the-art. Ann. Oper. Res. 140(1), 305–337 (2005). https://doi.org/10.1007/s10479-005-3975-3 6. Crew scheduling problem - mcgill university. (n.d.). 14 July 2022. http://cgm.cs.mcgill.ca/ ~avis/courses/567/notes/lec7.pdf 7. Office of the chief counsel. Office of the Chief Counsel | Federal Aviation Administration. (n.d.). Retrieved July 29, 2022, from https://www.faa.gov/about/office_org/headquarters_off ices/agc 8. Taboga, M.: Indicator function. Indicator function | Indicator random variable (n.d.). 29 July 2022. https://www.statlect.com/fundamentals-of-probability/indicator-functions 9. Vanderbei, R.J.: Chapter 23. Integer Programming. In Linear Programming: Foundations and Extensions (International Series in Operations Research & Management Science (196)), 4th ed. 2014 ed., Vol. 196, pp. 345–346 (2013). Springer. https://doi.org/10.1007/978-1-46147630-6 10. Office of The Federal Register. Code of Federal Regulations (CFR), Title 46, Shipping, Pts. 90–139, Revised as of October 1, 2020. NARA (2021)
Research on Business Management Based on New Retail Model: The Case of Yonghui Supermarket Chen Jing(B) University of British Columbia, Vancouver, Canada [email protected]
Abstract. The retail business is a fast-growing cornerstone of economic growth. In recent years, China’s economic growth has maintained a steady tempo, and with the increase of people’s buying power and consumption level, China’s retail companies have received new vigor, giving birth to the notion of “new retail.” Increasing rivalry among peers and rising costs have impeded the growth of new stores in recent years. Domestic new retail firms do not pay enough attention to cost management and lack expertise in it, so they cannot strengthen their competitive edge via cost control. Therefore, it’s important to analyze new retail firms in China’s cost control issues and provide solutions. This paper uses case study and quantitative analysis to collect financial data of Yonghui Supermarket in recent years, analyzes annual changes and trends of cost and supply chain indicators, and reveals the specific problems encountered in the management process of new retail enterprises, such as high investment in initial expansion, scarcity of composite talents, and high cost of logistics system configuring, so as to offer management optimization recommendations for new retail enterprises as a point of reference for other new retail enterprises. Keywords: New Retail · Cost management · Supply chain management · Yonghui Supermarket
1 Introduction Since the inception of contemporary retailing, there is no adequate explanation, thus industry players summarize and conclude in practice. History is full with undefined new things. New technologies have changed retail manufacturing and consumption since the 1960s. Customers are guiding retail expansion and reversing manufacturing in the second information revolution. As contemporary retailing influences traditional retail and e-commerce, retailers are transforming [1]. As companies try innovative retailing, issues arise. Whether it’s e-commerce with information technology as the main means of transaction or conventional retail shops, they’re testing new places, new technologies, and new situations. Many firms struggle to manage time, money, and people [2]. This paper mainly examines the current state of management control of new retail enterprises, identifies the issues in supply chain management and cost control of new © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 12–20, 2023. https://doi.org/10.1007/978-981-99-6441-3_2
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retail enterprises, and uses China’s new retail representative, Yonghui Supermarket, as a case study to analyze its efforts in the new retail model in depth. Through literature review and case study analysis, this study determines the advantages and disadvantages in supply chain management and cost control of the Yonghui Supermarket, suggests improvements to the disadvantages, investigates the measures it employs, and finally offers management optimization recommendations for new retail enterprises as a point of reference for other new retail enterprises.
2 Analysis on Yonghui Supermarket’s Management Yonghui Supermarket is a market leader in the retail sector. Yonghui Supermarket has explored two supply chain financing models, accounts receivable financing and pure credit financing, and has achieved certain results. This is a result of its growing strength, the increasing demand for financing from SMEs in the supply chain, and the maturation of digital technology. Currently, Yonghui Supermarket’s operation is founded on the paradigm of fresh food primarily driving the sales of other items, and its primary customer groups consist of housewives and office employees. The primary sectors are retail and apparel, as well as seafood, agricultural and ancillary goods, and daily food supply, among others. In addition, Yonghui Supermarket combines the upstream and downstream of the supply chain to save money on manufacturing and operation, and invests in creating a nice, clean, and sanitary retail environment to improve the shopping experience for its consumers. 2.1 Supply Chain Management of Yonghui Supermarket The supply chain functions as a meridian for grocery stores. The Yonghui Supermarket supply chain consists of four segments: procurement, logistics and transportation, inventory, and sales. Its supply chain is of the SPA mode kind. Also referred to as “vertically integrated.” After its IPO in 2010, Yonghui Supermarket has become an industry leader by implementing a supply chain management strategy in response to the “conversion of agro to super” national agenda. Yonghui Supermarket has built its own supply chain system by accessing the front end of the supply chain and tying upstream and downstream producers closely together. The company has established a wide range of cooperative relationships through last-mile distribution from upstream procurement to end-users, as well as its own nationwide supplier brand names, such as Yonghui Finance, Yonghui Logistics and Distribution, and Yonghui Food, which have resulted in business cooperation, quality management, and decreased production costs. Utilizing the forms of ownership involvement and management, the firm has built high-quality upstream suppliers of raw materials. Concurrently, Yonghui Supermarket built its own food business structure and formed a distribution subsidiary in the midst of the sales process, therefore constructing its own logistics and distribution network. Since 2015, Yonghui Supermarket has made extensive modifications to its current supply chain, offline physical locations, and internal structure. The firm has steadily transitioned from physical fresh food and the coexistence of three key business segments to a new retail development model including the coexistence of four major segments: cloud super, cloud creation, cloud commerce, and cloud investment (Table 1).
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C. Jing Table 1. Business unit.
Business Unit
Major business
Cloud Super
Involving red label stores and green label stores, covering twenty-one provinces in Chongqing, Sichuan and other regions
Cloud Creation
Yonghui Life Store, Super Species, Yonghui Life app and other businesses
Cloud Commerce
Reorganization of existing headquarters functions and business units
Cloud investment
Yonghui finance business, responsible for supply chain financing and other businesses
Yonghui Supermarket offers a number of procurement channels, including national centralized procurement, regional direct procurement, “agriculture and super docking,” foreign direct procurement, and supplier procurement. Since its introduction to the market, Yonghui Supermarket has developed its own “Yonghui Model” business strategy. For perishable and perishable fresh agricultural goods and processed products with a short shelf life, high transportation and storage needs, and a short shelf life, direct marketing and direct procurement in the form of farm-to-supermarket docking is the primary strategy. To meet the increased demand for product safety, quality, and convenience, modern product suppliers typically gradually modernize the product supply chain from the traditional model using the method of vertical synergy with hybrid enterprises; this supply chain transformation also presents an opportunity for farming enterprises (Fig. 1).
Fig. 1. Direct sourcing model in yonghui supermarket.
Yonghui Supermarket has derived its supply chain upstream and gradually expanded its own brands. Yonghui Supermarket takes building a vertical supply chain as the direction of supply chain transformation, improving the ability of supply chain tracking and traceability, optimizing supply chain structure and controlling procurement costs. In order to build a vertical supply chain system, Yonghui has been committed to vertical integration upstream of the supply chain, minimizing procurement intermediaries and
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cooperating directly with source suppliers to shorten the length of the upstream supply chain and realize vertical supply of products [4, 5]. The “agriculture-supermarket” docking format allows Yonghui Supermarket to cooperate directly with the source manufacturers of fresh food products, and in the process Yonghui Supermarket actively introduces differentiated main products. 2.2 Cost Management of Yonghui Supermarket Supply Chain Cost Management. Nevertheless, according to the available data, the majority of the retailer’s logistics expenses are represented in the “storage and logistical costs” component of the cost of sales. This research analyzes the logistics and storage expenses of Yonghui Supermarket for each year of its growth and utilize them as a quantitative indication of the present cost condition at the logistics management level (Table 2). Table 2. Trend of Freight and storage charges (in 100 million). Year
2016
2017
2018
2019
2020
2021
Freight and storage charges
4.45
5.65
9.92
11.29
11.74
12.36
Total Revenue
492.32
585.91
705.17
848.77
931.99
910.61
Percentage
0.90%
0.96%
1.41%
1.33%
1.26%
1.36%
26.97%
75.58%
13.81%
3.99%
5.28%
Year-on-year growth rate
15
1.50%
10
1.00%
5
0.50% 0.00%
0 Y16
Y17
Y18
Y19
Freight and storage charges
Y20
Y21
Percentage
Fig. 2. Trend of freight and storage charges (in 100 million).
It can be seen from Fig. 2, in 2016 and 2017, Yonghui Supermarket’s freight and storage costs as a percentage of operating sales stayed below 1%, indicating good cost control and the ability to build its own logistics infrastructure with minimal operational income. This index topped 1% in 2018, a considerable increase from previous years.
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C. Jing
Its income proportion is low. However, the percentage is small. Yonghui Supermarket’s freight and storage costs rose by RMB 420 million (75.58%) in 2018. Yonghui Supermarket’s expanded store presence is driving up logistical costs. The development of stores will raise storage and logistics expenses due to more orders and deliveries. The company’s logistics and distribution centers span 17 provinces and cities and occupy 450,000 square meters, according to its 2018 annual report. Temperature-controlled distribution facilities are also available. Distribution operations totaled 40.9 billion yuan, and logistics supply rate rose by 30.6% to 58.8%. In 2018, Yonghui Supermarket increased the number of outlets in the new retail sector. It improved warehousing and logistics transportation to improve online and offline retail transportation and prepare for expansion. Freight and storage charges soared.
Days of Inventory turnover
Fig. 3. Days of inventory turnover.
Another supply chain issue also comes from the inventory turnover side (Fig. 3). Yonghui Supermarket’s inventory turnover days started to climb annually in 2016. For a retail superstore like Yonghui Supermarket, which operates primarily with a high proportion of fresh food and food supplies, an excessive inventory backlog will cause spoilage, increase the cost of goods, occupy warehouses and increase logistics costs, and decrease the gross profit margin of the business. This demonstrates that if Yonghui Supermarket wishes to compete with several chain supermarkets, it must not only preserve its inherent advantages in procurement and logistics, but also enhance its capacity to plan and control the number of items. Administrative Cost Management. As the change of Yonghui Supermarket’s retail model and the expansion of the company’s size continue, the new retail needs for online and offline and logistics integration are rising, as is the annual rate of rise in management expenses. The percentage of management expenditures to operational revenue reached 2.53% in 2018, a 38.17% rise compared to the same period in 2017 (Table 3 and Fig. 4). In the following years, Yonghui Supermarket further optimized its overhead costs and saved expenses by adjusting its staff structure.
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Table 3. Trend of administrative expenses (in 100 million). Year
2016
2017
2018
2019
2020
2021
Administrative expense
9.69
12.89
17.81
20.13
22.93
21.55
Total Revenue
492.32
585.91
705.17
848.77
931.99
910.61
Percentage
1.97%
2.20%
2.53%
2.37%
2.46%
2.37%
33.02%
38.17%
13.03%
13.91%
6.02%
Year-on-year growth rate
Trend of Freight and Storage Charges (in 100 million)
Fig. 4. Trend of freight and storage charges.
2.3 Issues on Supply Chain Management and Cost Management Issues on Supply Chain. (1) Significant backlog of goods in stock. Yonghui Supermarket is a large supermarket chain that focuses mostly on fresh foods. Due to the special nature of the goods requiring fresh agricultural products with a short shelf life and high requirements for freshness, when the freshness of the products is not high, they are not promptly discounted and promoted, but are kept in the warehouse, causing a backlog of goods, which must eventually be discarded, thereby increasing the price of goods. On the other hand, owing to the enormous incoming amount of some items, the sales department did not account for the real sales situation, resulting in the buildup of goods in the warehouse department, which raised storage costs and incurred needless expenditures for the business [7]. The increasing trend of Yonghui Supermarket’s inventory turnover days from 2017 to 2019 indicates that the enterprise has a problem with inventory backlog, and the excessive inventory backlog will consume the enterprise’s capital, affect the cost of operation, reduce the enterprise’s profit, and impact the enterprise’s daily operations. (2) Unreasonable purchasing plan. Due to the lack of a professional procurement system, the company is unable to reasonably predict the market demand, which leads to the inventory being larger than the demand, resulting in a backlog of goods and increasing
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C. Jing
the burden of the company. Moreover, some of the fresh products are seasonal in nature, which makes the supermarkets unable to accurately predict the purchasing volume, and the purchasing personnel tend to neglect the storage of fresh products, which will increase the purchasing cost. (3) Less efficient distribution. There are downsides to the supermarket’s separate distribution logistics section, such as poor distribution efficiency. Because most supermarkets utilize a semi-automatic method of distribution, they mostly distribute and pack items manually, which reduces the enterprise’s efficiency owing to the low level of mechanical utilization. On the other hand, supermarkets rely mostly on fresh items as their primary commodities, and it is inevitable that the products will be damaged throughout the distribution process, which will lower their quality and make it impossible to satisfy consumers’ demand. (4) Problems with the inventory monitoring system. As a huge retail chain, Yonghui Supermarket sells a wide range of difficult products, resulting in a vast inventory. It is simple for inventory discrepancies to occur if management does not execute inventory checks on time, which will influence the purchasing department’s choice and result in losses for the supermarket business. And if the warehouse’s inventory is not arranged in a timely manner, it is possible for items to accumulate, which will delay the sale of goods, reduce the supermarket’s revenue, use the supermarket’s resources, etc. A fair inventory count may assist managers in calculating the rate of damage to items and intuitively understanding the difficulties of inventory management, so minimizing the enterprise’s loss and directing its long-term growth. As the internal audit department is under the direct supervision of the supermarket’s general manager, the management level is weak and cannot fully ensure the independence and accuracy of internal audit, which makes it very easy to engage in fraudulent behavior and complicates supermarket management. Issues on Cost Management (1) Separate sourcing model. Separate procurement technique will certainly raise the enterprise’s procurement costs and lead to inefficient capital management. Yonghui Supermarket employs the independent procurement technique, which prevents the quality of items from deteriorating in the middle of the supply chain [8]. In order to retain the supplier’s reputation, Yonghui Supermarket will seek for largescale suppliers for people’s everyday requirements whose manufacturing date, delivery time, and after-sales service are all assured. As a result, Yonghui Supermarket has a reliable partner to assure the uninterrupted flow of products. Because commodity transactions must adhere to the concept of independence, Yonghui Supermarket must still pay the deposit in advance if the quantity of items acquired at one time is quite significant. Therefore, based on the existing state of Yonghui Supermarket, it is difficult to regulate procurement expenses, but simple to lose a pricing edge. Not only is it difficult to manage the buying cost, but it is also simple to lose the pricing advantage, and most significantly, it cannot govern the interaction between buyers and sellers. The most significant aspect is that the interaction between buyers and sellers cannot be adequately managed. (2) Poor budget management capabilities. Only when the enterprise’s resources are efficiently distributed will the enterprise’s operational expenses decrease and economic advantages increase. There is still a lack of active communication between the departments of Yonghui Supermarket, resulting in the separation of the purchasing part and the selling part of the enterprise, so that the purchasing part is unclear about
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the specific situation and needs of the selling department, and can only decide the quantity of goods to be purchased based on the enterprise’s sales data, which can easily lead to budget errors.
3 Recommendations 3.1 Reduce Inventory Backlog The significance of inventory in the daily operations of big grocery chains is crucial. To guarantee the smooth execution of everyday operations, businesses must maintain a sufficient supply of safety stock. By analyzing the inventory turnover rate of Yonghui Supermarket, this paper has determined that the supermarket has a problem with inventory backlog; therefore, we can adopt the “zero inventory” management method to reduce the amount of inventory, on the premise of ensuring the safety stock, reduce the quantity of non-essential goods in stock, and decrease the liquidity ratio occupied by the inventory. To reduce losses and increase the inventory turnover rate of the enterprise, supermarkets should use zero inventory or low inventory mode for vegetables and fruits, make reasonable purchase plans, attempt to sell the inventory on the same day, and if necessary, take advantage of promotional discounts. 3.2 Improve Enterprise Information Management Inventory management could be enhanced to the degree that information management enables managers to have a better understanding of the organization. Utilizing the Internet to its fullest extent, and establishing a comprehensive internal control system, inventory management in all phases of the process can grasp the fundamental inventory status, facilitating procurement, storage, and sales. Information management paperless office has also become the mainstream of today’s corporate growth, which not only improves the efficiency of businesses but also reduces expenses and increases their worth [9]. Yonghui Supermarket can use information technology software to deliver information, which can facilitate the management of supermarket chains in various regions of the country, and can also grasp the inventory situation of each supermarket in a timely manner, to play the role of software management and communication in its entirety. 3.3 Improve Store Operation Efficiency In the contemporary retail businesses, where every square inch of space is a precious commodity, increasing the efficiency of store space usage is an efficient method for decreasing operational expenses. Improving the store’s customer flow is the first step that must be taken. Before selecting a site for a brick-and-mortar business, the target user groups should be correctly positioned and the consumer flow per unit of time should be assessed [10]. The second step is to focus on the unit pricing per consumer. By strengthening the correlation, synergy, and consistency of the items offered in the store to enhance the product linkage rate, so that customers may purchase several products after purchasing one, the unit ping efficiency in the physical shop can be increased.
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4 Conclusion The globe is presently experiencing a golden age of online economic development, and firms will pay more attention to the growth of digital retail. This paper makes extensive reference to many scholars’ relevant journal literature on new retail development models, analyzes the supply chain management and cost management of Yonghui Supermarket in the context of its case study, further analyzes the strengths and weaknesses of Yonghui Supermarket, a representative of the new retail industry, in its corporate management, and finally clearly emphasizes in its recommendations that cost control and process informatization are areas to which the new retail industry should pay attention and are important strategies for the long-term sustainable development of enterprises. This study focuses on the supply chain management and cost management of Yonghui Supermarket to reflect the management problems of the enterprise. Future research can be conducted for a specific link of supply chain management if it wants to draw wide attention to the enterprise, for example, to study the link of headway logistics and transportation management, parcel packing and distribution, etc., to gain an in-depth understanding of the impact of supply chain links on the new retail industry. In addition, this study uses key financial indicators to analyze cost management, but due to the difficulty of obtaining information from public sources, there is still a shortage of access to non-financial indicators for enterprises.
References 1. Huang, Z., Shi, X.: Exploring the development mode of “new retail species”- from boxma fresh life and super species. Business Econ. Res. 01, 26–29 (2019) 2. Davidson, W.R., Bates, A.D., Bass, S.J.: The retail life cycle. Retailing: The Evolution and Development of Retailing 55(6), 89–96 (1976) 3. Chen, J., Zhang, H., Sun, Y.: Implementing coordination contracts in a manufacturer stackelbergdual - channel supply chain. Omega 40(5), 571–583 (2012) 4. Wang, X., Ng, C.T.: New retail versus traditional retail in e-commerce: channel establishment, price competition, and consumer recognition. Ann. Oper. Res. 291(1), 921–937 (2020) 5. Cao, E., Ma, Y., Wan, C., et al.: Contracting with asymmetric cost information in a dualchannel supply chain. Oper. Res. Lett. 41(4), 410–414 (2013) 6. Yonghui Supermarket Annual Report (2021). https://www.yonghui.com.cn/upload/Inv/817 3137.PDF 7. Anna, D.C.: Issues in Supply Chain Management. Industrial Marketing Manage. (29), 65–83 (2004) 8. Zhao, Z.: A few thoughts on the “big cost view” of cost reduction. Finance and Accounting (21), 61 (2017) 9. Zhang, Y.: Exploring the transformation and development of traditional retail industry in the context of the internet. Circulation Economy 12, 001–002 (2018) 10. Zhu, L.: The construction and innovative development of logistics system under new retailing. Logistics Technol. Appl. 23(03), 116–118 (2018)
Supply Chain Management—A Case Study of Huawei’s Supply-Chain Chip Shortage Jiangke Chen(B) School of Management Science, CDUT Sino-British Collaborative Education, Chengdu 610059, China [email protected]
Abstract. Today’s business environment is more competitive and complex. Decreased trade barriers and more developed countries promoting businesses bring more businesses into the market. There is also more competition among organizations because businesses are introducing newer and cheaper innovations at a faster rate Innovation in organizational processes can make significant changes in the product output. Supply chain management is a potential area for technological innovations. Therefore, this paper will focus on the extent to which automation technology and blockchain technology support modern supply chains, and take Huawei as an example to study how Huawei’s global supply chain provides it with a competitive advantage. And makes a study on the current supply chain dilemma faced by Huawei and puts forward feasible solutions. The study found that automation can benefit enterprises by achieving faster and more efficient operations in inventory management and warehouse management. However, enterprises also need to be aware of the high cost of using automation. Blockchain technology can improve supply chain stability and security through smart contracts and shared ledger management. But there are also problems, such as a lack of laws and regulations that reduce the overall efficiency of the supply chain. Huawei’s use of reverse logistics and management of distribution channels have enhanced its supply chain’s competitive edge, but now it faces a chip shortage. For the current Huawei, finding backup suppliers, stabilizing consumer confidence, and maintaining investment in chip research and development are feasible ways to solve the existing problems. Keywords: Supply chain management · Automation · Blockchain · Huawei · Chip
1 Introduction In the context of economic globalization, the competition in the commercial field has become more intense and complex. As one of the core competitive advantages of enterprises, supply chain management has been gradually paid attention to by more and more corporate management because supply chain management reduces costs and improves the agility of enterprises. Grand View Research [1] also reported that both large and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 21–28, 2023. https://doi.org/10.1007/978-981-99-6441-3_3
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small enterprises are spending more on supply chain analysis to maintain their competitive edge in the market. Supply chain analysis plays an important role in accurate forecasting, supply chain optimization, minimizing waste and integrating business data. According to statistics [2], the global supply chain management market reached 15.58 billion dollars in 2020 and is expected to grow to 30.91 billion dollars in 2026. This paper will be divided into two aspects. On the one hand, they will critically analyze how modern supply chain technologies can help enterprises gain competitive advantages, which will be analyzed through automation and blockchain, respectively. On the other hand, it will critically analyze the role of Huawei’s supply chain in supporting its global competition and propose the challenges that Huawei may encounter and the measures that Huawei can take to cope with the challenges.
2 Definition of Concepts 2.1 Automation In supply chain management, enterprises use automation to enhance inventory management, warehouse management and logistics. One of the techniques for inventory management is automated data transfer which includes radio frequency data capture systems (RFDC) and bar code scanning, which can read data through bar codes and provide data on any gap between sales, shipments, and current stock for inventory management purposes [3]. As a result, automated data transfer technologies supplement distribution management transactions [4], which helps enterprises improve the efficiency of the whole supply chain and thus enhance their profitability. In terms of warehouse management, the warehouse management system automates collecting data and reduce menial tasks like billing, delivery order, and stock-taking [5]. It is suggested the system reduces labor costs, eliminates errors, and increases internal process efficiency, which leads to better delivery and better flexibility. At present, many enterprises have put automation into practice in their supply chains. In 2012, Knorr Germany introduced an intelligent machine vision system to check the quality of produced satchels. The vision system involved a micro-camera, a pattern recognition program, vision software, and a visualization panel. Since the micro camera replaced human visual inspection, monitoring manufacturing and inspection has become cost-effective and straightforward [6]. The use of automation in Knorr’s total productive management (TPM) has contributed to ongoing cost reduction and profitability increases over the years. Additionally, Conversight AI offers an intelligent assistant that has worked for organizations like Ford, Daimler Track, Dr.Argawal Eye Hospital, and Cargill [7]. Athena is a supply chain intelligent assistant that automatically senses supply chain demand and stock usage and then turns the data into reports that improve forecast accuracy [7]. Athena has helped businesses organize data on inventory, sales, and purchases and make analyses that allow businesses to optimize their supply chain systems. However, automation also has significant drawbacks in the supply chain, the most prominent of which is cost. Christiansen [8] reported that applying automation to the supply chain requires a huge investment from companies, with some automated machines costing millions of dollars to maintain. Thus, for the use of automation, it seems that only
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large enterprises with deep pockets can do it. For small and medium-sized enterprises, the maintenance cost of automatic machinery alone is not what they can afford. So, this kind of automation technology is still limited to the top companies in the world. 2.2 Blockchain Blockchain technology can improve supply chain competitive advantage through its functions of security and governance. Smart contracts and the management of shared ledgers are the two main advantages of blockchain technology in the supply chain. In terms of security, smart contracts in blockchain technology contribute a lot. They are pre-programmed principles that only execute upon satisfaction of certain conditions [9], which enhance the security of blockchain systems because only the right holders know the processes to implement the requirements. This helps companies avoid financial losses caused by artificial manipulation of order data in the supply chain. In addition, blockchain technology in supply chain management also addresses the issue of using shared transaction ledgers (such as enterprise assets or data exchange) [9]. Because the member nodes in a blockchain network do not rely on third parties to mediate transactions, but instead use all the confirmed protocols to determine the contents of the ledger, and the protocol changes must occur in more than one place at the same time, therefore, blockchain technology can avoid the current problems of traditional ledgers, which are prone to abuse and tampering [9]. This effectively avoids the loss of business opportunities caused by transaction fraud and provides support for enterprises to gain competitive advantages in the supply chain. There are also not uncommon examples today of companies using blockchain technology to support the reliability of their supply chains. For example, Bumble Bee Foods uses blockchain technology to track the history of tuna fish from the ocean to the customer. In 2019, Bumble Bee Foods integrated an SAP Cloud Platform blockchain-as-aservice technology to help verify the authenticity of its fish products. The technology has a QR code that reveals the supply chain history of the food to customers from tamperproof data on the blockchain [10]. Bumble Bee Food’s blockchain technology has reinforced customer trust because it enables traceability of their food’s supply chain from the ocean to cold storage and customers, which ensures that Bumble Bee Food’s supply chain is authentic and transparent, enabling it to win customers and increase orders. Another example happened in 2020, Honeywell introduced an anti-counterfeiting system that uses the blockchain to track airplane parts in the supply chain system. Its blockchain has a centralized database that reconstructs the history of airplane parts which helps Honeywell provide each piece’s tamper-proof supply chain history [11]. Blockchain technology helps Honeywell eliminate the possibility that information about aircraft parts could be manipulated in the supply chain, which improves the stability of its supply chain and enhances the company’s profitability. However, blockchain technology itself has its problems today. Smart contracts in blockchain technology are considered to be legally binding contracts [12]. Therefore, the legal issues involved in the deployment of smart contract systems should be regulated. However, there is no specialized organization that controls the deployment of smart contracts [12]. It can be seen that the binding nature of smart contracts is still uncertain at the legal level, which may cause enterprises to face legal problems in the supply chain.
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3 Role of Huawei’s Global Supply Chain in Supporting its Competitive Advantage Huawei Technologies produces and sells communication equipment. The organization started in 1987 in China, and has since spread to international countries Huawei’s global supply chain operations have the largest team spread out globally. Its reverse logistics technology and distribution channel management provide its supply chain with competitive advantages. 3.1 Reverse Logistics Huawei’s reverse logistics gives competitive advantages to its supply chain. As shown in Fig. 1, Huawei’s reverse logistics model can be divided into four steps. The first step could be called “Life Extension”, in which Huawei reproduces the disassembled products in its own factories [13]. The second step is “Resell”, qualified products in reproduction will be used by Huawei for sales [13], which saves the cost of producing new products and reduce the cost of supply chain. The third and fourth steps are carried out simultaneously, namely, “Remanufacture and Recycling”. For the defective products in production, Huawei will disassemble and screen them. The usable components will be recycled and put into reproduction, and the waste will be buried in landfills [13].
Fig. 1. Huawei’s reverse logistics model [13]
It can be seen that reverse logistics technology has helped Huawei reduce supply chain costs. By reusing, remanufacturing and recycling, Huawei can tap the potential value of product recycling. According to Huawei’s official website in 2019, it recovered more than 850 tonnes of electronic equipment through reverse logistics in Brazil from 2017 to 2019, with the cost of reprocessing the recovered products significantly lower than the cost of producing new products. Obviously, the cost reduction certainly gives Huawei a competitive advantage in its supply chain. However, reverse logistics also faces some challenges. The most important is the loss of product value caused by time delays. Sajadieh [14] claimed that reverse logistics will
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cause time delay, which leads to the delay of product shelf time. For products with high marginal value such as mobile phones, the market value will fall particularly sharply. This eventually leads to profit differentiation in the later stage and due to the complexity of reverse logistics recovery and the coordination of resources, it is difficult to accurately measure profit differentiation [14]. This shows that reverse logistics will damage product value due to delayed time to market, and for mobile phone manufacturers like Huawei, their product value will be more severely affected, which will affect Huawei’s overall earnings. 3.2 Distribution Channels of Huawei Huawei’s distribution channels also provide a competitive advantage for its supply chain operation. As shown in Fig. 2, Huawei uses a hierarchical system to organize global dealers. There are two levels of distribution partners. Tier 1 Channel partners include global Distributor (GD), Regional Distributor (RD), Global Partner (GP), Regional Partner (RP) and Value-added Partner (VAP). Tier 2 Channel partners include Gold, Silver and Authorized.
Fig. 2. Huawei Channel Structure [15]
These distributors play the role of middlemen in Huawei’s global supply chain, which is transactional and logistical. In terms of transactions, Huawei adds value to its distribution channels by introducing intermediary resources to establish market contacts and customer contacts [16]. On this basis, intermediaries also serve as a link between Huawei and customers, establishing a buying and selling relationship between Huawei and customers, and directly assuming the functions of marketing and sales. [16]. Huawei’s distribution channels strengthen its ties with customers, helping it reduce bullwhip effects in its supply chain. And as for logistics, Huawei’s intermediaries are responsible for the physical distribution of products, sorting and storing supplies at locations accessible to end customers and Tier 1 Channel Partners also break down Huawei’s volume production into smaller pieces for downstream middlemen (Tier 2 Channel partners) or retailers who transport smaller shipments to distribution channels [16]. Therefore, it can be inferred that Huawei makes full use of the function of distributors as intermediaries in its distribution channels, greatly improving the circulation of distributed products,
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reducing distribution costs and improving distribution efficiency, which enables Huawei to obtain economic benefits through efficient product distribution and rapid payment collection in the supply chain. However, it can be seen from Fig. 1 that Huawei’s distribution channels include 2 levels and involve 8 partners. Gaille [17] claimed that when the distribution channels go through multiple levels instead of simply from A to B, a time delay will occur in the circulation process and efficiency will decline. Therefore, Huawei needs to provide sufficient technical support in the distribution channels to ensure the efficiency of the distribution channels in the supply chain. Also, intermediaries in distribution channels will increase Huawei’s cost burden. Gaille [17] claims that there are additional costs when there is an intermediary between the organization and the end-user. In Huawei’s distribution channels, Huawei has to pay for its numerous intermediaries, which increases the cost of operating its supply chain. The cost increases will be negative for Huawei to gain competitive advantages in its supply chain.
4 Future Challenges of Huawei’s Global Supply Chain and their Implications Huawei has grown to surpass most telecommunications companies and its global reputation puts it at risk of situation-based risks. Since Huawei is a telecommunications giant, it can be caught up in the middle of international political wars. For instance, the organization became a target by the US when they had an agenda of creating a technological war with China. For example, the Trump administration has banned chipmakers that design chips using American technology and software from exporting chips to Huawei without licenses [18]. The politicization of technology policies in US and China relations affected how technology companies like Huawei do business internationally [18]. The ban directly harms Huawei’s supply chain from the source of suppliers, leading to a decline in production and sales of Huawei’s mobile phones. According to McDONALD [19], Huawei’s sales in the first half of 2021 dropped 29.4% from $70.2 billion to $49.6 billion year on year due to the impact of U.S. sanctions. The external factor is hard to amend in a short period of time because of part of national security. Therefore, based on Huawei’s global supply chain, it must constantly seek innovation to maintain its competitive advantage. The ability to trade-off between cost efficiency and flexibility can be challenging to supply chain systems of modern technological firms. Huawei must always stay updated in terms of innovation as a technological company to maintain global competition by developing the capability to meet cost efficiency and innovation. This poses a challenge to merging innovation and cost-efficiency. Huawei faces the challenge of creating variety and customization at low cost, high technology at low cost, and specialist products at low prices [20]. Providing high technology at a low cost requires the organization to invest in research and development on how to exploit the current market base. Businesses may want to offer variety and flexibility at a low cost, but the cost of innovations is usually high. According to disclosures on Huawei’s official website in 2022, although Huawei spends more than 10% of its sales revenue on research and development every year, and the figure has reached 22.4% in 2021, exceeding 14.27 billion yuan, Huawei has not
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made any substantial progress at present, such as solving the chip problem. Therefore, if Huawei cannot make technological breakthroughs under the pressure of U.S. sanctions, it will not be able to balance the issue of cost-effectiveness and innovation ability. To solve the chip supply problem, Huawei can address the chips dilemma by coordinating with backup suppliers. Such suppliers can supply goods and services as a backup in case there is an addressing supply chain disruption risks if the regular suppliers become incapacitated by external factors. And Huawei should continue to invest in innovation to achieve the goal of developing original chips as soon as possible. For a technology manufacturer like Huawei, mastering chip technology can help it maintain its competitive edge in the technology field. To deal with falling sales, Huawei should also have a contingency plan to address customer concerns during disruptions. In the era of social media, any adverse news for enterprises will be quickly spread, which will directly affect consumers’ confidence in enterprises and leading falling sales. Therefore, Huawei should have strategies to address customers who are waiting for products during external disruptions.
5 Conclusion To survive in the current market, businesses must use technologies in their supply chain systems to achieve a competitive advantage. Automation can help enterprises achieve faster and more efficient operations in inventory management and warehouse management, thus giving enterprises a competitive advantage in the supply chain. However, businesses also need to be aware of the high cost of using automation. Blockchain technology can enhance the stability and security of supply chains through smart contracts and shared ledger management. However, it also has the problem of a legal vacancy. Huawei’s use of reverse logistics and management of distribution channels have enhanced its supply chain’s competitive edge, but now it faces a chip shortage. For The current Huawei, finding backup suppliers, stabilizing consumer confidence and maintaining investment in chip research and development are feasible ways to solve the existing problems.
References 1. Grand View Research. Supply Chain Analytics Market Size, Share & Trends Analysis Report By Solution, By Service, By Deployment, By Enterprise Size, By End Use, By Region, And Segment Forecasts, 2022 – 2030. Published Date: Mar, 2022Report ID: GVR-1–68038–928– 9Number of Pages: 170Format: Electronic (PDF)Historical Data: 2017–2020 (2022) 2. Statistics. Size of the global supply chain management market worldwide from 2020 to 2026. Global supply chain management market size 2020–2026 | Statista (2022) 3. Bademosi, F.M., Issa, R.R.: Automation and robotics technologies deployment trends in construction. In: Automation and Robotics in the Architecture, Engineering, and Construction Industry, pp. 1–30. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-77163-8_1 4. Mehmeti, G., Musabelliu, B., Xhoxhi, O.: The review of factors that influence the supply chain performance. Academic J. Interdisciplinary Stud. 5(2), 181 (2016) 5. Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S.: The impact of supply chain management practices on competitive advantage and organizational performance. Omega 34(2), 107–124 (2006)
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6. Eggeling, K.A., Adler-Nissen, R.: The Synthetic Situation in Diplomacy: Scopic Media and the Digital Mediation of Estrangement. Global Studies Quarterly, 1 (2021) 7. Dash, R., McMurtrey, M., Rebman, C., Kar, U.K.: Application of artificial intelligence in automation of supply chain management. J. Strateg. Innov. Sustain. 14(3), 43–53 (2019) 8. Christiansen, L.: The Top Disadvantages of Business Process Automation. Available at: The Top Disadvantages of Business Process AutomationMenu (altametrics.com) (2020) 9. Kolokotronis, N., Limniotis, K., Shiaeles, S.N., Griffiths, R.: Blockchain Technologies for Enhanced Security and Privacy in the Internet of Things. ArXiv, abs/1903.04794 (2019) 10. Hooper, A., Holtbrügge, D.: Blockchain technology in international business: changing the agenda for global governance. Review of International Business and Strategy (2020) 11. Honeywell. Supply Chain (2022). https://www.honeywell.com/us/en/industries/supply-chain 12. Litan, A., Reynolds, M., Jones, L.C.: Managing the Risks of Enterprise Blockchain Smart Contracts. Published: 24 February 2020 ID: G00465806 (2020) 13. Karlborg, A.: Huawei Reverse Strategy under Circular Economy (2020). https://www.bing. com/ck/a?!&&p=a132e3eead3967160292f7d7651f5fbad9a9b3f0920198caa6c58c7d3804 75c4JmltdHM9MTY1NDI1MzI0MCZpZ3VpZD0zNTIzMGE3YS0zNDExLTRlODUtYjF mZi05MTFlMzEzODk3NzAmaW5zaWQ9NTE4Mg&ptn=3&fclid=8ab814b6-e32a-11ec9396-d2fb28130597&u=a1aHR0cHM6Ly93d3cuaXR1LmludC9lbi9JVFUtVC9Xb3Jrc2 hvcHMtYW5kLVNlbWluYXJzL2dzdy8yMDE0MDYvRG9jdW1lbnRzL1ByZXNlbnRhd GlvbnMvRm9ydW0tb24tRXdhc3RlLTIzLTA5LTIwMTQvUHJlczAxLUFuZGVyc0thcmx ib3JnLUUtV2FzdGUtR1NXMjAxNC1PcGVuaW5nQ2VyZW1vbnkucGRm&ntb=1 14. Sajadieh, M.S.: Global Supply Chain Management. In: Zanjirani Farahani, R., Asgari, N., Davarzani, H. (eds) Supply Chain and Logistics in National, International and Governmental Environment. Contributions to Management Science. Physica-Verlag HD (2009). https://doi. org/10.1007/978-3-7908-2156-7_3 15. Thongyod, S.: The Strategic Analysis of Huawei Investment & Holding Co., Ltd. MGN512 - Strategic Management. Third Semester, Academic Year 2018 Master of Business Administration, Stamford International University (2019) 16. Cole, R., Aitken, J.: ‘The role of intermediaries in establishing a sustainable supply chain’. J. Purchasing and Supply Manage. 26(2) (2020). https://doi.org/10.1016/j.pursup.2019.04.001 17. Gaille, B.: 12 Pros and Cons of Distribution Channels. 12 Pros and Cons of Distribution Channels - BrandonGaille.com (2015) 18. Williams, R.D., Center, P.T.C.: Beyond Huawei and TikTok: Untangling US Concerns Over Chinese Tech Companies and Digital Security (2020) 19. Mcdonald, J.: Huawei revenue sinks as smartphones hurt by US sanctions. Huawei revenue sinks as smartphones hurt by US sanctions - ABC News (go.com) (2021) 20. Berning, S.C.: The role of multinational enterprises in achieving sustainable development-the case of Huawei. European J. Sustainable Dev. 8(3), 194 (2019)
The Effect of Manufacturing Employment Changes on County-Level Partisan Voting Shares in the US Presidential Elections: An IV Analysis Yifan Gong(B) Cornell University, New York, NY 10021, USA [email protected]
Abstract. Employment has been an unavoidable topic in previous presidential and mid-term elections, which shows that the employment rate is an important factor affecting the results of U.S. presidential elections. This paper mainly studies the impact of changes in manufacturing employment in the United States on the results of five presidential elections from 2000 to 2016 at the county-level. This paper constructs a new import competition variable and analyzes it through instrumental variable method and 2SLS. This paper believes that whether to consider the lagging relationship between import competition and manufacturing employment change is an important factor to summarize the impact of manufacturing employment changes on the presidential election results, which to some extent complements the previous literature. At the same time, this paper also studies the research issues concerned or not concerned by the previous literature by classification (such as China shock, etc.). These discussions provide empirical evidence for the research conclusions of the previous literature under the county-level data. Keywords: Instrumental Variable Method · Manufacturing Employment · Presidential Election
1 Introduction 1.1 Research Background Ahead of the 2022 US midterm elections, the Biden Administration was taking a key step to ensure that federal funds will support the US manufacturing industry, including the US $1 trillion bipartisan infrastructure bill to strengthen the guarantee of local construction material procurement [1]. In the bipartisan infrastructure package that became law in November 2021, there is a requirement that all iron, steel, manufactured goods and building materials used in projects allocated to federal agencies are produced in the United States, otherwise no funds can be used. Drake, director of “Made in America” in the White House Office of Management and Budget, said there will be more good jobs in manufacturing. Increasing production in the United States will signify that large © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 29–52, 2023. https://doi.org/10.1007/978-981-99-6441-3_4
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companies will create more opportunities for small and medium-sized enterprises in the United States. President Biden has made this guidance the cornerstone of driving votes for him before the 2022 mid-term elections. He claimed that federal spending could be used to create more U.S. manufacturing jobs and reduce reliance on China and other countries whose geopolitical interests differ from those of the U.S. The similar political advocacy and policy advice took place in the 2016 presidential election process. In 2016, manufacturing workers heavily affected by trade played an important role in making the results of the US presidential election more reasonable. During the election campaign, the Republican candidate Trump believed that the decline in the US manufacturing industry was due to import competition, and claimed that China was “killing” the United States in trade, especially in manufacturing trade [2]. He promised to revive the American Dream and create manufacturing jobs. In order to realize this commitment, he proposed the extreme means of levying 45% tariffs on China, and raising tariffs on developing countries dominated by manufacturing exports such as Mexico. Since Trump took office in 2016, one of his first actions has been to withdraw the United States from the Trans-Pacific Partnership Agreement (TPP). From the above description, it is not difficult to see that the candidates and presidents of both the Republican Party and the Democratic Party have in recent years regarded the stabilization and development of domestic manufacturing employment and the reduction of imports of manufacturing products and other dependent products as one of the main policies of their campaign and ruling positions consolidating method. It is also the background for this paper to focus on the three key words of manufacturing imports, manufacturing employment and presidential elections. 1.2 Research Motivation and Aim The motivation for this study to focus on manufacturing is two-fold. First, the value of manufacturing products imported by the United States from the world each year accounts for about 73% of the total value, while the manufacturing products imported from China increased from 62% in 2000 to about 85% in 2016. It seems that the United States has a huge deficit in the international trade of manufacturing, which forms a strong contrast with the trade situation of non-manufacturing; the second is that this paper is to study the impact of manufacturing employment on the US presidential election. The outcome of the presidential election often depended on the swing states (such as Wisconsin and Ohio) whose pillar industries are agriculture or traditional industries, so focusing on import competition in manufacturing may be beneficial to the validity of our research. This paper focuses on the total imports of the United States from the world, but China’s import competition has been the focus of many scholars for a long time in the past. In 2001, China joined the World Trade Organization (WTO). In the past decade, China’s exports to most parts of the world almost doubled. This trade liberalization has led to significant changes in global trade flows and local labor markets, and triggered a series of impacts of Chinese competition on the U.S. labor market in highly traded sectors such as manufacturing. Among them, by analyzing the impact of Chinese competition on U.S. manufacturing employment from 1990 to 2007, Autor, Dorn and Hanson found that it is expected that an increase of $1,000 in import competition per worker would increase the local unemployment rate by 4.9%, reduced the employment participation
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rate by 2.1%, and increased the per capita expenditure of government social assistance by $58 [3]. On the other hand, some studies have proved that China’s accession to the WTO has brought considerable benefits to American consumers. It is estimated that 97% of Americans’ real income has increased due to the low commodity prices in China, while China’s economic crisis has directly led to a 2% decline in American consumer prices, which directly shows that China’s import competition will not bring serious inflation to the US. At the same time, through past statistical data, it can be found that the import trends of China and the world were similar. Therefore, in order to fully understand this impact, this paper attempts to expand the research object, that is, to consider the impact of manufacturing employment changes caused by world imports on the US election results, rather than limiting the scope to Sino-US trade. Meanwhile, this paper will also introduce and compare the data from China in the following chapters, and classify and discuss different situations for in-depth analysis.
2 Employment Change and Political Elections in the U.S. Much of the existing literature confirms that trade shocks can affect voting patterns by altering the effects of employment levels. Autor et al. found that import competition from China can reduce employment in manufacturing, thereby amplifying the problem of political polarization in US congressional elections, as measured by the number of moderate incumbents who lost their seats [3]. Borrowing from this conclusion, this paper focuses on the manufacturing employment rate and the results of the US election, and explores whether the polarization of partisan elections will be drawn again. Using data from voting patterns in six presidential elections, Jensen et al. extended this analysis to include services trade and exports and found that while employment declined due to increased imports produced greater polarization, increased exports resulting in employment expansion was associated with more support for incumbents [4]. Che et al. found that greater import competition from China was associated with increased electoral turnout and the share of Democrats’ votes in congressional elections [5]. Because trade affects counties in so many ways, it is unclear whether it will have a significant impact on a presidential election in which all counties votes. This paper uses this research conclusion as the research gap to set the research object of this paper, that is, the existing papers only focus on the national level, the state level election level and other divided congressional electoral district levels, and no paper or seldom explores how about the support of Democrats and Republicans in the independent county level election results. In addition, studies have pointed out that interaction of competing importing countries may offset shocks of export-oriented countries. However, Donald Trump’s emphasis on trade protections and manufacturing jobs suggests that to the extent that voters’ concerns about trade affect the election, it could give him an edge in counties that specialize in manufacturing or lost manufacturing jobs. In fact, in a side note to their paper on polarization, Autor et al. found evidence that U.S. manufacturing employment has taken a big hit due to Chinese import competition, which directly helped and boosted Trump’s victory in the general election [6]. In 2016, Donald Trump’s share of votes in each state was higher than even previous Republican candidates, especially in swing states.
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3 Methodology 3.1 Empirical Approach Since the instrumental variable is considered to be related to the explanatory variable of interest, it is exogenous to the explained variable. By reviewing the literature, this paper draws on findings of Dippel et al. that labor market adjustments across industries explain almost all the effects of import risk on voting patterns, that is, the risk of import competition affects the final outcome of the election by affecting changes in the employment market [7]. In addition, there is no direct correlation between the county-level election data and the national-level election data. With regard to the above statement, this paper attempts to discuss whether the import competition is a single chain correspondence with the results of the US presidential election. Among them, the most important point in this paper is to clarify the uncorrelation of inflation factor in the empirical analysis of this instrumental variable method. In our inherent idea, from a global perspective, a country’s imports or international trade may affect GDP, exchange rate, inflation and interest rate. However, this paper argues that the US did not meet the corresponding conditions. First of all, as a developed country, the United States cannot maintain low inflation for a long time under the background of the slow increase trend of global inflation rate; Second, some people say that this year’s high level of inflation will probably affect the U.S. mid-term elections. They believe that the reason for this inflation is the restriction of U.S. crude oil import caused by the RussianUkrainian conflict and the inability of the United States to import crops and industrial products caused by the pandemic in China, resulting in demand-oriented inflation in the United States. However, this paper argues that US inflation has accelerated and become more serious long before the conflict between Russia and Ukraine. The fundamental reason for the current round of inflation in the United States is that the U.S. government and relevant departments have adopted extreme monetary and fiscal policies. The US inflation is so serious this time, which is closely related to the rapid growth of the US base currency, M1 and M2 [8]. In addition, many papers have proved that in the changes of inflation over the years, compared with the government’s fiscal policy, the import factor can be ignored. Meanwhile, it is not difficult to find out that before the US national voter polling day (the first Tuesday after the first Monday in November of the election year) between 2000 and 2016, the incumbent presidents will try to create a better election environment by reducing the inflation rate. It can be seen that the imported inflation caused by the import problem would not be considered in this paper. Meanwhile, this paper also wants to prove through two other evidence from the United States that imports will not directly lead to inflation in the United States. First, according to the above description, the root cause of inflation is the excessive supply of money. In international trade, the trade surplus (that is, the export volume in a certain period is greater than the import volume) will cause the accumulation of money, which may lead to domestic inflation, while the trade deficit will reduce the number of circulating money on the market, thus avoiding inflation to a certain extent. It is easy to find out that the United States has been in a state of trade deficit since 2000, so it has avoided inflation caused by changes in currency circulation in international trade; Second, many research conclusions show that manufacturing import competition from
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33
abroad, especially China and other developing countries, will reduce the employment rate of bottom workers in the United States, which may reduce the average wage of American society and increase inflation. However, research of Auer and Fischer confirmed that import competition from China and other low wage countries would curb producer price inflation of manufactured goods in the United States by more than 2 percentage points every year, which fully showed that the hypothesis of the second point is not tenable [9]. Therefore, this paper excludes the consideration of the impact of imports on inflation. Moreover, this paper is measuring import competition locally, but inflation is usually measured nationally. Because employment is local, but consumer goods can be purchased online, so there is price arbitrage and only very small variation for most consumer goods. So, even if inflation is affected, its effect should be constant across all counties, while employment effects are different across all counties. Inflation was also quite low and constant for the years that this paper is considering. So, this paper confirms that the above analysis address the instrumental variable concern. Based on these analyses and summaries above, this paper constructs an instrumental variable of import competition to study the association between manufacturing employment and U.S. presidential election outcomes in the following content. Through fixed effect panel regression and heterogeneity test, this paper also tests that there is no direct or indirect correlation between import competition variables and presidential election results, so that the next step can be set for instrumental variables. Taking the exposure of import competition from China and the whole world as instrumental variables, this paper mainly explores the impact of American manufacturing employment on the result of the presidential elections. First of all, as we all know, the two major traditional parties in the United States are the Democratic Party and the Republican Party. However, this does not mean that there are only these two parties in the United States. In fact, from the perspective of the 2020 U.S. presidential election, there are presidential candidates from at least four parties competing for the presidency [10]. However, although the voting shares won by the opposition parties was much smaller than that of the two major parties, it might still interfere with the results in this study. First, its existence will reduce the voting shares won by the two major parties to a certain extent, that is, the sum of the voting shares won by the Republican Party and the Democratic Party is not equal to 1. The consequence of such situation is that this paper needs to regress the presidential voting data of Republicans and Democrats at the same time, which will take some unnecessary length of this paper. In addition, because the voting shares won by the opposition parties was not necessarily small with uncertainty (for example, in a voting district, the voting shares won by the opposition parties was 5% in 2000 and rose to 10% in 2004), which might lead to the simultaneous rise or fall on the voting shares of the two major parties in the results of two or more consecutive elections (for example, in an election district in 2012, the votes of the opposition parties were 3%, and the votes of the Republican Party and the Democratic Party were 55% and 42% respectively; in 2016, the votes of the opposition parties increased to 6%, while the votes of the Republican Party and the Democratic Party decreased to 53% and 41% respectively). It may lead to misjudgment of the election results in the process of running the data, resulting in our wrong interpretation of the data.
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In order to prevent such a situation from happening, this paper calculates the major voting percentages of Republicans and Democrats (that is, the relative percentage of the two parties) based on the number of votes in each voting district, instead of using the official total voting percentages of the United States (that is, the absolute percentages). The calculation formulas of the two are as follows: RepVotesTotalPercent =
Republicans Votes × 100% Republicans Votes + Democrats Votes + Opposition Votes
(1)
Republicans Votes × 100% Republicans Votes + Democrats Votes
(2)
RepVotesMajorPercent =
the left-hand side of the above formulas represent the total and major voting percentages of the Republican Party respectively. Since in previous US presidential elections, either Republicans or Democrats have always won the majority of votes in the voting districts, using the major percentages can offset the interference risk of the opposition party; meanwhile, we only need to pay attention to the results of one party in the follow-up return (for example, only the Republican Party), the return results of Democratic Party can be calculated for subsequent analysis. Next, we need to build the index of instrumental variable, that is, the model of import competition exposure. This paper takes the study of Autor, Dorn and Hanson on the shocks of Chinese imports on the local labor market in the United States as the theoretical basis and model construction reference, and changes it on the basis of constructed model to meet the needs of this paper [3]. The import competition model in their paper is based on the Heckscher-Ohlin theorem and model, defines the labor force level and labor wage level respectively through the formulas, and believes that the employment in the trade sector depends on the growth of U.S. imports from China, which empowers the growth of China’s export-supply capacity (M CjU A Cj ). The growth is measured by the labor force of region i (L Ti ) and weighted by the share of region i in the industry j employment (L ijt / L ujt ), whose final formula was as follows:
IPW uit =
Lijt M ucjt Lujt Lit
(3)
j
where L it is the start of period employment (year t) in region i and ΔM ucjt is the observed change in U.S. imports from world in industry j between the start and end of the period. However, this paper argues that the model the above paper built has one point which are worth noting and should be improved for fitting the study of this paper, that is, the period of time they studied was one year or ten years, which means that the import volume from China for a specific manufacturing industry they studied was the total import volume of the whole period, then it is not problematic to define the number of employment as the beginning of a period. However, for the data and ideas of this paper, the original import competition data used in this paper is the import difference between the current period and the previous period. Since the population of the specific voting region will change during the two periods, and the employment level will also vary to some extent, it is not appropriate to use only the number of jobs in the current period. Meanwhile, different from the literature, this paper will study the separate voting
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35
district, rather than the set combination of voting areas, so the personnel flow must be more frequent. In order to avoid the above risk and better meet the research needs, this paper makes the following modifications based on the above model: ICE uit =
Lijt Mucjt Lij(t−1) Mucj(t−1) ( − ) Lkjt Lit Lkj(t−1) Li(t−1)
(4)
j
where ICE uit is the index model of import competition exposure that will be calculated and used in the following regression and analysis. Lijt is total employment in voting district i, industry j, in year t; Lkjt is total employment in all voting districts, industry j, in year t; Lit is total employment in voting district i, in year t for all manufacturing industry types; Mucjt is the imports of the US from the world in industry j in year t. The variables with subscript (t-1) are expressed as the value level of the variables in the previous year. The main difference between IPW uit and ICE uit is that the latter one has considered the change per capita in a specific industry j, which makes it possible to obtain more reasonable results according to the formula even if the number of population and employment in specific voting district changes. For example, if the import volume of a specific manufacturing industry decreases and the total population and employment level in the current year have risen compared with the previous year, the ICE uit is likely to be negative, and vice versa. In addition, in the following sections, this paper will also explore the shocks of China’s import data on U.S. manufacturing industry employment, so the name of the variables will naturally change. Additional instructions will be provided later in this paper. 3.2 Data and Measurement There are multiple data sources in this paper. All data sources will be added to the references and identified with asterisks. First of all, this paper downloaded industry-level manufacturing imports data of the world and China and manufacturing employment data in County Business Patterns (CBP) from Census Bureau of the United States. In this data source, this paper identifies the following information. First, Unlike Autor et al., Hochschild and Wallace and Dorn and Hanson [3, 11, 12] who divided the study area into Congressional Areas, Metropolitan or Micropolitan Areas (MSA) and Commuting Zones respectively, this paper sets the study area (i.e. voting district) as the countylevel administrative region (including counties and independent cities) of each state in the United States. There are two reasons for this setting: First, because the Commuting Zones designated by the author through labor market mobility or traffic conditions do not have official statistical U.S. election data, and these areas are not necessarily connected by complete counties, the author of the above research will inevitably make errors in the process of transforming the regional election results; Second, we cannot ignore the function of many counties as places of residence [13]. Many people who work in one MSA may live in another. Compared with the MSA statistics of employment population whose collection objects are the employment units, it will be more accurate to carry out employment and population statistics through the fixed residence records at the county-level. In addition, the county-level data in the same time period is about ten times that of Commuting Zones or MSA, so it is more likely to obtain a more accurate
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impact of employment on the election results; Second, according to the standard of North American Industry Classification System (NAICS), the manufacturing industry in the United States is divided into 21 industries. Since some counties do not necessarily own all manufacturing industries, we select 14 of them as separate industries j, and the remaining nine are calculated as a whole industry (see the Appendix 1); Third, since the NAICS standard was just adopted in the United States in 2000, and the CBP data was only updated to 2019 in the writing stage of this paper. Therefore, due to the availability of data, the data collection scope of this paper is set as 2000–2016, including five U.S. presidential elections in 2000, 2004, 2008, 2012 and 2016. Moreover, data for the two years prior to the election year (for example, data of 2002 and 2003 correspond to 2004) were also collected for regression analysis below. The U.S. presidential election data is downloaded from Voting and Elections Collection, Library of CQ Press. In the presidential election data, we deleted some counties with missing election data for some election years and all counties in Alaska (because the electoral district for each election is not fixed), and finally deleted 261 of the 3243 counties. For the criteria that deleting the counties, there are three main points: First, the accessibility of presidential election data (since some counties may be too small or newly established during 2000–2016, there may not be statistical results in all five presidential elections); Second, the availability of manufacturing employment (some counties may not have all 21 manufacturing sectors according to the NAICS system mentioned in the article); Third, a small number of counties may not have data statistics of control variables. Most of the deleted counties are counties with characteristics of small scale and small employment population and total population. After deletion, this paper will adjust the data in the follow-up, but on the whole, it may not have much influence on the stability of the regression results. According to the definition Bureau of Labor Statistics and Fred Economics Research Data, OECD, this paper defines the 16–64 year-old group as the working population and collect the relevant population data from U.S. County Population Data, SEER, National Cancer Institute. Finally, based on the findings of Galbraith and Hale and Neugart on the relationship between income, unemployment insurance, employment level and electoral behavior, this paper sets four relevant control variables for the subsequent model, which are average earnings per job (EPJ), per capita net earnings (CNE), per capita personal income (CPI) and per capita unemployment insurance compensation (UIC) [14, 15]. The county-level data of all of the control variables can be found and downloaded from Regional Economic Accounts, Bureau of Economic Analysis, U.S. Department of Commerce. The sorted data will be short panel data with county as panel and section length greater than time length. Besides, because all variables meet the normal distribution, the datasets will not be processed logarithmically in this paper. The description and statistics of each variable is as follows (Table 1).
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Table 1. Description and Statistics of Each Variable. Variable Name
Variable Meaning
Mean
SD
Max
Min
ICE uit
Current import competition
22.57
132.6
64.98
−35.61
ICE ui(t−1)
Import competition one year in advance
28.91
137.9
39.78
−25.23
Eitm
Changes in manufacturing −0.019 employment
1.808
80.81
−54.29
RepVotesMajorPercentit
Support rate of US election results
61.15
14.56
95.05
2.76
EPJ it
Average earnings per job
37161
106645
140375
12178
CNE it
per capita net earnings
19763
7409
127846
6296
CPI it
per capita personal income
32301
10129
200184
13245
UIC it
per capita unemployment insurance compensation
177.7
150.3
1326
10
3.3 Data Analysis Method and Classification The instrumental variable strategy, outlined in Sect. 3.1, will be applied and classified in this section (Fig. 1).
Fig. 1. Relationship between change of manufacturing employment and import competition (M_da is the 4-year period import competition and ce is the 4-year change of manufacturing employment).
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Before the instrumental analysis, we need to do an ordinary fixed effect panel regression for original manufacturing employment change and Republican voting shares first for comparison in the following chapter. RepVotesMajorPercent it =α0 + β0 Eitm + γa EPJit +γb CNEit + γc CPIit + γd UICit + δt + εt
(5)
This paper will apply the two stage least square (2SLS) panel regression to do the empirical analysis. The regression equations of the first and second stages are as follows: Eitm = α1 + β1 ICEuit + γ1 EPJit + γ2 CNEit + γ3 CPIit + γ4 UICit + δt + εt
(6)
RepVotesMajorPercent it =α2 + β2 Eitm + γ5 EPJit +γ6 CNEit + γ7 CPIit + γ8 UICit + δt + εt
(7)
where Eitm is the yearly change in the manufacturing employment share of the working age population in county i, and Eitm is the predicted value of the Eitm from the estimation of Eq. (5). δt is the year fixed effects. In order to fully discuss the impact of employment on the election results with instrument, this paper will conduct regression analysis on the following Classifications:
A. First, in the U.S. presidential election, how voters decide which presidential candidate to vote for is a complex topic. There may be many factors that affect the results of the U.S. election (for example, the weather will temporarily change voters’ voting psychology) [16]. In the existing literature, Bakker, B., Rooduijn, M. and Schumacher and Plescia pointed out that people’s comparative psychology and expectations may have a greater impact on the election results. In the existing articles, there is little explanation of where voters’ psychological expectations and comparisons come from (that is, whether they will compare the ruling situation of the current president in the third and fourth year of its term of office, or whether the current president’s ruling situation will be compared with the incoming president, so as to make their own choice) [17, 18]. In this classification, this paper makes a hypothesis, that is, voters will compare the performance of the current president during his term of office with that of the previous president, or compare the performance of the then president in the final year with that in the third year, so as to obtain their own judgment, psychological expectation and voting behavior. In order to test this hypothesis, this paper adjusts the research period to four years (i.e. one term of office) and one year (e.g. 2012 and 2015 correspond to the 2016 general presidential election) to discuss whether the voting shares of the two major parties in the U.S. presidential election under different classifications have changed or different. B. Second, since the change of employment level lags behind the import competition (the specific transmission mechanism will be explained in the Discussion section), in this classification, the import competition will advance one year and correspond to the employment level in the election year (for example, the import competition in 2015 corresponds to the employment level in 2016). The specific equation is as follows. Besides, in this classification, the time period t is only set as one year period,
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39
because this paper infers from common sense that the manufacturing import situation before the two presidential terms will not affect the employment level of the current term (e.g., the import situation of Obama’s first term will not affect the change of manufacturing employment during Trump’s term). Eitm = α1 + β1 ICE ui(t−1) + γ1 EPJ it + γ2 CNE it + γ3 CPI it + γ4 UIC it + δt + εt (8) Lij(t−1) Mucj(t−1) Lij(t−2) Mucj(t−2 ICE ui(t−1) = ( − ) (9) Lkj(t−1) Li(t−1) Lkj(t−2) Li(t−2) j
C. Third, This paper finds that the existing literature did not fully consider whether the employment situation with instrument affected the results of the U.S. election during the 2008 financial crisis. Therefore, this paper chooses to delete the situation in 2008, regress the samples in the remaining years, and compare and analyze the results with the same as the Classification A. D. Fourth, due to the policy preferences of the Republican Party and the Democratic Party from 2012 to 2016 and the changes in the election strategy of the 2016 presidential election, this paper makes the hypothesis of this classification, that is, by deleting the results of the 2016 election, the absolute value of the Republican Party’s support rate change in the regression without advanced year on the import competition will narrow on the basis of the original change trend in the second stage. Because the Republican Party paid more attention to the bottom workers in the manufacturing industry in 2012–2016, and President Trump also announced during the election that in order to promote the recovery of domestic manufacturing and traditional industries, he would increase tariffs on China and many countries in the world to set trade barriers to promote the employment rate of relevant industries. This paper speculates that this political adjustment should expand the election advantage for the Republican Party. E. Fifth, since the contest for the votes of the swing states has always been the key to the outcome of the U.S. presidential election, many literature also discuss the situation of the swing states in the United States. This paper analyzes data from deep red states (consistently supporting the Republican Party) and deep blue states (consistently supporting the Democratic Party), as well as some swing states in this category, to derive the relationship between employment and elections in various states. This paper conjectures that because most swing states are in the central and southern United States, most of them take agriculture, basic industry and manufacturing as their pillar industries. According to the ruling tendency of the Republican Party to take root in the countryside and support the development of large manufacturing enterprises, this paper believes that in the election results at the county level, the support rate of the Republican Party should be the same as that in crimson state, and increase with the increase of manufacturing employment. F. Sixth, applying the methods in Classification A and B to China’s data to see whether there will be consistent (or opposite) results. Finally, the data results of China and the world are integrated and analyzed. According to the above classifications, this paper forecasts the signs of the results, especially for the signs in the Classification A, B and F. This paper holds that the signs
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of instrumental variables and core explanatory variables should be opposite in the first and second stage regression of 2SLS respectively. This paper holds that due to the lag effect between import competition and manufacturing employment change, the coefficient signs of instrument variable in the first stage should be different because of whether to advance the import shock by one period. Previous literature has proved that import shocks do have a negative impact on manufacturing employment. Due to the existence of the above lag effect, this paper believes that the coefficient of instrumental variables in the first stage of 2SLS regression in which import shocks are advanced one period (ICE ui(t−1) ) is negative. The differences in the first stage will lead to the differences in the signs of the core explanatory variables of the second stage regression. These differences are the focus of this paper and provide an empirical basis for the later discussion and interpretation of this paper.
4 Findings and Discussions 4.1 Findings According to the classifications of Sect. 3.3, the ordinary panel regression results that original manufacturing employment change to Republican elections results are shown in Table 2 For the F-statistics of all first stage regressions are larger than 10, according to the study of Staiger Stock and Watson, there is no weak instrument problem in the variable setting of this paper [19]. At the same time, we can also see that the coefficient significance of direct panel regression of manufacturing employment change variable to the presidential election results is not too high. According to common sense, the regression effect of employment on election results should be relatively high. Therefore, this also confirms the necessity of solving endogeneity by introducing instrumental variables. Table 2. Panel regression of manufacturing employment change to presidential elections result. 4-year regression
1-year regression
C
−2.211e + 03*** (−33.72)
7.032e + 01***
Eitm
−1.355e + 02 (−1.06)
8.528e + 01*
Residual Standard Error
12.97
13.47
Degree of Freedom
11921
14904
Adjusted R-Squared
0.2063
0.08627
F-statistic
517.7
282.5
(167.263) (2.647)
Note: the significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1; Eitm is the original manufacturing employment change. Meanwhile, this paper considers the control variables in each regression, and the relevant results will be shown in the following tables when necessary
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Table 3. 2SLS without one period advanced (Classification A). (1)
(2)
(3)
(4)
4-year regression 4-year regression 1-year regression First stage Second stage First stage
1-year regression Second stage
−3.260e + 00*** (−14.060)
−2.491e + 03*** (−34.234)
−6.880e-01*** (−7.574)
−2.257e + 03*** (−33.725)
ICEuit /Eitm
3.587e-05*** (23.181)
−1.064e + 02*** (−8.701)
2.573e-05*** (20.501)
−1.341e + 02*** (−3.764)
EPJ
−3.063e-07*** (−4.8340)
−5.612e-04*** (−31.424)
−6.480e-08** (−2.641)
−5.522e-04*** (−30.901)
CNE
−6.957e-09 (−0.044)
8.767e-04*** (19.718)
3.166e-08 (0.519)
8.715e-04*** (19.543)
UIC
−3.044e-05*** (−6.55)
−3.960e-02*** (−29.445)
−2.700e-06 (−1.497)
−3.736e-02*** (−28.341)
CPI
2.021e-07 (1.890)
−5.132e-04*** (−16.915)
3.050e-08 (0.733)
−5.256e-04*** (−17.298)
Residual Standard Error
0.0456
12.93
0.01774
12.96
Degree of Freedom
11929
11929
11921
11921
Adjusted R-Squared
0.06297
0.2112
0.03699
0.207
F-statistic
134.7
533.5
77.35
519.8
C
Note: The significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1; For ICEuit /Eitm , when the result is the first stage regression, the core variable is the instrumental variable ICEuit ; When the result is the second stage regression, the core explanatory variable is the predicted manufacturing
employment change Eitm . The following tables are the same as this note.
Compared with the results in Table 2 and Table 3, the results of 2SLS are more significant than those of ordinary panel regressions, which proves that the import competition instrument has eliminated the endogeneity problem in the variable of manufacturing employment change to some extent. For the Classification A, we can see the results of the first stage regression from Table 3 Column 1 and Column 3, the ICE uit of both 4-year period and 1-year period are positive, which are 0.003587 and 0.002573 respectively. We can find that the coefficient of ICE uit of 4-year period is larger than that of 1-year period; For the results of the second stage in Table 3 Column 2 and Column 4, there are also the same sign in the parameters of core explanatory variables Eitm . In order to prove the correctness of the ICE uit model modified in this paper, this paper also calculates the IPW uit according to
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Autor, Dorn and Hanson’s formula and brings it into the first stage regression. However, the results show that the coefficient of the variable IPW uit is not significant, indicating that this variable is not well suitable for the research needs of this paper. For the Classification B, the results shown in the Table 4 reflects that when the ICE uit is advanced by one period (which is ICE ui(t−1) ), the import competition exposure from the world will lead to the opposite conclusion to the current period in the Classification A, that is, if the ICE ui(t−1) increases by one unit, the percentage change of manufacturing employment in each county will decrease by 0.0002806%; in the second period, the manufacturing employment will increase by 0.001% would result in a 0.0115% drop in the Republican voting shares. This trend is roughly in line with previous studies by scholars. For the Classification C and D, all of the results in Table 5 and Table 6 illustrate that the change trend of ICE uit and Eitm are roughly similar as those of the Classification A, but the magnitude of the absolute value are larger than the value of the Classification A.
Table 4. 2SLS without one period advanced (Classification B). (1)First Stage
(2)Second Stage
−3.768e-01*** (−4.125)
−1.759e + 03*** (−12.614)
ICEuit /Eitm
−2.806e-06* (−2.266)
1.150e + 03*** (3.629)
Residual Standard Error
0.01805
12.96
C
Degree of Freedom
11921
11921
Adjusted R-Squared
0.003465
0.2069
F-statistic
17.911
519.6
For the Classification E whose results shown in Table 7, in addition to the negative coefficient of dark blue states, the value is −2.469, indicating that when the annual change of manufacturing employment rate increases by 1%, the supporting rate of the Republican Party decreases by 2.469% on average in the counties of these states. However, in swing states and dark red states, the coefficients are positive. For the Classification F of China data in Table 8, this paper finds that compared with the Classification A, the change trend of their core variables is the same. But it is easy to find that the shock effect of China’s imports exposure on manufacturing employment in the U.S. is significantly higher than that of world imports, and sometimes can even reach dozens of times.
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Table 5. 2SLS without data in 2008 (Classification C). (1)No advance period First Stage
(2)No advance period Second Stage
(3)With advance period First Stage
−1.630e + 00*** (−9.875)
−2.736e + 03*** (−24.735)
−1.242e + 00*** −9.924e + 02*** (−7.476) (−3.791)
ICEuit /Eitm
2.702e-05*** (17.433)
−1.983e + 02*** (−5.563)
−4.686e-06** (−3.118)
1.219e + 03*** (6.217)
Residual Standard Error
0.01976
12.28
0.02008
12.27
Degree of Freedom
8939
8939
8939
8939
Adjusted R-Squared
0.0414
0.1918
0.00989
0.1925
F-statistic
65.39
354.9
15.89
356.5
C
(4)With advance period Second Stage
Table 6. 2SLS without data in 2016 (Classification D). (1)1-year period with advanced year First Stage
(2)1-year period with advanced year Second Stage
(3)1-year period without advanced year First Stage
(4)1-year period without advanced year Second Stage
−1.242e + 00*** (−7.476)
−9.343e + 02*** (−3.619)
−2.607e + 00*** (-5.956)
−3.563e + 03*** (−16.372)
ICEuit /Eitm
−4.686e-06** (−3.118)
1.155e + 03*** (5.972)
1.215e-05** (2.955)
−4.411e + 02*** (−5.563)
Residual Standard Error
0.02008
12.1
0.0524
12.28
Degree of Freedom
8947
8947
8939
8939
Adjusted R-Squared
0.04779
0.1948
0.00898
0.1918
F-statistic
25.89
360.4
14.51
354.9
C
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Table 7. Second stage regression for dark blue, dark red and swing states with advanced year on import competition (Classification E). (1)Second Stage
(2)Second Stage
(3)Second Stage
−7.578e + 02
−2.946e + 03***
−2.348e + 03***
(−1.258)
(−6.791)
(−8.595)
−2.469e + 02*
2.191e + 03***
1.283e + 02**
(0.203)
(3.697)
(2.865)
Residual Standard Error
11.08
12.3
8.64
Degree of Freedom
533
2133
961
Adjusted R-Squared
0.3048
0.119
0.2348
F-statistic
40.39
49.16
50.46
C
ICEuit /Eitm
4.2 Discussions The Instrumental Transmission Mechanism between World and Chinese Imports and U.S. Manufacturing Employment. This section will try to rationalize and analyze the empirical findings of the previous section. Many classification results, including Classification A, confirm that for the current period regression data, in the first stage, the exposure effect of import competition on the change of manufacturing employment rate is often positive, while in the second stage, the exposure effect of manufacturing employment change on the Republican voting result is negative; If this paper advances the import competition data for the previous period and carry out the regression of the first stage and the second stage with other variables unchanged, we will find the opposite result, that is, the exposure of import competition on the change of manufacturing employment rate is negative, and the impact of manufacturing employment change on the Republican voting result is positive. This paper holds that the reason why the current research results are different from the advancing research results is that the change of manufacturing employment lags behind the change of import. Different studies in various fields have many cases of research lag or advance, such as the study of the relationship between the number of confirmed cases and the pandemic risk spread rate in Taiwan every day during the COVID-19 [20]. Return to this paper, it may explain the lagging relationship between employment (or unemployment) and import first through a narrative description. An explanatory logic case can be found in Appendix 2. As far as the empirical results of this paper are concerned, we can understand it in this way. Taking 2004 as an example, we can call the import competition in 2003 a “complete effect” on the changes in manufacturing employment in 2004, that is, it can fully explain the reasons for the changes in manufacturing employment in 2004 with high probability. As the increase in imports hit the original manufacturing companies in the United States, many low-skilled workers at the bottom of the manufacturing value chain were forced to lose their jobs, resulting in a decline in the manufacturing employment rate. However, it can be seen from the introduction that from 2000 to
2.415e-08 (0.577)
9.565e-04 *** (21.38)
−5.182e-02*** (−30.86)
−4.520e-04*** (−14.76)
1.219e-07 (0.762)
−2.596e-05*** (−5.473)
1.351e-07 ( 1.238)
0.04655
11929
0.02391
49.72
CNE
UIC
CPI
Residual Standard Error
Degree of Freedom
Adjusted R-Squared
F-statistic
558.7
0.219
11929
12.87
−3.958e-06* (−2.174)
−6.182e-04*** (−33.62)
−1.901e-07** (−2.909)
EPJ
49.84
0.02398
11921
0.01786
4.000e-08 (0.652)
−6.193e-08* (−2.503)
−6.231e + 02*** 1.008e-04*** (-13.98) (15.994)
−6.339e-01*** (−6.924)
1-year period without advanced year First Stage
3.244e-05*** (6.203)
−2.775e + 00*** –3.848e + 03*** (−11.750) (−28.74)
4-year period Second Stage
4-year period First Stage
(3)
ICEuit /Eitm
C
(2)
(1)
521.3
0.2074
11921
12.96
−5.248e-04 (−17.276)
−3.748e-02*** (−28.424)
8.761e-04*** (19.637)
−5.537e-04*** (−30.979)
−2.083e + 02*** (-4.592)
−2.286e + 03*** (−33.721)
1-year period without advanced year Second Stage
(4)
Table 8. 2SLS for China’s data (Classification F).
8.372
0.003695
11921
0.01805
6.682e-09 0.158
−1.122e-06 (−0.609)
6.999e-08 (1.128)
−8.338e-09 (−0.331)
−1.822e-05** (-2.809)
−3.702e-01*** (−4.050)
1-year period with advanced year First Stage
(5)
544.2
0.2146
11921
12.9
−5.581e-04*** (−18.383)
−3.240e-02*** (−23.567)
6.817e-04*** (14.480)
−4.907e-04*** (−26.523)
2.907e + 03*** (11.423)
−1.076e + 0*** (−9.092)
1-year period with advanced year Second Stage
(6)
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2016, the total value of manufacturing goods imported from the world or China did not decline due to fluctuations in domestic manufacturing employment or other vital economic factors, but on the contrary, it was continuously rising. In this case, we have two different explanations for the 2004 manufacturing import competition corresponding to the increase in manufacturing employment in 2004. The first is that we use the limit method and regard the import competition in 2004 as a “new complete effect” on the change of manufacturing employment in that year. Then, combining the existing research and China’s empirical results in classification 6, we can obtain one conclusion: As Varas’s research shows, international trade, especially imports, actually creates job positions. She found that between 2010 and 2016, more than 60 percent of U.S. imports were used by U.S. manufacturing companies to make final products. Imports reduce domestic manufacturing costs and enable manufacturing companies to hire more workers due to lower raw material prices abroad [21]. Varas’s conclusion has also been confirmed by the analysis of relevant analysts like Perry and Guillot and government statistics [22– 24]. At the same time, she believes that the shock of China’s import competition on the world has declined year by year after China joined the WTO in 2001 and became normalized around 2010. The use of imported products through continued Sino-US trade liberalization is a net gain for manufacturing company and labor. Another explanation can be called “incomplete effect” or “partial effect”, that is, due to the influence of lag, the manufacturing import shock in 2004 can only partially explain the changes in manufacturing employment in that year. Regarding the changes in manufacturing employment in 2004, the explanatory power of this year’s import competition or the effective value of the shock may not be as high as that of the previous year. In a sense, this year’s import competition has even become smaller. Then, due to this information asymmetry, it is very likely that the change in manufacturing employment has increased in a statistical sense. Performance of Political Election in the Results. In the empirical results, this paper also obtains the regression results of the election year data without financial crisis year (2008), the regression results without the 2016 election data, the regression results of three election state groups with different characteristics, and the trend of control variables. Each group of results has its own inner meaning. From the comparison between Table 3 and Table 5 it can be seen that in the regression without advanced period, the regression result of no 2008 data is larger than that of classification1, while in the regression result of lag period, the former is smaller than the latter. This shows that the impact of Chinese imports on U.S. manufacturing employment in 2008, when U.S. imports slowed down due to the financial crisis, has been exaggerated. Meanwhile, we are concerned about the results of the second phase of the presidential election. Since 2008 is the ruling year of Republican President George W. Bush, according to his research results, voters tend not to vote for the ruling party when unexpected events or imports have a negative impact on employment. The regression results of the election data show that the decline in the Republican vote compared with classification 1 is reasonable. In order to explain the remaining results, and to summarize the commonalities shown by all the regression results, we need to understand the political philosophy of the two parties in the United States from 1996 to 2016. The main difference between the Republican
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and Democratic traditions is that the Republicans are rooted in the countryside or towns, while the Democrats tend to build urban credibility. From 1996–2012, Republicans were considered “the party of the rich” because people said they were supporting policies that benefited farmer entrepreneur and CEO’s but not workers, especially those in the service sectors. Republicans believed that if they make policies that help businesses, then the businesses can hire farmers and manufacturing workers. Therefore, combined with the explanation above, we can see that under the influence of the “complete effect”, the supporting rate of the Republican Party is positively correlated with the predicted change of manufacturing employment. This is because in the United States, central and southern states (such as Texas and Kansas) will have more villages than middle-class and poor states such as New York state and California, and many large enterprises dominated by traditional industries are also located in these places. The Republican party guarantees the development of these enterprises, which means that they will be supported by more workers related to manufacturing and traditional industries. However, at the time, Democrats were considered “socialists” because they supported labor unions or trade unions, they organized labor movements and listened to their needs, so at the stage of this study, the support rate of Democrats should be much higher than that of Republicans, because they focus on grassroots labor and not just the state’s big business. And that changed in 2016, Trump became president as a Republican. Unlike previous Republicans, he talked a lot about manufacturing. This changed the way Republicans talked – Republicans started talking more about working class poor people and less about big businesses. However, Democrats have been really successful in Silicon Valley, most rich tech people are democrats. So from 2016, Democrats have become known more as the “party of the rich”. Although the ruling concepts of the two parties at the national level may be changed, at the county level, due to the advantages of the Republican Party’s roots in rural areas and towns, they will naturally come into contact with more poor people or basic labor workers. Therefore, the Republican Party has always had a greater advantage over the Democratic Party in the evaluation of government satisfaction at the county level for a long time. From this, we can explain the remaining two issues: On the issue of swing states, because the swing states are mostly located in the central region of the United States and are dominated by agriculture and old industries, they tend to favor the Republican Party in elections. According to the description in the previous paragraph, we delete the data for 2016, and the obtained Republican approval rate should have increased significantly, which is consistent with our regression results.
5 Conclusion Combined with all the findings and discussions, this paper thinks that there is a positive correlation between the change of support rate of the Republican Party and the change of manufacturing employment due to its own ruling policy and concern groups. In general, this paper summarizes the main findings and conclusions as follows: 1. In this paper, the research time of instrumental variables is divided into current period and advance period. Different from previous studies, after analyzing the results, this paper believes that the hypothesis before the empirical results is correct, that is, the
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import competition in advance will have the “complete effect” proposed by this paper for the first time. This will draw a conclusion similar to previous studies, that is, import competition makes the local manufacturing employment rate decline. The current import competition may have a “new complete effect”, that is, imports stimulate the manufacturing and semi-finished product processing industry in the United States to a certain extent, thus improving the employment of the manufacturing industry. 2. Under the premise of “complete effect”, this paper believes that there is a positive correlation between manufacturing employment and the change of Republican support rate. This is due to the Republican Party’s political philosophy and ruling strategy, that is, its strong power in the countryside, especially in many central and southern states. The main supporters are religious organizations, large enterprises in traditional manufacturing and industry, manufacturing workers and veterans. The white, especially male whites are the most important resources of the Republican Party. This has also directly led to an obvious link between the level of manufacturing employment and the level of Republican support. 3. Meanwhile, this paper also found two interesting phenomena. Firstly, comparing the two columns on the right of Table 6 with Table 4, this paper finds that after deleting the data in 2016, the support advantage of the Republican Party has declined slightly, which is different from the previous conjecture. This paper holds that the reason for this situation is probably that the political adjustment of the Republican Party has affected the growing American processing industry in manufacturing sector. As mentioned above, after 2010, the import of semi-finished manufacturing products has brought a certain degree of manufacturing employment recovery to the United States, and the Republican Party’s tendency to restrict international trade may affect their interests. Second, comparing the two columns on the right of Table 8 with Table 4, it is not difficult to see from the second stage regressions that the coefficient of the core explanatory variable of Chinese data regression is larger than that of world data regression. This paper holds that the reason for this phenomenon is due to the role of “new complete effect”. Since 2000, the United States has mostly imported manufacturing end products from China; however, compared with China, the United States may import more manufacturing semi-finished products from the whole world. Since the increase of semi-finished products can stimulate employment by increasing processing industries in the manufacturing sector, though the US manufacturing imports from China are part of the US manufacturing imports from the world, the “new complete effect” may offset the impact of some changes in employment rate caused by China’s import competition on the outcome of the US election. This paper absorbs, integrates and modifies some existing research settings and achievements in this field, such as variables and model construction in empirical approach part, data sources and fitting degree of empirical results with previous literature. However, in terms of innovation, this paper believes that there are some methods that have not been tried by previous studies in this field, and draws new conclusions through these. First of all, because many scholars believed that the import competition of China’s manufacturing industry will end around 2010, they only focused on the relevant data before 2010. However, this paper studies the data after 2000 to 2016, and the relevant
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conclusions make up for the relative gap in this field and help to improve the comprehensiveness of the conclusions; Second, this paper uses the county-level data as the minimum unit of election results, which has not been tried in previous literature. This paper holds that the complexity of county-level data can make up for the previous research and play a supplementary role; Third, in this field, much scholars’ research were carried out with China as the research object. This paper explores whether the import competition in the whole world will have the same or opposite impact on the election results compared with China, but this paper does not find the evidence for the gap between the two objects; Fourth, because the statistics of the unemployment rate in the United States are not detailed enough to be counted by industrial sectors, this paper studies the change of the employment rate level as a substitute for directly quoting the unemployment rate data, so as to skillfully avoid the occurrence of such problems. Finally, this paper also considers the different types of situations in 2008, 2016 and swing states. These classifications can give a more comprehensive explanation for the study of the supporting situation of Republicans and Democrats in county-level elections.
Appendix Appendix 1. All Manufacturing Sectors According to NAICS System. NAICS codes
Names of Manufacturing Sectors
311///
*Food Manufacturing
312///
*Beverage and Tobacco Product Manufacturing
313///
*Textile Mills
314///
*Textile Product Mills
315///
*Apparel Manufacturing
316///
*Leather and Allied Product Manufacturing
321///
Wood Product Manufacturing
322///
*Paper Manufacturing
323///
*Printing and Related Support Activities
324///
Petroleum and Coal Products Manufacturing
325///
*Chemical Manufacturing
326///
*Plastics and Rubber Products Manufacturing
327///
Nonmetallic Mineral Product Manufacturing
331///
Primary Metal Manufacturing
332///
*Fabricated Metal Product Manufacturing
333///
*Machinery Manufacturing
334///
*Computer and Electronic Product Manufacturing
335///
Electrical Equipment, Appliance, and Component Manufacturing
336///
*Transportation Equipment Manufacturing (continued)
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(continued) NAICS codes
Names of Manufacturing Sectors
337///
Furniture and Related Product Manufacturing
339///
Miscellaneous Manufacturing
Note: The sectors with asterisk are used as separate sectors when calculating import competition variables in this paper, and the rest are treated as a whole.
Appendix 2. Example of lagging relationship between employment (or unemployment) and import.
References 1. The White House. The Biden-Harris Plan to Revitalize American Manufacturing and Secure Critical Supply Chains in 2022 | The White House (2022). https://www.whitehouse.gov/ briefing-room/statements-releases/2022/02/24/the-biden-harris-plan-to-revitalize-americanmanufacturing-and-secure-critical-supply-chains-in-2022/ 2. Appelbaum, B.: On Trade, Donald Trump Breaks With 200 Years of Economic Orthodoxy (Published 2016). Nytimes.com (2016). 1), negative impact γ(0 < γ < 1), the probability of the manufacturer setting a high price is x(0 < x < 1), and the probability of the manufacturer setting a low price is 1 − x(0 < 1 − x < 1). Then, assuming for the retailer, the regulatory penalty β for the retailer’s unmarked price, the excess profit R brought to the retailer by the unknown price, and R > β is satisfied, which is, only when the excess profit obtained by the retailer by charging the channel fee is greater than the punishment by market regular, the retailer will violate the law and adopt the strategy of do not mark the price. The probability that the retailer clearly marked the price is y(0 < y < 1), and the probability that the retailer does not clearly mark the price is 1 − y(0 < 1 − y < 1). Finally, for the consumers, they get the utility E when the demand is satisfied, get negative utility-E when the demand is not satisfied, consumers’ expectation of the product (the higher the commodity price, the higher the expectation), and the actual utility level provided by the product (higher price) The psychological disappointment H (the difference between the expected utility when consumers buy high-priced ice cream and the actual utility obtained after purchase). The proportion of consumers buying is z(0 < z < 1). The proportion of consumers who do not buy is 1 − z(0 < 1 − z < 1).
3 Results and Discussion Table 1 can be prepared based on the above assumptions and parameters, there are three participants’ interests by their different strategies were displayed in this table. The first element in each small grid represents the profit obtained by the strategy selection corresponding to the manufacturer. And the second formula is the profit of retailers from their different strategies. The third formula is the benefit for consumers.
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Table 1. Income matrix Retailer
Manufacturer
Higher Price P1 (x)
Lower Price P2 (1 − x)
Customer Buy (z)
Not (1 − z)
Marked Price (y)
αP1 D − W + C (P3 − P1 )D + W E − H − P1
αP1 D − W −P1 D + W −E
Unclear price (1 − y)
γ P1 D − W + C (P3 − P1 )D + W + R − βE − H − P1
γ P1 D − W P1 D + W − β −E
Marked Price (y)
αP2 D (P4 − P2 )D E − P2
αP2 D −P2 D −E
Unclear price (1 − y)
γ P2 D (P4 − P2 )D + R − β E − P2
γ P2 D −P2 D − β −E
3.1 Constructing the Return Expectation Function Firstly, if the manufacturer chooses the “high price” strategy, the expected return isV11 , the expected return of choosing the “low price” strategy is V12 , and the average expected return is V1 respectively expressed as below: V11 = zy(αP1 − W + C) + y(1 − z)(αP1 D − W ) + (1 − y)z(γ P1 D − W + C)+ (1 − y)(1 − z)(γ P1 D − W )
(1)
V12 = zy(αP2 D) + (1 − z)y(αP2 D) + (1 − y)z(γ P2 D) + (1 − y)(1 − z)(γ P2 D) (2) V1 = xV11 + (1 − x)V12
(3)
Then, if the expected return of the retailer choosing the “clearly marked price” strategy is V21 , the expected return of choosing the “unmarked price” strategy is V22 , And the Average return is V2 , respectively expressed as below: V21 = zP3 D + zR − zxR − P1 D + W − β + βx
(4)
V22 = zP + D + 2R − zxR − P2 D − β + βx
(5)
V2 = yV21 + (1 − y)V22
(6)
Finally, if the expected benefit of consumers choosing the “buy” strategy is V31 , the expected benefit of choosing the “do not buy” strategy isV32 , and the average expected benefit is V3 , respectively expressed as below: V31 = (P2 − H − P1 ) + E
(7)
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V32 = −E
(8)
V3 = zV31 + (1 − z)V32
(9)
3.2 Replicate Dynamic Equation Solution According to the analysis in the previous step, using the principle of replication dynamic equation. The manufacturer, retailer, and consumer’s replication dynamic equation are obtained as following. dy = y(y − 1)(E − zE − zR + xW − zβ − P2 D − xP1 D+ dt xP2 D − xzW + xzP1 D − xzP2 D
(10)
dx = x(x − 1)(W − zC − γ P1 D + γ P2 D − yαP1 D+ dt yαP2 D + yγ P1 D − yγ P2 D
(11)
F(y) =
F(x) =
F(z) =
dz = z(z − 1)(P2 − 2E + xH + xP1 − xP2 ) dt
(12)
3.3 Constructing the Jacobian Matrix Friedman proposed that the stability of the evolutionary equilibrium of the system can be judged by the local stability of the Jacobian matrix, and the replication dynamic system of the manufacturer, retailer, and consumer can be formed by the above three replication dynamic equations simultaneously so that Jacobian of this System can be obtained by replication dynamic equations. ⎡
⎤ A11 A12 A13 J = ⎣ A21 A22 A23 ⎦ A31 A32 A33
(13)
A11 = (2x − 1)(W − zC − γ P1 D + γ P2 D − αyP1 D+ αyP2 D + γ yP1 D − γ yP2 D
(14)
A12 = −xD(P1 − P2 )(α − γ )(x − 1)
(15)
A13 = xC(x − 1)
(16)
A21 = y(y − 1)(z − 1)(W − P1 D + P2 D)
(17)
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A22 = −(2y − 1)(E − zE − zR + xW + zβ − P2 D − xP1 D + xP2 D + zP2 D − zxW + zxP1 D − zxP2 D
(18)
A23 = −xC(x − 1)
(19)
A31 = z(z − 1)(H + P1 − P2 )
(20)
A32 = 0
(21)
A33 = (2z − 1)(P2 − 2E + xH + xP1 − xP2 )
(22)
Let F(x) = F(y) = F(z) = 0, a total of eight local equilibrium points of the system are gotten from that equation. Substitute each equilibrium point into the matrix to get the corresponding Jacobian matrix. The eigenvalues of each matrix are shown in the following table. Using the Lyapunov indirect method: the eigenvalues of the Jacobian matrix all have negative real parts, then the equilibrium point is the asymptotically stable point, that is, the ESS stability point of the system; if at least one of the eigenvalues of the Jacobian matrix has a positive real part, then the equilibrium point is an unstable point; if all other eigenvalues in the Jacobian matrix have negative real parts except the features with zero real part, the equilibrium point is in a critical state, and the stability cannot be determined by the eigenvalue sign (Table 2). 3.4 Stability Analysis of Equilibrium Point Each equilibrium point will have stability under different circumstances, according to the assumption, when the parameters satisfy E < D ∗ P2 and E < 0.5 ∗ P2 , and γP1 D < W + γP2 D, there is an ESS equilibrium at the equilibrium point (0, 0, 0), at Table 2. The eigenvalues equilibrium points
eigenvalues t1
eigenvalues t2
eigenvalues t3
S1 (0,0,0)
E − P2 D
2E − P2
γ P1 D − W − γ P 2 D
S2 (1,0,0)
2E − H − P1
W − γ P 1 D + γ P2 D
E + W − P1 D
S3 (0,1,0)
2E − P2
P2 D − E
αP1 D − W − αP2 D
S4 (0,0,1)
P2 − 2E
β −R
C − W + γ P 1 D − γ P2 D
S5 (1,1,0)
2E − H − P1
W − αP1 D + αP2 D
P1 D − W − E
S6 (1,0,1)
β −R
H − 2E + P1
W − C − γ P 1 D + γ P2 D
S7 (0,1,1)
P2 − 2E
R−β
C − W + αP1 D − αP2 D
S8 (1,1,1)
R−β
H − 2E + P1
W − C − αP1 D + αP2 D
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this time, the manufacture adopts a low price strategy and retailer Merchants adopt the strategy of unmarked prices, and the consumers do not purchase. Then, when the parameters satisfy E < 0.5 ∗ (H + P1 ), W + γP2 D < γP1 D and E + W < DP1 , there is an ESS equilibrium at the equilibrium point (1, 0, 0), and the manufacturer adopts a high price Strategy, retailers adopt the strategy of unmarked price, and the consumers do not purchase. After that, when the parameters satisfy E < 0.5 ∗ P2 and E > P2 D,and αP1 D < W + αP2 D there is an ESS equilibrium at the equilibrium point (0, 1, 0). At this time, the manufacturer adopts a low pricing strategy, and the retailers adopt the strategy of a clearly marked price, and the consumers do not purchase. Then, when the parameters satisfy P < 2E, β < R and C + γP1 D < W + γP2 D there is an ESS equilibrium at the equilibrium point (0, 0, 1). At which time the manufacturer adopts a low pricing strategy, the retailers adopt the strategy of an unmarked price, and the consumer buys. Additionally, when the parameters satisfy E < 0.5(H + P1 ), W + αP2 D < αP1 D, and P1 D < W + E there is an ESS equilibrium at the equilibrium point (1, 1, 0) at which time the manufacturer adopts a high price Strategy, retailers adopt the strategy of clear price tag, and consumers do not purchase. Similarly, when the parameters satisfy β < R, H + P2 < 2E and W + γP2 D < C + γP1 D, there is ESS equilibrium at the equilibrium point (1, 0, 1). At this time, only when the excess profit of the unmarked price far exceeds the regulatory penalty, the retailer will take risks and adopt the strategy of unmarked price. When the actual effect is greater than half of the high price and the psychological gap, consumers will pay. Also, when the profit brought by the channel fee exceeds the channel fee, the manufacturer paying the channel fee will receive a larger profit than the channel fee itself. When the parameters satisfy P2 < 2E,R < β and C + αP2 D < W + αP2 D, there is an ESS equilibrium at the equilibrium point (0, 1, 1). At this time, the manufacturer adopts a low pricing strategy, the retailer adopts the strategy of a clearly marked price, and consumers’ purchase. Finally, when the parameters satisfy R < β,H + P1 < 2E and W + αP2 D < C + αP1 D, there is an ESS equilibrium at the equilibrium point (1, 1, 1). At this time the manufacturer adopts a high pricing strategy, and the retailer adopts the strategy of clear code list price while consumers’ purchase.
4 Numerical Simulation Analysis In order to further explore the changes of various parameters in the model, especially the influence of the changes in online reputation on the decision-making of the tripartite subjects of the game, Matlab2018 software is used to simulate the dynamic evolutionary game process of manufacturers, retailers, and consumers in different initial states. According to the dynamic simulation, the influencing factors such as the probability of strategic choice of each party, the network word of mouth, the channel fee, and the psychological gap of consumers are discussed. The initial values of each parameter are set as follows:P1 = 5, P2 = 4, α = 1.2, γ = 0.8, β = 3, E = 5, D = 2, C = 2.5, W = 3, R = 4.5, H = 3.
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4.1 Effect of the Initial Probability of the Three Parties’ Analysis When the other parameter conditions are unchanged, the effect of the initial probability of the three parties of the game on the outcome of the evolutionary game behavior is shown in Fig. 1. Assume that the initial probabilities of the three parties are the same, i.e. When the initial probabilities of all three parties are low, immediate, x and z converge at 1, y converges at 0, and the equilibrium point tends to be (1, 0, 1), that is, the manufacturer chooses the high price strategy, the retailer chooses the unclear price tag, and the consumer chooses to buy. When the initial probability of the three parties is in a medium state, that is, the same result can be obtained according to the diagram, that is, the equilibrium point tends to be close to (1, 0, 1). When the initial probabilities of all three parties are high, i.e., the equilibrium point is still close to (1, 0, 1), according to the diagram. The conclusion obtained is the same as the stability analysis of the equilibrium point, that is, the equilibrium point (1, 0, 1) is the local stability point of the system.
Fig. 1. The influence of the initial probabilities
4.2 The Influence of Online Word-of-Mouth Factors Analysis The influencing factors of network word of mouth, including the influence of positive network word of mouth, and the influence of negative network word of mouth, both aspects, without considering other parameter changes, in order to make the three parties of the game do not have any tendency to each of the two strategies, hypothetic. First, the influence of positive online word of mouth on evolutionary game behavior is analyzed. As shown in Fig. 2, when 0, 1, 1.5 is taken, respectively, as the value increases, x and
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z are close to the equilibrium point 1, that is, the positive impact of online reputation will always affect the manufacturer to adopt a high price strategy, affecting consumers to buy the ice cream. For retailers, no matter how it changes, it always tends to be close to equilibrium point 0, which means that the positive impact of online word-of-mouth cannot have an impact on retailers’ strategy of unclear price marking.
Fig. 2. The influence of positive IWOM
As shown in Fig. 3, when the negative network word of mouth, close to the equilibrium point 0, that is, the manufacturer will choose the strategy of setting a low price for ice cream at this time, when taking 0.6 and 1 respectively, gradually approaching the equilibrium point 1, which means that when the network word of mouth begins to gradually improve, the manufacturer will choose the strategy of setting a high price. According to the diagram, it can be found that the change of negative online reputation still does not affect the strategy choice of retailers, that is, no matter how it changes, retailers will choose the strategy of unclear price. At that time, it was close to equilibrium point 0, which meant that negative online word of mouth had an impact on consumer decision-making, prompting consumers to adopt a strategy of not buying ice cream. When the negative online reputation began to gradually improve, that is, when the values were 0.6 and 1, respectively, and tended to be close to the equilibrium point 1, consumers changed their strategy and chose to buy ice cream.
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Fig. 3. The influence of negative IWOM
5 Conclusion To sum up, when IWOM is equal to α, manufacturer and consumer will always choose the high price and purchase strategy, because positive online word-of-mouth will always bring positive feedback to the manufacturer’s high price strategy. At the same time, a higher price can make the manufacturer obtain higher profits. Manufacturers are bound to choose the high price strategy in order to pursue profits. However, when the negative network reputation γ is in a low state, the manufacturer will choose to return to their reputation by exchanging the higher price strategy to lower price. Consumers’ herd mentality will have an impact on their purchase strategy choices, that is, no matter what the status of positive online word of mouth α is, it will always attract consumers and urge them to pay. When online word-of-mouth is in a negative state, that is, when IWOM is γ, when γ is in a high or medium level, it will still not have a full impact on consumers’ purchasing strategy. Once γ is in a low level, consumers’ herd mentality will urge them to stop paying for this kind of ice cream with poor IWOM. For retailers, changes in IWOM can never have an impact on their strategic choices, because retailers are downstream of the supply chain and will not be deeply bound to a certain commodity, so online word-of-mouth will not have an impact on retailers’ strategic choices. What can directly affect retailers’ strategies are the excess profit R brought by unclear pricing and regulatory penalties for retailers with unclear pricing β. Therefore, the choice of the strategy combination of "ice cream Assassin" is reasonable for manufacturers and retailers to pursue the goal of maximizing profits.
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References 1. Liang, X.: Research on Supply Chain Enterprise Cooperation Based on Game Theory, Wuhan University of Technology, MA thesis (2008) 2. Yang, B.: Research on Supply Chain Revenue Optimization under New Retail Model Based on Game Theory, Jilin University, MA thesis (2019) 3. Li, S. X., Huang, Z., Ashley, A.: Manufacturer-Retailer Supply Chain Cooperation Through Franchising: A Chance Constrained Game Approach. Infor Information Systems & Operational Research (2002) 4. Wang, H.: Research on Supply Chain Pricing Strategy and Coordination Mechanism Considering Internet Word-of-Mouth. Jiangnan University, MA thesis (2021) 5. Li, Y.: Research on the Impact of Online Reviews on Online Consumers Purchasing Decisions and Product Pricing. Huazhong University of Science and Technology, MA thesis (2016) 6. Wang, X.: Retailer Buyer Power and Channel Fee, Southwestern University of Finance and Economics, MA thesis (2006) 7. Zhang, Z., Yu, Y.: Monopoly power of retailers, channel fees and economic regulation. Finan. Trade Econ. 03, 60–66 (2006) 8. Xue, F., Chen, G., Qian, Q., Wen, D.: Research on three-degree price discrimination analysis from the perspective of static game of incomplete information. Technol. Econ. 04, 65–76 (2021) 9. Liu, W.: Research on Product Discrimination Pricing Strategies Considering Consumer Brand Preferences, Southwest Jiao tong University, MA thesis (2019) 10. Fat, W.: The same is the internet red ice cream, why some can only be remembered products, while some are remembered brand. Sales Market. (Manage. Edn) 08, 46–49 (2020)
Empirical Analysis of Liquidity Risk of Chinese Listed Commercial Banks Ce Shen(B) School of Liaoning University, Shenyang 110036, Liaoning, China [email protected]
Abstract. Liquidity risk has a huge influence on commercial banks. Many scholars have developed and derived different models from different perspectives to measure and prevent liquidity risk of commercial banks. This article selected thirty-seven commercial banks as examples, using their financial data in 2021, and use twelve variables to construct the liquidity risk evaluation index. These selected banks were divided according to their different business natures which included state-owned banks, joint-equity banks, city commercial banks and rural commercial banks. This article uses factor analysis to calculate the liquidity scores, ranks the result and from several perspectives explains the result. Results of factor analysis shows that state owned banks perform worse than non-state-owned banks, but state-owned banks have higher scores on risk resilience, profit and liquidity factors. This article shows different result compared with other scholars’ study, may because of the different macro environments, insufficient data or an imprecise empirical process. This article considers that the special characteristics of years after COVID- 19 and different flexibility of state-owned banks and non-state-owned ones contribute to the result. In the end, this article suggested three aspects, from state owned banks, non-state-owned banks and regulation department to improve or maintain the liquidity situation. Keywords: Liquidity Risk · Factor Analysis · Listed Commercial Bank
1 Introduction Liquidity risk of commercial banks generates mainly because commercial banks fail to handle the liquidity dilemma brought by either assets or liabilities. Compared with credit risk or market risk, liquidity risk is a more comprehensive risk. When commercial banks lack liquidity, they cannot acquire enough money by increasing liabilities or quickly converting into cash, which can influence their profitability. In the 21st century, the frequency of global financial crisis and the outbreak of COVID- 19, the world economy has been in a state of depression and nearly all the countries and districts have been badly influenced. Changes in the capital market have been bound to hit the banking sector. Under the new character of this era, it is more urgent to measure and prevent the liquidity risk of commercial banks. In history, Chinese banking sector was badly influenced by factors like politics, economy and international relations and still has a long way to go © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 571–581, 2023. https://doi.org/10.1007/978-981-99-6441-3_52
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compared banking sectors of Europe and United States. In China for a long history, stateowned nature and non- state-owned nature have affected profitability and liquidity of commercial banks and this nature-controlled difference should be corrected. Therefore, studying liquidity risk of different types of commercial banks under new characters of era benefits the prevention of liquidity crisis, the promotion of fair competition and the perfection of regulation. The aim of this article is to select thirty-seven listed commercial banks in China, using their statistics from the annual report of 2021 and constructing a valuation system with twelve indicators, use the factor analysis model to calculate separate liquidity score and compare the results of different types of banks. The first part of this article describes the background and studies of other scholars. The second part includes the method and basic description of samples and the valuation system. The third part shows the process of factor analysis and liquidity scores. The fourth part presents the improvement suggestions and possible shortcomings. Scholars generally believes that liquidity risk of banks comes from the lack of solvency, which is determined by whether the bank has sufficient liquid assets or whether the bank can complete the financing in time. Diamond first came up with the D-D model [1]. They concluded that demand deposits of bank can effectively spread risk for intertemporal consumers, reaching an equilibrium that is more sufficient than financial market, that because of the special first come-first serve character of banks, when panic arrives, a run on a bank is optimal for depositors and that once a run on a bank happens, even healthy banks cannot avoid bankruptcy. As for determinants of liquidity risk, Chinese scholar Li based on data from annual report of 2020 of sixteen listed commercial banks, selected liquidity ratio, loan-todeposit ratio, non-performing loan ratio, core tier one capital, ROA, deposit ratio and debt ratio as explaining variables and calculated through multiple linear regression model [2]. The conclusion was that among seven variables, only core tier one capital and deposit ratio were significant both of which measure the banks’ ability of absorbing funds. That article suggested three ways to improve, including expanding channels for capital replenishment, appropriately raising deposit interest rate and constructing a comprehensive regulation system of liquidity. Wang, Wen and Zhou used time series analysis model to measure the influences of deposit-to-loan ration, non-performing ratio, capital adequacy ratio and economic growth rate on liquidity [3]. The result was that the influence of capital adequacy rate on liquidity wasn’t significant. Besides, more and more scholars combine commercial banks’ liquidity with technical improvements. Pei and Fu selected fifteen Chinese listed banks from 2014 to 2019 as samples and used fixed effects model and mediating effect model to empirically test the influence and transmission channels of the development of online finance [4]. They found that the development of online finance significantly lower the liquidity of commercial banks. Deng, Jiang and Song studied how the innovation of loan facility influenced the liquidity creation of commercial banks [5]. Results showed that loan facility could significantly increase the banks’ ability to create liquidity, that policy effect of this tool had more significant influence on banks with smaller scale and more dependent on interbank finance and that mid-term lending facility had stronger influence. This study benefits the implement of Chinese monetary policies, the regulation of central bank’ s liquidity and the prevention of systemic financial risk.
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Measures of bank’s liquidity have been developed variously. Zhang constructed a valuation system using fifteen indicators, divided forty bank samples into state-owned banks, joint-equity banks, city commercial banks and country commercial banks and used factor analysis model to calculate liquidity scores [6]. The author analyzed issues of different types of banks and made separate suggestion. Sun selected listed banks to construct stress test model of liquidity risk. He pointed that at present Chinese banking sector had enough liquidity reserve, but under the heavy pressure of the depression of economy, the banking sector was more likely to generate systemic liquidity risk. Scholars Liu, Zhao and Tian studied the mismatch of liquidity of Chinese listed banks based on time-varying model [7]. They concluded that there were significant heterogeneity and time variability, that macro-economic situation and changes in assets price should be more focused on and that relevant regulation system should be built. Considering the natures of banks, Gu and Chang used the stress test model of William and Van den to test the liquidity risk of the four state-owned banks in 2008 and made suggestions on how to apply on stress test models on state-owned banks such as constructing a complete risk valuation system and improving the application of test results in reality [8]. Che selected several non-state-owned banks as samples, using Principle Component Analysis and index method to calculate liquidity risk [9]. The conclusion was that nearly 70% of samples had high liquidity risk and most of them suffered from weak ability of storing assets and weak liquidity about assets and liabilities. Nie and Huang added aging situation into studying the relation between monetary policy and liquidity of state-owned banks, using VAR model. Results showed that in a long term, aging, monetary deposit reserve ratio, rediscount rate and open market business had equilibrium relations with liquidity of state-owned banks [10]. Generally speaking, measures of liquidity risk are varying, models are still developing and state-owned banks have better liquidity situations than non-state- owned ones, possessing better profitability.
2 Methodology As a classic method in multivariate statistical analysis, factor analysis has been applied in many fields like economics, sociology and psychology. It mainly based on internal dependencies among variables, uses a few abstract variables to describe data structure and main information of numerous variables. The main advantage is that factor analysis can simplify the analysis process covering all aspects when dealing with a lot of data and cumbersome data projects. Factor analysis includes two types, type Q about samples and type R about variables. In this article, type R is used to describe thirteen variables concerning liquidity of thirty-seven listed banks in China. The expression of factor analysis is: Xi = ai1 F1 + ai2 F2 + . . . . . . aip Fp +ε i , i = 1, 2, . . . . . . p
(1)
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F1, F2,……, Fp refer to common factors and εi refers to special factor of Xi.In matrix form, the expression changes into: X = AF +
(2)
(3) ⎡
⎤ ⎡ ⎤ ⎡ ⎤ X1 F1 ε1 ⎢ .. ⎥ ⎢ .. ⎥ ⎢ .. ⎥ X = ⎣ . ⎦, F = ⎣ . ⎦, = ⎣ . ⎦ X2
F2
(4)
ε2
Data used in this article comes from 2021 annual report of thirty-seven Chinese listed banks. Listed banks are selected according to their properties, including six state - owned banks, eleven joint-equity banks, twelve city commercial banks and eight rural commercial banks, as table one presents. According to how scholars divide indicators when analyzing liquidity risk and how they analysis the influence direction of each indicator, this article sets up the evaluation system with twelve indicators as table two presents. This article uses Rstudio to do all the analysis (Tables 1 and 2). Table 1. Bank samples of four properties. State-owned
Joint-equity
City commercial
Rural commercial
BC
CMB
Bank of Nanjing
Chongqing RCB
ABC
SPD Bank
Bank of Ningbo
Qingdao RCB
ICBC
China CITIC Bank
Bank of Beijing
Changshu RCB
CBC
CEB
Bank of Jiangsu
Zijin RCB
BCM
HB
Bank of Guiyang
Wuxi RCB
PSBC
CMB
Bank of Hangzhou
Zhangjiagang RCB
GDB
Bank of Shanghai
Jiangyin RCB
CIB
Bank of Chengdu
Suzhou RCB
Ping An Bank
Bank of Zhengzhou
Zhe Shang Bank
Bank of Changsha
Bo Hai Bank
Bank of Qingdao Bank of Xian
Empirical Analysis of Liquidity Risk of Chinese Listed Commercial
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Table 2. Evaluation index system. Variables
Indicators
Influence
X1
Liquidity ratio
Positive
X2
Return on total assets
Positive
X3
Deposit composition ratio
Negative
X4
Interbank debt ratio
Negative
X5
Loan-to debt ratio
Negative
X6
Demand deposit ratio
Negative
X7
Core tier one capital
Positive
X8
Provision coverage
Positive
X9
Loan allocation ratio
Positive
X10
Leverage ratio
Positive
X11
Return on equity
Positive
X12
Net interest margin
Positive
3 Results and Discussion Unify influence direction. Among all the indicators, the ones with negative influence on liquidity risk are fewer than the ones with positive influence, therefore negative indicators firstly are changed into positive ones by calculating the reverse of these indicators. Describe the statistics. Table three shows the basic information about thirteen indicators of thirty-seven listed banks. Generally, statistical analysis needs data normally distributed, using skew and kurtosis to describe the difference between real data and ideal data. In table three, all twelve variables have values of skew greater than zero, which means these variables have large degree of deflection and have more extreme numbers on the right side of the data. Return on total assets has the greatest value. As for kurtosis, only core tier one capital, provision coverage, loan allocation ratio and leverage ratio have negative values, which means compared with normal distribution, their distributions are more gently. The other eight variables have positive values, having more steep distributions. Return on total assets also has the greatest value and the most steeply distribution (Table 3). Test the data. The validity of factor analysis depends on correlations among original data. This article uses KMO test and Bartlett’s sphericity to test whether correlation exists. Table four shows the result of two tests. In KMO test, the overall MSA is 0.59 higher than 0.5, the condition of the model, meaning that data selected satisfies the factor analysis. In the Bartlett’ s test, P-value is far less than 0.5 level of significance, and is close to zero. The result rejects the null hypothesis that no correlation exists. Therefore, according to those two tests, data selected is suitable to do factor analysis (Table 4).
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C. Shen Table 3. Descriptive statistics.
vars
n
mean
sd
median
min
max
skew
kurtosis
X1
37
69.47
21.26
64.25
X2
37
1. 10
1.50
0.85
41.5
157.79
2.11
5.97
0.5
9.83
5.31
27.94
X3
37
0.02
0.01
0.01
0.01
0.03
0.71
0.07
X4
37
0.13
X5
37
0.01
0.11
0.09
0.04
0.51
2.19
4.43
0.00
0.01
0.01
0.02
0.54
0.42
X6
37
0.03
0.01
0.02
0.02
0.04
0.72
0.08
X7
37
10.11
1.51
9.82
7.93
13.59
0.74
-0.45
X8
37
290.54
126.03
268.73
135.63
567.71
0.66
-0.83
X9
37
3.40
0.76
3.36
2.23
4.86
0.18
-1.38
X10
37
7.01
0.88
6.95
5.66
8.72
0.24
-1.10
X11
37
11.29
2.41
10.89
6.59
17.60
0.78
0.70
X12
37
2.11
0.31
2.13
1.56
3.06
0.68
0.77
Table 4. Tests’ results. Test
Indicator
Value
Kaiser-Meyer-Olkin factor adequacy
Overall MSA
0.59
Bartlett’s test of homogeneity of variance
Bartlett’s K-squared
3855.4
df
11
P-value
< 2.2e−16
Extract the numbers of common factors. This article uses principal components method to calculate the cumulative variance contribution rate. The contribution rate of each variable and cumulative variance contribution rate is presented in table five. Table five shows that the first five numbers have a cumulative variance contribution rate more than eighty per cent, satisfying the condition of this model. Therefore, this article uses the first five numbers as common factors (Table 5). Rotate factor loading matrix and name the common factors. Factor loading matrix rotated can expand the differences of numbers of each row, making them closer to either one or zero in order to highlight main factors with high loading on each common factor. Table six shows the factor loading matrix with rotation. Table seven shows factor rotation matrix. As Table 6 shows, thirteen indicators can be classified into the six common factors according to their loading on each common factor. Table eight presents the classification result. Each common factor is named in terms of its main economic meaning. Common factor one includes provision coverage, loan allocation ratio and return on total
Empirical Analysis of Liquidity Risk of Chinese Listed Commercial
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Table 5. Contribution rate. Xi
Contribution rate
Xi
Cumulative rate
1
0.298
1
0.298
2
0.185
2
0.483
3
0.146
3
0.629
4
0.100
4
0.729
5
0.078
5
0.807
6
0 061
6
0 869
7
0.042
7
0.911
8
0 038
8
0 949
9
0.026
9
0.975
10
0 020
10
0 995
11
0.005
11
1.000
12
0.000
12
1.000
Table 6. Factor loading matrix with rotation. Variables
PA1
PA2
PA3
PA4
PA5
X1
0.14
0.52
0.05
0.3
0.2
X2
−0.05
−0. 15
−0.24
0.2
0.0
X3
-0.01
0.99
−0.03
0.0
0.1
X4
0.12
0.19
0.03
0.1
0.8
X5
0.39
0. 11
0.10
−0. 1
0.7s
X6
-0.01
0.99
−0.03
0.0
0.1
X7
0.15
−0.05
0.69
0.0
0.2
X8
0.97
0. 16
−0.09
0.1
0.2
X9
0.80
0.21
0.20
0.1
0.2
X10
−0.16
−0.10
0.98
0.1
−0.1
X11
0.60
−0.19
0.00
0.1
0.1
X12
0.26
0.12
−0.02
0.9
0.0
assets, which describe financial soundness of the bank, named financial soundness factor. Common factor two includes liquidity ratio, deposit composition ratio and demand deposit ratio, named time limit factor. Common factor three includes core tier one capital and leverage ratio, showing the risk resilience of the bank, named risk resilience factor. Common factor four includes return on total assets and net interest margin, reflecting the income situation of the bank, named profit factor. Common factor five contains interbank
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debt ratio and loan-to-debt ratio, mainly presenting all kinds of liabilities of the bank, named liquidity factor (Tables 7 and 8). Table 7. Factor rotation matrix. V1
V2
V3
V4
V5
V6
1
0.60
−0.57
-0.01
0.20
−0.28
0.45
2
0.58
0.71
0.33
0.15
0.08
0.12
3
0.19
−0.28
0.93
−0.02
0.15
0.03
4
0.21
−0.22
−0. 07
0.62
0.47
−0.55
5
0.29
−0. 16
−0.12
−0.59
0.71
0.14
6
0.38
0. 11
−0.12
0.45
0.40
0.69
Table 8. Factors classification Common factor
F1
F2
F3
F4
F5
Name
Financial soundness factor
Time limit factor
Risk resilience factor
Profit factor
Liquidity factor
Provision coverage
Liquidity ratio
Core tier one capital
Return on total assets
Interbank debt ratio
Loan allocation ratio
Deposit composition ratio
Leverage ratio
Net interest margin
Loan-todebt ratio
Return on equity
Demand deposit ratio
Calculate factor scores. By calculating scores of each indicator on each common factor and taking the first five factors’ variance contribution rate as weight, this article calculates the general formula, composite scores and separate scores of each common factor. The highest five and lowest five banks are shown in table nine (Table 9). F = 0.4F1 +0.24F2 +0. 18F3 +0. 12F4 +0.07F5
(5)
According to the table nine, the highest five banks with high liquidity scores are Jiangyin RCB, Chongqing RCB, Zhangjiagang RCB, Changshu RCB and Wuxi RCB, all of which are rural commercial banks. The lowest five banks with low liquidity scores are Qingdao RCB, GDB, BC, China CITIC Bank and Zhe Shang Bank, most of which are city commercial banks. According to table ten, state-owned banks score higher in risk resilience factor, profit factor and liquidity factor than other three types of banks.
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Table 9. Composite scores and rank, the highest five and the lowest five banks Bank
Composite score
BC
−0 70
Rank 35
China CITIC Bank
−0.63
34
GDB
−0.84
36
Zhe Shang Bank
−0.58
33
Bank of Qingdao
−0.86
37
Chongqing RCB
0.85
2
Changshu RCB
0.74
4
Wuxi RCB
0.72
5
Zhangjiagang RCB
0.77
3
Jiangyin RCB
1.20
1
Generally speaking, state-owned banks should have higher composite scores than any other type of banks, because state-owned banks have enough financial and political support, higher credit and more operating skills. While rural commercial banks which are small in size and lack special operating skills should face more liquidity risk. This article mainly considers two parts. Firstly, the time chosen has its specific characteristics. In 2021, nearly all the countries suffered from economic, political and environmental destruction. The economy was in bad situation, the capital market was weak and international trades were badly affected by international political relations. That consumption and investment lack incentive made companies hard to get enough money to complete capital turnover and even to repay their loans from banks. Commercial banks, especially ones with large capital volume like state owned banks had made allot of loans before the weak economy couldn’t collect the loans in time, putting pressure on their liquidity. Besides, compared with any other types of banks, state-owned banks have to undertake a bigger part of social responsibility, especially when the nation’s economy has serious issues. Therefore, these state-owned banks may put the principles of liquidity and security behind. Rural commercial banks mostly focus on their locations, conduct business in a specific district and can attain capital support from bigger banks when in trouble. People who deposit in rural commercial banks mostly are the ones aren’t interested in multiple investments but in the deposit interest. And people or companies that borrow from rural commercial banks usually can’t get huge loans considering the capital volume of these banks. Therefore, rural commercial banks don’t have as high liquidity risk as state-owned risk. Secondly, the flexibility of operating among different types of banks is variable. State owned banks compared with other three types of commercial banks, have a much larger capital flow and more complicated interest relations. The other three bank types can adjust their businesses more easily than state owned banks because they don’t have as many constraints as state owned banks. A little change in the businesses of state owned can take a longer time to complete. Besides, state owned have had an advantage of government
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support for a long time. Although the reform and opening up policy encourages and stimulates the development of all kinds of economic forms, the state-owned forms still dominate. And the long history of state- owned forms becomes an obstacle of change. For example, if a state-owned company and a non-state-owned company want to borrow money from the same bank, the bank will probably make loan to the state owned one no matter how well the non-state- owned company operates its business. This tilt of resources to state owned economic forms has a long history and make some or most enterprises of this type satisfied with present and change slower compared with nonstate-owned economic forms. The same is true for state owned banks. The state-owned banks always have government as their backup. When the weak economy affects banks with small capital volume, these banks are more active and willing to change their operating models to fight against risk. The liquidity scores may in some degree reflect the efficiency of business operating.
4 Conclusion In conclusion, according to the result of this article, there are three suggestions to the liquidity issue of different types of commercial banks. As for state-owned banks, although in this economic situation dropping social responsibility behind their own interest is unrealistic, at least the leaders should adopt more flexible mechanisms to improve business and to increase their liquidity. And their habit of depending on the government should be abandoned and cultivate innovative thinking gradually. As for non-state-owned commercial banks, they can consider development multiple businesses, diversifying the use of money. Besides, regulation departments should regulate regulation standard for different type of commercial banks while make different specific regulation considering the reality of different types, combining commonality with individuality. The result is different from those of other scholars, most of whom concluded that state owned banks have higher liquidity scores than non-state-owned ones. The result may be affected by different study time or by technical errors such as insufficient data and imprecise empirical process.
References 1. Dybvig, D.: Deposit insurance and liquidity. Fed. Reserve Bank Minneap. Q. Rev. 24(3), 401–419 (2000) 2. Li, M.J.: Study on influencing factors of liquidity of commercial banks. Sci. Techn. Innov. Product. 12, 84–86 (2021) 3. Wang, C., Wen, X.M., Zhou, Y.L.: Research on measurement and management of bank liquidity risk under the new trend. Res. Finan. Educ. 32(04), 55–61+68 (2019) 4. Pei, P., Fu, S.: The influence of internet finance development on the liquidity of commercial banks-empirical evidence from 15 listed banks in China. De Econ. 12, 80–87 (2020) 5. Deng, W., Jiang, N., Song, M.: Do lending facilities improve the liquidity creation of commercial banks in China. Stud. Int. Finan. 07, 58–67 (2022) 6. Zhang, Y.H.: On liquidity risk evaluation of China’s listed commercial banks based on factor analysis. J. Tongling Univ. 21(01), 33–39 (2022)
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7. Sun, Y.J.: Research on liquidity risk of commercial banks based on stress test model. J Sanmenxia Polytech. 20(01), 124–128 (2021) 8. Gu, S.H., Chang, Y.: Stress test of liquidity risk of Chinese state-owned banks. J. Shenyang Univ. (Soc. Sci.) 15(06), 756–760 (2013) 9. Che, M.: Research on liquidity risk of non-state owned commercial banks. CO-Oper. Econ. Sci. 22, 56–57 (2021) 10. Nie, G.H., Huang, M.Q.: The empirical analysis of the monetary policy and the liquidity of state-owned banks under the background of aging. Res. Finan. Educ. 29(02), 3–9 (2016)
Analysis on the Influence of Various Elements on the Net Profit of a Steel Group in Southwest China and the Prediction Based on Arima Model Zhaoxin He(B) School of Resources and Civil Engineering, NEU, Shenyang 110000, Liaoning, China [email protected]
Abstract. This paper uses the method of quantitative research and adopts the coefficient of variation method and Arima time series model to analyze the adverse effects of the steel industry in Southwest China, the upstream industry of real estate, in the context of the COVID-19 pandemic and the continuous regulation of China’s real estate policy. Carry out the analysis of the influence degree of each factor and forecast the net profit. The study found that among the many unfavorable factors, the reduction in demand, the rising cost of imported raw materials, and the increasing labor costs have a greater impact on net profit, while equipment depreciation expenses, domestic raw materials and common power sources have a significant impact on net profit. Less impact. In response to the two major factors, the price of imported raw materials and labor costs, the national policy adopted the establishment of a company that purchases mineral resources in a unified way – China Minerals Group to enhance its voice, and internal enterprises can adopt methods to improve personnel efficiency to reduce labor costs. Keywords: coefficient of variation method · Arima time series model · net profit of steel group under the impact of Chinese real estate
1 Introduction In the context of the epidemic, the economies of all countries are declining, and the price of real estate in China continues to decline. In addition, the Central Economic Work Conference has issued a series of policies to limit the rise in real estate since the end of 2016, when it was clearly stated that houses are for living, not for speculation., such as the policy introduced in July 2021 that the sale of second-hand houses in densely populated central cities shall not be higher than the guide price of buildings, which makes the housing price almost collapse. The steel industry, which is an upper-class real estate industry, has also been hit hard [2]. At this stage, the steel industry is facing many factors such as overcapacity, reduced demand, falling steel prices, rising costs, and resource constraints. Under the impact of various factors, many steel industries have fallen into losses. In response to falling steel prices, China has been preparing to establish a unified external resources procurement bureau, China Minerals Group, to boost the country’s influence as a buyer [1]. This study analyzes the net profit of a steel © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 582–593, 2023. https://doi.org/10.1007/978-981-99-6441-3_53
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group in Southwest China from January 2017 to the present. According to reading the literature in many academic literature libraries such as web of science and CNKI, it is found that few papers use the coefficient of variation method to judge the weight index of a specific steel enterprise’s internal net profit, so as to visualize the data and adjust it for unreasonable places. Few papers use the Arima model to study the price trend of the steel industry, so this paper makes up for the gap in this aspect by analyzing the net profit of a specific enterprise through the coefficient of variation method and the Arima model [3]. Through the internally obtained data from 2017 to August 2022, combined with the general price trend of international imported raw materials landing in Qingdao Port, the data was reorganized and preprocessed, and the processed data was calculated using the coefficient of variation method to obtain the value of each independent variable. The weight index (i.e. the degree of importance) is used to predict the price trend in the next 4 months through the Arima model. After the predicted value is obtained, the original two independent variables are modified according to the two policies that have been carried out by the country and the enterprise, and a new Arima forecast is obtained. Model, compare the difference between the two prediction models before and after, confirm the feasibility of the two policies and prove the accuracy of the weight index calculated by the coefficient of variation method [4].
2 Research Methods 2.1 Data Sample The data of this research comes from the production and operation analysis financial report of a certain steel group from 2017 to 2022. The production and operation analysis is roughly divided into raw material procurement costs; material financing costs; current costs and processing expenses; Impact; main economic and technical indicators; electricity price impact; variable cost impact; labor cost per ton of steel; freight per ton of sales, a total of ten dimensions [5]. 2.2 Variable Description Dependent Variable. The explained variable (dependent variable) of this study is the net profit of the steel group. Main Independent Variables. The control variables for this study were selected as the following indicators: x_1: Cost of ore raw materials, a numerical categorical variable. The original data consists of 8 groups of data, which are sorted into 6 groups of data, namely Brazilian fine ore, 58% Australian fine ore, lump ore, iron ore concentrate of a certain mountain pipeline, and Panxi vanadium. Titanium concentrate, domestic iron concentrate 62%. x_2: Fuel cost, a numerical categorical variable. There are 7 groups of raw data, namely imported coke, self-produced 83 coke, injection coal, lean coal, cleaned coal, and anthracite. Since the general price trend of injection coal is similar to that of lean coal,
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and injection coal has only been used for one year in 2017, the data are compressed into 6 sets of data: imported coke, self-produced 83 coke, lean coal, cleaned coal, and anthracite. x_3: The impact of financing and interest discount on materials, a numerical categorical variable, there are 6 groups of data: Brazilian fine ore, Australian fine ore, concentrate, anthracite, coke financing, and coke interest discount. x_4–x_8: It includes the manufacturing costs of sintering, pellets, ironmaking, steelmaking, and bars, respectively. The manufacturing cost of each part includes the use of raw materials, fuel and power consumption, material spare parts, and manufacturing costs., In-unit transportation costs, reduction and recycling, wages and benefits. x_9: Labor cost, a numerical categorical variable. There are 7 groups of data, including total employee wages, social insurance expenses, welfare expenses, education expenses, labor union expenses, labor protection expenses, and housing provident fund. x_10: Expenses during the process, a numerical categorical variable, a total of 4 groups of data, namely taxes and surcharges, management expenses, financial expenses, and operating expenses. x_11: The freight borne by the seller, a numerical categorical variable, with a total of 4 sets of data, namely the freight generated by road transportation, railway transportation, warehousing and storage, and mechanical hoisting. x_12: The remaining influencing factors are roughly divided into 4 aspects, which are the cost of equipment depreciation, inventory corrosion, the impact of excessive inventory in the previous period, and the loss caused by shutdown. The selection parameters of this research are based on the operating analysis financial report of the steel group and the data provided by the China Iron and Steel Association, and try to avoid unnecessary research caused by the multivariate collinearity problem. By August 2022, the per capita steel output, overall production cost, total sales and net profit are analyzed for the core indicators of the survival and development of the steel group, and it is judged that the main factors such as raw material cost, labor cost, market influence, etc. will affect the net profit. Impact and leverage the Arima time series model to forecast its sales for later decision making [6].
3 Results 3.1 Coefficient of Variation Method First, start with the independent variables of each dimension mentioned in the data description, and then analyze their influence on the dependent variables. The focus is mainly placed on major dimensions such as raw material costs, financing discounts, equipment depreciation expenses, labor costs, marketing costs and sales freight. Firstly, the weight of each indicator is analyzed according to the weight calculation result, and the weight analysis matrix is obtained through the weight calculation, and finally the analysis is summarized. Table 1 shows the weight calculation results of the coefficient of variation method. According to the results, the weight of each index is analyzed. The index variability in
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Table 1. Weight calculation result table category item Net profit (10,000 yuan)
mean
standard deviation
cv coefficient
Weights
8357.125
5654.485
0.677
16.453
11224.239
2531.692
0.226
5.485
Brazil fine ore cost (10,000 yuan)
3308.913
1085.655
0.328
7.978
58% purchase cost of fine ore (10,000 yuan)
5493.661
1744.967
0.318
7.724
Purchase cost of fine ore in South Africa (10,000 yuan)
1905.246
617.297
0.324
7.879
Anthracite purchase cost (10,000 yuan)
9486.901
2073.014
0.219
5.314
Pipeline iron concentrate cost (10,000 yuan)
Coke purchase cost (10,000 yuan)
44010.8
20881.16
0.474
11.537
Total sales (10,000 yuan)
268414.157
60102.565
0.224
5.445
4484.932
464.013
0.103
2.516
12.166
2.974
0.244
5.944
4363.245
396.795
0.091
2.211
38.372
9.246
0.241
5.86
0.143
3.469
0.338
8.223
Wire price Wire production Rebar price Bar production Angle steel price Section steel production
4482.466
639.51
8.594
2.906
Equipment depreciation expense
4133.779
287.664
0.07
1.692
Labor cost
6898.486
643.964
0.093
2.27
the above figure is the standard deviation. The larger the standard deviation, the greater the weight; the calculation formula of the coefficient of variation is: CV =
SD × 100% Mean
(1)
The coefficient of variation is an index of variation expressed as a relative number. It is obtained by comparing the total distance, average difference or standard deviation in the variation index with the average; after deducting the net profit of the dependent variable, the maximum value of the index weight is the coke purchase cost (10,000 yuan) (11.537%), and the minimum value is Equipment depreciation expense (1.692%).
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The classification of different dimensions is more refined, and it can be further obtained that coke is the most influential factor in the cost of raw materials due to the largest amount of coke used and the price increase of coke in recent years. The imported Brazilian fine ore, South African fine ore and 58% fine ore have a greater impact on the procurement cost, which further affects the output of section steel, wire rod and bar. As a domestic mountain pipeline concentrate, the raw material procurement cost is only It is one level higher than anthracite, an auxiliary power material also produced domestically. In the production cost, the influencing factors of labor cost are more significant, and the equipment depreciation expense is the least affected because the equipment loss and depreciation situation are similar to the previous year, and the annual equipment depreciation expense is basically constant [7]. 3.2 Arima Time Series Forecasting On this basis, we set up the Arima time series sales model, The Arima model requires that the series meet the stationarity. By looking at the ADF test results, according to the analysis t value, analyze whether it can significantly reject the hypothesis that the series is not stationary (Table 2). Table 2. ADF inspection form variable
Difference order t
p
AIC
threshold 1%
Net profit 0 (10,000 yuan) 1 2
5%
10%
−0.424 0.906
1078.836 −3.548 −2.913 −2.594
−1.829 0.366
1055.117 −3.555 −2.916 −2.596
−6.637 0.000*** 1038.901 −3.555 −2.916 −2.596
Note: ***, **, * represent the significance levels of 1%, 5%, and 10%, respectively The above table shows the results of the ADF test. The results of the series test show that based on the variable net profit: when the difference is 2-order, the significant p value is 0.000***, and the level is significant, rejecting the null hypothesis, the sequence is stationary time series
Figure 1 shows the time series diagram of the variable as net profit after the secondorder difference of the original data. Figures 2 and 3 show the autocorrelation plot (ACF), including coefficients, upper and lower confidence limits. x: number of delays. y: autocorrelation coefficient. blue bar: partial autocorrelation coefficient. green line: upper bound of PACF95% confidence interval. yellow line: lower bound of PACF95% confidence interval (Table 3)
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Fig. 1. The time series diagram of the variable as net profit after the second-order difference of the original data. Note: ***, **, * represent the significance levels of 1%, 5%, and 10%, respectively the above table shows the results of the ADF test. The results of the series test show that based on the variable net profit: when the difference is 2-order, the significant p value is 0.000***, and the level is significant, rejecting the null hypothesis, the sequence is stationary time series.
Fig. 2. Autocorrelation plot (ACF)
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Fig. 3. Autocorrelation plot (ACF)
Table 3. Results of ARIMA model (0,0,3) test variable
symbol
value
Df Residuals
64
Number of samples
N
68
Q statistic
Q6(P-value)
0.008(0.927)
Q12(P-value)
3.53(0.740)
Q18(P-value)
16.554(0.167)
Q24(P-value)
22.461(0.212)
Information Guidelines goodness of fit
Q30(P-value)
30.321(0.174)
AIC
1307.826
BIC
1318.924
R2
0.638
Note: ***, **, * represent the significance levels of 1%, 5%, and 10%
Based on the AIC information criterion to find the optimal parameters, the model result is the ARIMA model (0,0,3) test table, based on the variable: net profit (10,000 yuan), from the analysis of the Q statistic results, it can be obtained: Q6 does not appear on the level Significant, the assumption that the residuals of the model are white noise sequences cannot be rejected. At the same time, the goodness of fit of the model R2 is 0.638, the model performance is relatively good, and the model basically meets the requirements. x: number of delays y: autocorrelation coefficient blue bar: partial autocorrelation coefficient green line: upper bound of PACF95% confidence interval
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Fig. 4. Autocorrelation plot(ACF)
Fig. 5. autocorrelation plot(ACF)
yellow line: lower bound of PACF95% confidence interval Figures 4 and 5 show that the correlation coefficients are basically within two intervals and belong to the white noise sequence (Table 4) Based on the variable net profit (10,000 yuan), the optimal parameters were also found based on the AIC information criterion. The model result is the ARIMA model (0,0,3) test table and based on 2-difference data, the model formula is as follows (Fig. 6, Table 5) [8]: y(t) = 8357.125 + 0.894∗ε(t − 1) + 1.005∗ε(t − 2) + 0.419∗ε(t − 3) The time series graph is derived from the model formula
(2)
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standard deviation
t
p > |t|
0.025
0.975
8357.125
1311.096
6.374
0
5787.423
10926.827
ma.L1Net profit (10000 yuan)
0.894
0.112
7.978
0
0.674
1.113
ma.L2.Net profit (10000 yuan)
1.005
0.125
8.03
0
0.759
1.25
ma.L3.Net profit (10000 yuan)
0.419
0.111
3.783
0
0.202
0.636
Constant
Note: ***, **, * represent the significance levels of 1%, 5%, and 10%
Fig. 6. Forecasted time series plot
Table 5. Predictive value Time
Prediction results Unit: 10000 yuan
September 2022
546.644
October 2022
2777.585
November 2022
6121.326
December 2022
8357.125
4 Discussion Combining the obtained weight values of the respective variables and the profit forecast for the next four months, an assumption can be established. Under the situation of sluggish market demand that cannot be intervened in the severe winter of real estate,
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in order to survive, enterprises can control the cost of imported ore raw materials and reduce staff. Increase efficiency and reduce costs [9]. In response to these two approaches, both companies and the Chinese government have taken measures to deal with them. At the beginning of 2020, when the cost of raw materials has risen sharply, China has already planned to establish a central enterprise to purchase ore raw materials in a unified manner. The operating principle of the enterprise is as follows: each iron and steel enterprise first reports the raw materials it needs for a quarter or a month to the enterprise, and then the enterprise makes a unified external purchase. Because of the unified external purchase, there is no internal vicious competition. This policy enables Chinese steel enterprises to have a certain degree of right to speak in terms of purchase price externally, and also plays a role in the rational allocation of resources internally, effectively preventing a phenomenon which some enterprises with convenient transportation along the coast from competing on price with steel enterprises in inland cities due to the price advantage of raw materials. On June 16, 2022, the Financial Times reported that China will establish a company called China Mineral Resources Group Co., Ltd. to break the iron ore monopoly between Australia and South Africa. The company has held its inaugural meeting in Beijing on July 25, 2022. It can be seen from the price chart that the landing price of South African fine ore in July was US$29/ton lower than that of June, and the landing price of Australian fine ore in July was 6.6% from the previous month. Monthly decrease of 22 US dollars/ton, the landing price of South African fine ore in August decreased by 2 US dollars/ton from July, and the South African fine ore landing price in August decreased by 1 US dollar/ton from July, and these price reductions took into account the previously signed orders and contracts. The price of imported ore will fall further in the future when the new central SOEs gradually gain real control over the source of domestic steel enterprises. The company reduced staff and increased efficiency. Before encountering the real estate demand dilemma in the severe winter, the steel company proposed to reduce staff by 50% in four years and increase the per capita steel output per ton. In the four-year plan formulated at that time, the reduction was reduced in the first year 20% of the staff, 15% of the staff in the second year, 10% of the staff in the third year, and 5% of the staff in the fourth year. The target of reducing staff by 50% will be completed after four years. As of the end of 2021, in the past year, the number of employees has dropped from 28,899 at the end of 2020 to 22,806, a total reduction of 6,093, or a decrease of 21.1%. The company completed the first year of indicators. This behavior has brought about a significant increase in the actual per capita steel output, and also reduced operating costs by nearly 15%. After the intervention of the two major factors, we re-predicted the sales model based on the price of ore raw materials and the greatly reduced labor cost after the intervention of the two factors, and obtained the following Arima model—Fig. 7. As can be seen from the model, we can see that when we cannot interfere with market 0demand, we can only intervene at our own level and perspective, that is, under the condition of intervening and optimizing the cost of ore raw materials and labor costs, the business situation of the enterprise will improve, turn losses into profits. The new sales forecasting model once again proved the accuracy of the previous analysis of the size ratio of the influencing factors of sales profit (Table 6).
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Fig. 7. Revised forecast time series plot
Table 6. Predictive value Time
Prediction results Unit: 10000 yuan
September 2022
3958.271
October 2022
5848.431
November 2022
7640.297
December 2022
8856.238
The shortcomings of this experiment: firstly, the Arima model is more suitable for measuring predictions with less uncontrollable factors. In this experiment, the future uncontrollable factors are considered as known factors in advance and act on the data used in the experiment. After this method predicts There may be a certain error between the results and the actual phenomenon in the future. Secondly, because of the inability to change the real estate winter caused by the two major factors of the epidemic and policies, this experiment cannot take the market demand into account in all data type variables, and can only change the supply-side variables in the supply-demand relationship.
5 Conclusions This research uses the coefficient of variation method and the Arima model to calculate the weight value of each factor affecting profit and forecast the net profit in the next four months for a steel group in Southwest China. It points out that the price of imported ore raw materials and the high but inefficient labor cost are the two major factors affecting the net profit, and obtains the result by taking into account the impact of the decisions of the two countries and enterprises that have been implemented and changing the variables. The net profit forecast for the next four months after the intervention of the two major
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decisions clarifies the correctness of the state’s establishment of an enterprise to purchase ore raw materials in a unified manner and the reduction of staff and efficiency of the steel group. The weight values of the respective variables obtained in this research visualize each cost element and provide a reference for the decision-making direction of the steel group. The determination of the staff policy and the positive net profit forecast have also added some confidence to the group to continue and actively engage in the steelmaking industry in the harsh real estate winter environment. Because the prediction model used in this study is not suitable for the situation with many uncontrollable factors, the inaccuracy can only be controlled to a considerable range by artificial adjustment, and the predicted results can be There will be errors with the actual situation in the future. Referring to the “Research on Trading Strategy Based on EMD-LSTM Prediction Model and Sharpe Ratio Risk Backtracking”, it is found that the LSTM model is better than the Arima model to deal with uncertainties and instability. It is predicted that its curve fitting is better than that of Arima, and the research direction of this paper will be further improved for machine learning and deep learning [9].
References 1. Liu, W.: Current Situation and Thinking of Internet Development in China’s Iron and Steel Industry. China Metallurgy. ISSN 1006-9356, CN 11-3729/TF 2. Suo, H.: The establishment of a new state-owned enterprise, China unifies the right to speak of iron ore resources. China Business News2022-07-25 (A03) 3. Fang, L., Shen, L.: Analysis of price trend of mineral resources assets based on ARMA model. China Mining (08) (2010) 4. Chen, Y., Tong, L.: Combined trading strategy of bitcoin and gold. Economics and Management Science (FEMS 2022) (5), 99–110 (2016) 5. Chen, H.: Sales Monitoring and Analysis of New Retail under big data background. University of International Business and Economics (03) (2019) 6. Lu, X., Cheng, C.: Application of multiple linear regression and ARIMA combination model in hot rolled spot price prediction. China Comput. Commun. 34(05) (2022) 7. Sun, Z.: A grey correlation analysis model based on variation coefficient method and its application. China Western Leather 39(08) (2017). ISSN: 1671-1602 8. Peng, Y.: Introduction to the ARIMA Model. Electron. World (10) (2014) 9. Ren, Y.: Suggestions and thoughts on reducing staff and increasing efficiency in state-owned coal enterprises. Econ. Outlook Bohai and Huanhai (06) (2019). ISSN1004-9754 10. Tong, L., Zhang, Y., Ji, Y.: Research on trading strategy based on EMD-LSTM prediction model and sharpe ratio risk backtracking. China Market (01) (2023). ISSN1005-6432
Are There Any Winners on Both Sides of the Trade Conflict: Evidence from China and U.S. Lingyun Zhao(B) Nanjing Auditing University, 86 Yushan West Road, Jiangpu Street, Pukou District, Nanjing 211815, China [email protected]
Abstract. The trade conflict has imposed severe hits on China’s and American financial markets, which is reflected by the yield of five indexes. This paper assesses the impact of tariffs on five indexes, which include SSEC, SZSE, NASDAQ, DJI and S&P 500. Then, the paper builds a VAR model and an ARMAGARCH model to investigate the variations in the volatility of yield. This article illustrates that the tariffs injure financial markets, but the American market suffers less. As the time of tariffs increases, the influence of tariffs has been reduced, approximately to zero. The tariffs lead to damage in other aspects: unemployment, a decline in some industries dependent on exports and a tense international situation. The influence has spread to other nations, especially small open economies. The policy-makers of both countries should realize the negative influence of tariffs on the global economy, and seek other ways to encourage the development of the economy. Keywords: Trade War · Yield Volatility · Financial Market
1 Introduction On the 23rd of March in 2018, the president of the USA, Donald Trump announced that import goods worth about $60 billion produced in China would be collected tariffs and Chinese companies would be confined from investing in mergers and acquisitions in America. Since then, the American government has raised the tax nine times, including different domains and scales. Financial markets in both countries experienced shocks to a different degree. The literature shows that the USA endures considerable losses of social benefits, although China may enjoy modest gains or losses [1]. According to J Wu and his team, the tariffs on Chinese imports were the attribute for 67% of the indirect tariff burden that emerged from the tit-for-tat tariff competition, which totalled almost 23 billion US dollars (USD) [2]. In addition, for US importers, the replacement of imports from other countries with products imported from China is the most likely overall outcome of these new country-specific taxes. As a result, the US trade imbalance with other nations will increase as it decreases with China. There won’t be a considerable transformation in the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 594–605, 2023. https://doi.org/10.1007/978-981-99-6441-3_54
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gross trade deficit in America with the rest of the world [3]. The increased tariffs on imports alone cause a 0.1% and 0.2% drop in the US and China’s GDP, respectively. There is a decrease of 9.8 billion and 35.2 billion USD in the corresponding variation in the US and China, respectively [4]. According to M Amiti et al., the tariff has a full incidence on domestic consumers and will reduce real income in the United States by $16.8 billion in one year by the end of 2018 [5]. The financial impact has spread to other aspects. If everything else were equal, the China-US trade war would have significantly increased the bankruptcy rate for farm debtors by 25.7% [6]. Research reveals that there are also some benefits for the relevant industries. The Sino-US trade conflict will encourage structural reform in China’s cattle and sheep industries and compel their modernization. Additionally, it will encourage China’s forage grass industry’s optimization and adjustment and open doors for the sector’s expansion [7]. While input exposure may not always result in a substantial difference, enterprises with large output exposure to China’s market suffer significantly more from the trade war [8]. Furthermore, M Guo and his team suggest that although some tiny open economies might modestly benefit, others might suffer collateral damage.[9]. When it comes to politics, a power crisis caused by the United States’ lack of strategic vision has accelerated the trend toward a new multipolar world with multiple power centres [10]. Overall, trade risks are more ambiguous due to the complexity of the Sino-US trade war. All these observations demonstrate the difficulty of evaluating the financial effects of tariffs on the financial markets in two countries, which encourages future research on the effects of tariffs on the effectiveness of the free mobility of capital and goods globally. This paper builds its search for additional correspondences between tariffs and financial market volatility on this theory. The following parts of the article are illustrated below: Sect. 2 is research design, including data source, unit root test and identification strategy; Sect. 3 is empirical results, including VAR model result and ARMA-GARCH model result; Sect. 4 is discussion; Sect. 5 is the conclusion. This paper researches the five indexes, which contain SSEC, SZSE, NASDAQ, DJI and S&P 500.
2 Research Design 2.1 Data Sources The index figures in this report were taken from the Choice website between September 23, 2019, and January 24, 2020. As one of the most well-known financial websites in the world, it provides the majority of real-time financial information from all markets. For financial organizations and investors, it combines information search with statistical analysis. The closing price is used to calculate all historical index data in the article. In this situation, the study could yield a precise estimate of how the index’s market expectations fluctuate and are impacted by several tariffs. The index data and trading days are matched in this study. Since various indexes have different trading days, the research only includes data on the days when all of the indices are trading. Stata is commonly employed in this study as a tool to address the issues that come up as the research moves forward with its investigations.
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The logarithmic term is used as the index price and returns throughout the research in this paper, which is calculated as: lnprice = ln(1 + price)
(1)
pricet − pricet−1 pricet−1
(2)
lnyieldt = ln(1 + yieldt )
(3)
yieldt =
2.2 Unit Root Test A process known as a unit root test is then used to determine if the time series is stationary. This is because the majority of quantitative analyses of time series rely on the assumption that there is a stationary time series. Before starting the search, the paper should first check if the data is stationary. When performing the unit root test, it is a general hypothesis that the time series xt can be presented as: p−1 xt = ct + βxt−1 + Φi xt−i + εi (4) i=1
Table 1. ADF test Variables
t-statistic
p-value
SSEC
−1.659
0.7686
SZSE
−1.015
0.9420
NASDAQ
−2.373
0.3943
DJI
−3.253
0.0744*
S&P 500
−2.255
0.4588
SSEC
−16.385
0.0000***
SZSE
−16.782
0.0000***
NASDAQ
−18.061
0.0000***
DJI
−17.317
0.0000***
S&P 500
−16.815
0.0000***
Index
Yield
Note: The thresholds for t-value are −3.960 (1%), −3.410 (5%), −3.120 (10%). ***, **, and * indicate the level of significance of 1%, 5%, and 10%, respectively
The null hypothesis of the text is that the coefficient β = 1, indicating that there is a unit root in the series and is not stationary, while the substitute assumption is that β < 1, suggesting that the series under test is stationary.
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The test results for the index and yield data are identified in Table 1 suggests that the index data series underperform in the stationary test, but the yield data series perform well in the stationary test, and the yield data series performs well in the stationary test. The index time series for five indexes are not significantly stationary under 95% confidence intervals and the yield time series for five indexes are significantly stationary under 95% confidence intervals. According to the analysis, the following models could be constructed with these fixed series. 2.3 VAR Model Identification The vector autoregression model (VAR) is a useful model utilized to investigate the correlation between several time series during time. The assumption that all variables have exerted an impact on the rest of the variables underlies a VAR model in a single system, making predictions of this multivariate time series in total. The paper puts six stationary series: logarithmic yield for the exchange rate, the logarithmic yield for SSEC, SZSE, NASDAQ, DJI and S&P 500 into the Vector Autoregression system. To illustrate, considering a VAR model with two variables (x1 , x2 ) given only one lag term (p = 1), the system can be manifested like: x1,t = c11 x1,t−1 + c12 x2,t−1 + εx1t
(5)
x2,t = c21 x1,t−1 + c22 x2,t−1 + εx2t
(6)
it can be compactly presented as a vector group like: Xt = C · XLag + Et
(7)
εx1t c11 c12 x1,t x1,t−1 ,C = , XLag = ,E = . where xt = x2,t c21 c22 x2,t−1 εx2t In this case, with six variables (yield for exchange rate, yield for SSEC, yield for SZSE, yield for NASDAQ, yield for DJI, yield for S&P 500), a VAR (p) model can be presented as:
yt = Γ0 + Γ1 yt−1 + · · · + Γp yt−p + εt ⎡
⎤
⎡
⎤
⎡
⎤
⎡
(8) ⎤
y1t Γ1t ε1t β11 . . . ⎢ .. ⎥ ⎢ .. ⎥ ⎢ .. ⎥ ⎢ .. .. ⎥ where yt = ⎣ . ⎦, Γt = ⎣ . ⎦, εt = ⎣ . ⎦, Γ1 = ⎣ . . ⎦, · · · · · · , Γp = yit Γit εit βi1 . . . ⎡ ⎤ β1p . . . ⎢ .. .. ⎥ ⎣ . . ⎦.
βip . . . In, yt appertains to the six response variables in this system. Γ0 , Γ1 , · · · , ΓP are the coefficient matrix for corresponding terms. εt is the error term matrix in period t. In this case, the VAR is estimated to be 6∗11 in total, which is too large to analyze, so impulse response figures are an important instrument for investigating the interactions
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between or among variables in a VAR system. Response plots generate an exogenous impulse and observe how the system responds to it from period to period. In general, the response effect is figured as: ψs =
∂yt+s ∂εt
(9)
The formulation suggests that the response effect should be the change of yi,t+s , the value of variable i in the timestamp t + s, when variable j increases by one unit in the disturbance term εjt at the timestamp t. Described as a function of time interval s, it becomes the useful impulse response function (IRF). A graph is a perfect way to visualize this interaction between variables in a VAR model. All impulse responses within a VAR system can be drawn as graphs, and these impulse response graphs are an essential part of investigating a VAR model. 2.4 ARMA-GARCH-X Model Specification For real-world market figures, the variance represents the risk of an asset, and since the risk of a market cannot remain constant all the time, the variance fluctuates from period to period. ARCH models assume that a high variance in the time series of the previous period may also be high in the next period, an autoregressive logic resembling the AR model for predicting future variance. Typically, an ARCH (p) model can be written as: 2 2 σt2 = α0 + α1 εt−1 + . . . + α13 εt−p
(10)
where the σt is the forecast variance in the period. εt appertains to the actual variance in the period. α0 is a constant. If the GARCH term is the addition to the original ARCH model, the Generalized ARCH(GARCH) model is created. GARCH (1,1) is used as an example for further explanation. GARCH (1,1) with three terms can be written as: 2 2 + β1 σt−1 + γ exchange ratet σt2 = α0 + α1 εt−1
(11)
2 σ 2 , · · · · · · to the formula, (11) If keeping adding the GARCH equation for σt−1, t−2 can finally be presented as: 2 2 2 σt2 = α0 + α1 εt−1 + α2 εt−2 + · · · + αp εt−p + ···
(12)
which is an ARCH (∞) Model with infinite terms. Generally, a GARCH (p, q) model can be presented as: 2 2 2 2 2 σt2 = α0 + α1 εt−1 + α2 εt−2 + · · · + αp εt−p + · · · + β1 σt−1 + · · · + βp σt−q
(13)
Given that market returns are positively relevant to the risk contained in the market, the risk is almost the most underlying attribute of the market. To investigate problems about risk, the paper creates an ARMA-GARCH model to research return volatility. The ARMA-GARCH model is a double-equation with values and variances. In spite that the ARMA part is computed for its value, the GARCH part should be focused on concluding about market risk.
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3 Empirical Results 3.1 VAR Model Estimation Results In this section, the paper puts six stationary series: logarithmic yield for the exchange rate, the logarithmic yield for SSEC, SZSE, NASDAQ, DJI and S&P 500 into the Vector Autoregression system. First, the appropriate order p for this VAR (p) model was determined using the different VARSOC order selection criteria in Stata; the results are plotted in Table 2, suggesting that the paper can utilize a VAR with 10 orders. Table 2. VAR model identification Lag
LL
LR
df
p
FPE
AIC
HQIC
0
12151.5
1
6.0e−28
−45.6599
−45.6411
12221.2
139.27
36
0.000
5.3e−28*
−45.7864*
−45.6542*
2
12257.2
71.993
36
0.000
5.3e−28
−45.7864
−45.541
3
12275.6
36.888
36
0.428
5.6e−28
−45.7204
−45.3617
4
12294.6
37.984
36
0.379
6.0e−28
−45.6564
−45.1845
5
12315.8
42.378
36
0.215
6.3e−28
−45.6007
−45.0156
6
12338.9
46.285
36
0.117
6.6e−28
−45.5524
−44.854
7
12360.2
42.6
36
0.208
7.0e−28
−45.4971
−44.6855
8
12378.8
37.104
36
0.418
7.5e−28
−45.4315
−44.5066
9
12396.6
35.588
36
0.488
8.1e−28
−45.3631
−44.3249
10
12422.3
51.379*
36
0.046
8.4e−28
−45.3243
−44.1729
11
12445.6
46.565
36
0.112
8.8e−28
−45.2765
−44.0118
12
12462.1
32.992
36
0.612
9.5e−28
−45.2032
−43.8253
In this paper, after constructing the VAR (10) model, we use a code-named varstable tool in Stata to examine the eigenvalue stability condition in a subsequent step of measuring the parameters of the vector autoregression and plot a unit circle to visually represent the figures. As long as all the points representing the eigenvalues are within the circle, the VAR estimate is stable. The results of the plot are given in Table 3 and illustrate that the stability conditions for this VAR system are being met (Fig. 1). Figure 2 shows the impulse response results of yield for the exchange rate, yield for SSEC, the yield for SZSE, the yield for NASDAQ, the yield for DJI, and the yield for S&P 500. It can be illustrated from the figures that one unit change in yield for exchange rate would cause a short-term decrease in yield for the five indexes, but then they would fluctuate around 0. From the long-term point of view, the impact of repress on yield for exchange rate from five indexes gradually dies down and plagued at 0 (20 trading days later). Exchange rate shocks react quickly to the financial markets of both countries. Specifically, an exchange rate shock of one unit in period t = 0 has an effect of around −0.2% on the US financial markets in the current period, and a much larger effect on
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Fig. 1. VAR stability
China, with the SSCE and SZSE, returns falling by around 0.25%. The net effect on the financial markets of both countries is also negative over the next 10 periods. 3.2 ARMA-GARCH Model Result To establish an ARMA-GARCH model, the article seeks the right AR and MA part of yield for SSEC, the yield for SZSE, the yield for NASDAQ, the yield for DJI and the yield for S&P 500. Compute the partial autocorrelation plot (PACF plot) of the series in Stata, and Fig. 3 plots the figure for yield SSEC. The black rectangle is a baseline for identifying statistically significant terms in the AR model, where the lag 3 term considerably impacts the data. For SZSE, the lag 3 term significantly produces a considerable effect on the data. For the NASDAQ, the lag 1 term exerts considerable impacts on the data. For the DJI, a lag of 1 term of the original series has a significant impact on the data. For the S&P 500, lag 3 term exerts noticeable impacts on the data. This paper employs an Autocorrelation Plot (ACF Plot) to identify the MA part of the series, and figures plotted by employing Stata are presented in Fig. 3. Figure 3 illustrates that for SSEC, the lag 3 term is an ideal choice for the moving average process. For SSEC, the lag 3 term is an ideal choice for the moving average process. For SZSE, the lag 3 term is an ideal choice for the moving average process. For NASDAQ, lag 1 term is a perfect choice for the moving average process. For DJI, lag 1 term satisfies the moving average process. For S&P 500, lag 3 term is a satisfactory choice for the moving average process. After calculating the AR and MA terms, the ARMA terms of the five indexes are finally presented. For SZSE, the ideal ARMA term of ARMA (3,3) covers the AR terms of lag 3 and the MA term of lag 1. For SSEC, the ARMA term of ARMA (3,3) covers the AR terms of lag 3 and the MA term of lag 1. For NASDAQ, the ARMA term of ARMA (1,1) covers the AR terms of lag 3 and the MA term of lag 1. For DJI, the ARMA term
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NASDAQ
SSEC
DJI
SZSE
601
S&P 500
Fig. 2. Impulse and response
of ARMA (1,1) includes the AR terms of lag 3 and the MA term of lag 1. For S&P 500, the ARMA term of ARMA (3,3) covers the AR terms of lag 3 and the MA term of lag 1. The paper adds the nine days that the USA officially announced the imposed tariffs as dummy variables. After the date that tariffs were added, dummyt = 1. If not, dummyt = 0. From Table 3, ARCH for NASDAQ, NJI and S&P 500 is all statistically significant and the GARCH for NASDAQ, NJI, S&P 500 and SSEC is all statistically significant. It could be told that the yield for NASDAQ, NJI, S&P 500 and SSEC series has conditional heteroscedasticity.
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PACF
ACF SSEC
SZSE
NASDAQ
DJI
S&P 500
Fig. 3. PACF and ACF
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Table 3. ARMA-GARCH-X estimation results (1)
(2)
(3)
(4)
(5)
NASDAQ
DJI
S&P 500
SSEC
SZEC
Dummy 1
−0.1264
0.0448
0.2137
0.6297***
0.3531***
0.3403
0.2818
0.3939
0.1811
0.1510
Dummy 2
0.3287
−0.1240
−41.4063***
0.9399
0.7634
2.2400
3.1001
0.6985
1.6756
1.8377
−0.7782
−0.8280
40.7350***
−0.8144
−0.6961
2.2697
3.1212
0.0000
1.7388
1.8714
Dummy 4
0.3763
−0.2254
−0.6032
−0.2123
−0.3322
0.6029
0.8330
1.1140
0.5568
0.4575
Dummy 5
2.1259***
2.6819***
3.1398***
0.6408
0.7226*
0.5313
0.7070
0.9530
0.4732
0.3915
−2.0824***
−1.7514***
−1.7879***
0.1086
0.1334
0.3232
0.3145
0.3542
0.2084
0.1821
0.8539**
0.3603
0.3302
−0.9446***
−0.7446***
0.3482
0.3198
0.4017
0.2130
0.1891
Dummy 8
−1.5103***
−0.5294**
−0.7879**
−0.6946***
−0.7060***
0.3623
0.2293
0.3339
0.2618
0.2504
Dummy 9
−0.0964
−0.6674
−0.6159
0.5890**
0.5308*
0.5797
0.5496
0.7907
0.2964
0.3003
0.1879***
0.1772***
0.1936***
0.0422
−0.0234
0.0373
0.0268
0.0333
0.0284
0.0318
0.6093***
0.6297***
0.6909***
0.6479***
0.0993
0.0825
0.0739
0.0547
0.2407
0.9829
−10.7622***
−11.1287***
12.0668***
−10.7316***
−8.8958***
0.3157
0.3230
0.4313
0.7029
1.0708
Dummy
Dummy 3
Dummy 6 Dummy 7
GARCH (1, 1) ARCH, L1 GARCH, L1 Constant
Note: Dummy represents the lag order. For every lag, the first row represents the estimated coefficient of the term, and the second row is the standard error of the estimators. *** 、** and* represent the level of significance of 1%, 5% and 10%, respectively.
From the estimation results of exogenous variables, only the fifth added tariff caused the increase of volatility in the USA’s stock market. Only the first and the last added tariffs caused the increased volatility in China’s stock market.
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4 Discussion This article finds that financial markets in the USA and China both suffer from tariffs. It is noteworthy that if the shock of the exchange rate is continuous, both would be affected negatively. While China’s financial markets are more disturbed by trade conflict, it has been detrimental to both countries. During the conflict, the added tariff led to the appreciation of dollars and the depreciation of RMB. It induced different financial results compared with that of a liberal economy. In a liberal economy, the appreciation of dollars leads to the flow of capital and the global market would increase the procession of the dollar. On one hand, the global demand for dollars increases, leading to more dollars flowing into the American financial market. In this way, the USA experienced a net capital inflow. Similarly, China experienced a net capital outflow. As a result, the stock price or the yield in the American market increased, and that in China’s market experienced a decline. On the other hand, the appreciation of the dollar increases the purchasing power of the dollar, expanding the import and shrinking the export of the USA. There are many transnational companies in the USA, whose benefits suffer during the restrain of export. However, the tariffs impose restrictions on export in China, and the Chinese government has done counter-measures. All these actions negatively influence the free mobility of goods and capital. Moreover, the shock induces piles of new effects. For instance, the restriction of exports from China lifts the price of domestic goods in America. For industries that rely heavily on exports, their demand would decrease considerably, and their unemployment problems would become more severe. To some extent, the price increase suppresses consumption, which does not benefit domestic industries. Overall, theoretical analysis can not directly conclude the net effect of financial conflicts on both countries. For policy-makers, the policy of tariffs has a destructive effect on the free mobility of capital and goods, leading to the destruction of the free global market. There are many ways to protect domestic industry and price level, whereas tariffs are not the ideal one.
5 Conclusion The conflict between the USA and China has exerted a significant impact on their financial markets. During this period, different indexes have different reactions to the shock. This paper researches how the American market and China’s markets are influenced by several times of tariffs. This paper focuses on the volatility of the market. The research concludes that the shock of tariffs negatively impacts both countries, and the extent is different. China’s market suffered more severely, but the USA experienced a more volatile financial market. With time, the tariffs could not induce dramatic damage to the financial market. As a result, tariffs are not an effective tool to solve the problem of trade deficit. Also, tariffs trigger other problems, like unemployment and consumption suppression, which is not good for the global economy. Policy-makers should seek other ways to revive the manufacturing industry. Both China and the US experienced a net loss from this trade conflict.
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References 1. Guo, M., Lu, L., Sheng, L., et al.: The day after tomorrow: evaluating the burden of trump’s trade war. Asian Econ. Papers 17(1), 101–120 (2018) 2. Jie, W., Wood, J., Keunyeob, O., et al.: Evaluating the cumulative impact of the US–China trade war along global value chains. The World Econ. 44(12), 3516–3533 (2021) 3. Lau, L.J.: A Better Alternative to a Trade War. China and the World (2018) 4. Tsutsumi, M.: The Economic Consequences of the 2018 US-China Trade Conflict: A CGE Simulation Analysis. CIS Discussion paper series (2018) 5. Amiti, M., Redding, S., Weinstein, D.: The Impact of the 2018 Trade War on U.S. Prices and Welfare. Stephen Redding (2019) 6. Yu, W., Turvey, C.G.: The impact of the China–USA trade war on USA Chapter 12 farm bankruptcies. Agric. Financ. Rev. 81(3), 386–414 (2020) 7. Dong, Q., Chang-Hai, M.A., Zhao, H.F., et al.: Influence and countermeasures of sino-us trade war on the development of cattle and sheep industry in China. Chin. J. Animal Sci. 8, 4 (2019) 8. Chen, Y., Fang, J., Liu, D.: The Effects of Trump’s Trade War on U.S. Financial Markets. Social Science Electronic Publishing 9. Guo, M., Lin, L.U., Sheng, L., et al.: Evaluating the Global Economic Burden of Trump’s Trade War. Academic Monthly (2018) 10. Arežina, S.: U.S.-china relations under the trump administration: changes and challenges. China Q. Int. Strategic Stud. 05(03), 289–315 (2019)
Investment Strategy in a Down Market: Application of Market Neutral Strategy in Energy, Utilities and Technology Sector Wenjun Yang(B) University of Washington, Seattle, WA 98195, USA [email protected]
Abstract. In face of the challenges of Covid-19, the Europe energy crisis, and inflation, the US stock market entered a bear market period in 2022. During a down market period, market neutral strategy is widely preferred, since it can minimize market risks by constructing a zero-beta portfolio. This paper analyzes the effectiveness of the market neutral strategy in practice. Three portfolios, that simulate different levels of stock selection skills, are set up from August 15, 2022, to September 9, 2022, when the market is sinking. The returns of the portfolios deliver three messages. First of all, market neutral strategy can outperform the downward market, regardless of stock selection abilities, but it does not guarantee returns higher than the risk-free rate, which the strategy thoracically intends. Secondly, higher stock selection skills may create higher returns. An investor, who is excel at analyzing both industry and stock, performs better than an investor who is adept at a single aspect. Lastly, the choice of stocks is more important than the choice of industries. If a portfolio is well diversified based on stocks’ values, it can still generate a decent return even if the industry is underperformed. Keywords: Down Market · Market Neutral Strategy · Stock Selection
1 Introduction The US stock market was not performing well in the first half of 2022. The plummet was partly due to the global economic slowdown caused by Covid-19 and the energy crisis resulting from the Russia-Ukraine war. Also, the US faced a serious problem of inflation. US inflation rate, which had not exceeded 4% for more than 20 years before the pandemic, was 9.1% in June. When inflation dropped to 8.5% in July, investors turned optimistic. The US stock market rebounded in July and August, but the positive momentum was depleted by August 16. Meanwhile, on August 26, Fed Chairman Jerome Powell delivered a hawkish message, indicating the US central bank’s determination to cool down surging inflation. In reaction to Powell’s speech, the stock market declined further. Again, the US stock market entered a downward period. According to market cycles, there are always ups and downs in the market. Investors are willing to embrace up markets, but when down markets come, they often find it © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 606–615, 2023. https://doi.org/10.1007/978-981-99-6441-3_55
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difficult to handle. In a down market, it is uneasy to profit through a long-only strategy, which is the most widely used strategy in the market. This strategy makes profits when the stock price is growing, so it is no longer effective when most of the stocks in the market are sinking. In a bear market, investors are suggested to stay out, buy a few stocks long, or go short [1]. Indeed, short selling has a stronger relationship with price change during extreme days than during normal days [2]. Nonetheless, a short-only strategy in down markets may bear greater risk than a long-only strategy in up markets. Price stop dropping when it comes to zero, whereas there is no ceiling for price rise, so a short-only strategy can lead to infinite losses if stocks do not fall as expected. To reduce the risk of betting in one direction, the long-short strategy is developed. Long-short strategy is taking a long position in stocks expected to rise and taking a short position in stocks expected to fall. The two actions are treated as a single integrated portfolio. The long-short strategy benefits from freedom in shorting unattractive assets and the opportunity to leverage excess returns [3]. Given the added flexibility that a longshort portfolio provides, it can be expected to perform better than a long-only portfolio based on the same set of insights [4]. Also, the long-short strategy allows traders to make money in any market [5]. If the market is going down and the short stock falls more than the long stock, the investor will profit. A special case of the long-short strategy is market neutral strategy. Compared with long-short portfolios, whose returns vary with the market, returns to market-neutral portfolios are uncorrelated to the market [6]. The market-neutral status is achieved through a balance of beta. Some investors may refer to dollar-neutral as market-neutral. Nevertheless, as stocks exhibit different volatility characteristics, hedging stocks in amount may not necessarily minimize the portfolio’s volatility [7]. Therefore, it is more appropriate to describe a beta-neutral portfolio as market-neutral. Theoretically, market neutral strategy is a more moderate choice than traditional strategies, but in practice, its effectiveness is affected by multiple risks. On one hand, absolute market neutrality is unlikely to achieve in reality. Previous research suggests that the dependence between hedge fund returns and market returns is often significant and positive, even for market neutral funds [8]. Not even proficient portfolio managers can realize market neutrality, the strategy is still affected by market risks. On the other hand, the strategy is still greatly influenced by non-systematic risks. One obvious risk comes from investors’ stock selection skills [9]. As systematic risks are minimized, a portfolio profits or loses depending on the relative performance of the stocks. A welldiversified portfolio has the possibility of enjoying the market neutral strategy, profiting when the market is going down [10]. Nevertheless, not every investor possesses excellent stock selection skills. In addition to insights into the market and stocks, investment sentiment also affects stock selection, especially for retail investors. Their sentiment is easily affected by market fluctuations, and changes in retail sentiment may induce comovement in stock returns [11]. As a result, even though market neutral strategy aims to provide positive returns that can outperform the risk-free rate, it is uncertain whether this objective can be realized in reality. In this paper, three conclusions are drawn after analyzing three trades. Firstly, all three trades perform better than the market, indicating that market neutral strategy can outperform the overall market in a down period, no matter what level of stock selection
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skills the investors possess. However, the utilities trade receives a return lower than the risk-free rate, which reveals that the strategy does not guarantee an intended minimum return that is close to the risk-free rate. The second indication is that higher stock selection skills may generate higher returns. Among the three investigated trades, the energy portfolio, whose industry and stocks are carefully selected, creates the highest return. Finally, the last revelation states that the choice of stocks is more important than the choice of industries. During the analyzed period, energy and utilities are two of the bestperforming sectors in the market, while the technology sector performs less desirable. However, the utilities trade, whose stocks are chosen randomly, has a return less than the risk-free rate. Meanwhile, the technology portfolio, whose stocks are carefully selected, performs better than the utilities portfolio. Therefore, bad stock selection can lead to unsatisfying returns even in a well-performing industry.
2 Method In this paper, the market neutral strategy is applied to 3 different sectors: energy, utilities, and technology. All three trades are placed between August 15, 2022, and September 9, 2022, when the overall market plummets. 2.1 Market Neutral Strategy A fundamental concept of market neutral strategy is beta. Beta is a measurement of the volatility of a stock compared to the market. An absolute value of beta greater than 1 reflects greater volatility, while an absolute value of beta less than 1 means lower volatility. A positive beta indicates the investment is in the same direction as the market, and a negative beta reflects the opposite. Generally, an investment with a larger beta has a higher risk and a greater expected return. The market neutral strategy seeks to earn positive returns regardless of the market’s swings by purchasing undervalued stocks and short-selling overvalued stocks. By definition, a portfolio utilizing market neutral strategy has a beta of zero. However, in reality, it is difficult to construct a portfolio with a beta exactly equal to zero, since the number of shares purchased can only be integers. Therefore, the portfolio is considered market-neutral as long as its beta is close to zero. Zero-beta portfolio has minimized systematic risk, so its return is expected to match the risk-free rate of return. The most remarkable feature of the market neutral strategy is stability. Since beta is restricted to zero, the volatility of a market neutral portfolio is low. This may not be satisfying in an upmarket, where upward fluctuations create more opportunities for long positions. The market neutral strategy benefits less from the upward swings than the long-only strategy does. However, during a down market, a market neutral strategy can effectively limit losses caused by downward fluctuations. Although market neutral strategy minimizes systematic risk, it is not a risk-free strategy. One common risk comes from investors’ ability to select industries and stocks. If an investor is proficient in choosing stocks, the return is expected to be higher than the expected return in theory. For a retail investor who has limited selection skills, it is uncertain whether the market neutral strategy can still result in a risk-free rate of return.
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2.2 Stock Selection To find out whether the market neutral strategy stays effective under different levels of selection skill, three trades that stimulate different levels of stock selection skills are composed. In the first trade, it is assumed that the investor is excelled at selecting both industries and stocks. An industry is chosen based on macroeconomic analysis, and stocks are chosen for technical reasons. The first portfolio is constituted of two energy stocks: Sunrun Inc. (RUN) and Callon Petroleum Company (CPE). RUN (see Fig. 1) is a renewable energy company in the US, offering solar energy generation systems and battery energy storage products for families.
Close Price
41 37 33 29 25 8-15
8-20
8-25
8-30
9-4
9-9
Fig. 1. Sunrun Inc.’s stock price from August 15, 2022, to September 9, 2022. Source: Yahoo Finance [12]
46
Close Price
43 40 37 34
8-15
8-20
8-25
8-30
9-4
9-9
Fig. 2. Callon Petroleum Company’s stock price from August 15, 2022, to September 9, 2022 Source: Yahoo Finance [12]
The other company, CPE (see Fig. 2), is an independent oil and gas company engaged in the exploration, development, acquisition, and production of oil and gas assets in West and South Texas. In the second trade, the investor is supposed to be skilled in selecting industries but is weak at choosing stocks. This portfolio includes two utilities stocks: Nrg Energy Inc.
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(NRG) and Consolidated Edison (ED). Utilities sector is selected due to macroeconomic reasons, but the two stocks are picked randomly. NRG (see Fig. 3) is a US integrated power company, involved in energy generation and retail electricity. The company has an energy portfolio that includes coal, oil, natural gas, and renewable energy. It serves 24 states in the US, including Texas, Massachusetts, New York, and Pennsylvania. 44
Close Price
43 42 41 40
8-15
8-20
8-25
8-30
9-4
9-9
Fig. 3. Nrg Energy Inc.’s stock price from August 15, 2022, to September 9, 2022. Source: Yahoo Finance [12]
Close Price
102 100 98 96 94 8-15
8-20
8-25
8-30
9-4
9-9
Fig. 4. Consolidated Edison’s stock price from August 15, 2022, to September 9, 2022. Source: Yahoo Finance [12]
Meanwhile, ED (see Fig. 4) is an investor-owned energy company whose gas, electricity, clean energy, and steam provide energy services mainly for New York City and Westchester County. Finally, in the last trade, it is presumed that the investor is adept at picking stocks but is inferior at analyzing industries. While the technology sector is chosen at random, the two stocks are chosen based on their PEG ratio. PEG ratio is a measurement determining the rationality of the price of a stock, regarding earnings per share, and the company’s expected growth. A PEG greater than 1 indicates the stock is overvalued, while a less than 1 PEG reflects the stock is undervalued. It is frequently used to evaluate stocks in fast-growing industries. Advanced Micro Devices Inc. (AMD) (see Fig. 5) is in such an industry. This company specializes in designing and manufacturing
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various innovative microprocessor and low-power processor solutions for the computer, communication, and consumer electronics industries. It develops the industry’s leading computer processors and related technologies. 110
Close Price
100 90 80 70
8-15
8-20
8-25
8-30
9-4
9-9
Fig. 5. Advanced Micro Devices Inc.’s stock price from August 15, 2022, to September 9, 2022. Source: Yahoo Finance [12]
The other included company is Intel Corporation (INTC) (see Fig. 6), which is also a semiconductor company. It focuses on manufacturing PC parts and CPUs and it is also one of the world’s largest semiconductor chip manufacturers. 38
Close Price
36 34 32 30 28 8-15
8-20
8-25
8-30
9-4
9-9
Fig. 6. Intel Corporation’s stock price from August 15, 2022, to September 9, 2022. Source: Yahoo Finance [12]
2.3 Benchmarks The expected return of the market neutral strategy is the risk-free rate. In this paper, the US 1-month treasury bill yield is chosen to be the risk-free rate. It is priced at 2.55% on September 9, 2022. Besides the risk-free rate, two other market indexes have been chosen to evaluate the performance of trades. The first index is the S&P 500 Index (see Fig. 7), which tracks the stock performance of 500 large companies listed on the US
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exchange. The included companies are selected by a committed based on criteria such as market capitalization, liquidity, and representation of the industries. Therefore, the S&P 500 Index is considered to be a representative indicator of the overall market’s performance. In this paper, the S&P 500 Index is used as a comparable standard in the energy trade and the utilities trade. 4400
Close Price
4200 4000 3800 3600
8-15
8-20
8-25
8-30
9-4
9-9
Fig. 7. The S&P 500 Index from August 15, 2022, to September 9, 2022. Source: Yahoo Finance [12]
The second index is the Nasdaq Composite Index (see Fig. 8). It consists of the stocks that are listed on the Nasdaq stock exchange. Since a large proportion of stocks in Nasdaq are from the technology sector, the Nasdaq Composite Index is regarded as a barometer reflecting the performance of the technology market. It is used as a benchmark in the semiconductor trade. 14000
Close Prcie
13000 12000 11000 10000
8-15
8-20
8-25
8-30
9-4
9-9
Fig. 8. The Nasdaq Composite Index from August 15, 2022, to September 9, 2022. Source: Yahoo Finance [12]
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3 Results In this paper, three trades from different sectors are investigated. In the first trade, the portfolio is composed of a renewable energy stock and an oil stock. Since early 2022, conventional energy prices had surged significantly due to the Russian-Ukraine conflict, inflation, and unusual weather conditions. People started looking for cheaper and cleaner alternatives, which are renewable energy sources. Moreover, on August 23, 2022, renewable energy was one of the best-performing industries in the market. RUN’s 30day line intersected with its 5-day line from below, trying to go beyond the short-term moving average line. This sign indicated that the stock has the potential to rise shortly. Meanwhile, CPE was moving far below its 200-day line. Therefore, 1000 shares of RUN were longed at $31.28, while 597 shares of CPE were shorted at $43.64. Till September 9, 2022, this portfolio has a positive return of 13.94%, which is much higher than the expected return of 2.55%. It also outperforms the overall market, whose return is − 5.35%. The following Table 1 gives a summary of the energy trade. Table 1. Details of the energy portfolio Name
Action
Quantity
Price Paid
Price Sold
Profit/Loss
Risk-free Rate
Market Index
RUN
Long
1000
$ 31.28
$ 38.45
13.94%
2.55%
−5.35%
CPE
Short
597
$ 43.64
$ 42.26
Table 2. Details of the utilities portfolio Name
Action
NRG
Long
ED
Short
Quantity
Price Paid
Price Sold
Profit/Loss
Risk-free Rate
Market Index
750
$ 41.23
$ 43.29
−0.93%
2.55%
−5.35%
1256
$ 98.47
$ 100.85
The second trade is placed according to Fed Chairman Jerome Powell’s talk on August 26, 2022. He claimed that the US government would act forcefully to bring down high inflation. The economy is expected to experience a recession. During an economic recession, utility stocks are relatively safe choices, since utility services are often steady and revenues are stable. Therefore, two stocks are picked randomly from the utilities sector. 750 shares of NRG were bought at $41.23 on August 30, 2022, and its price was $43.29 on September 9, 2022. On the other side, 1256 shares of ED were short-sold at $98.47 on August 30, 2022, and its closing price was $100.85 on September 9, 2022. The portfolio results in a 0.93% loss, failing to reach the expected return of 2.55%. However, it still performs better than the return of −5.35% from the overall market. The following Table 2 gives a summary of the utilities trade. Lastly, in the third trade, the technology sector is randomly selected, while the stocks are picked based on their PEG ratio. AMD had a PEG of Qualcomm had a PEG of 0.79,
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suggesting its stock price had not fully matched its fundamentals yet. In the meantime, INTC had a PEG of 3.75, indicating its current price, concerning its fundamentals, might be overvalued. Thus, AMD was bought at $99.94 for 97 shares, and INTC was shorted at $35.87 for 850 shares on August 16, 2022. During the observation period, the technology market decreased by 7.74%, but this portfolio received a positive return of 5.83%, which excels both the market and expectation. The following Table 3 gives a summary of the technology trade. Table 3. Details of the technology portfolio Name
Action
AMD
Long
INTC
Short
Quantity
Price Paid
Price Sold
Profit/Loss
Risk-free Rate
Market Index
97
$ 99.94
$ 85.45
5.83%
2.55%
−7.74%
850
$ 35.87
$ 31.46
All three trades outperform the market. Among them, the energy trade performs the best, receiving the highest return. It is because energy is one of the best performing sectors during the analyzed period, and the reasonings for stock selection are rational. The second-best trade is the technology trade. Even though the technology sector suffers during this down period, this portfolio benefits from the short position. The stock in the short position drops more than the stock in the long position does, so profits from shorting are greater than losses from longing. The utilities sector is not well-performed as the other two trades. It has a negative return, which is underperformed compared to the risk-free rate. The utilities sector is also one of the best performing sectors in the trading period. Nonetheless, stocks are selected at random, since the investor is supposed to be deficient in stock selection. The stock in the short position has greater potential to grow, causing great losses in shorting and minor profits in longing.
4 Conclusion Challenges from the energy crisis and inflation frustrate investors, dragging the US stock market into a bear market period. However, there are methods to make money in the down market. In this paper, market neutral strategy is applied to three trades, which simulate different levels of stock selection abilities, during a down market period. After four weeks of trading, the trades demonstrate that market neutral strategy can outperform the overall market in a down period, regardless of stock selection skills. The returns of all three trades can exceed the market. Nevertheless, the utilities trade has a negative return, which is lower than the risk-free rate. This result implies that market neutral strategy does not guarantee a specific return, though it intends to create positive returns in all market conditions. The results also imply that higher stock selection skills can result in higher returns. Among the three investigated trades, the energy portfolio, whose industry and stocks are carefully selected, creates the highest return. If investors understand the industry
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well, they may set up wrong portfolios by longing the overvalued stocks and shorting the undervalued stocks. On the other side, if investors master stock analysis, they may miss the benefits brought by the sector. Only when investors are adept at analyzing both industries and stocks, can their profits be most ensured. Finally, the last revelation is that the choice of stocks has a greater influence on return than the choice of industries does. As the three analyzed trades show, even if the industry is well performing in the market, the portfolio does not have a satisfying return if stocks are not carefully selected. On the contrary, if stocks are selected based on an evaluation of their values, the portfolio can still generate a decent return even if the industry performs terribly.
References 1. Schultz, H.D.: Bear Market Investing Strategies. In: Wiley Trading, p. 96. John Wiley, Chichester (2002) 2. Geraci, M., Garbaraviˇcius, T., Veredas, D.: Short selling in extreme events. J. Financ. Stab. 39, 90–103 (2018) 3. Jelicic, D., Munro, J.: Market neutral investing. Derivatives use. Trading Regul. 5(3) (1999) 4. Jacobs, B.I., Levy, K.N., Starer, D.: Long-short portfolio management: an integrated approach. The J. Portfolio Manag. 25(2), 23–32 (1999) 5. Clifford, D.: Risk isolation using market neutral strategy. J. Trading 1(2), 80–82 (2006) 6. Badrinath, S.G., Gubellini, S.: On the characteristics and performance of long-short, marketneutral and bear mutual funds. J. Bank. Finance 35(7), 1762–1776 (2011) 7. Ineichen, A.M.: Who’s long? market-neutral versus long/short. J. Altern. Investments 4(4), 62–69 (2001) 8. Patton, A.J.: Are “market neutral” hedge funds really market neutral? Rev. Financ. Stud. 22(7), 2495–2530 (2009) 9. Finn, M.T.: Market neutral investing. J. Financ. Plann. (1998) 10. Agnew, A.S.:: Diversification and Market Neutral Portfolios in S&P500. Williams Honors College. Honors Research Projects 378 (2016) 11. Kumar, A., Lee, C.M.C.: Retail investor sentiment and return comovements. The J. Financ. 61(5), 2451–2486 (2006). https://doi.org/10.1111/j.1540-6261.2006.01063.x 12. Yahoo Finance
Factors Analysis on Affecting the Sales Volume of K-Pack in Smartfood Kewei Yang(B) University of Utah, Salt Lake City, UT 84112, USA [email protected]
Abstract. Maximizing corporate profitability is one of the goals of managers. Sales plays an important role in the development of a company, as it suggests its performance and success. Based on SMARTFOOD’s low-carbohydrate food product which is labeled K-Pack, the article examines various factors that affect the sales volume of K-Pack, shows the current sales strengths and weaknesses of K-Pack through a SWOT analysis, and makes suggestions for the sales situation of K-Pack. In terms of the factors to predict the sales and profits, the regression results show that except Locations and City index, other variables, such as, price, advertising, store volume, greatly affect the sales amount and help the company to realize the key factors or approach to improve its sales in the future to obtain a sustainable development. Reduce the impact of sales in terms of city index and location on results. Keywords: Profit · Regression · SWOT · Low-carbohydrate Food
1 Introduction Profitability plays an important role in the structure and growth of a company, as it measures its performance and success. It also enhances the reputation of the company. Maximizing corporate profits is one of the main goals of managers. Therefore, the company’s profitability and its ability to better withstand negative shocks and promote system stability is also a key issue [1]. Good or bad sales results are usually reflected by the amount of earnings, usually in the form of a list of revenues and expenses of the company over a period of time. To determine which factors have a significant impact on sales results, income statements are often used to forecast profitability. Companies need to adopt the right sales approach to help K-pack take over the market quickly. More importantly, adopting a sound sales strategy in the pre-launch phase of a product can greatly help the product’s development. Developing a strategy essentially means developing a complex action plan to achieve the main goals [2]. The main elements that are part of it are mainly related to the background and information needed for the market, setting goals and objectives, the way of thinking of the sales force and their visionary abilities, the strategies used, and the actions as a way. Getting the most correct sales strategy based on the analysis of the sales results is the key factor that determines whether it is profitable or not [3]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 616–622, 2023. https://doi.org/10.1007/978-981-99-6441-3_56
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In order to find the relationship between K-Pack sales data and these factors, this paper conducts a regression analysis to investigate which independent variables have a significant impact on K-Pack sales based on the relationship between multiple independent variables and sales data. Before starting the analysis, it was predicted that region and city index would not have a significant impact on sales results while others would have a significant impact on the sales amount. In order to enable K-Pack to better define its sales targets and directions in the market, SWOT analysis and regression analysis models provide the company with sufficient sales recommendations and directions. Since the sales amount of the product is influenced by the volume of goods, number of stores, sales in different locations, advertising investment, etc. Based on several analysis methods it can be discussed that these factors can significantly affect profitability.
2 SWOT Analysis of Smartfood Company 2.1 Overview of Smartfood Company SMARTFOOD is about to launch a new product, K-Pack, a new low carb food product to their snack/diet food line. The product, tentatively called the K-Pack, is packaged to look like a typical candy bar. Investors needs to forecast sales and profits to achieve maximum annual sales. In order to predict sales and profits, and to achieve maximum annual sales, a number of variables are set into the model: three commodity prices (50 cents, 60 cents, 70 cents), two advertising levels ($3 million and $3.5 million), and two store locations (bakery and breakfast sections). Each city’s grocery store marketing mix variables are different. K-pack sales are measured by conducting an audit of stores in each city. The goal is to use the sample data to determine the final marketing mix. Thus, this paper conducts SWOT analysis to gain a better strategy and avoid the weak points. “SWOT analysis is a tool used for the strategy of the organization’s company, institution, which can provide effective strategies. SWOT consists of various subsystems under the whole system, one is its own environmental factors, and the other is external factors, and it is necessary to analyze these environmental factors for strategic strategies.” 2.2 SWOT Analysis SWOT stands for Strengths, Weaknesses, Opportunities and Threats. Strengths and weaknesses are internal to the company (as shown in Table 1), i.e., factors that can be controlled such as intellectual property, company members, etc., while opportunities and threats are external, i.e., things that cannot be changed, such as competitors, buying trends, etc. [4]. In terms of strengths, “Several people who attempt to lose weight do not use the recommended combination of reduced caloric intake and increased physical activity. Despite the use of various approaches, such as increased exercise, reduced fat intake, and calorie reduction, the duration of these strategies was too short; only 20% of the required time was spent. Poor long-term adherence to fat intake restriction has stimulated the introduction of alternative dietary strategies to achieve effective weight loss. One such strategy is the introduction of a low-carbohydrate diet, where carbohydrates are
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replaced by a protein- and fat-rich diet [5]. The rationale for using such diets is to promote satiety and trigger energy expenditure, both of which can promote weight loss.” As a new low-carb food, a certain amount of advertising and merchandising will be more effective than regular food, and consumers will be more willing to pay for the new product due to the new packaging and taste. In addition, due to the short life cycle of snack products and the small variety of competing products, if diet snacks can enter the market early, they will gain a huge market share advantage. As for weakness, “Low-fat and low-carbohydrate diets are the most popular and in the last two years, the number of low-carbohydrate products available in stores has sharply increased. Consequently, the word “carb” is used extensively in food packaging; however, many low-carbohydrate products are expensive and tasteless. A recent report shows some dieters forking 138 $over dollars a week for the diet-based foods compared to about $55 dollars a week for a normal diet” Since K-packs are brand new products, the quality of the products cannot be guaranteed. Moreover, as a food product, the safety level of the new food product should be given priority attention, and the credibility of the brand needs to be improved as much as possible [6]. Since it is a combination of a weight loss product and a snack, the taste may not be as good compared to a snack, and the effect is not as obvious compared to a weight loss drug, so it is more important to pay attention to the market positioning of K-packs and clarify the target consumer group of the product. The lack of exposure during the period when the product is first launched is also a weakness. For the opportunity, Due to the huge size of the food industry, K-packs can be sold in restaurants, coffee shops, grocery stores, kiosks, etc. In addition, due to the growing takeout industry and Uber eats, DoorDash and other platforms to provide online delivery and pick-up services to promote new product sales. Social media will be another way to attract new customers, whether it’s by offering special offers or promotions to help K-packs expand their sales. “In response to the excessive intake of carbohydrates, the restaurant industry has modified the packaging statements of food products, they have reduced the carbohydrate content of food increased consumer demand for K-Pack. Restaurants are offering low-carb foods to satisfy consumers and keep them away from large amounts of carbohydrates. An aggressive sales program means that these products are a perfect alternative to high-carb foods, thus promoting K-Pack consumption.” [7]. As for the threats, K-packs may be threatened by the price of similar food products in the market. The most important point is that since K-packs are new food products, the cost price of raw materials may be much higher than that of ordinary food products, which is a threat that cannot be ignored in the early stage of product sales. In addition, in view of the current situation, the global epidemic of COVID-19 has reduced consumer demand for the new product, and the product is not sold as normally during the epidemic due to economic factors.
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Table 1. SWOT conclusion. Strengths High attraction level Short product life cycle
Weakness Effectiveness of the product Unclear market positioning in the early stage of the product
Opportunities Willingness to buy Driven by other industries Alternatives to high-carb foods
Threats Raw material prices the impact of COVID
3 Regression and Results 3.1 Data and Variables First of all, the price should be treated by using dummy variables. To determine what variables would influence the sale most or least significantly. And it’s going to change variables constantly to determine which variable is unnecessary. As it has set a dummy variables P1 = 1 when Price is 60 otherwise is 0, and P2 = 1 when Price is 70 otherwise is 0, this study uses regression model to analyze it. Dependent variable is Sales and the independent variables are Price, Advertising, Location, Store volume [8]. As for City index, and uses C1, C2, C3 to distinguish the 4 cities, Due to C3 is highly correlated with other independent variables, so C3 is removed in this model. The regression results are summarized is Table 2. Table 2. The original variables regression. Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 90.0%
Upper 90.0%
Cons
57.77
89.70
0.64
0.52
−120.49
236.02
−91.35
206.88
P1
−31.74
18.23
−1.74
0.09
−67.97
4.48
−62.04
−1.44
P2
−49.06
20.14
−2.44
0.02
−89.09
−9.03
−82.55
−15.57
A
56.91
21.90
2.60
0.01
13.39
100.43
20.51
93.32
L
12.43
14.85
0.84
0.40
−17.07
41.93
−12.25
37.11
V
3.61
1.88
1.92
0.06
−0.12
7.35
0.49
6.74
C1
8.40
21.32
0.39
0.69
−33.97
50.76
−27.04
43.84
C2
2.51
26.37
0.10
0.92
−49.89
54.92
−41.33
46.35
C3
0
0
65535
#NUM!
0
0
0
0
3.2 Results of OLS Model Because R square and Adjusted R Square is too small, lower values indicate that the independent variable does not explain much of the variation in the dependent variable,
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it has small percentage of variation that can fit our model. According to the table, the equation is Sales = 57.77 − 31.71P1 − 49.06P2 + 56.91A + 12.43L + 3.61V + 8.40C1 + 2.51C2.
It is known the significance level is 10%. Assume P > 0.1, do not reject the null hypothesis, so it seems that L, C1, C2 are not significant with Sales. Then removed them from the regression model. When those 3 variables are removed, the results as shown in Tables 3 and 4. Sales = 67.79−31.75P1−49.1P2+53.98A+3.62V , it’s clear to see those variables are very significant to the model. So, the conclusion is that City Index, Location are not significant to the Sales, conversely, other factors have strong impact on sales. Table 3. The value of R square. Regression Statistics Multiple R
0.43685509
R Square
0.19084237
Adjusted R Square
0.126477558
Standard Error
72.69198177
Observations
96
Table 4. Regression without location and city index. Coefficients Standard t Stat Error Cons
P-value Lower 95%
89.699
0.644 0.521
−31.742
18.228
−1.741 0.085
−67.965
P2
−89.091
−49.059
20.144
−2.435 0.017
56.911
21.899
2.599 0.011
L
12.430
14.846
0.837 0.405
Lower 90.0%
−120.492 236.023 −91.346
57.765
P1 A
Upper 95%
4.482 −62.043
206.877 −1.441
−9.028 −82.545 −15.573
13.391 100.431 −17.073
Upper 90.0%
20.507
93.315
41.932 −12.249
37.108
V
3.614
1.879
1.924 0.058
−0.120
0.491
6.738
C1
8.397
21.318
0.394 0.695
−33.969
50.762 −27.042
43.835
C2
2.512
26.370
0.095 0.924
−49.894
54.918 −41.325
46.349
7.348
4 Discussion The results of the study show that price, advertising and store volume have the greatest impact on the total sales amount for K-Pack sales results. For the company, price is undoubtedly the most influential factor in sales, and price is determined by consumer
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demand. And no matter how the price changes, it always determines whether the goods become part of the sales. And the advertising determines the degree of marketing of the goods, the range of influence of the goods. For this example, the marketing strategy of a new food product greatly influences the number and scope of the initial sales of the product, in whatever form it is presented to consumers, as a new product that can cause some consumers to want to buy it. “Advertising is an innovative sales method that influences consumers’ motivation to buy a specific product or changes their perception of the product, and the main role of advertising is to influence consumers’ buying behavior. Advertising creates the image of a product and makes it more memorable. These brand perceptions influence consumers to consider, evaluate, and purchase products. And advertising leads from perception to purchase completion.” [9]. SWOT model indicates that the sales of K-Pack have a great potential for development, the market demand for this product will be great, and the social evaluation of this kind of food will be more positive, so the demand for K-Pack will gradually increase, if Smartfood can enter the market as soon as possible I am sure that K-Pack will quickly occupy the snack food industry, dominate the snack food industry and expand their sales with more flavors. For the regression model, it can be concluded that the number and location of the city do not have much influence on the sales of K-Pack. For most regions and different cities, the difference in income and prosperity does not have much influence on the daily necessities such as food, but the number of products, the investment in advertising and the price are the important factors affecting the sales of K-Pack [10].
5 Conclusions This paper focuses on the future food market forecast of K-Pack and its market competitiveness. The SWOT analysis and the regression model analysis are used to determine the main parts of K-Pack in the market sales based on these two approaches. The article summarizes the current sales situation of K-Pack in the snack food industry and the current shortage of smart food companies as well as the shortcomings of smart food companies. Finally, by explaining the SWOT model, it is certain that K-pack is very innovative, accompanied by an effective marketing strategy and a huge financial support and longterm supply, launching a strong impact on the traditional snack industry [11]. Based on the regression results, the future market trend of K-Pack is very positive through social, economic and technological considerations, gaining support from society and government for healthy food through various aspects. There are several implications in this paper. The company should pay more attention to the price level of K-pack in the market, increase the number of products and lower the unit selling price in the pre-sales stage as much as possible, so that more consumers can spend less amount to experience the new product, and the lower unit price will get more positive feedback in the society, which will increase the brand loyalty and dependence. In addition, as people become more aware of healthy eating, the demand for K-pack increases along with it. If the company wants to make K-pack more competitive it still needs to address several issues, the first being the competitiveness of the snack market, where consumers have little loyalty to this new product and will be less profitable once
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a product more appealing to them comes along. COVID 19 has been a big hit for most of the product market and Many people have been harmed by the rising unemployment rate, which has led to a decrease in citizen consumption, and all food supplies and demands are extremely unstable, and if smart companies can identify these problems and implement the right decisions will help them dominate the market. For example, by advertising their products and running promotions in different cities [12]. However, in the first stage of product development, the company should pay more attention to the development opportunities of K-pack in the market, whether or not location and city index have a great impact on the sales results, to be able to firmly product development in the early stages of the dominant snack market is a very critical factor, in addition to then the next step in the development of the current situation is only reasonable [13]. Also, as people become more aware of health, they are paying more and more attention to their health condition, increasing the sales of K-pack. With the spread of COVID 19 makes the economy slow down, but on the other hand, it lowers the price level in the market and stimulates more consumers to K-pack, therefore increasing the company’s profit acquisition.
References 1. Buzzell, R.D., Bradley, T.G., Ralph, G.M.S.: Market share-a key to profitability. Harvard Bus. Rev. 53(1), 97–106 (1975) 2. Hofstrand, D.: Understanding profitability. Ag Dec. Makers 2, C3-24 (2009) 3. Gurau, M.A.: The use of profitability index in economic evaluation of industrial investment projects. Proc. Manuf. Syst. 7(1), 55–58 (2012) 4. GURL, Emet: SWOT analysis: A theoretical review (2017) 5. Freeman, J., Hayes, C.: “Low-carbohydrate” food facts and fallacies. Diabetes Spectrum 17(3), 137–140 (2004) 6. Erlanson-Albertsson, C., Mei, J.: The effect of low carbohydrate on energy metabolism. Int. J. Obes. 29(2), S26–S30 (2005) 7. Jauho, M., et al.: How do trendy diets emerge? An exploratory social media study on the low-carbohydrate diet in Finland. Food, Cult. Soc. 26(2), 344–369 (2021) 8. Poole, M.A., O’Farrell, P.N.: The assumptions of the linear regression model. Trans. Instit.; Br. Geogr. (52), 145 (1971) 9. Nangoy, C.L., lfa Tumbuan, W.J.F: The effect of advertising and sales promotion on consumer buying decision of Indovision TV Cable Provider. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis Dan Akuntansi 6(3) (2018) 10. Benzaghta, M.A., et al.: SWOT analysis applications: an integrative literature review. J. Global Bus. Insights 6(1), 55–73 (2021). https://doi.org/10.5038/2640-6489.6.1.1148 11. Factors Influencing the Purchasing Decision Process of Low-Carbohydrate Products Victoria A. Seitz, Nabil Razzouk & Warintra Triyangkulsri 23–38 12. Triyangkulsri, W.: Factors influencing purchasing decision process of low-carbohydrate products (2005) 13. Tin, S.T., Mhurchu, C.N., Bullen, C.: Supermarket sales data: feasibility and applicability in population food and nutrition monitoring. Nut. Rev. 65(1), 20–30 (2008). https://doi.org/10. 1111/j.1753-4887.2007.tb00264.x
Resignation of Board of Directors Secretaries in Their Tenures and Business Violations Hongji Gao1 , Yangbo Xing2(B) , and Fulin Yu3 1 School of Law, Tianjin University of Finance and Economics, Tianjin 300000, China 2 School of Accountancy, Southwestern University of Finance and Economics,
Chengdu 610000, Sichuan, China [email protected] 3 School of Finance and Investment, Guangdong University of Finance, Guangzhou 510000, China
Abstract. The revised Company Law in 2006 stipulates that Secretary of the board is a senior executive of the company and responsible for information disclosure. In recent years, the strange appearance of secretary of the board of directors with short tenure and frequent abnormal resignation has attracted wide attention from scholars. In view of this, this paper empirically investigates the relationship between the turnover of directors during their tenure and corporate violations. The findings are as follows: First, compared with other companies, the companies where directors leave during their tenure are more likely to have violations. Second, compared with the normal outgoing secretary, the outgoing secretary’s company has a higher probability of violations in the same year. In addition, by controlling for year and industry or removing control variables, the conclusion of this paper remains unchanged. The results of this paper show that the resignation of directors during their tenure sends a negative signal, and the company is prone to violations, which needs to attract the attention of regulators and investors. Keywords: Secretary of Board · Information Disclosure · Violations of Companies
1 Introduction Protecting investors’ rights and interests has always been one of the important tasks of listed companies. The White Paper on Investor Protection in China’s capital market pointed out that it is more difficult and important to protect the rights and interests of investors in China’s A-share market, where retail investors are the main force. Gao believed that the secretary of the board of directors (here in after referred to as “Secretary of the Board”) plays an especially important role as the convergent point of interests of all parties in the capital market. Therefore, this article focuses on the listed company chairman secretary term departures and its influence to the investors, violations of relationship with the company, mainly discussed from three aspects: the identity of the H. Gao, Y. Xing, F. Yu—Contributed equally. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 623–633, 2023. https://doi.org/10.1007/978-981-99-6441-3_57
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chairman secretary positioning and should perform their duties, in recent years, the listed company chairman secretary left behind the present situation and the possible reasons, dong secret behind the abnormal termination and violations of PLC and the interests of the investors. The term “secretary of the board of directors” originated from the term “company secretary” in common law countries. Wang explained that China transformed this term to give the company unique Chinese characteristics, so it was called “secretary of the board of directors”. Under the Company law and corporate governance rules, the secretary of the board of directors is a senior management position, but it is also a responsibility. Zhong emphasized that the main senior management positions of the company should be defined by the articles of association or the resolution of the board of directors, and only the business responsible for the responsibility of “company secretary” must be included. In terms of responsibilities and authority, Bu believed that it mainly includes taking charge of corporate information disclosure affairs, investor relations management affairs of listed companies, assisting the board of directors of listed companies to strengthen the construction of corporate governance mechanism and promoting the standardized operation of the company. Zhang pointed out that since 2021, 494 A-share companies have disclosed the resignation announcement of director secretary (excluding acting secretary), an in-crease of nearly 20% compared with 417 in the same period in the past year. Basically, there will be two director secretaries leaving every day on average. According to Zhou, first of all, the company’s operation is not standardized, information disclosure is not compliant, and the board secretary “suffers together” with senior executives, directors and surveillance, or “takes the blame on behalf of others”, which affects the development prospect of the board secretary’s career. Secondly, when the secretary sees many negative situations in his own industry, he will move to a more developed enterprise or industry. Thirdly, some companies ignore secretary directors and do not let them know the company’s major decisions or matters. Du found that the higher the education level of Dong Mi, the older the age, the higher the probability of abnormal resignation. Directors with larger shareholding ratio have lower probability of abnormal turnover. Moreover, the financial situation of the company where the secretary of the board leaves abnormally is less optimistic than that of the normal company. At the same time, Ji found that the board secretary who resigned on the disclosure date of the annual report had a higher risk of violation than the board secretary who resigned on the non-disclosure date of the annual report, and the disclosed information needed to be verified. Yu found that the informal information transmission such as the resignation of the secretary of the director made the company’s idiosyncratic information better integrated into the stock price, and the synchronization between the resignation of the secretary of the director and the stock price changes decreased. In addition, Wang found that there was no part-time executive chairman secretary in the company, concurrently chairman secretary company executives are more likely to appear as a wrong information, hidden disclosure makes shares or collapse phenomenon, its reason mainly lies in part-time executive chairman secretary, within the company are more likely to increase due to the operation of the interests of the individual behavior. However, Zhao pointed out that when the company strengthens the disclosure of relevant social responsibility behaviors,
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the risk of the company’s stock price falling or crash will be appropriately reduced within one year. Jiang found that when the in-formation disclosed by the secretary of the board is asymmetric, such as investment and market value expansion, which are recognized by the market, the financial data may have misleading effect, thus causing disadvantages to small and medium investors. Referring to the existing relevant literature, it can be found that researchers have made a more detailed explanation and explanation of the source of the secretary of the board of directors and the hub relationship between the capital market and the hub between the company, as well as the differences in the departure of the secretary of the board of directors of different ages, academic backgrounds, and tenures. Similarly, some researchers have found that the turnover rate of board secretaries has been high in recent years. However, the above-mentioned literature in the wave of abnormal departure of the board secretary hidden in the illegal and illegal incidents and the departure of the board secretary whether the departure of the board secretary caused the problem of information disclosure and its impact on the majority of investors are not sufficient, so this article will discuss the following two issues. First, the relationship between the violation of laws and regulations of listed companies and the resignation of the company’s secretary. Second, the impact of the high turnover rate of board secretaries on investors.
2 Methodology 2.1 Sample Selection and Data Sources Taking Chinese A-share listed companies as the research object, this paper empirically investigates the relationship between resignation of Secretary of the Board during tenure and corporate violations. The data of enterprise Secretary of the Board is from the Choice database “Management Post Changes”. The data of board of director secretary are from the Choice database “Management Post Changes”. Considering the availability of data, this paper selects the resignation events of board of director secretary from 2004 to 2019, and deletes the missing samples of Board of director secretary’ resignation time. The company’s violation data came from the Choice financial terminal “Statistics on Violation Treatment of Listed Companies”, combined with the classification standard of “Statistics on Violation Treatment of Listed Companies”, The five categories of violations such as “fictitious profit”, “fictitious assets”, “delayed disclosure”, “false statement” and “material omission” are defined as violations related to the company’s information disclosure work. Other financial data comes from the CSMAR and CNRDS databases. In this paper, the samples are processed as follows: Firstly, the samples of financial listed companies are deleted. Secondly, Samples with missing relevant data were deleted. Lastly, the continuous variables were reduced by 1%.
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2.2 The Number of Violations of Listed Companies Figure 1 below shows the number of violations of listed companies in each month from 2006 to 2019. This paper will use stock codes to match the violations of companies with the resignation of board secretaries during their tenure.
the number of violations of listed companies
300
270 238
250 200 149
150 100 50
35
39
37
50
49
246 217
169 165
73
0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Fig. 1. Histograms of corporate violations from 2008 to 2019
2.3 Trend Chart of the Number of Board Secretaries Leaving Figure 2 depicts the total number of board secretaries and employees of listed companies in China. As is shown in the figure, the total number of directors secretaries in the Ashare market in 2001 was 1157, and 2 directors secretaries left abnormally. In 2006, with the implementation of the new Company Law, the total number of directors’ secretaries reached 1448, and 54 of them quit abnormally, accounting for 64.3% of the total number of directors’ secretaries that year. In 2018, with the expansion of the capital market, the total number of directors secretaries reached 3,595, and the number of abnormal departures reached 353, accounting for 45.3% of the total number of directors secretaries who resigned that year. To sum up, the proportion of abnormal turnover of secretary directors is relatively high, and the total number of directors grows rapidly.
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the number of different kinds of secretary trend
4000 3000 2000 1000 0 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Total secretary number resignation of secretary abnormal resignation of secretary
Year
Fig. 2. Listed companies in the total number of the secretary and abnormal design trend
3 Results and Discussion 3.1 Specification of Model This paper focuses on the association between the resignation of the secretary of the board of directors during his tenure and corporate violations, and analyzes it from the following two aspects: first, compared with other companies, whether the company where the secretary of the board of directors leaves during his tenure has a higher probability of disclosure violations. Second, compared with the normal resignation of the board secretary in the company, the secretary of the board of directors who quit during the tenure of the company has a higher probability of violations, the model of this paper is set as follows: LOGIT (Yi,t ) = α + βResignationi,t + γ Controlsi,t + Yeari,t + Firmi,t + εi,t
(1)
where Yi,t respectively stands for financial restatement and information disclosure violation, and Resignationi,t stands for the company where the executive resigned on the annual report day. This paper selected corresponding control variables. See Table 1 for detailed variable descriptions. This paper assumes that the resignation of the board secretary during the term has the function of marking corporate governance defects, that is, compared with the companies whose board secretary does not resign or resigns normally, the probability of serious violations in the next year will increase significantly in the companies whose board secretary resigns during the term. Table 2 shows the descriptive statistics of the main variables in this paper. It can be seen from Table 2 that the average value of “Foccur” in all samples is 5%, indicating that the quality of financial information of A-share listed companies is low and the inquiry letters are frequent. In all the samples, about 7.0% of the companies have the phenomenon of resignation during the tenure of secretary of the board, indicating that the resignation phenomenon occurs frequently during the tenure of secretary of the board.
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H. Gao et al. Table 1. Variable descriptions
Explained variable Foccur
Enterprise violation, if the company is disclosed in the year of the violation is 1, otherwise 0
Explanatory variable Resignation The secretary of the board leaves during the term of office, if there is a company executive who demission during non-tenure, set it as 1. Otherwise, set it as 0 Control variables Size
Company size, natural logarithm of total assets
Lev
Leverage, total liabilities divided by total assets
ROA
Return on total assets, net profit divided by total assets
Growth
Development rate, the company’s current operating income divided by the previous year’s one
Expense
Overhead ratio, overhead divided by operating income
Big1
Ownership concentration, shareholding ratio of the largest shareholder
MTB
Market to book equity ratio, market value divided by net assets
Brdsize
The size of the board, the number of directors plus 1 take the natural logarithm
SEO
Seasoned Equity Offering, the company in the year of Seasoned Equity Offering to take 1, otherwise 0
Audit
Audit quality: the company audited by the Big Four accounting firms in the current year is set as 1, otherwise set as 0
Other variables were in the normal range, indicating that continuous variables were not seriously affected by extreme values after the shrinking of the tail. Table 3 is the descriptive statistical results by the sample. As shown in the table, the mean value of violations in the company where the outgoing director secretary works during his tenure is 11%, and the mean value of violations in other companies is 5%. The results are all significant at 1% level by T test of the companies. The above results indicate that there is a high probability of corporate violations in the companies where the outgoing secretary works during the tenure, which preliminarily proves the conclusion of this paper. Meanwhile, other variables were in the normal range and most of them passed the t-test, indicating that the quality of the control variables in this paper was good. Table 4 examines the relationship between the resignation of board secretaries during their tenure and corporate violations. In this paper, different methods are used to verify the significance and coefficient between variables: first, year and industry are not controlled. Secondly, both control variables and year and industry are used. The results show that the probability of corporate violations in the current year is higher in the companies where the secretary of the board leaves during his tenure, and the correlation coefficients with the explained variable (Foccur) of regression (1), (2) are 0.602 and 0.588, all of which are significantly positive at the 1% level.
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Table 2. Descriptive statistics variables
N
mean
Std.Dev
min
max
Skewness
Kurtosis
Foccur
24356
0.05
0.23
0.00
1.00
3.92
16.37
Resignation
24356
0.07
0.25
0.00
1.00
3.43
12.79
Size
24356
21.94
1.29
18.93
25.84
0.58
3.42
Lev
24356
0.46
0.22
0.05
1.23
0.33
2.98
ROA
24356
0.04
0.06
0.26
0.24
-0.88
8.38
Big1
24356
35.35
15.16
8.76
75.52
0.46
2.54
Growth
24356
0.22
0.60
0.68
4.43
4.45
29.06
Audit
24356
0.06
0.23
0.00
1.00
3.80
15.42
Expense
24356
0.11
0.11
0.01
0.84
3.89
23.05
SEO
24356
0.13
0.34
0.00
1.00
2.18
5.75
MTB
24356
4.50
4.01
0.99
30.03
3.91
22.68
Brdsize
24356
2.36
0.23
1.79
2.94
0.12
3.21
Table 3. Descriptive statistics by sample variables Foccur
Resignation = 1, N = 1655
Resignation = 0, N = 22701
Mean Diff
mean
mean
max
T-test −0.059***
0.11
min
max
0.05
min
0
1
0
1
Size
21.72
18.9
25.84
21.96
18.9
25.84
0.234***
Lev
0.46
0.05
1.23
0.46
0.05
1.23
0.006*
ROA
0.03
0.26
0.24
0.04
0.26
0.24
0.008***
Big1
34.49
8.76
75.52
35.41
8.76
75.52
0.927**
Growth
0.28
0.68
4.43
0.22
0.68
4.43
−0.060***
Audit
0.05
0
1
0.06
0
1
Expense
0.12
0.01
0.84
0.1
0.01
0.84
SEO
0.12
0
1
0.13
0
1
MTB
4.89
0.99
30.03
4.47
0.99
30.03
−0.417***
Brdsize
2.38
1.79
2.94
2.36
1.79
2.94
−0.025***
0.011* −0.016*** 0.011*
Table 4 reports the regression results of resignation during the tenure of board secretaries and corporate violations. The regression results show that Foccur is significantly negatively correlated with the Resignation at 1% level (coefficient = 0.588, t = 6.74), indicating that the Resignation of the board secretary during his tenure resulted in an increase of about 59 percentage points in corporate violations. The above results show that the resignation of board secretaries during their tenure does significantly lead to the
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H. Gao et al. Table 4. Board of director secretary tenure and business violations
variable
(1) Foccur
(2) Foccur
Resignation
0.602***
(−6.92)
0.588***
(6.74)
Size
−0.0115
(−0.39)
−0.180***
(−6.08)
Lev
0.408**
(−2.45)
1.294***
(−8.55)
ROA
−4.570***
(−8.87)
−3.980***
(−8.77)
Big1
−0.0153***
(6.61)
−.0127***
(−6.24)
Growth
0.107**
(−2.56)
0.118***
(−2.9)
Audit
−0.927***
(−4.13)
−0.681***
(−3.51)
Expense
0.292
(−1.29)
0.0135
(−0.06)
SEO
0.0929
(1.17)
−0.039
(−0.48)
MTB
0.0309***
(−5.65)
0.00965*
(−1.75)
Brdsize
0.298**
(−2.22)
0.192
(−1.57)
Constant
−2.981***
(−4.78)
−0.26
(−0.40)
Year
No
Yes
Indcd
No
Yes
Obs
24356
24356
Pseudo R2
0.05
0.08
Note: (1) values reported in parentheses are t-statistics. (2) “*”, “**” and “***” rep-resent 10%, 5% and 1% significance levels, respectively. (3) This paper is clustered by company and year, with robust standard error adjustments
increase of violations in listed companies, and the hypothesis is initially supported by empirical evidence. The practical implication of this conclusion is that if senior executives ignore their main responsibilities, the efficiency of corporate governance will be reduced, which will lead to the decline of accounting performance. In terms of control variables, Foccur is significantly negatively correlated with Big1, indicating that with the increase of the shareholding proportion of the largest shareholder, it can exert a supervisory effect on the management to a certain extent, which may lead to the improvement of decision-making efficiency and the reduction of corporate violations. In countries with weak investor protection, ownership concentration helps reduce agency costs, and the shareholding ratio of the largest shareholder should be positively correlated with corporate performance. Foccur is negatively correlated with ROA, indicating that high corporate returns do have a restraining effect on corporate violations. In addition, Foccur is significantly positively correlated with Lev, indicating that the higher the asset-liability ratio, the more likely it is to produce violations.
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3.2 Further Analysis This paper takes board secretaries who quit during their tenure as the research object, on the basis of the particularity of board secretaries who quit during their tenure, that is to say, board secretaries who quit normally do not have the relevant “results” of board secretaries who quit during their tenure, or the correlation is not so significant. Based on the above analysis, this part selects the resignation samples of all board secretaries and divides them into board secretaries who resignation during tenure and board secretaries who resignation during non-tenure, and investigates the relationship between board secretaries who resignation during non-tenure and corporate violations. According to Table 5, compared with board secretaries who quit during tenure, the probability of violation in the company of board secretaries who quit during non-tenure is smaller, and the significance level is reduced, which is only significantly positive at 5%. To sum up, it is basically proved that, compared with the secretary of the board of directors who leaves office normally, the secretary of the board of directors who leaves office during the term of office has particularity. Table 5. Further analysis variable
(1) Foccur
(2) Foccur
Resignation
0.273***
(−3.45)
0.193**
(−2.31)
Size
−0.0178
(−0.60)
−0.187***
(−6.08)
Lev
0.389**
(−2.34)
1.286***
(−8.55)
ROA
−4.669***
(−8.93)
−4.073***
(−8.77)
Big1
−0.0152***
(−6.57)
−.0127***
(−6.24)
Growth
0.109***
(−2.58)
0.120***
(−2.9)
Audit
−0.915***
(−4.09)
−0.666***
(−3.51)
Expense
0.308
(−1.36)
0.0184
(−0.06)
SEO
0.0908
(−1.14)
−0.0413
(−0.48)
MTB
0.0310***
(−5.65)
0.00966*
(−1.75)
Brdsize
0.308**
(−2.27)
0.214*
(−1.57)
Constant
−2.981***
(−4.78)
−0.26
(−0.40)
Year
No
Indcd
No
Yes
Obs
24356
24356
Pseudo R2
0.05
0.08
Yes
Note:(1) values reported in parentheses are t-statistics. (2) “*”, “**” and “***” represent 10%, 5% and 1% significance levels, respectively. (3) This paper is clustered by company and year, with robust standard error adjustments
632
H. Gao et al.
4 Conclusion This paper manually collects the empirical data of board secretary resignation during tenure of listed companies in Shanghai and Shenzhen from 2008 to 2019, and empirically studies the relationship between board secretary resignation behavior during tenure and the violation probability of listed companies. The results show that the listed companies whose board secretaries leave during their tenure have significantly higher probability of violation. The policy implications of this paper are as follows: Firstly, the secretary of the board of director leaves office during the term of office, which leads to the gradual loss of the role of the secretary of the board of directors in corporate governance. Therefore, for the securities regulator, it is necessary to restrict the resignation behavior of the secretary of the board of directors during the term of office to make his tenure more long-term. Secondly, for stock exchanges, they should further make more detailed provisions on listing rules, term of office and responsibilities of board secretaries of listed companies, so that the rights and responsibilities of board secretaries as senior executives are equal. Lastly, for investors, the resignation of the secretary of the board of directors during his tenure usually means that the company lacks corresponding corporate governance links, which leads to the decline of performance and the increase of the probability of violation. Therefore, investors can take the resignation of the secretary of the board of directors during his tenure as a negative signal to keep alert to such listed companies and make prudent decisions. Compared with the 100-year career development of board secretary in the West, the development history of board secretary system in China’s capital market is only more than 20 years. Therefore, the system of board secretary and its operation still have many theoretical and practical problems to be discussed. The research of this paper focuses on the impact of board secretary resignation during tenure on the probability of company violation. For the research of board secretary in China’s emerging capital market, it may be worthy of further discussion on the following issues: Firstly, the influence of the restriction of corporate governance structure on the performance of the duties of the secretary of the board of directors. As we all know, the secretary of the board of directors is an important link in corporate governance, so the performance of the secretary function of the board of directors is inevitably closely related to the existing corporate governance structure of China’s listed companies. It will be of great significance to explore the impact of corporate governance mechanism on the secretary function of the board of directors. Secondly, the main functions of the secretary of the board of directors, including improving the internal governance of the company, managing investor relations and ensuring information compliance and disclosure, focus on the relationship between them. Lastly, since the career development path of board secretaries is an important incentive that affects the degree of due diligence of board secretaries, the career development of board secretaries in the company is also an important issue.
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References 1. Gao, Q., Wu, L.: Can serving as secretary of directors improve the quality of information disclosure – an empirical test of the proposed revision of the new requirements for secretary of directors’ qualifications. Acc. Res. 1, 47–54 (2008) 2. Wang, C.: Education Level, Information Pathway and Stock Price Fluctuation of Executive Secretaries. Shanghai Jiao Tong University (2019) 3. Zhong, J.Y.: What kind of official is dong secretary. Board Directors 04, 62–63 (2018) 4. Bu, J.: Research on the position of board secretary, duty performance and investor protection. Northeast Univ. Finance Econ (2019). 10.27006 /, dc nki. Gdbcu. 2019.000016 5. Zhang, A.: The story behind the chairman secretary left. Shanghai Securities J (2021). https:// doi.org/10.28719/n.c.NkiNSHZJ.2021.003763 6. Zhou, Q.Y.: Bid farewell to the high risk of executive secretary career. Board of Directors 11, 14–15 (2018) 7. Du, X.Q., Feng, W.T., Pei, H.M.: The economic consequences of abnormal turnover of board secretaries in IPO companies: empirical evidence from China’s capital market. Investment Res. 32(08), 47–64 (2013) 8. Ji, Y.: Why do secretary of the board of directors choose to leave the company. Southwest Univ. Finan. Econ. (2021). https://doi.org/10.27412/,dcnki.Gxncu.2021.001458 9. Yu, B.B., Zheng, D.Y., Luo, Y.R.: Chairman secretary departure, information environment and stock price synchronicity. J. Mod. Bus. (16), 135–138 (2021). https://doi.org/10.14097/ j.carolcarrollnki.5392/2021.16.044 10. Wang, C.L., Yu, W.: A study on the effectiveness of the real executive serving as secretary of the board of directors: from the perspective of stock price crash risk. Investment Res. 36(10), 106–128 (2017) 11. Zhao, W.Y.: The information disclosure of listed companies and stock price crash risk. Foreign Econ. Trade Univ. (2021). 10.27015 /, dcnki. Gdwju. 2021.000363 12. Jiang, C.: Market value management of listed companies and protection of interests of small and medium investors: a case study of tongkun shares. Finance Account. Mon. 1–9 (2022)
Research on the Influencing Factors of Marriage Rate Among Young People in China Yuxuan Chen1(B) and Junji Yang2 1 School of Yuxuan Chen, Xijing University, Xian, Shanxi Province, China
[email protected] 2 School of Junji Yang, Hubei University of Economics, Wuhan, Hubei Province, China
Abstract. With the continuous development and progress of society, people’s thoughts are constantly broadened, especially single women. Nowadays, the marriage rate of the society is declining, which has aroused the concern of the society. Based on the data of China’s marriage rate in recent years, this paper uses chart analysis, linear regression model and collects some ideas of contemporary young people to analyze the reasons for the marriage rate in today’s society. Previously, some scholars have studied the reasons for the low marriage rate, but they all stayed at the national and social levels, so this study is mainly analyzed at the individual level. The study found that there are six reasons. First, the higher the education, the later the marriage. Second, women’s independent thoughts are constantly strengthened. Third, the enterprise employment system is harsh on women, so that women have to marry late or not. Fourth, the heavy burden on society and families has made many young people afraid to marry. Fifth, young people have increasingly high expectations of marriage and partners. Sixth, young people yearn for freedom more, so many young people choose to marry later for freedom. Keywords: Marriage Rate · Young People · Linear Regression Model · Diversification
1 Introduction In China, from ancient times to the present, marriage is the only way on the road of life. In ancient times, there are many rules, and now there is mutual help and relief in time of poverty. From the emotional point of view, it allows two people to support each other and stay together; From an economic point of view, marriage can continuously aggregate each other’s abilities, so as to achieve the reproduction of the next generation. In general, marriage is an important guarantee for family continuity. But in the 21st century, Marriage is no longer the inevitable choice of life, but has become a free option. With the development of industrialization and urbanization, the great division of labor in socialization and the reality of the stranger society make people get used to being alone [1]. The average life expectancy of Chinese population Y. Chen, J. Yang—Contributed equally. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 634–643, 2023. https://doi.org/10.1007/978-981-99-6441-3_58
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is increasing, and the aging population is increasing, which means that the proportion of the marriageable age population suitable for marriage in the total population is also declining, and the decline in the marriageable age population affects the marriage rate [2]. The decrease of marriage rate has also increased parents’ anxiety. However, how to reduce cost of living, increase development expectations, let everyone don’t worry about the future and be happy in life [3]. The available literature shows that the theoretical circles have carried out relevant research mainly from the following aspects. Firstly, from the national level: the marriage rate and the aging of the birth rate are closely related and complement each other [4]. The delay in marriage and the aging of the age structure are the main reasons for the current reduction of the marriage rate [5]. The national system is becoming more and more perfect, and the divorce procedures are becoming simpler and faster, which also facilitates divorce [6]. The rising divorce rate also increased the fear of marriage among unmarried people, thereby reducing the marriage rate. Secondly, from a social perspective: Chinese society is becoming more and more open, with more and more foreign exchanges, leading to the younger generation’s thinking and horizons more and more open, so they have no standard answer to the question, whether they must get married or not. With the transformation and development of society, the social tolerance for unmarried cohabitation, divorce and other phenomena has also greatly improved. Not getting married or divorce is no longer a shameful thing, but has become a choice for many people to pursue self-worth and a happy life [7]. With the development of industrialization and urbanization, the great division of labor in socialization and the reality of the stranger society make people get used to being alone. The traditional concept of marriage and childbirth has undergone tremendous changes. In the past, the concept of marriage was based on men, men were the head of the family, the owner decided the right, and the status of women was very inferior to that of men, such as women should obey men. In the modern concept of equality of all people gradually emerged, women’s social status increased, and many female suitors would not obey people’s previous ideas of independence. With the rise of women’s status, the cost of marriage is getting higher and higher. Buying a house and a car is the premise of marriage, which will greatly increase the cost of marriage and put great pressure on men. Marriage may mean a new burden of reincarnation [8]. Women not only have higher and higher requirements on material conditions, but also have higher and higher requirements on mate selection criteria [9]. To a certain extent, it reflects the progress of society, but it also reduces the marriage rate. To summarize, individual selection factors are on the rise, and family factors are declining. The enhancement of women’s independence has made dependent marriage and men’s survival unnecessary. Young people’s social circle narrowing is not conducive to smooth divorce, late marriage or even no marriage is more and more common. And in China, self-demand-oriented consumption types, such as selfpleasant consumption, cost-effective pursuit and appearance economy, have emerged as the times require [10]. Therefore, young people have less and less demand for marriage. The problem of “low marriage rate” is becoming more and more common among the younger generation,
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so this paper will talk about the reasons for this problem, from the perspective of young people. Chinese scholars have made a detailed analysis of the low marriage rate in China, which is clear and unique on the whole, mainly from the perspectives of the country, society, individuals and marriage itself. In addition to these reasons, this paper will look at this problem from the ability of young people, because starting from the parties themselves is more conducive to the solution of the problem. So this paper will start from the young people around, analyze this problem from the perspective of the parties and whether marriage is still necessary in this era. This paper will promote the research of this problem.
2 Methodology First of all, the opinions of this article are all from the real opinions of young people in the network. Then, six influential factors are selected from many viewpoints leading to low marriage rate. According to the influencing factors, find the data from the database and draw the chart by making the chart. Through the chart, the relationship between the marriage rate and these six influencing factors can be seen more clearly. The data comes from the National Bureau of Statistics. After downloading the data, import it into an Excel table, and use the drawing method that comes with Excel to make images. The curves of the marriage rate and divorce rate and the images of the marriage rate and influencing factors are respectively made, and the data is analyzed based on the images. Using a simple linear regression equation and correlation coefficient r can determine that the marriage rate is highly correlated with education, housing prices and working conditions. 2.1 Data Sources In order to establish a simple linear regression model, this paper finds data on China’s marriage rate and divorce rate from 2004 to 2020, and data on Chinese undergraduate graduates, social female employees and housing prices from 2005 to 2022, and these data are all derived from national statistics Bureau. In order to make the established data better to construct a simple linear regression equation and to make the data highly correlated, in the discussion of factor influence, the above factors are all selected from the data from 2013 to 2020 and China marriage rate and divorce Data from 2004 to 2020 were selected for the discussion of rates. After data selection, six influencing variables were found. 2.2 Variable Description This article aims to establish an effective linear regression model to explain why the marriage rate in China is now at a low level. There are six variables here, which are SSR (regression sum of squares), SST (total sum of Squares), R2 (Coefficient of determination), Yˆ (Dependent variable Predicted), βˆ (Regression coefficients) and β1 (Starting point). Then build a simple linear regression equation.
Yˆ = βˆ + β1 X + ε
(1)
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n Xi − X Yi − Y i βˆ = 2 n i Xi − X
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(2)
ˆ β1 = Y − βX
(3)
2 n Y − Y i i SSR = R2 = 2 n SST i Yi − Y
(4)
where βˆ is regression coefficients representing the independent variable increasing by one unit or decreasing by one unit is the average increase or decrease in the dependent variable, β1 is starting point used to predict what the value of the dependent variable will be when the independent variable is zero. The two variables βˆ and β1 are the important basis for forming the regression equation. After the construction is completed, Yˆ can be obtained, and it is used to represent the predicted value, and Yˆ is an important basis for the composition of SSR, SSR It is used to predict the difference between Yˆ and Y , and SST is used to represent the total fluctuation degree of the data in the original sample. Generally speaking, the larger the SST, the greater the fluctuation degree. After dividing SSR by SST, R2 can be obtained, and R2 is used to indicate the quality of the model fitting. Usually, when R^2 is closer to 1, the better the model is established, so R2 is a very important indicator. This is also an important basis for this paper to test the fitting situation (see Table 1).
Table 1. Variable description
Explained variable
Variable symbol
Variable meaning
SSR
Regression sum of squares
SST
Total sum of squares
R2 Yˆ
Coefficient of determination Dependent variable Predicted
βˆ
Regression coefficients
3 Results and Discussion From Figs. 1 and 2, it can see that the number of married people is decreasing year by year and the number of graduates is increasing year by year. The number of married people is inversely proportional to the number of graduates. Therefore, it can be concluded that the marriage rate is related to education. The higher the education, the later the marriage. The competition in today’s society is becoming more and more fierce. Young people have to improve their advantages by improving their academic qualifications. The higher
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academic qualifications they get, the longer learning career they have, so they get married latter. First, the longer they stay in school, the later they enter society and meet different people. Second, with the continuous improvement of education and more and more extensive knowledge, people’s expectations for marriage are higher and higher. Marriage is no longer for the purpose of having children, but more for the spiritual companionship of the other half. The purpose of marriage is more personalized and diversified. Some people get married to relieve their loneliness and make life interesting. Some people get married in order to find a person with common interests and thoughts and make life more colorful. Some people get married just to complete the task, but no love. Last but not least, they have all kinds of reasons to get married (see Figs. 3 and 4). 1500 1400 1300
Marriages(number)
1200 1100 1000 900
y = -5.7009x + 3229 R² = 0.9524
800 700 600
300
320
340
360
380
400
420
440
undergraduate graduation(number) Fig. 1. Linear regression equation plot of undergraduate graduates and marriage rates
From this figure, it can see that the number of married people is decreasing year by year, and the number of women working is increasing year by year. The number of marriages is inversely proportional to the number of women working. It can be concluded that the number of married people is related to the number of women who work. The more women work, the less the number of married people. It can also be seen that women’s independent thoughts are gradually strengthened. Women’s self-awareness is constantly awakening. Considering that their personal needs must give way to their family responsibilities after marriage, many women choose to marry later or even not. Although society now requires equality for all, many enterprises have higher requirements for women. In order to improve the efficiency of the company, many enterprises will impose stricter requirements on women in recruitment, such as not being able to get married and have children within three years. Women have to marry and have children late for their own career. If they get married and have children, they may face unemployment. So, for their own career, women have to choose to marry later or not. However, in terms of technology and ability, women are not inferior to men. Women spend a lot of time and
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(Number of people/Ten thousand)
1600 1400 1200 1000 800 600 400 200 0 2013
2014
2015
Marriages(number)
2016
2017
2018
2019
2020
Undergraduate graduation(number)
Fig. 2. Graph of undergraduate graduates and marriage rates
female employees(number)
95 90 85 80 75 70 65 60
y = -0.0662x + 146.35 R² = 0.9759
55 50
800
900
1000
1100
1200
1300
1400
Marriages(number) Fig. 3. Linear regression plot of marriage rate and female employees
energy to realize their dreams and goals, so they cannot give up halfway. Only through late marriage and nonmarriage can their dreams be realized. This is a manifestation of the enhancement of women’s independent consciousness and social progress. This is also a very important reason for the low marriage rate in today’s society (see Figs. 5 and 6). It can be seen from the figure that the number of married people is decreasing year by year, the house price is rising year by year, and the house price is rising rapidly. The number of marriages is inversely proportional to the housing price. It can be concluded that the marriage rate is related to the house price. The higher the house price, the lower
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1600 1400 1200 1000 800 600 400 200 0 2013
2014
2015
marriages(number)
2016
2017
2018
2019
2020
female employees(number)
Fig. 4. Graph of marriage rate and female employees
11000 10000
(house price)
9000 8000 7000 6000
y = -8.2478x + 16725 R² = 0.9829
5000 4000 800
900
1000
1100
1200
1300
1400
Marriages(number) Fig. 5. Simple linear regression plot of house prices and marriage rates
the marriage rate. Too high house prices have brought great pressure to marriage, so many young people dare not get married or do not have the financial ability to get married. Marriage is also a new pressure for contemporary young people. They will face the pressure of housing loans, the pressure of supporting the elderly and the pressure of raising children. A marriage full of pressure will become increasingly unhappy, lose love and patience, often quarrel, so they will divorce finally. Therefore, many young people do not marry because they cannot afford these pressures and do not want to divorce. Different from the people before, now young people have higher and higher standards for choosing a spouse. They have higher standards for attaching importance to their personality and character, which also increases the difficulty of marriage. People’s
1600 1400 1200 1000 800 600 400 200 0
12000 10000 8000 6000 4000 2000 2013 2014 2015 2016 2017 2018 2019 2020 marriages(number)
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(yuan/square meter)
(Number of people/Ten thousand)
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0
house price
Fig. 6. Graph of house prices and marriage rates
requirements for the quality of life are also getting higher and higher. Their ways of thinking are becoming more diversified. They have more personalized choices for their way of life, and there is no longer only the option of getting married and having children. For example, even without a partner, a person can eat, shop and go to the party. Even without the material foundation of two people, one can buy a house and a car. When a person’s material base is rich enough, his desire for a partner will also decline. This will also affect the marriage rate. The younger generation have higher and higher requirements for freedom and selfworth. Marriage is a constraint on a person to a certain extent. So young people choose to marry later or not in order to gain more freedom. This also reflects the progress of society and the openness of social thinking. In order to pursue the quality of life and freedom, many young couples are unwilling to make ends meet and choose divorce. So, the divorce rate is getting higher and higher (see Fig. 7). It can be seen from this figure that the number of married people first increased and then decreased, and the number of divorced people decreased year by year. Since 2013, the number of marriages has been inversely proportional to the number of divorces. Since the post-90s came of age, the number of married people has decreased year by year, and the number of divorced people has increased year by year. It can be concluded that the number of marriages is related to the number of divorces. The higher the number of divorces, the greater the pressure on single people. They will become more and more distrustful of marriage, feel that marriage is unreliable or even betrayal, so they are afraid of getting married. To sum up, this study has drawn six factors that affect the low marriage rate. They are economic pressure, female independence, enterprise system, degree of freedom, education and personality. These six factors reflect the relationship between marriage and material and spiritual aspects. They are closely related to marriage. Marriage is based on marriage, but there must also be material protection, otherwise it will definitely lead to divorce. These six factors also reflect the improvement of the comprehensive quality of the younger generation. They have higher requirements for their marriage. From the above four charts, first of all, it can be seen that the number of married people is
1600
12‰
1400
10‰
1200
8‰
1000 800
6‰
600
4‰
400
2‰ 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
200 0
(ratio/‰)
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642
Marriages(number)
Divorces(number)
Divorce rate
Marriage rate
0‰
Fig. 7. Change in marriage and divorce rates in China from 2002 to 2020
decreasing year by year, and the speed is very fast. This shows that the low marriage rate has become a problem rather than an ordinary phenomenon. It can clearly see the relationship between the marriage rate and these factors. These six factors are inversely proportional to the number of married people. Therefore, charts are a very important method for analyzing problems. The marriage rate is closely related to these six factors. This is also an important reason from the perspective of young people themselves.
4 Conclusion In this study, from the personal level of young people, there are six factors leading to the marriage rate were obtained. First, the higher the education, the later the marriage. In order to improve their social competitiveness, many young people choose to improve their academic qualifications. Second, women’s independent thoughts are constantly strengthened. They no longer choose to rely on men, but choose to rely on themselves. Third, the enterprise employment system is harsh on women, so that women have to marry late or not. Fourth, the heavy burden on society and families has made many young people afraid to marry. Fifth, young people have increasingly high expectations of marriage and partners. Sixth, young people yearn for freedom more, so many young people choose to marry later for freedom. Through this study, people can not only know the low marriage rate, but also know the reasons for the low marriage rate. It also can eliminate some prejudice against young people, and fill the reasons for choosing not to marry at the level of young people. Through this research, society should give young people a lot of space and freedom, please do not judge them with old ideas. Today’s society is a pluralistic society, and everyone is unique. If they are all evaluated by a unified standard, society will become patterned and lose creativity. Labor is the fundamental driving force of social development. However, creativity is the driving force of social development. Therefore, society should develop diversification.
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Of course, young people should not only consider their own development, but also look at marriage from the perspective of the country and society. Young people are the main force of the future society. They should not only contribute to economic and scientific and technological development, but also to population. Therefore, it is the responsibility of young people to raise the fertility rate and reduce the aging population. At present, China’s aging problem is serious, which will increase the social burden. Therefore, the country alleviates the aging by increasing the fertility rate. For the sake of social development, young people should consider not only their own interests, but also society and the country. Everyone should be a useful person for social development.
References 1. Xue, S.J.: Objective view of the decline in marriage rate. Henan Daily 2019-08-21(007). https://doi.org/10.28371/n.cnki.nhnrb.2019.004233 2. Gong, C.Z., Yu, X.M.: Calculation and improvement of marriage rate and divorce rate indicators (01), 32–35 (2022). https://doi.org/10.19456/j.cnki.tjyzx.2022.01.008 3. Tu, T.: Choosing a mate is not an easy task. Times Post (13), 33 (2019) 4. Peng, S.P.: The number of marriage registrations in China has declined for seven consecutive years. China Women’s daily 2021-11-29. https://doi.org/10.28067/n.cnki.ncfnb.2021.003595 5. Jian, S.: Demographic interpretation of the new low marriage rate. People’s Forum 08, 72–74 (2020) 6. Zhai, Z.W., Liu, W.L.: Are Chinese people really not getting married -- Viewing Chinese people’s marriage and nonmarriage from the perspective of queues. Explor. Contention (02), 122–130+160 (2020) 7. In 2018, the national marriage rate hit a new low in six years, and the divorce rate rose for 15 consecutive years. China Economic Weekly (06), 7 (2019) 8. Luo.: China’s marriage rate hit a 10-year low: why don’t young people get married. Times Post (19), 30–31 (2019) 9. Youth Marriage and Love Survey | Nearly half of the college students surveyed look for a partner without looking at their faces? China Youth Daily 2022–07–30 10. Yuan, H.: Will we fall into a “low-desire society”. Times Post (23), 32 (2019)
Constrained Portfolio Optimization: A Comparison of Markowitz Model and Single Index Model Qiucen Lin(B) Business School, Soochow University, Suzhou 215021, China [email protected]
Abstract. This paper studies the constrained portfolio optimization for Markowitz Model and Index Model, illustrating a comparison between the performance of two models. A recent twenty years of historical return data for ten stocks in technology, financial services and industrials sectors are chosen and processed. Using the data, the optimal portfolio weights of two models are calculated and a prediction-based portfolio optimization model is constructed to capture short-term investment opportunities. Meanwhile, five additional optimization constraints are added to simulate real-world investment. After finding the regions of permissible portfolios, details of comparative analysis are presented, including the weights of portfolios, returns, volatility and Sharpe ratio. The results show that some constraints have the same effect on the portfolio while some do not. Markowitz Model and Index Model have quite similar performances concerning both the Minimal Risk Portfolio and Efficient Risky Portfolio, and under any constraint, the relative advantages between each other are insignificant. The study aims to provide some useful guidance for future data analysis in portfolio construction and investment recommendations. Keywords: Optimal Portfolio · Markowitz Model · Index Model · Constraints · Sharpe Ratio
1 Introduction Investment has become an increasingly important thing, and diversifying can help investors maintain capital. Thus, research into portfolio optimization matters a lot. Modern portfolio theory is a useful technique for choosing investments to maximize their total returns while maintaining a manageable degree of risk [1]. Markowitz Model and Index Model have always been used in determining the optimal portfolios of stocks chosen and have been widely compared. Constraints, however, may affect the performance and volatility of built-in portfolios as well as the applicability of the Markowitz Model and Index Model. This paper creates a portfolio simulation using the Excel Solver Add-ins for the Markowitz model and Index model, and additional constraints to get more closely to the real investment world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 644–654, 2023. https://doi.org/10.1007/978-981-99-6441-3_59
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Since the 1950s, portfolio theory has developed rapidly. Harry Markowitz [1] gives a brief overview of the key attributes of one strategy for resolving what is now known as the standard portfolio selection model. In 1963, Sharpe puts forward a simplified model for portfolio analysis - the single-index model for evaluating the risk and return characteristics of portfolios [2]. Later, the Markowitz model is further developed, with the classical capital asset pricing model established [3] and the sensitivity between securities return and market portfolio return are analyzed [4]. Additionally, research on the capital asset pricing model is being done, extending the assumptions and presenting a more straightforward and condensed arbitrage pricing model [5]. After that, related concepts have been increasingly applied by academics in financial fields. Major reasons are explained why portfolio theory has not been applied and offer several approaches to solve them [6] and the interest rate effects into the single-index model looks promising [7]. By carefully adjusting the inputs and imposing limits based on important priors and fundamental investing considerations, the practical worth of portfolios may be increased [8]. A portfolio optimization model employing the mean absolute deviation risk function can keep the benefits of the traditional Markowitz’s model while removing most of the problems with it [9]. The main problem with traditional mean-variance analysis is that it ignores the impact of measurement error on ideal portfolio allocations. A straightforward simulation approach can reveal information about the distribution of portfolio weights [10]. After that, the effectiveness of portfolio optimization is examined with conditional value-at-risk objectives and constraints [11]. The aim of this article is to find the optimized portfolios for the Markowitz Model and the Index Model under different additional constraints. According to the results, most of the indexes exhibit comparable performance on both models when comparing the various constraints. The Markowitz model exhibits a little advantage, at the cost of a larger number of estimates. Some constraints behave in the same way as the free constraint, while some constraints will result in a higher better profit. The analysis of results would help investors gain insight into the impact of different constraints on portfolios and the difference between the application of two models. The rest of this paper is structured as follows. Section 2 introduces the theoretical knowledge of the Markowitz Model, Index Model and five additional constraints. Section 3 describes the process of data collection and analysis. Section 4 analyzes portfolio optimization results and compares the performance of two models under the constraints. In the final Section, the conclusion is presented.
2 Problem Formulation This paper investigates the performance of Markowitz Model (MM) and Index Model (IM), which will be introduced in this section. The Markowitz model provides a conceptual basis for examining risk, return, and related interrelationships. The idea of efficient portfolios was developed using the framework in Portfolio Selection [1]. Markowitz has established rules for diversification based on his research, considering investors’ attitudes toward risk and return as well as an accurate quantification of risk. According to this model, a security’s value can be determined by looking at its mean return, standard deviation (risk), and correlation with other securities in a portfolio. He advised the investors to put their attention on selecting portfolios
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based on their general risk and return characteristics. Large number of portfolios can be constructed by combining security and by varying the proportion of investment among assets. The mean in the Markowitz Model is calculated by (1) rp = xi ∗ ri where ri is the return to stock; xi is the investment ratio of stock. The variance is calculated by xi ∗ ri ∗ Cov(ri, rj) min σ 2 (rp) =
(2)
Next, Single index model, which is also called the Index model is introduced. The index model is a simple asset pricing model which is used to measure the risk and return of a stock [2]. Compared to the Markowitz model, the estimation of the covariance matrix problem is made simpler by the index model, which also improves the analysis of security expected returns, also called risk premiums. It is predicated on the idea that the error e is stock-independent. Thus, the market’s uniform movement is the only reason stocks fluctuate together, consistently [14]. To further clarify that the equation reflects the return on any security independently of how the return on the market varies, the single-index model considers that e and the market return are uncorrelated. From time series-regression analysis, estimates of alpha, beta, and covariance of errors are frequently produced. The excess return in the Index Model is calculated by (3) ri − rf = αi + βi rm − rf + εi where rf refers to the risk-free rate; rm means the market portfolio’s return; αi is alpha; βi is beta; εi refers to the residual return. In addition to the Markowitz Model and Index Model, several extensions are proposed to enrich the two portfolio optimizations models with real world constraints. In the paper, the analysis is based on five practical constraints. • Constraint 1: "Free Problem” (C) There are no further optimization restrictions for this condition. This is used to observe optimized portfolio performance if there is no constraint. • Constraint 2: “Exclusion of the Index Asset” (C2) This restriction is in place to determine whether the wide index’s inclusion in the portfolio has a positive or negative impact [15]. The SPX index is not considered from the portfolio, and Eq. (4) can be used to display the formula, where w1 indicates the weight of SPX index in the portfolio. w1 = 0
(4)
• Constraint 3: “No Short Positions” (C3) A U.S. open-ended mutual fund is not permitted to have any short positions; therefore this additional optimization constraint is intended to imitate the typical restrictions that are present in the U.S. mutual fund business. The mathematical expression be shown as Eq. (5). wi ≥ 0, for ∀i
(5)
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• Constraint 4:"Arbitrary Box” (C4) This additional optimization constraint is configured to imitate certain arbitrary box weight constraints that the customer may supply. This illustrates the optimized portfolio where individual stock weights are less than 100%. And it can be expressed by equation below. |wi| ≤ 1, for ∀i
(6)
• Constraint 5: “Regulation T Problem” (C5) This new optimization constraint mimics the Financial Industry Regulatory’s Regulation T, which permits broker-dealers to allow their clients to hold positions that are at least 50% backed by their account equity. In other words, the absolute value of the sum of the whole stock purchase should be less than 200%. The equation is referred to as: 11
|wi| ≤ 2
(7)
i=1
3 Data Collection and Analysis In this research, this paper uses the adjusted price of 10 stocks during the early May 2001 until early May 2021 period which belong to technology, financial services and industrials sectors, is selected to conduct the portfolio. The 1-month Fed Funds rate and SPX 500 index are used as market index data and risk-free rate return in two models, respectively, to simulate the performance of investments. In particular, the 10 firms are Wells Fargo & Company (WFC), Southwest Airlines Co. (LUV), The Progressive Corporation (PGR), Landstar System, Inc.(LSTR), Cisco Systems, Inc.(CSCO), The Toronto-Dominion Bank (TD CN), FedEx Corporation (FDX), Microsoft Corporation (MSFT), Adobe Inc. (ADBE) and SAP SE (SAP). Wells Fargo is a multinational American financial services corporation with management offices both domestically and abroad, ranking one of the 500 largest companies in the United States; Southwest Airlines is the biggest low-cost carrier in the world and one of the top airlines operating in the USA; The third-largest and first commercial vehicle insurer in the US is Progressive Corporation, an American insurance firm; Landstar System, Inc. Has more than 10,000 owner-operators in North America; Cisco Systems, Inc. is an American multinational technology group of companies, developing and manufacturing high-tech services and products; Toronto-Dominion Bank is a famous company in Canada that offers banking and financial services, with over 26 million customers globally; FedEx Corporation is an American multinational holding company with an emphasis on services, e-commerce, and delivery; The largest software company in the world, Microsoft Corporation, with a market value of over $1 trillion as of 2019; Adobe Inc. is an American multinational computer software company that specializes in software for creating and distributing a wide range of content; The German multinational software company SAP SE creates business applications to manage client relationships and business processes (Fig. 1).
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600 500 400 300 200 100 0
5-11-01 5-11-02 5-11-03 5-11-04 5-11-05 5-11-06 5-11-07 5-11-08 5-11-09 5-11-10 5-11-11 5-11-12 5-11-13 5-11-14 5-11-15 5-11-16 5-11-17 5-11-18 5-11-19 5-11-20 5-11-21
STOCK PRICE (US DOLLARS)
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WFC TD CN
LUV FDX
PGR MSFT
LSTR ADBE
CSCO SAP
Fig. 1. Stock Price Trend of 10 companies. Source: Yahoo! Finance.
Table 1. Return and Risk performance of SPX and 10 stocks. SPX Average Return (%)
WFC LUV PGR
7.5
8.9
Standard 14.9 Deviation (%)
28.1
9.85 15.4
31.8
21.1
Beta
1.00
1.05
1.15
Alpha (%)
0.00
0.95
1.18 10.0
Residual Standard Deviation (%)
0.00 23.4
26.9
0.71
18.2
LSTR CSCO TD CN
FDX MSFT ADBE SAP
17.4
13.0
13.2
19.6
12.0
26.7
23.3
31.8
33.9
23.9
0.80
9.71 11.0
30.8
1.32
11.4
−0.2
20.8
23.8
18.1
0.79
1.10
1.00
1.42
1.48
5.07
4.63
5.59
8.85
0.81
13.9
21.1
17.9
23.8
25.8
For building up the efficient set of portfolios, certain parameters of stocks are calculated using the Excel to measure how these stocks perform the return and risk (Table 1). Correlation in the context of diverse portfolios refers to the degree of relationship between the price changes of the various assets held in the portfolio. From the results of correlation table, all the correlation coefficient is in a moderate level (smaller than 0.65), showing that the volatility of portfolio is not very high.
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Table 2. Correlation table. SPX WFC LUV PGR LSTR CSCO TD CN FDX MSFT ADBE SAP SPX
1.00 0.56
0.54
0.50
0.50
0.64
0.64
0.61
0.64
0.66
0.65
WFC
0.56 1.00
0.41
0.30
0.32
0.25
0.54
0.43
0.29
0.30
0.30
LUV
0.54 0.41
1.00
0.35
0.35
0.33
0.44
0.51
0.27
0.39
0.32
PGR
0.50 0.30
0.35
1.00
0.29
0.30
0.37
0.36
0.25
0.45
0.34
LSTR
0.50 0.32
0.35
0.29
1.00
0.35
0.37
0.48
0.27
0.33
0.26
CSCO
0.64 0.25
0.33
0.30
0.35
1.00
0.41
0.34
0.48
0.49
0.50
TD CN 0.64 0.54
0.44
0.37
0.37
0.41
1.00
0.40
0.35
0.46
0.47
PG
0.61 0.43
0.51
0.36
0.48
0.34
0.40
1.00
0.33
0.49
0.32
MSFT
0.64 0.29
0.27
0.25
0.27
0.48
0.35
0.33
1.00
0.51
0.45
KO
0.66 0.30
0.39
0.45
0.33
0.49
0.46
0.49
0.51
1.00
0.53
MCD
0.65 0.30
0.32
0.34
0.26
0.50
0.47
0.32
0.45
0.53
1.00
4 Analysis on Different Constraints in MM and IM Using the data from Table 2, the minimal variance portfolio of Markowitz model is constructed under five constraints individually. The performance of the constrained portfolios is analyzed using three criteria: (i) portfolio standard deviation, (ii) portfolio return, and (iii) Sharpe ratio. Results in Tables 3 and 4 imply the weights of stocks and the portfolio’s risk and return. Table 3. Weights in Min Variance portfolio under MM SPX WFC
LUV
C1 0.93 −0.09 −.05
PGR LSTR CSCO TD CN FDX
MS FT AD BE SAP
0.17
0.07
−0.08 0.25
−0.04 0.07
−0.13
−0.11
C2 0.00 −0.02 −0.03 0.30
0.15
−0.01 0.45
0.05
0.26
−0.11
−0.04
0.00
0.04
C3 0.52 0.00
0.16
0.08
0.00
0.00
0.00
C4 0.93 −0.09 −0.05 0.17
0.00
0.07
−0.08 0.25
0.20
−0.04 0.07
−0.13
−0.11
C5 0.93 −0.09 −0.05 0.17
0.07
−0.08 0.25
−0.04 0.07
−0.13
−0.11
In the “Free Problem” (C1), the results of Markowitz model by Solver demonstrate that the standard deviation of lowest risk portfolio is as low as 12.70%, given the assumption that investors are highly risk averse. SPX is a well-diversified index asset, with the lowest standard deviation of 14.9%. Thus, greater weight is placed on SPX and it accounts for 93.44% of the total portfolio. It’s noticeable that the weights of C4 and C5 are exactly the same as C1, since there is not any overly radical leverage. Accordingly, the performances of these constraints
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Standard Deviation
Sharpe ratio
Constraint 1
8.21%
12.70%
0.646
Constraint 2
13.02%
14.36%
0.907
Constraint 3
10.47%
14.06%
0.745
Constraint 4
8.21%
12.70%
0.646
Constraint 5
8.21%
12.70%
0.646
are identical in this optimization portfolio, with a return of 8.21% and the Sharpe ratio of 0.646. The performance of the portfolio improves under Constraint 3, showing that imposing no-short-sale constraints on the portfolio weights could improve the performance of portfolios. Under Constraint 2, the minimum risk is higher than initial condition after removing SPX for further diversification, with 14.36% in Markowitz model. And its Sharpe ratio turns into 0.907, which is the highest among all constraint settings. In addition to the risk, the return is an indicator of the performance of portfolio as well and should be taken into consideration, thus the Max Sharpe portfolio is constructed in the same simulations (Table 5). Table 5. Weights in Max Sharpe portfolio under MM SPX
WFC
LUV
PGR LSTR CSCO TD CN
FDX
MSFT ADBE SAP
C1 −1.03 −0.04 −0.08 0.55
0.49
−0.09 0.50
0.08 0.46
0.17
0.00
C2 0.00
−0.10 −0.09 0.39
0.36
−0.14 0.31 −0.01 0.25
0.10
−0.07
C3 0.00
0.00
0.35
0.31
0.00
0.14
0.00 0.17
0.04
0.00
C4 −1.00 −0.04 −0.08 0.55
0.49
−0.09 0.49
0.07 0.45
0.17
−0.01
C5 −0.31 −0.03 −0.06 0.42
0.38
−0.08 0.31
0.00 0.29
0.09
−0.02
0.00
The maximum Sharpe portfolio shows that risk and return are generally in balance. The Sharpe ratio for the Min-Variance portfolio is 8.21%, while the Sharpe ratio for Constraint 1 is equal to 1.083, reflecting a larger risk of standard deviation being 23.04%, or roughly three times that much. Different from Minimum Variance Portfolio, Constraint 4 and 5 exert certain influence on the maximum Sharpe ratio portfolio. A less efficient portfolio than C1 could be observed from Table 6, where the maximum Sharpe ratio becomes 1.082 in Constraint 4 and 1.055 in Constraint 5. Constraint 3 generally outperforms other constraints, with a Sharpe ratio of 0.965 and a lower return of 15.418%, respectively. Next, the constrained optimizing portfolios are conducted for the Index model and comparison between two models is made. Tables 7, 8, 9 and 10 indicate the optimized
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Table 6. Performance of Max Sharpe portfolio under MM. Return
Standard Deviation
Sharpe ratio
Constraint 1
23.04%
21.28%
1.083
Constraint 2
16.82%
16.32%
1.031
Constraint 3
15.18%
15.73%
0.965
Constraint 4
22.85%
21.10%
1.082
Constraint 5
18.00%
17.06%
1.055
portfolios under IM. And a comparison is made to observe which model of portfolio selection offers a better option for making decisions to investors. Table 7. Weights in Min Variance portfolio under SPX
WFC
LUV
PGR
LSTR
CSCO
TD CN
FDX
MS FT
AD BE
SAP
C1
1.02
−0.02
−0.03
0.14
0.08
−0.09
0.18
−0.04
0.00
−0.12
−0.12
C2
0.00
0.06
0.02
0.27
0.18
−0.02
0.41
0.06
0.13
−0.05
−0.06
C3
0.52
0.00
0.00
0.17
0.09
0.00
0.22
0.00
0.00
0.00
0.00
C4
1.00
−0.01
−0.03
0.15
0.08
−0.09
0.19
−0.04
0.00
−0.12
−0.12
C5
1.02
−0.02
−0.03
0.14
0.08
−0.09
0.18
−0.04
0.00
−0.12
−0.12
The weights of objects are quite similar in MM and IM. Under Constraint 1, Constraint 4 and Constraint 5, SPX is given the highest weight, which is 0.02 higher than the one for the Markowitz model. Under Constraint 2, TD CN is given the highest weight for minimum risk portfolio in both models. Table 8. Performance of Min Variance portfolio under IM. Standard Deviation
Sharpe ratio
Constraint 1
Return 7.52%
12.88%
0.584
Constraint 2
13.10%
14.47%
0.906
Constraint 3
10.53%
13.98%
0.753
Constraint 4
7.52%
12.88%
0.584
Constraint 5
7.52%
12.88%
0.584
Like the Markowitz model, Constraint 4 and Constraint 5 have no effects on the minimum variance under the Index model, predicting the same risk, return and Sharpe
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ratio as free constraint. Under the remaining two constraints, the performance of two models shows nearly identical. The differences of the Sharpe ratio for both models are smaller than 0.01,which is not a significant number. Table 9. Weights in Max Sharpe portfolio under IM. SPX
WFC
LUV
PGR
LS TR
CS CO
TD CN
FDX
MS FT
ADBE
SAP
C1
−1.88
0.04
0.04
0.67
0.58
−0.01
0.58
0.23
0.38
0.35
0.03
C2
0.00
−0.06
−0.05
0.39
0.34
−0.11
0.24
0.06
0.14
0.14
−0.08
C3
0.00
0.00
0.00
0.35
0.31
0.00
0.16
0.01
0.08
0.09
0.00
C4
−1.00
0.00
0.00
0.53
0.46
−0.05
0.43
0.15
0.27
0.24
−0.02
C5
−0.39
−0.01
−0.03
0.60
0.29
−0.05
0.35
−0.01
0.00
0.26
0.00
Table 10. Performance of Max Sharpe portfolio under IM. Return
Standard Deviation
Sharpe ratio
Constraint 1
28.32%
24.99%
1.133
Constraint 2
16.69%
16.33%
1.022
Constraint 3
15.47%
15.79%
0.980
Constraint 4
22.62%
20.22%
1.118
Constraint 5
19.16%
18.80%
1.019
In terms of the most efficient portfolio, the simulation of Index model shows quite similar performance compared to the Markowitz model. Constraint 1 has the highest return and Sharpe ratio, 28.32% and 1.133 separately. Under Constraint 4, Index model achieves the most efficient portfolio by decreasing the weight on SPX to – 1, totally identical to Markowitz model. Consequently, the Sharpe ratio of MM is 1.118 and the value for IM is 1.082.
5 Result Analysis Generally, the predictions made by the Markowitz and Index models regarding weight distributions and their optimizing subjects are quite similar. When minimizing the risks (standard deviation) of portfolios, Markowitz model presents a comparative advantage to Index model in all constraints except the “No Short Positions” (C3). The maximum Sharpe ratio is 0.062 higher, which is not considered as a very significant number. In terms of the maximum efficiency portfolio, Index model is more suitable for the portfolio simulation of maximum efficiency under Constraint 1, Constraint 3 and Constraint 4, with approximately 0.05 higher in Sharpe ratio. Instead, the
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Markowitz model performs better in the construction of portfolios with maximum Sharpe ratios without the index asset (C2) and when investors are forbidden from excessively leveraging their assets by more than twice the initial equity (C5). When comparing all the constraints, different constraints do exert different effects on the predictions. In Min-Variance portfolios, Constraint 4 and 5 perform the same as the free constraint, since there is not any overly radical leverage. They are therefore not constrained by this change in constraint and are unaffected by it. The Sharpe ratio under C2 and C3 predicts a higher than the free one, imposing no-short-sale constraints on the portfolio weights and removing index asset could largely improve the performance of portfolios. This simple remedy for dealing with estimation errors performs quite well. While in the Max-Sharpe ratio portfolios, the free constraint (C1) predicts the most efficient portfolios in both models, with a Sharpe ratio of 1.083 and 1.133 each. Constraint 3 suggests the lowest Sharpe ratio among all the settings, illustrating that outputs of the models prohibiting short positions are less efficient than under other constraints.
6 Conclusion Nowadays, portfolio optimization has become an increasingly important area in finance. The optimized portfolios have always been chosen using the Markowitz Model and Index Model, which have been extensively compared. This paper compared Markowitz model and Index model for the constrained portfolio optimization problem, where five additional constraints in real investment are set. Several data analysis using Solver is done to obtain optimal solution and a thorough comparison of the effects of two models are presented. The results show that the Markowitz model and Index model show an extremely similar result concerning portfolio optimization, with similar data of weights of stocks, returns, standard deviations and Sharpe Ratios, no matter under which constraint. More accurately, the Markowitz model has a little comparative advantage, with a lower standard deviation and a higher Sharpe ratio in more situations, though the discrepancy is not very significant. However, along with the advantage are several problems in the application. For the Markowitz model, numerous estimates of expected returns, variances and covariances are needed, thus the complexity of calculation would increase a lot as the stock size goes up. Compared with it, the Index model largely simplifies the estimation of covariance matrix problem. Admittedly, there are still several limitations in the research. The volatility of the stock market has been increasing since Covid-19. It cannot be guaranteed that the analysis of the history could predict future investment accurately. Meanwhile, only a basic investment portfolio of two models is constructed according to the historical data, and the number of stocks, which is only 10, seems not quite sufficient to represent the entire industry. Thus, more financial data and reality factors should be taken into consideration in future research. In a word, the paper serves as a motivation for more insightful research on portfolio optimization problem. In the future, it will be meaningful to analyze the performance on real instances of a larger problem size or consider other constraints such as transaction cost.
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References 1. Markowitz, H.: Portfolio Selection. Journal of Finance 77–80 (1952) 2. Sharpe, W.F.: A Simplified model for Portfolio Analysis. Manage. Sci. 9(2), 277–293 (1963) 3. Black, F., Jensen, M.C., Scholes, M.: The capital asset pricing model: Some empirical tests (1972) 4. Mossin, J.: Equilibrium in a capital asset market. Journal of the econometric society, 768–783 (1996) 5. Ross, S.: The Arbitrage Theory of Capital Asset Pricing. Journal Of Economic Theory 13(3), 341–360 (1976) 6. Elton, E.J., Gruber, M.J., Padberg, M.W.: Simple criteria for optimal portfolio selection The. Journal of Finance 31(5), 1341–1357 (1976) 7. Lloyd, W.P., Shick, R.A.: A test of Stone’s two-index model of returns. J. Fina. Quantit. Anal. 12(3), 363–376 (1977) 8. Michaud, R.O.: The Markowitz optimization enigma: Is‘optimized’optimal? Financ. Anal. J. 45(1), 31–42 (1989) 9. Konno, H., Yamazaki, H.: Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Manage. Sci. 37(5), 519–531 (1991) 10. Jorion, P.: Portfolio optimization in practice. Financ. Anal. J. 48(1), 68–74 (1992) 11. Krokhmal, P., Palmquist, J., Uryasev, S.: Portfolio optimization with conditional value-at-risk objective and constraints. J. Risk 4, 43–68 (2002) 12. Sharpe, W.F.: Capital asset prices: A theory of market equilibrium under conditions of risk. J. Financ. 19(3), 425–442 (1964) 13. Varian, H.: A portfolio of Nobel laureates: Markowitz, Miller and Sharpe. J. Econ. Perspect. 7(1), 159–169 (1993)
Tesla Stock Price Timeseries Analysis and Forecasting Chen Yang(B) Chengdu University of Technology, Chengdu, Sichuan, China [email protected]
Abstract. This paper studies the prediction of Tesla’s stock price by establishing a stock price prediction model. Due to the importance of stock investment in the trading market, it is indispensable to predict the future trend of the stock market, avoid losses and realize the maximum profit for investors. First of all, this article selects the historical closing price of Tesla stock as the sample data, and uses SPSS statistical software package as the application tool. In order to analyze and make projections about the stock prices, the ARIMA model was used. According to the fitting trend, this model has a good prediction, but it is not suitable for long-term judgment because of many fluctuation factors. But in a short period of time, there is rarely a huge disparity between the value that was projected for the stock price and the value that really occurred. This indicates that the ARIMA model has a good short-term prediction effect on Tesla stock price, which has great reference value, both of which can provide assistance for stock investors. In addition, in the short-term forecast, Tesla’s stock price is stable and has an upward trend. In general, Tesla stock is a short-term investment. Keywords: Tesla · ARIMA Model · Stock Price Forecast
1 Introduction After the 21st century, stock investment has become an indispensable part of national finance. For investors, it is of great significance to predict the future development trend of stocks for their investment and financial planning. With the prosperity of the stock market, investors continue to seek a variety of ways to determine the optimal portfolio to maximize their own benefits. In recent years, several social platforms have emerged in China and abroad to predict stock prices. Internet users are free to publish their own experiences in predicting the trend of stock prices. Scholars are also actively studying more accurate and effective stock price prediction algorithms. Romanuke Vadim (2022) used arima model to study, and found that the model based on stationary series can get better prediction results [1]. Goyal Megha constructed arima model for the export quantity of Indian agricultural products, and found that the deviation between the estimated value and the actual value of arima model was the smallest without interference from other external factors [2]. Ansari Saleh Ahmar and Liu Song found that the prediction effect only has reference function in the short term, and the long-term prediction will © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 655–663, 2023. https://doi.org/10.1007/978-981-99-6441-3_60
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be influenced by macro, micro and investor psychology [3, 4]. This paper will mainly introduce the ARIMA model, model the five-year historical data of Tesla, and establish a stock price prediction model to predict the stock price trend of Tesla in the next 88 days. Then the prediction effect of ARIMA model is evaluated, and some reasonable investment suggestions are provided for investors through the analysis results.
2 Data Preprocessing
Fig. 1. Original sequence timing diagram.
The trend chart of Tesla stock closing price data is drawn in Fig. 1 above. It can be seen that the sequence has an obvious time trend and does not conform to the characteristics of a stable sequence. Since this is a change in a certain time period of a day, the sequence diagram shows that there is no seasonal change, so it is not necessary to carry out seasonal decomposition. Then, the sequence is processed by first-order difference and its sequence diagram is drawn. It can be found that the sequence fluctuates around the X-axis after first-order difference, which is consistent with the general characteristics of stationary. Therefore, we preliminarily determine that the first-order difference sequence is a stationary time series (Fig. 2).
3 Arima Model Introduction and Modeling Process 3.1 ARMA Model ARMA model is the first random time series analysis model established by Box and Jenkins in the 1970s. It is a group of random variables with some regular changes in time, which can be described by econometric models. The principle of ARMA model is
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Fig. 2. First Order Difference Timing Diagram.
to use the weighted modeling of past value, current value and lagged random disturbance term to explain and predict the change rule of time series in prediction. At the same time, it should be noted that the time series must be stable when establishing ARMA model for time series prediction and analysis. Assuming that yt is a stationary time series, we discuss the classification of yt time series: when yt is a linear function of its pre-value and random term, it is called p-order autoregressive model, denoted by AR (p). The expression is: Yt = β0 + β1 Yt−1 + β2 Yt−2 + . . . + βp Yt−p + εt
(1)
When yt is a linear function of the error and random term of the current and previous values, it is called MA (q). The expression is as follows: Yt = εt + α1 εt−1 + α2 εt−2 + . . . + αq εt−q
(2)
When yt is the error and random term of the current value and the pre-value, and the linear function of the pre-value, it is denoted by ARMA (p, q). Expression: Yt = β0 + β1 Yt−1 + β2 Yt−2 + . . . + βp Yt−p + εt + α1 εt−1 + α2 εt−2 + . . . + αq εt−q (3) In terms of popular points, ARMA (p, q) is a “mixed” model, which combines AR model with MA model to minimize the number of parameters used.
4 ARMA Model Identification and Estimation First of all, the two prerequisites for establishing ARMA model are to satisfy the stationary and white noise requirements for this time series. Here, ADF test is used to study the stability of the sequence, and the autocorrelation diagram of the residual sequence
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is observed to determine whether it is white noise. Secondly, for model selection and order determination, the properties of ACF and PACF coefficients and the comparison of BIC values are used to determine. ADF Test. ADF test is also called unit root test. If there is unit root in the detected sequence, the sequence is not stationary. Because there is no special ADF test in SPSS, we use eviews to replace the ADF test. The results in Table 1 are the first-order difference of the original sequence.
Table 1. ADF test results. t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
−37.99516
0.0000
Test critical values:
1% level
−2.566665
5% level
−1.941057
As can be seen from the table results, at 1% and 5% aboriginality levels, ADF test values are less than the critical value, p-value = 0.0000 indicates that the sequence is stable. Therefore, the first-order difference sequence obtained by processing the original sequence is stable, which confirms our preliminary judgment. White Noise Test. The random error term in the model must be white noise, otherwise the model does not have economic significance. The expression is: H0: ρ1 = ρ2 =… ρm = 0 H1: there is at least one ρk = 0. The original assumption is that the autocorrelation function is zero and the lag m-order sequence values are independent of each other. Optional hypothesis: there is at least one autocorrelation function is not zero, there is correlation between the sequences of lag m period. When p > = α, the original assumption that the sequence is white noise is accepted conversely, reject the original assumption, the sequence is non-white noise. Methods Based on the Properties of Autocorrelation Function and Partial Autocorrelation Function. Both ACF function image and PACF function image require trailing to establish ARMA (p, q) model, and we will choose p and q parameters according to ACF and PACF Image Properties.
4.1 Introduction of Modeling Process When choosing ARMA models, Box and Jenkins advocated that parsimony principle should be followed and proposed the systematic methodology of modeling process, which is called Box-Jenkins methodology. The method includes four stages: Firstly, ADF test is used to determine whether the observation sequence is a stationary sequence. If not, it will be transformed into stationary sequence by difference processing. Secondly, according to the properties of ACF coefficient and PACF coefficient and BIC information criterion, the optimal model is determined and analyzed. Third, estimate the unknown
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parameters of the model; fourthly, whether the model meets the assumptions and whether the residual sequence is white noise are tested. If so, the established model is the best model without changing the values of p and q parameters. Fifth, using the model to predict.
5 Empirical Analysis of Historical Closing Price of Tesla 5.1 Data Sources The data in this paper are from the daily closing stock price data of Tesla in Investing from January 3, 2017 to May 27, 2022, with a total of 1361 sample data. Next, based on the theory of ARMA model, using SPSS to analyze and predict the future stock price trend of Tesla. 5.2 Empirical Analysis Determine the Optimal ARIMA Model. In the above, according to the sequence diagram of the first-order difference sequence and the ADF test, we know that the sequence obtained by the first-order difference treatment of the original sequence is stable, and according to the corresponding theory, we know that d = 1 when only one difference is carried out, and then the values of p and q are determined through the ACF and PACF function images (Fig. 3).
Fig. 3. ACF function image.
From the ACF image of the first-order difference sequence in the above graph, it is shown that the whole is trailing and there is no confidence interval beyond the upper and lower limits, so we temporarily set the p value to 0. Similarly, the PACF image of the first-order difference sequence in the above Fig. 4 is also tailed, and there is no confidence interval beyond the upper and lower limits at 1, so we temporarily set the q value as 0.
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Fig. 4. PACF function image.
Because the values of p and q can not be determined clearly in ACF and PACF images, that is to say, the optimal ARIMA model can not be determined. Therefore, we will determine the best model through the BIC information criterion, which takes the minimum BIC as the standard. That is to say, the model is the best model when the BIC value is the smallest under the condition of ensuring that the parameters are obvious (Table 2). Table 2. Summary of BIC statistics. Model
BIC
ARIMA (0,1,0)
5.809
ARIMA (4,1,4)
5.807
ARIMA (5,1,5)
5.835
ARIMA (6,1,6)
5.819
The best model is ARIMA (4,1,4) according to BIC minimum criterion. Model Estimation. Next, the model is estimated, and the following Table 3 is the specific estimation results. In the estimation process, we found that the constant P value is 0.264, and it is not obvious. after eliminating, the model is re-estimated and tested. The results show that the coefficients of the model are still obvious and the BIC Statistics are smaller, so the constant term is better removed to fit the model. According to the estimation results, the final model expression is: Yt = −2.711Yt−1 − 3.398Yt−2 − 2.289Yt−3 − 0.692Yt−4 + εt − 2.703εt−1 − 3.361εt−2 − 2.202εt−3 − 0.614εt−4
(4)
Among them, εt is a residual sequence.
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Table 3. ARIMA (4, 1, 4) estimation results. Variable
Coefficient
Std.Error
T
P-value
AR(1)
−2.711
0.103
−26.316
0.000
AR(2)
−3.398
0.214
−15.880
0.000
AR(3)
−2.289
0.201
−11.413
0.000
AR(4)
−0.692
0.085
−8.131
0.000
MA(1)
−2.703
0.114
−23.756
0.000
MA(2)
−3.361
0.244
−13.797
0.000
MA(3)
−2.202
0.236
−9.319
0.000
MA(4)
−0.614
0.102
−5.998
0.000
6 Model Test White Noise Test. Next, a white noise test is conducted on the residual series after parameter estimation. If the residual series are non-white noise series, the established ARIMA model still needs to be improved. Here, the P-value of ljung-box test is used to determine whether the sequence is white noise. The judgment standard takes 0.05 as the boundary. Table 4. Model Statistics. Model goodness of fit statistics
Ljung-Box Q (18)
Model
R2
MAPE
MAE
DF
P-value
ARIMA (4,1,4)
0.997
2.701
8.736
10
0.148
It can be seen from the above Table 4 that the model R2 is 99.7%, indicating that the fitting effect is good, and the p-value of Ljung-Box test is 0.148, indicating that the sequence is in line with white noise. At the same time, it also indicates that the model is optimal without adjusting p and q parameters. Test of Residual ACF and PACF Diagrams. Next, the ACF diagram and PACF diagram of the residual series are tested. The purpose of the test is to test whether there is correlation in the residual series. It can be seen from the figure that the correlation is low, because most of the residuals are within the two confidence intervals, indicating that there is basically no correlation. In other words, this part of the information that the past data can affect the current data has been eliminated by the model (Fig. 5). In summary, ARIMA (4,1,4) model meets the requirements and can be used to predict the future stock price of Tesla.
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Fig. 5. ACF and PACF Function Diagrams for Residuals.
6.1 Model Forecast ARIMA (4, 1, 4) model is used to predict the stock price of 88 days after May 27, 2022 in Tesla. The results are as follows:
Fig. 6. Tesla stock price forecast.
The actual value in Fig. 6 basically coincides with the fitting value estimated by the model, which indicates that the model has a good fitting effect. By comparing the predicted results with the actual stock prices, the fitting trend diagram can be seen that the predicted results are still good. Therefore, this model can obtain a good result for the
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prediction of stock price trends in the short term, but in the long run, there are too many factors affecting the fluctuation of stock prices, such as macro policy regulation or the influence of major social events. At this time, ARIMA model cannot accurately predict the stock price.
7 Conclusion In this paper, a total of 1361 groups of daily closing price data of Tesla from January 3, 2017 to May 27, 2022 are processed, and the ARIMA model is constructed based on the sequence of the first-order difference of the group of data. At the same time, the stock price of Tesla in the next 88 days is predicted. The results show that there is little difference between the actual value and the fitting value during the sample period. The future prediction shows that the stock price of Tesla will be in a stable state, and it is feasible to predict and analyze the stock price of Tesla by using the ARIMA (4,1,4) model. Therefore, it is concluded that ARIMA model has great reference value for short-term stock price prediction, but in the long term, the prediction results have great deviation. When investors make investment decisions, they can use ARIMA model to predict short-term stock prices and formulate short-term investment plans. In the long run, more accurate stock price prediction models need to be continuously explored.
References 1. Vadim, R.: Arima Model Optimal Selection for Time Series Forecasting. Maritime Technical Journal 224(1) (2022) 2. Megha, G., Goyal, S.K., Subodh, A., Nitin, K.: Forecasting of Indian Agricultural Export Using ARIMA Model. J. Comm. Mobiliz. Sustai. Develop. 16(3) (2022) 3. Ahmar, A.S., Singh, P.K., Van Thanh, N., Viet Tinh, N., Vo Minh, H.: Prediction of BRIC Stock Price Using ARIMA, Sutte ARIMA, and Holt-Winters. Computers, Materials & Continua 70(1) (2022) 4. Song, L., Shuai, Z.: Empirical study on stock price forecasting using ARIMA model. Econ. Res. J. 25, 76–78 (2021)
The Application of CAPM and Fama-French Three-Factor Model to the Investment Choice in Individual Stocks in China’s A-share Market and the Explanatory Power of Returns Wang Henan(B) Southwestern University of Finance and Economics, No. 555 Liutai Avenue, Wenjiang District, Chengdu 611100, China [email protected]
Abstract. Based on the capital asset pricing model (CAPM) and the Fama-French three-factor model, this paper analyzes the monthly rate of return (RoR) of all 50 stocks of the FTSE China A50 index from January 2002 to January 2022 and the corresponding three-factor index, and gets corresponding α values of these 50 stocks. The series analysis which consist of comparing the regression results of the CAPM and the Fama-French three-factor model and sortting the α values of all 50-value stocks under the two models concludes that stocks from Midea Group, Haitian Flavor Industry, Xinhua Insurance, China Communications Construction have high investment value. Then, based on the differences between the two models, this paper studies the explanatory power of model factors on the RoR of individual stocks, and concludes that the three-factor model can explain some stocks’ RoR better. Keywords: China’s stock markets · FTSE China A50 index · CAPM · Fama-French Three-Factor Model
1 Introduction Capital Asset Pricing Model (CAPM) is the cornerstone of capital asset pricing theory. After Harry Markowitz proposed portfolio selection theory, Sharpe introduced the concept of market portfolio on this basis, and assumed that asset returns were linearly related to market portfolio. Therefore, the risk-return characteristics of any risk asset can be measured by the mean, standard deviation and sensitivity to market portfolio. The core idea of the CAPM is that the expected RoR (RoR) of a single asset or portfolio is positively correlated with its systemic risk. However, in 1992, Eugene Fama and Kenneth French found that the β value in the CAPM could not explain the difference in the RoR between different stocks. They found that the two factors of the market capitalization (SMB) and book-to-market (HML) factors can explain most of the stock price changes, and these two factors can replace © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 664–678, 2023. https://doi.org/10.1007/978-981-99-6441-3_61
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some other risk factors. Therefore, they constructed three factors to explain the changes in stock returns by simulating market risk, SMB risk and HML risk. Through the introduction of relevant literature at home and abroad, we find that the empirical research of CAPM and Fama-French three-factor model has made great achievements, and is widely used in the research of stock market returns and risks. Different scholars have also used a large number of US stock market data in different periods to construct and estimate the model, and then empirically test the above two models. In order to test the applicability of CAPM and Fama-French three-factor model in China ‘s A-share market, this paper analyses the data of 50 stocks of A50 index from January 2002 to January 2022 and the corresponding stock market factor index. Then based on α valuation in selecting investment stocks, this paper compares and analyzes the explanatory power of the factors of the two models for the return of individual stocks.
2 Literature Review The theoretical system of capital asset pricing has a long history. William Sharpe, John Lintner and Jan Mossin further developed it into CAPM on the basis of Harry Markowitz ‘s modern portfolio management theory [1–3]. As one of the three cornerstones of modern financial theory, CAPM gives an accurate prediction method for determining the relationship between asset risk and its expected RoR, which is favored by scholars at home and abroad. Black et al.conducted the most famous time series test of the CAPM [4], later known as the BJS method. The final test results show that the time series method basically conforms to the CAPM. Fama and MacBeth proposed the most famous crosssectional test of the CAPM [5], called the FM method, and found that there is a positive correlation between the β value and the expected return of the portfolio. Therefore, CAPM divides the risks faced by the company into systemic risks and non-systematic risks, and believes that on the basis of general equilibrium, the return on assets depends only on systemic risks. Due to the strict assumptions of the CAPM and the theory that only the RoR is attributed to the systemic risk of a single market, it is challenged from various aspects. On this basis, Fama and French proposed the Fama-French three-factor model through the study of the US stock market [6], and it is widely used in practice. The Fama-French three-factor model improves the CAPM by retaining the market portfolio factor and introducing the market capitalization (SMB) and book-to-market (HML) factors factors. Fama and French found that the β value of the stock market can not fully explain the difference in the RoR in the study of the factors affecting the difference in the RoR of different stocks in the United States market, while the SMB and HML of listed companies can further explain the difference in the RoR of stocks. In the stock markets of the United States and other foreign countries, the CAPM and the Fama-French three-factor model are widely used and deeply loved by scholars. For China‘s stock market, because the development of CAPM is more mature, Chinese scholars prefer to use CAPM to analyze China‘s stock market. This paper uses the above two models to study some individual stocks in China‘s A-share market and provides empirical evidence for China‘s asset pricing model.
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3 Model 3.1 CAPM Model Assumption. In the CAPM, we assume that there are a large number of investors in the market, and the wealth of each investor is negligible relative to the total wealth of all investors. Then, all investors are rational investors under the mean-variance framework, so this means that investors pursue portfolio returns maximization and variance minimization. In addition, we must meet the homogeneous expectations hypothesis, that is, investors of various assets expected returns, variance and covariance of the same expectations. In terms of trading objects, investors’ trading objects are limited to assets in the open financial market, and non-tradable assets, such as human capital, are not considered in this model. Investors can borrow or lend any amount of assets at a fixed risk-free interest rate. There are no securities transaction fees and taxes. The Formula and Meaning of CAPM
E(ri ) = rf + βi E(rM ) − rf
(1)
where E(ri ) is the expected RoR for each risk asset. rf . is risk-free RoR. E(rM ) is the expected RoR for the market risk portfolio. Where E(rM ) − rf represents the risk premium of the market risk portfolio. βi reflects a coefficient of the size of the nondiversifiable risk. Thus, the risk premium of any risk asset (portfolio) is proportional to the risk premium of the market risk asset portfolio and the βi value of the risk asset (portfolio). 3.2 Fama-French Three-Factor Model Model Assumption. In the Fama-French three-factor model, we assume that there are a large number of investors in the mark, and all investors plan their portfolios in the same securities holding period. For trading objects, the scope of investors’ investment is limited to assets traded in the open financial market. In addition, there is no securities transaction costs (commissions and service fees and taxes). Investors have the same expectations for the mean, variance and covariance of the return on securities. Moreover, all investors have the same views on the evaluation of securities and the economic situation. Formula and Meaning of Fama-French Three-Factor Model E(Rit ) − Rft = βi E Rmt − Rft + si E(SMBt ) + hi E(HMIt )
(2)
the market yield of time where Rft represents the risk-free RoR for timet. Rmt represents t. Rit denotes the yield of asset i at time t. E Rmt − Rft is the market risk premium, the SMBt simulated portfolio yield of the SMB (Size) factor for time t, and the HMIt simulated portfolio yield of the HML for time t. Where βi 、si 、hi are the coefficients of the three factors, and their regression models are as follows: (3) Rit − Rft = ai + βi Rmt − Rft + si SMBt + hi HMIt + εit
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4 Empirical Result 4.1 Data Selection The data selected in this paper are the monthly returns of all 50 stocks in the FTSE China A50 Index. For the convenience of observing the data, the above 50 stocks are numbered 1–50 in this order, as shown in Table 1. The data source is the CSMAR database. The time interval of the constituency is from January 2002 to January 2022. The risk-free interest rate is selected from the central bank ‘s announcement of the March benchmark interest rate from 2002 to 2022. The three-factor model index was selected from January 2002 to January 2022. The monthly data of the comprehensive A-share market (excluding the SSE STAR MARKET and the GEM). The above data and the mean and standard deviation of the RoR of individual stocks are shown in Table 1. Table 1. Descriptive statistics of selected stocks Stock name
Stock code
Number
Mean rate of return(%)
Standard deviation for returns
Ping An Bank
000001
1
16.6126
1.3610
Vanke A
000002
2
17.4812
1.3665
Midea Group
000333
3
19.8799
1.6308
Gree Electric Appliances
000651
4
17.4812
1.3665
Wuliangye
000858
5
17.2391
1.3651
Merchants Shekou
001979
6
27.8018
1.5321
Yanghe Brewery
002304
7
15.9597
1.5049
S.F. Holding
002352
8
16.0072
1.5200
Hikvision
002415
9
18.3483
1.5344
Wen’s Foodstuffs Group
300498
10
24.9664
1.5406
Mindray Bio-Medical Electronics
300760
11
−14.1041
1.2277
Pudong Development Bank
600000
12
16.1746
1.3598
Minsheng Bank
600016
13
16.2125
1.3569
Shanghai International 600018 Port
14
16.1746
1.3598
Baoshan Iron & Steel
15
16.8846
1.3633
600019
(continued)
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Stock name
Stock code
Number
Mean rate of return(%)
Standard deviation for returns
China Petroleum & Chemical
600028
16
16.2125
1.3569
CITIC Securities
600030
17
18.8186
1.3698
China Merchants Bank 600036
18
16.8846
1.3633
PolyDevelopments and Holdings Group
600048
19
17.2753
1.4298
China Unicom
600050
20
18.7451
1.3728
SAIC Motor
600104
21
16.1746
1.3598
Hengrui Pharmaceuticals
600276
22
16.2125
1.3569
Kweichow Moutai
600519
23
16.1746
1.3598
Conch Cement
600585
24
16.1746
1.3598
Yili Industrial Group
600887
25
16.2125
1.3569
Yangtze Power
600900
26
16.9780
1.4060
China Shenhua Energy 601088
27
17.0184
1.4907
Foxconn Industrial Internet
601138
28
−12.9071
1.1882
Industrial Bank
601166
29
16.6936
1.4508
Bank of Beijing
601169
30
17.1407
1.4781
China Railway Construction
601186
31
16.8732
1.5004
Guotai Junan Securities
601211
32
18.9890
1.5499
Agricultural Bank of China
601288
33
16.9829
1.5317
Ping An Insurance
601318
34
17.0212
1.4622
Bank of Communications
601328
35
17.0212
1.4622
New China Life Insurance
601336
36
15.9960
1.5292
360 Security
601360
37
−12.0452
1.1470
China Railway Group
601390
38
16.4509
1.4968
Industrial and Commercial Bank
601398
39
16.2695
1.4355 (continued)
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Table 1. (continued) Stock name
Stock code
Number
Mean rate of return(%)
Standard deviation for returns
China Pacific Insurance
601601
40
17.0184
1.4907
China Life Insurance
601628
41
16.3793
1.4473
China State Construction Engineering
601668
42
16.1559
1.4888
Crrc Corp
601766
43
18.9194
1.5118
China Communications Construction
601800
44
16.8267
1.5387
China Everbright Bank 601818
45
16.0447
1.5335
PetroChina
601857
46
16.9036
1.4864
China Construction Bank
601939
47
17.1407
1.4781
Bank of China
601988
48
17.2753
1.4298
China CITIC Bank
601998
49
16.6008
1.4591
Haitian Flavouring and 603288 Food
50
24.8147
1.6450
Note: Since the stock return data downloaded in this article are monthly data, the α, average RoR, and standard deviation of the RoR that appear in this article are annualized data (monthly data mean or RoR multiplied by 12). And for the name of the stock, the full name of some stock companies is relatively long, which is abbreviated in this paper
4.2 Analysis of Regression Results CAPM. First of all, this paper filters the RoR data of individual stocks, deletes the monthly data corresponding to the month of suspension, and saves the effective value. Then, taking 30 months as a cycle, the RoR of individual stocks and the market risk premium are rolling regression. Then, for each stock, the regression of α and β obtained from each cycle is regressed, and the mean value of α is obtained as shown in Fig. 1.
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Fig. 1. The α value estimated by the selected stocks based on the CAPM.
After obtaining the mean value of α and β, the t value is calculated for T test, and the significance of α and β is obtained. The number of each stock, the α value and its t value are shown in Table 2. Table 2. α and corresponding t values estimated based on CAPM Number
alpha(t)
Number
alpha(t)
1
7.6394***
26
17.6187*** (7.9060)
(10.2925) 2
11.4552*** (11.2965)
27
17.6534*** (6.8557)
3
25.1795*** (5.3594)
28
−25.4467*** (−7.7106)
4
11.1694*** (10.9958)
29
18.1399*** (7.4962)
5
14.4298*** (11.4237)
30
17.2692*** (6.7436)
6
16.2501** (2.3314)
31
20.4195*** (7.2716)
7
14.6346*** (5.0000)
32
21.4877*** (3.1380)
8
16.0814*** (5.2125)
33
16.7936*** (4.7876) (continued)
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Table 2. (continued) Number
alpha(t)
Number
alpha(t)
9
14.8714***
34
17.1332*** (7.0861)
10
13.5997** (2.1276)
35
17.2038*** (7.0400)
11
−20.9539*** (−5.9737)
36
22.5313*** (5.9185)
12
14.0100*** (11.5738)
37
−20.1582*** (−9.1253)
13
11.2001*** (11.6757)
38
17.3344*** (6.4878)
14
10.0792*** (10.9755)
39
17.5010*** (7.1088)
15
11.2800*** (11.5293)
40
17.2666*** (6.5675)
16
11.2001*** (11.6757)
41
17.8989*** (7.3513)
17
16.4399*** (8.1641)
42
15.6722*** (5.6678)
18
12.6772*** (8.4440)
43
18.6144*** (6.5435)
19
18.5128*** (7.7496)
44
24.0525*** (6.3872)
20
17.0811*** (9.1381)
45
16.6919*** (4.7300)
21
9.8668*** (11.3894)
46
16.1188*** (6.2282)
22
11.2001*** (11.6757)
47
17.2692*** (6.7436)
23
13.8880*** (11.2473)
48
18.5128*** (7.7496)
24
12.9720*** (9.1865)
49
18.1698*** (6.9783)
25
11.2001*** (11.6757)
50
30.6376*** (5.6883)
(4.7485)
Note: * * * represents significant at 1% level, * * represents significant at 5% level, * represents significant at 10% level. Therefore, we can find that for all 50 stocks in the FTSE China A50 index, their α means are significant.
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Fama-French Three-Factor Model First of all, this paper filters the RoR data of individual stocks, deletes the monthly data corresponding to the month of suspension, and saves the effective value. Then, taking 30 months as a cycle, the RoR of individual stocks and market risk premium factor, SMB, HML are rolling regression. Then, for each stock, the regression of α, β, s and h obtained from each cycle is regressed, and the mean value of α is obtained as shown in Fig. 2. After obtaining the mean of the four coefficients, their t values are calculated for T test, and then the significance of the four factors is obtained. The number, α value and its t value of each stock are shown in Table 3.
Fig. 2. α value estimated by the selected stocks based on the Fama-French three-factor model.
Comparing α in CAPM and Fama-French Three-Factor Model. According to the Alpha strategy, for individual stocks, whether in the CAPM or the Fama-French threefactor model, α represents the excess RoR of individual stocks, that is, if the α value of individual stocks is larger, the stock has more investment value. Therefore, this paper sorts the α values of all 50-value stocks under the CAPM and the Fama-French three-factor model as shown in Fig. 3 and Fig. 4. It can be found from the figure that the third (Midea Group), the 50th (Haitian Flavor Industry), the 36th (Xinhua Insurance), and the 44th (China Communications Construction) have higher α values in both models, so these stocks have higher investment values. The Explanatory Power of CAPM and Fama-French Three-Factor Model on Stock Returns. The CAPM and the Fama-French three-factor model can explain the RoR of individual stocks through factors. For example, the CAPM explains the non-diversified risk through the market risk premium factor, that is, the impact of system risk on the RoR of individual stocks. On this basis, the Fama-French three-factor model explains the RoR of individual stocks by adding SMB and HML.
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Table 3. α and corresponding t values estimated based on the Fama-French three-factor model Number
alpha(t)
Number
alpha(t)
1
6.3431***
26
13.0052*** (5.3790)
(8.0816) 2
12.7477*** (9.1373)
27
18.4890*** (6.5760)
3
29.6697*** (6.4341)
28
−32.9457*** (−9.4999)
4
12.4334*** (8.9985)
29
25.4035*** (10.0432)
5
13.6457*** (10.7004)
30
26.8972*** (7.2479)
6
16.7426** (2.1740)
31
16.0846*** (5.7006)
7
11.4071*** (4.0237)
32
16.9159** (2.3745)
8
19.4973*** (4.9630)
33
8.3759*** (2.7442)
9
19.5359*** (5.4882)
34
24.5898*** (8.9697)
10
22.6305*** (3.1034)
35
26.2176*** (10.1771)
11
−19.9503*** (−5.5906)
36
25.5411*** (6.9811)
12
13.1755*** (10.4452)
37
−18.1205*** (−5.1729)
13
13.6207*** (10.2256)
38
22.0622*** (6.7427)
14
8.4508*** (8.7871)
39
19.4235*** (6.0354)
15
13.8927*** (10.4616)
40
22.1202*** (6.9814)
16
13.6207*** (10.2256)
41
16.6563*** (5.2012)
17
16.5056*** (7.6158)
42
16.3604*** (4.2777)
18
15.2950*** (8.6741)
43
14.1980*** (5.0361) (continued)
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W. Henan Table 3. (continued)
Number
alpha(t)
Number
alpha(t)
19
10.2735***
44
20.0026*** (4.7395)
20
14.9490*** (7.1572)
45
6.7435** (2.5086)
21
9.8297*** (11.1375)
46
19.4573*** (7.0922)
22
13.6207*** (10.2256)
47
26.8972*** (7.2479)
23
13.1562*** (10.5321)
48
10.2735*** (3.6909)
24
12.6025*** (9.1805)
49
23.1953*** (7.3948)
25
13.6207*** (10.2256)
50
27.3827*** (5.0247)
(3.6909)
Note: * * * represents significant at 1% level, * * represents significant at 5% level, * represents significant at 10% level. From this we can see that for all 50 stocks in the FTSE China A50, their α values means are significant.
Fig. 3. Sorted α values of the stocks based on CAPM estimation.
Therefore, in order to study the explanatory power of the two models for the RoR of 50 stocks, this paper uses the MATLAB program to make a difference between the α values of each stock in each window in the process of rolling regression of the two models, and then the difference is obtained. The linear regression is performed, and the
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Fig. 4. Sorted α values of the stocks based on Fama-French three-factor model estimation.
mean value of the obtained α difference is tested by T test to test its significance. The mean value of α difference and its t value are as shown in Table 4, and the mean value of α difference is as shown in Fig. 5. Table 4. Estimated α differences and corresponding t values for CAPM and Fama-French threefactor model Number
Alpha difference(t)
Number
Alpha difference(t)
1
1.2963***
26
4.6135*** (5.3041)
(3.1112) 2
−1.2925* (−1.6841)
27
−0.8356 (−1.2618)
3
−4.4901*** (−4.6509)
28
7.4990*** (8.6109)
4
−1.2640* (−1.6557)
29
−7.2636*** (−7.6870)
5
0.7840** (2.5533)
30
−9.6280*** (−5.7911)
6
−0.4925 (−0.4742)
31
4.3349*** (4.9569)
7
3.2275*** (4.6484)
32
4.5718*** (4.8884) (continued)
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Number
Alpha difference(t)
Number
Alpha difference(t)
8
−3.4159***
33
8.4177*** (10.2988)
9
−4.6645*** (−4.0939)
34
−7.4565*** (−6.5485)
10
−9.0308*** (−7.9125)
35
−9.0138*** (−8.0310)
11
−1.0036** (−2.3385)
36
−3.0098*** (−4.4936)
12
0.8346** (2.5822)
37
−2.0377 (−1.3281)
13
−2.4206*** (−3.2589)
38
−4.7278*** (−3.7142)
14
1.6284*** (4.4020)
39
−1.9225 (−1.5732)
15
−2.6127*** (−4.0641)
40
−4.8536*** (−3.9187)
16
−2.4206*** (−3.2589)
41
1.2426 (1.1384)
17
−0.0657 (−0.1537)
42
−0.6881 (−0.4955)
18
−2.6178*** (−3.8961)
43
4.4164*** (4.4593)
19
8.2393*** (7.4572)
44
4.0500*** (4.0967)
20
2.1321*** (3.2370)
45
9.9484*** (7.0422)
21
0.0371 (0.1211)
46
−3.3385*** (−3.0884)
22
−2.4206*** (−3.2589)
47
−9.6280*** (−5.7911)
23
0.7318** (2.3292)
48
8.2393*** (7.4572)
24
0.3695 (1.1167)
49
−5.0254*** (−4.4578)
25
−2.4206*** (−3.2589)
50
3.2549*** (2.8277)
(−3.1622)
Note: * * * represents significant at 1% level, * * represents significant at 5% level, * represents significant at 10% level.
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It can be seen from this that if the mean value of the α difference of a stock is significantly not zero, then the two factors added by the Fama-French three-factor model on the basis of the CAPM have a certain explanatory effect on the α value of the stock. If the mean value of the α difference is negative, it shows that the SMB and the HML have a negative explanation for the RoR of the stock. If the mean value of the α difference is positive, it shows that the SMB and the HML have certain explanatory power for the return of individual stocks. If the mean value of the α difference of a stock is significantly zero, then the two models have almost the same explanatory power for the return of the stock.
Fig. 5. Estimated α difference between CAPM and Fama-French three-factor model.
5 Conclusion Using the data of all 50 stocks of A50 index from 2002 to 2022, CAPM and Fama-French three-factor model, this paper studies the application of the two models in the investment choice in individual stocks and their explanatory power to the RoR of individual stocks. Among them, we conduct a rolling regression of individual stock data. The main purpose is to get the average α value of each stock, and then t test to test its significance. According to the Alpha strategy, we can know that if in the model, the greater the α value of individual stocks, the higher its excess RoR, and the more investment value this stock has. For the 50 stocks of the A50 index, the empirical results are that Midea Group, Haitian Flavor Industry, Xinhua Insurance, China Communications Construction and other individual stocks have higher α values in both models, that is, these stocks have higher investment value. Furthermore, the difference in the α value of individual stocks in the two models leads to the study of the explanatory power of the two models on the RoR of individual stocks.This paper analyzes the explanatory power of the two factors added by the FamaFrench three-factor model on the basis of the CAPM by making a difference in the α
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value of each window when the two models are rolling regression, and making a linear regression of the difference. The empirical result is that for Wen ‘s shares, Bank of Beijing, Bank of Communications, Construction Bank and other stocks, the added SMB and HML have a negative explanatory effect on their RoR. For China Everbright Bank, Poly Development, Agricultural Bank, Bank of China and other stocks, the added SMB and HML have a certain explanatory power for their RoR, while for CITIC Securities, SAIC Group and other stocks, the two models have almost the same explanatory power for their RoR. With the accumulation and enrichment of practical experience in the stock market, the capital asset pricing theory system is also gradually improved, from the CAPM single factor model to the three-factor, multi-factor model. According to the empirical analysis of this paper, the CAPM and the Fama-French three-factor model ‘s explanatory power for individual stocks in the A50 index, it can be found that when selecting individual stocks for investment, the overall return on investment can be improved by selecting individual stocks with larger α values or increasing the proportion of stocks with larger α values in the portfolio, and the explanatory power of model factors for the return on individual stocks can be used as a reference to select portfolios with higher relative investment value.
References 1. Sharpe, W.F.: Capital asset prices: a theory of market equilibrium under conditions of risk. The Journal of Finance 19(3) (1964) 2. Lintner, J.: The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Stochastic optimization models in finance. Academic Press (1975) 3. Mossin, J.: Equilibrium in a capital asset market. Econometrica: Journal of the Econometric Society (1966) 4. Fischer, B., Myron, S.: The Pricing of Options and Corporate Liabilities. Journal of Political Economy 81(3) (1973) 5. Fama, E.F., MacBeth, J.D.: Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy 81(3) (1973) 6. Fama, E.F., French, K.R.: The cross-section of expected stock returns. The Journal of Finance 47(2) (1992)
Optimal Capital Structure of China’s Small and Medium Listed Companies: A Case of RC Ltd. Mianlun Zhang(B) Department of Statistics, University of Warwick, Coventry CV4 7AL, United Kingdom [email protected]
Abstract. Firms raise funds through equity financing and/or debt financing. The combination of the two constitutes the capital structure of the company, and the weighted average cost of the two is called the cost of capital. An optimal capital structure enables an enterprise to minimize its cost of capital and maximize its firm value. To closely examine this issue, this paper selects a Shanghai Stock Exchange-listed Chinese company, the RC Ltd., as the case company, and conduct a detailed analysis and discussion of its current capital structure and future leverage strategy. It is found that the RC Ltd., as well as its peer companies, has not sufficiently exploited the benefits of debt, which results in a higher cost of capital and a lower firm value than what can be, otherwise, achieved through raising its current leverage. It should also be noted, however, the RC Ltd. is in a period of rapid expansion accompanied by a relatively higher operational risk, together with China’s strict Covid policy and economic uncertainty, the company should take a conservative approach and gradually adjust to its optimal capital structure. Keywords: Capital Cost · Optimization of Capital Structure · Listed Enterprise · Case Study · Food Industry
1 Introduction There are two types of financing methods for enterprises: equity financing and debt financing. The combination of the two constitutes the capital structure of the company. An optimal capital structure enables an enterprise to minimize its cost of capital and maximize its firm value. Early capital structure theories start from Modigliani-Miller theorem to the trade-off theory, and to the pecking order theory, and to various extensions of these theories. Those studies were late introduced to China and were faced with many challenges due to the unique characteristics of China’s underdeveloped capital market and its different legal framework. Further, many Chinese enterprises do not have sufficient understanding and awareness of capital structure strategy, resulting in a widespread illusion that the cost of equity is cheap or even free, leaving a blind spot on the important strategic issue of capital structure and cost of capital. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 679–691, 2023. https://doi.org/10.1007/978-981-99-6441-3_62
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Hence, the strategic goal of building an optimal capital structure from the macro infrastructure of the market to the individual enterprise for Chinese enterprises is still a big challenge and has a long way to go. This study conducts a case-based analysis on this issue and aim to contribute and shed light on the search for optimal capital structure for Chinese companies.
2 Capital Structure Theory 2.1 Studies in the West Modigliani and Miller started the discussion of optimal capital structure in perfect capital markets. Late studies in the 1970s took into account the tax benefits of borrowing and costs of financial distress, and information asymmetry, and developed various extensions of capital structure theories, such as the trade-off theory and pecking order theory, which better explain the real-world problems of capital structure [1, 2]. In general, these studies agree that there exists an optimal capital structure for firms and firms should adjust to their optimal debt ratio to minimize their cost of capital and maximize the firm value (Fig. 1).
Fig. 1. Weighted Average Cost of Capital (WACC) [3].
The weighted average cost of capital of an enterprise is equal to: Equity Debt + RE × WACC = RD × (1 − TC ) × Debt + Equity Debt + Equity
(1)
where RE, the cost of equity, is derived from the Capital Asset Pricing Model (CAPM): ˜ = Rf + βim(E(Rm) ˜ − Rf ) E(Ri)
(2)
where, Ri = cost of equity, Rm = market return, Rf = risk-free rate In Practice, each of these theories encounters some degree of limitations. (1) Each tends to explain only partially the financing behavior of enterprises. (2) Most research is
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based on the US market. However, studies show that the legal environment determines, to some extent, the financing behavior of enterprises in different countries [4]. The analysis based on the capital structure of 10 developing countries argue that the role of different institutional environments in determining capital structure decisions still needs to be further understood [5]. 2.2 Studies in China Early studies in China are mainly based on western theories and examine the impact of factors such as size, profitability, and tax on the capital structure of Chinese enterprises. Li and Liu conduct a good literature review on this [6]. Late scholars start to introduce the influence of Chinese institutional factors and find that institutional factors have a strong influence on the financing of Chinese enterprises, such as the state-owned enterprises (SOEs) and industries supported by favorable industrial policies, tight monetary policy, and IPO restrictions [7–9]. At the same time, the regulatory authorities give priority to SOEs in terms of listing, which makes it more probable for SOEs to obtain financing [10]. Cheng Liubing, Ye Fan and Liu Feng compare differences in the capital structure under a free capital market system and a regulated capital market system. They argue that the capital structure of Chinese listed enterprises does not conform to the expectations of the trade-off theory and pecking order theory in free capital markets but is consistent with the characteristics of the capital structure in regulated markets. In China, Enterprises have limited freedom to choose their financing methods [11].
3 A Case Study on the RC Ltd. In this section, we conduct empirical analysis on the RC Ltd. And its closely related industry peer companies. All data from the analysis are obtained from the annual report of these listed companies. 3.1 Company Profile and Financial Status The RC Ltd. Was founded in 2001 and listed on the main board of Shanghai Stock Exchange in 2019. It was one of the earliest compound seasoning manufacturers in China. Compared to 165 listed food enterprises in China, the RC Ltd. Has a low liquidity risk, its liquidity measures (e.g., current ratio = 3.9; quick ratio = 3.6) are twice better than the industry average, and its debt to the equity ratio (11%) is only one-tenth of the food industry. Meanwhile, the RC Ltd. Has strong profitability, with a gross profit margin (41.3%) and net profit margin (23.3%) significantly higher than the industry average (18.6% and 5.2%, respectively). However, it should be noted that, prior to 2019, its gross profit and net profit growth rates were higher than the sales growth rates, while, after the pandemic, operating costs developed a rapid upward trend leading to a decline in profit margins which lagged behind the sales. In terms of capital and asset utilization efficiency, the RC Ltd.’s asset turnover ratio (0.44) is significantly lower than the industry average (0.93) due to market listing and the significant capital raised. ROA
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has fallen back significantly (from 28% to 10%), which calls for the improvement of asset utilization efficiency. Its operational efficiency measure, WCR to sales ratio is moving higher (from 6.5% in 2017 to 10.9% in 2021), the growth rate of WCR exceeded the growth rate of sales, which suggests that operating efficiency was on a downward trend. ROE dropped from 35% to 12% post IPO, so improving the company’s ROE is one of the main challenges going forward. 3.2 Status of Capital Structure (1) Capital Structure (Fig. 2).
Fig. 2. Owners’ Equity.
(2) Debt situation (Fig. 3).
Fig. 3. Structure of Liability
3.3 Costs of Capital of RC Ltd. and Peer Companies Estimating the Cost of Capital. (1) Beta estimation and equity capital cost measurement Rit − Rft = αi + βi(RMt − Rft) + εit
(3)
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Beta is estimated by using the above regression. Normally, five years of data on monthly stock returns of the company, i.e., a total of 60 observations, are generally required. Since the RC Ltd. Was listed in August 2019, there are only 3 years of stock return data, so the accuracy and stability of the Beta can be weaker than would be estimated using 5 years of data (Table 1). Table 1. Data for Estimating the Beta of the RC Ltd. Date
Ri - Rf
Rm - Rf Date
Ri - Rf
Rm - Rf Date
Ri - Rf
Rm - Rf
01/09/2019
61.07% −2.01% 01/09/2019 −23.69% −7.84% 01/09/2019
5.47% −1.62%
01/10/2019
17.70% −1.90% 01/10/2020 −7.47%
0.18% −2.82%
−2.54% 01/10/2021
01/11/2019 −14.64% −4.63% 01/11/2020 −16.22% 2.11% 01/12/2019
−0.10% 3.76%
01/12/2020 30.29%
01/01/2020 −15.93% −4.70% 01/01/2021 −1.20% 01/02/2020 01/03/2020 01/04/2020 01/05/2020 01/06/2020
01/11/2021
3.30% −1.70%
−0.30% 01/12/2021
21.97% −0.06%
−2.54% 01/01/2022 −25.14% −9.65%
0.06% −5.31% 01/02/2021 −26.57% −1.95% 01/02/2022 14.08% −6.33% 01/03/2021 11.34% 5.68% 2.85%
01/04/2021 −11.89% −2.47% 01/04/2022
52.84% −1.88% 01/05/2021 −1.20% 5.60% 2.48%
01/06/2021 −3.71%
0.26% 0.93%
−4.45% 01/03/2022 −28.61% −8.22% 2.33%
−1.51% −8.46%
01/05/2022
1.45% 2.58%
−2.99% 01/06/2022
17.85% 4.75%
01/07/2020 −18.73% 8.63%
01/07/2021 −22.21% −7.36% 01/07/2022 −13.71% −5.99%
01/08/2020
01/08/2021 −23.02% 2.06%
19.36% 0.09%
01/08/2022 −11.90% −1.55%
Ri = monthly stock return of RC, Rm = SSE Composite Index Monthly Return, Rf = One-Year Treasury Yield
After performing the above regression, RC’s β is 1.09. Next step is to calculate the RC Ltd.’s cost of equity by using the CAPM given the market risk-free interest rate of 1.7% and the market risk premium of 7%: E(Ri) = 1.7% + (1.09) × (7%) = 9.33% The cost of equity of the RC Ltd. Equals to 9.33%. Given the lack of observations in the RC Ltd.’s Beta estimation, this paper also uses the average β of other listed peer companies in the closest industry and use the industry β to estimate the RC Ltd.’s cost of equity. As shown in the figures below, the industry’s average β is 0.65 and the industry’s cost of equity is 6.28%, both lower than those of the RC Ltd. (Fig. 4 and Fig. 5) (2) Estimating the weighted average cost of capital of the RC Ltd. Equity Debt + RE × (4) WACC = RD × (1 − TC ) × Debt + Equity Debt + Equity Using the RC Ltd.’s cost of equity, it has: WACC = 4.0% × (1–15%) × 11% + 9.33% × 89% = 8.7% Using industry’s cost of equity, it has: WACC = 4.0% × (1–15%) × 11% + 6.28% × 89% = 6.0%
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Fig. 4. Betas of RC Ltd. And Peer Companies.
Fig. 5. Costs of Equity of RC Ltd. And Peer Companies.
Therefore, the estimation the RC Ltd.’s cost of capital cost is between 6.0% and 8.7%. Comparison with Peer Companies. 8 closely related listed peer companies are selected for comparative analysis, as shown in Fig. 6 and Fig. 7, the RC Ltd.’s current debt ratio (11.35%) is higher than that of the industry average (1.77%), and the RC Ltd.’s cost of capital (8.66%) is higher than the industry average (6.21%). Issues with Quantitative Analysis. As can be seen, the debt ratios of the peer companies are relatively low, and the costs of capital varies. However, compared with the debt-to-equity ratio of 113% for China’s entire food industry (165 listed enterprises) the RC Ltd.’s current debt-to-equity ratio is only 1/10 of the food industry, which suggests a significantly low financial risk for the RC Ltd. Therefore, based on the trade-off theory, a reasonable increase in the proportion of debt financing could help reduce the RC Ltd.’s cost of capital and optimize its capital structure.
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Fig. 6. Debt/ (Debt Equity) of RC Ltd. And Peer Companies (TTM).
Fig. 7. Costs of Capital of RC Ltd. And Peer Companies (MRQ).
3.4 Qualitative Analysis of Capital Structure In addition to quantitative analysis, this paper also conducts a qualitative analysis. Table 2 presents the results of the qualitative analysis. According to the above analysis: among the 9 factors related to debt financing, the ratio of factors that support, do not support, and have no effect on debt financing is 5: 2: 2. Thus, the qualitative results favour debt financing. It is worth noting, however, that the RC Ltd. is in a period of rapid growth and expansion, and its operational risks are relatively high at this stage. Further, the uncertainty caused by China’s strict Covid policy and economic downturn amplifies its operational risk. Therefore, according to the principle of matching between operational risk and financial risk, high operational risk should be accompanied by low financial risk. In addition, the type of financing will also depend on the overall market environment and the valuation of the enterprise. If the overall market is at a low point and the enterprise’s stock price is below its intrinsic value, equity financing should not be chosen, as equity financing at this point will drive up the firm’s cost of equity, leading to an increase in its overall cost of capital and resulting in a lower firm value.
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3.5 Discussion on Debt Financing Ratio in Optimal Capital Structure Through the above quantitative and qualitative analysis, the RC Ltd. Should adjust towards its optimal capital structure by increasing its debt level. The next question to ask is: how much debt can the RC Ltd. Increase to achieve a better capital structure and a lower cost of capital? To answer this question, this paper uses the interest coverage ratio (interest coverage ratio = EBIT/interest expense) as a measure to determine the RC Ltd.’s new capital structure. For the most recent 12 months, the average interest coverage ratio for the Chinese food industry was 5.4 times and the debt-to-capital ratio was 36%, while the RC Ltd.’s interest coverage ratio was 18.6 times and the debt-to-capital was 13.1%, which suggests Table 2. Qualitative Analysis of RC’s Debt Financing. Influencing factors
Freely chosen capital market systems Trade-off theory
Regulated capital Current market systems situation of RC Pecking order theory
Debt financing scoring
Company size Positive correlation – larger companies have lower risk of bankruptcy and can take on more debt
Negative correlation – larger companies have low information asymmetry, equity financing with low cost
Positive correlation – the larger the company, the lower the credit risk and the more loans it can get, but there is no clear requirement of SEO regulations
Relative big +1 firm for banks and there are more borrowing channels with lower interest rates
Tangible assets ratio
Negative correlation – the higher the scale of tangible assets, the lower the information asymmetry of the enterprises, the lower the cost of equity financing, and the lower the proportion of debt
Positive correlation – the larger the scale of tangible assets, the more assets are pledged as collateral, the lower the risk to the bank and the more loans it can obtain, but there is no clear requirement of SEO regulations
High tangible + 1 asset ratio and strong ability to obtain loans
Positive correlation – more physical assets provide more collateral for liabilities
(continued)
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Table 2. (continued) Influencing factors
Freely chosen capital market systems Trade-off theory
Regulated capital Current market systems situation of RC Pecking order theory
Debt financing scoring 0
Profitability
Positive correlation – more profitable companies are less likely to go bankrupt and can leverage more debt
Negative correlation – companies with strong profitability are able to internally finance more through retained earnings
Positive correlation-more profitable, less credit risk for the bank, more loans available Negative correlation – the more profitable the company that meets SEO requirements, the higher the probability of successful SEO implementation and lower debt levels
Strong profitability. Various theoretical positive and negative offsets
Growth opportunities
Negative correlation – companies with more growth opportunities have higher risk, higher cost of financial distress and lower debt levels
Positive correlation – the higher the growth, the greater the information asymmetry, the higher the cost of equity financing, and the higher the debt ratio of enterprises
Negative correlation – higher growth companies have higher credit risk and receive fewer loans
In a period of -1 rapid development, the operational risk is high and financial risk should be reduced
Enterprise taxation
Tax laws favor 1 Internal debt financing capital; 2 Debt financing; 3 Equity financing
Same as trade-off Enjoy tax theory shield
+1
(continued)
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Influencing factors
Freely chosen capital market systems Trade-off theory
Regulated capital Current market systems situation of RC Pecking order theory
Debt financing scoring
Agency costs of equity financing; Keeping control power unchanged; Signaling effects under information asymmetry
Borrowing reduces agency costs of equity financing; Control power can be maintained; Adverse capital market readings can be avoided
1. Major 0 shareholder in charge, no agency cost: not prefer loans 2. Need to maintain the control of major shareholder: prefer loan 3. The debt ratio of industry is low, borrowing will send a negative signal: not prefer loan 4. issuing shares will also send a negative signal: prefer loan
Financial distress costs
The cost of firms getting into financial difficulties due to excessive liabilities is very high
The Altman +1 Z-Score is well above 3; the interest coverage ratio for the last 12 months is 18.6 times. The risk of financial distress is small (continued)
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Table 2. (continued) Influencing factors
Freely chosen capital market systems Trade-off theory
Regulated capital Current market systems situation of RC Pecking order theory
Agency costs of debt financing; maintaining a robust dividend policy; financial flexibility and credit ratings
Inability to fully utilize the tax shield; interest conflicts between shareholders and creditors occur; limit the firm’s ability to pay stable dividends; reduce the company’s financial flexibility and affecting its credit rating
Government regulation
Not applicable Not applicable
Debt financing scoring
The current +1 debt is low and there will be no problem of excessive debt Moderate borrowing is currently available
Restrictions on equity financing result in limited access to funding opportunities, and the timing should be fully utilized when having the funding authority
Propose to apply for equity financing
-1
an ample room to raise debt. Taking into account the internal and external environment, the current development stage of the RC Ltd. And the operational risks it faces, it believes that 10 times of the interest coverage ratio (twice of the industry average) is a safe region for the RC Ltd. to increase its debt. Thus, the calculation is based on 10 times of interest coverage ratio and the RC Ltd.’s operating performance in 2021 and 2022 (forecast). Table 3 presents the results of the estimation. To reach the 10 times interest coverage ratio, the RC Ltd. Could raise its debt level to 250 million yuan in 2011 (debt-to-capital ratio of 27.5%), and to 300 million yuan in 2022 (debt-to-capital ratio of 27.7%). Based on the RC Ltd.’s own cost of equity, the corresponding cost of capital for the RC Ltd.
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Table 3. Calculation of the capital cost based on the historical data of RC Unit: ten thousand yuan. Project
2021
Prediction for Dec. 2022
Debt
7,956
9,980
Equity
66,090
79,216
EBIT
9,723
11,654
Interest costs
287
349
Interest coverage ratio
33.8
33.4
Debt/Total Capital
10.7%
11.2%
Debt/Total Capital (New)
27.5%
27.7%
WACC_1
8.7%
8.7%
WACC_1 (New)
7.7%
7.7%
WACC_2
5.9%
5.9%
WACC_2 (New)
5.5%
5.5%
Note: (1) Debt/Total Capital (New) is the new debt-to-capital ratio after increasing debt. (2) WACC_1 and WACC_1 (New) refers to the original cost of capital and the new cost of capital with additional debt, with reference to the RC Ltd.’s cost of equity of 9.3% (3) WACC_2 and WACC_2 (New) refers to the original cost of capital and the new cost of capital with additional debt, with reference to the industry average cost of equity of 6.3%.
Can be reduced from about 8.7% to about 7.7%; based on the industry average cost of equity, the RC Ltd.’s cost of capital can be reduced from about 5.9% to about 5.5%. As mentioned above, taking into account the internal and external environment and the current stage of development and operational risks faced by the RC Ltd., the cost of capital can be reduced to between 5.5% and 7.7% on the basis of a still strong balance sheet and low financial risk (i.e., 10 times interest coverage ratio). It is worth mentioning that once the RC Ltd. Has reached its new debt ratio as suggested above, it should try to maintain this leverage ratio by not trying to issue more debt which could lead to a high cost of financial distress. Equity financing or equity and debt financing can be considered for the RC Ltd.’s future financing needs.
4 Conclusion Based on the capital structure and asset pricing theories, this study analyzes and discusses the optimal capital structure and cost of capital of a selected case company, the RC Ltd. The study finds that similar to its peer companies in the closest industry, the RC Ltd. Has a low debt ratio, with a debt-to-capital ratio only one-tenth that of the broader food industry. While this low debt ratio reflects the earnings and debt characteristics of its industry sector, it is clear that the RC Ltd. And its peer enterprises have not sufficiently exploited the benefits of debt, e.g., boosting earning, lowering the cost of capital, and
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improving corporate governance. Therefore, an appropriate increase in its debt ratio can help create value in different ways, while maintaining its financial stability. Based on this, this study has performed related estimations and set an interest coverage ratio at 10 times for the RC Ltd. to safely raise its debt ratio and adjust to a better capital structure. Although this study focuses on the case of the RC Ltd., the results of the study reflect the issues of listed companies in Chinese food industry in terms of capital structure policies and strategies. Hopefully this case-based study on the RC Ltd offers a theoretical direction and practical guidance for many Chinese firms and entrepreneurs who are searching for the optimal capital structure for their companies.
References 1. Jensen, M.C., Meckling, W.: Theory of Firm: Managerial Behavior, Agency Cost, and Ownership Structure. Journal of Financial Economics (3), 326 (1976) 2. Leary, M.T., Roberts, M.R.: The Pecking Order, Debt Capacity, and Information Asymmetry. J. Financ. Econ. 95(3), 332–355 (2010) 3. Hawawini, G., Viallet, C.J.: Finance for Executives: Managing for Value Creation, 6th Edition. Cengage (2019) 4. La Porta, R.F., Lopez-de-Silances, A.S., Vishny, R.W.: Legal Determinants of External Finance. Journal of Finance 3(52), 1131–1150 (1997) 5. Booth, L., Aivazian, V., Demirguc-Kunt, A., Maksimovic, V.: Capital structures in developing countries. Journal of Finance 1(56), 7–130 (2001) 6. Li, S., Liu, Z.: A review of the factors affecting the capital structure of Listed Companies. Accounting Research 8, 31–35 (2003) 7. Donghua, C., Zhen, L., Fu, X.: Industrial policy and corporate finance. Working Paper (2010) 8. Song, X., Wu, Y., Ning, J.: Monetary policy Company growth and dynamic adjustment of capital structure. International Financial Research 11, 46–55 (2014) 9. Jie, G., Yingbo, Z.: Company timing or government timing - the impact of IPO market timing on capital structure under China’s specific institutional background. Financial Research (7), 137–153 (2012) 10. Jigao, Z., Zhengfei, L.: Property right nature, equity refinancing and resource allocation efficiency. Financial Research (1), 131–148 (2011) 11. Cheng, L., Ye, F., Liu, F.: Capital market regulation and company capital structure. China Industrial Economy 11, 155–171 (2017)
The Main Challenge Faced by the Migrant Population and Recommended Policies: A Case of Hangzhou Yilin Jiang(B) Hangzhou Foreign Languages School Cambridge A-Level Center, Hangzhou 310023, China [email protected]
Abstract. The increasing migrant population has become a crucial issue in the development of China as they contribute a lot to boosting economic growth, accelerating urbanization, achieving a balanced development of society, and promoting equalization of public services. The purpose of this study is to investigate the major challenge faced by the migrant population in Hangzhou and provide recommended policies to tackle the most significant issue. By conducting survey research on 428 participants from three diverse neighborhoods in Hangzhou, the results demonstrated that the major challenge encountered by the migrant population in Hangzhou is housing. Also, the prior choice of accommodation for the migrant population was rental houses, and the assumed affordable rents for the migrant population accounted for less than 20% or 25% of the total income. For those people who decided to relocate to other cities or return to their hometowns, the top reason was the high cost of living in Hangzhou. In order to deal with the problem, the Hangzhou government might need to increase the number of “bluecollar apartments” (temporary rental housing) in Hangzhou and provide subsidies for the migrant population to reduce their burden on rent in case those workers leave Hangzhou due to high living costs. Keywords: Migrant Population · Housing Condition · Willingness to stay · Policy-making
1 Introduction The migrant population refers to people whose domicile places are different from the cities they currently live in [1]. Since China’s reform and opening-up in 1978, the country has gradually evolved from an agricultural-based economy to an industrialized economy, attracting more people to move from rural areas to urban areas [2]. Through the analysis of the data on the migrant population, the total migrant population continued to grow for seven consecutive years from 2009 to 2015, increasing from 211 million in 2009 to 247 million in 2015. Subsequently, the size of the migrant population entered a period of quantitative adjustment, falling to 241 million in 2018. The data of the seventh census showed that the size of the migrant population reached 376 million in 2020, and the proportion of the migrant population to the total population was as high as 26.64%, which was much higher than expected [3]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 692–701, 2023. https://doi.org/10.1007/978-981-99-6441-3_63
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Though Zhejiang is a relatively small province, its migrant population is the second largest in the country. Hangzhou, Zhejiang’s provincial capital city, has the highest migrant population (about 5 million people) in the province [4]. Such a high attraction to the migrant population is the result of Zhejiang’s accelerated economic development. According to the Zhejiang Provincial Bureau of Statistics, Zhejiang’s GDP jumped from 2.74 trillion yuan in 2010 to 6.47 trillion yuan in 2020, with continued stable and rapid economic and social development and more active population movement [6]. Standing at the forefront of Internet + and cross-border e-commerce, Hangzhou attracts many high-tech companies such as Alibaba, and NetEase to settle there, which are capable of providing more job opportunities for talents with high educational levels. Also, Hangzhou, as a host city of the G20 Summit in 2016 and the 19th Asian Games in the upcoming 2023, has a strong attraction to the migrant population. However, migrant workers often face difficulties in their daily lives, such as housing, employment, and healthcare services. Paying more attention to these problems is significant since the migrant population has a great impact on accelerating economic growth, promoting equalization of public services, and achieving a balanced development of society. In that case, successfully conducting social management and furnishing stable and effective public policies for the migrant population has become an urgent and significant issue for the local government. This article will mainly focus on figuring out the major challenges encountered by the migrant population in Hangzhou and will provide recommended policies to tackle those difficulties based on the results of the investigation.
2 Literature Review Many scholars have paid attention to the migrant population from various perspectives. Some of them focused on the reasons that caused them to emigrate, others studied their impacts on the destinations, and many theories were proposed to support their opinions. E.G. Ravenstein (1889) proposed his explanation of migration [7]. His ideas included that people mainly tended to move from agricultural areas to industrial areas, mostly for economic reasons. Long-distance migrants were mainly heading to metropolitan areas. Also, females moved more frequently at short distances, while males participated more in international migration. Ravenstein’s law can be used to explain Hangzhou’s current situation with the migrant population since Hangzhou has a strong momentum of economic growth. Zhejiang province has the most developed private economy in the whole country, and Hangzhou, as the provincial city of Zhejiang, contributed 24.78% of the total provincial GDP during the first half of 2022 [6]. Such steady economic development and a dynamic market make it appealing to the migrant population around the country. According to the data of the 7th census, the proportion of the migrant population in Zhejiang, a “laborintensive province”, is much higher than the national level, and Hangzhou is the city with the largest number of people in the province. In 2020, the total migrant population in Hangzhou reached 679.99 million people [6]. Based on Ravenstein’s theory, other scholars theorized factors that stimulate people to move from agricultural areas to industrial cities. W. Arthur Lewis (1954) proposed
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a model in “Economic Development with Unlimited Supplies of Labour”, where he concluded that the labor force tended to transfer from the rural agriculture sector to the urban industrial sector due to the productivity and income differences (usually 30% higher salaries) between these two sectors. The higher profits earned by urban manufacturing industries promoted the expansion of the manufacturing companies, causing further transfer of the labor force from rural to urban [8]. From an economic perspective, John R. Harris and Michael Todaro (1970) also presented a model. However, unlike Lewis’s model, Harris–Todaro’s model assumed that migration was determined by the difference in expected income received in the rural and urban areas, instead of the difference in real income in the two areas. If the expected income in urban areas was higher than that in the countryside, and unemployment was relatively high, migrating from rural to urban seemed reasonable [9]. Later, Everett S. Lee (1966) published his work “A Theory of Migration”, in which he proposed his Push-Pull Theory. Lee concluded the push factors that caused people to leave their old residences, such as discrimination, war, and loss of wealth and the pull factors that attracted people to the new destinations, such as better working conditions, an attractive climate, and security. In general, people mainly migrated to other places in pursuit of better job opportunities and higher living standards [10]. When examining the factors that drive the migrant population to Zhejiang, a similar pattern emerges. According to the Zhejiang Provincial Bureau of Statistics, in 2020, approximately 3.62 million people left their domiciles because of work and employment, accounting for 38.59% of the total migrant population within the province. As for the migrant population from other provinces, 82.18% of the influx of people came to Zhejiang due to employment [4]. Being the most attractive city to migrant people in Zhejiang province, the Hangzhou government released policies associated with the migrant population. For education, “The Implementation Measures for Enrolling Children of Migrants in Hangzhou City”, published in 2019, guaranteed the rights and interests of children of the migrant population to enjoy 15 years of basic education and take college entrance examinations in Hangzhou. The government also introduced policies to solve the housing problems of the migrant population [11]. According to “The Suggestions on Accelerating the Construction of Temporary Rental Housing”, by the end of 2020, the city had started 98 “Blue-Collar Apartment” (temporary rental housing) projects with 41,900 sets of housing. Among them, 35 projects with 16,300 sets of housing have issued lease acceptance notices [12]. Improving the migrant population’s working skills was also considered by the government. According to the “Hangzhou Occupational Skills Enhancement Action Implementation Plan”, after the migrant workers finish particular occupational training, they can receive training subsidies in accordance with the provisions [13]. Although the government has already published various policies to facilitate the migrant population’s life in Hangzhou, a limited amount of research has been done to investigate the major challenges encountered by the migrant population. Some scholars proposed the main difficulty is housing [14], while others considered the problem to be the cognition of urban identity [15]. Identifying the major challenges of the migrant population is relatively urgent because it may cause a reduction in the labor force and
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the “brain drain” in Hangzhou. Also, the case of Hangzhou has implications for other cities, as it has achieved some success in managing such a large migrant population. In addition, current policies might just be a try, which is not beneficial to all the migrant population. For example, the “Blue-collar apartment” provided by the government is about 40000 sets in total, but for Hangzhou’s millions of migrant populations, it’s just a drop in the bucket. Hence, this article will specifically investigate and analyze the major challenge faced by the migrant population in Hangzhou and provide some recommended policies to deal with the issue.
3 Methodology In this paper, survey research was the major method used to find the current living standards and major challenges encountered by the migrant population. A total of 428 people filled out the questionnaires, with 282 males (65.9%) and 146 females (34.1%). Due to time and money constraints, it was not possible to cover the survey of the migrant population in all the neighborhoods of Hangzhou. Under these circumstances, the participants came from three diverse chosen neighborhoods with different functional orientations and different industrial structures, which are the Beishan subdistrict, the Gongchenqiao subdistrict, and the Zhuantang subdistrict in Hangzhou. Those participants were all migrant workers from various cities across the country to Hangzhou. The questionnaire was developed in an online format, and the staff in the neighborhoods were requested to send the questionnaire to the Wechat groups of the migrant population in their neighborhood. The questionnaire involved 19 questions, which were designed independently. These questions were aimed at learning about the basic information of these migrant workers, such as age, gender, educational attainment, and job, and questions like “What is your current housing situation?”, “Have you signed an employment contract and purchased social insurance?”, and “What is the most difficult problem encountered in Hangzhou?” were designed to investigate their current living standards and their major challenges. The procedure of the research was that first, the participants received the link to the questionnaire in the Wechat group, then they were asked to complete the questions associated with their background information, current living situation, and major challenges, and finally, they were thanked for finishing this survey. The whole process took about 5 min on average to complete one questionnaire.
4 Results Table 1 shows the age of the migrant population surveyed. Among the 428 respondents, the majority of them are young and middle-aged workers from 20–39 years old, which accounts for 66.82% of all the respondents. Table 2 demonstrates the educational level of respondents. The number of people who graduated from junior high school is the highest, which is 23.13% among all degrees. Table 3 demonstrates the major challenges that the respondents felt they encountered in Hangzhou. More than half (50.47%) of them believed the greatest difficulty in living in Hangzhou was housing, followed by employment (19.63%).
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Number
Percentage
Under 20
18
4.21%
20–29
138
32.24%
30–39
148
34.58%
40–49
75
17.52%
50–59
46
10.75%
Over 60
3
0.70%
Table 2. The educational level. Educational level
Number
Percentage
Primary school
8
Junior high school
99
23.13%
Senior high school
85
19.86%
Technical secondary school
57
13.32%
Junior college education
83
19.39%
Undergraduate
87
20.33%
Graduate
8
1.87%
Doctor
1
0.23%
1.87%
Table 3. The major challenge faced when living in Hangzhou. Major challenge
Number
Percentage
Housing
216
50.47%
Education for kids
45
10.51%
Healthcare
35
8.18%
Transportation
21
4.91%
Employment
84
19.63%
Fit into a new community
27
6.31%
Table 4 shows the current housing conditions of all the respondents. For most migrant workers (62.47%) surveyed, rental houses are the prior choice for them in Hangzhou, and the number of moving houses of the migrant population in Hangzhou is shown in Table 5. 35.98% of the migrant population have already moved houses more than twice, and 19.86% of the migrant population still lived in their original place when they first came to Hangzhou. Table 6 reflects how much income respondents think it is affordable
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to spend on rental houses. Most people (78.74%) think that lower than 20% or 25% of the income spent on rent is affordable for them living in Hangzhou. Table 4. The housing conditions. Housing condition
Number
Percentage
Rented house
268
62.62%
Self-purchased house
59
13.79%
Company dormitory
80
18.69%
Within construction plant
16
3.74%
Relatives/friends’ house
5
1.17%
Table 5. The number of moving houses in Hangzhou. Number of moving houses
Number
Percentage
0
85
19.86%
1
106
24.77%
2
83
19.39%
More than 2
154
35.98%
Table 6. The assumption about the affordable money spent on housing. Rent/income
Number
Percentage
Lower than 1/5
159
37.15%
Lower than 1/4
178
41.59%
Lower than 1/3
69
16.12%
Lower than 1/2
21
4.91%
Lower than 2/3
1
0.23%
Table 7 shows the willingness of respondents to continue staying in Hangzhou. Among all the participants, about 57% decided to stay in Hangzhou, while about 43% tended to go back home or go to other cities. Table 8 presents further reasons why 184 respondents decided not to stay in Hangzhou. 47.28% of them tend to leave because of high living costs, and the second reason, which 36.96% of the respondents chose, is difficulties in employment.
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Number Percentage
Continue living in Hangzhou 244
57.01%
Go hometown or other cities
42.99%
184
Table 8. The main reason for refusing to stay in Hangzhou. Reason
Number Percentage
high living costs
87
47.28%
cannot find suitable jobs
68
36.96%
cannot fit into the community 14
7.61%
other personal reasons
8.15%
15
5 Conclusion This article used survey research to examine the major challenges encountered by the migrant population in Hangzhou. Our findings revealed that the major challenge that the migrant population in Hangzhou faced was housing. We also further investigated the current housing conditions of respondents and their willingness to stay in Hangzhou. First and foremost, the most significant result of this research showed that the major challenge that the migrant population faced in Hangzhou was housing. This outcome filled a gap in this field since previous research rarely studied the major challenges that the migrant population encountered. One study looked into the working conditions, education for children, housing, social security, and discrimination of the migrant population and discovered that high education fees were the most difficult obstacle for migrant workers [16]. However, since this research was completed 16 years ago, there is a big time lag until now. The government, like Hangzhou, has already introduced policies to ensure 15 years of basic education for migrant kids [11], so it is possible that the major challenge of the migrant population changes from high education expenses to housing over time. Also, the previously mentioned research done in 2006 was from a national perspective, while our research specifically focused on Hangzhou. Hence, the situation of each city may be different from the whole nation due to various local government policies. Secondly, the results demonstrated that the prior housing option for the migrant population in Hangzhou was rental housing, which was consistent with the previous studies revealing that the top housing choice for migrant workers was rental housing around the whole country [17]. Research on Hubei provinces in China also showed similar results, that only 8.9% of the migrant population lived in self-purchased houses while 80.2% of them lived in rental houses [18]. When it comes to considering the rent in Hangzhou, our data indicated that the majority of people (78.74%) believed rent that accounted for less than 20% or 25% of
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their income was affordable for them. This result was similar to the earlier research outcome where 79.9% of the migrant population thought less than 20% or 25% of their income used for paying rent was reasonable [14]. Besides, our data showed that rent was associated with the willingness of the migrant population to continue staying in Hangzhou since 47.28% of the respondents who decided to move to other cities or go back home pointed out that high living costs weakened their determination to stay in Hangzhou. Previous research concluded that the migrant population’s affordability of housing had a positive influence on their willingness to stay. If the migrant population had access to more affordable housing, they would have a stronger willingness to stay longer [19]. Other research analyzing Hangzhou’s migrant population also supported our result [20]. It summarized that migrant worker in Hangzhou had high pressure on housing because about one-third of them spent over 30% of their income on rent and 14% of them planned to go back to their rural hometowns. In addition, an interesting result was found in this questionnaire. Among all the respondents who were determined to relocate to other cities, 36.96% claimed that the lack of access to suitable jobs forced them to leave. It seemed to be inconsistent with previous data, which showed that many of the migrant population reached Hangzhou in pursuit of better jobs. However, in reality, being unable to find proper jobs compelled those migrant workers to leave. Future studies can focus on investigating the reasons that cause this contradiction and furnishing policies to resolve the issue. Our study has several limitations. Firstly, the sample size of the questionnaire survey was relatively small. The 428 respondents chosen from three neighborhoods might not reflect the ideas of all migrant workers in Hangzhou since migrant workers in other neighborhoods had different incomes, educational levels, working conditions, or other factors that caused them to face different challenges when living in Hangzhou. In addition, only the relationship between the rent and the willingness to stay in Hangzhou was summarized when investigating the suitable rent of the migrant population’s houses because of the limited data collected. In the future, the government may need to increase the number of “blue-collar apartments” (temporary rental housing) in Hangzhou and provide subsidies for the migrant population to reduce their burden on rent, which may help to solve the major challenge that the migrant population in Hangzhou face and increase their willingness to stay longer. Future studies can also focus on investigating the suitable rent for both the migrant population and landlords so that the government can have a more intuitive data reference when making policies associated with the migrant population’s housing problems.
References 1. National Bureau of Statistics of China: Communiqué of the Seventh National Population Census (No. 7),” National Bureau of Statistics of China, 11 May 2021. http://www.stats.gov. cn/english/PressRelease/202105/t20210510_1817192.html. Accessed 28 Aug 2022 2. Wu, J., Yu, Z., Wei, Y., Yang, L.: Changing distribution of migrant population and its influencing factors in urban China: economic transition, public policy, and amenities. Habitat Int. 94, 102063 (2019). https://doi.org/10.1016/j.habitatint.2019.102063
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3. Wang, P.: Grasp the changing trend of migrant population characteristic,” Chinese Social Sciences Today,. 04 August 2021. [https://epaper.csstoday.net/epaper/read.do?m=i&iid=6094& eid=41882&sid=192964&idate=12_2021-08-04. Accessed 28 Aug 2022 4. Zhejiang Provincial Bureau of Statistics: Analysis of the Seventh Population Census of Zhejiang Province: Migrant Population. Zhejiang Provincial Bureau of Statistics, 22 July 2022. http://tjj.zj.gov.cn/art/2022/7/22/art_1229129214_4956222.html. Accessed 28 Aug 2022 5. Zheng, Y.: The second largest population growth in China, what is the attractiveness of Zhejiang? Zhejiang Daily, 05 August 2022. http://zjrb.zjol.com.cn/html/2022-08/05/content_3 576342.htm?div=-1. Accessed 28 Aug 2022 6. Zhejiang Provincial Bureau of Statistics: GDP of Zhejiang Province and each cities,” Zhejiang Provincial Bureau of Statistics, 03 August 2022. http://data.tjj.zj.gov.cn/page/zbcx/zbDetail. jsp?taskId=0cbf79bde4694a5c9b31b0614a2a1995&orgCode=33. Accessed 28 Aug 2022 7. Ravenstein, E.G.: The Laws of Migration. J. Roy. Stat. Soc. 52(2), 241 (1889). https://doi. org/10.2307/2979333 8. Lewis, W.A.: Economic development with unlimited supplies of labour. Manch. Sch. 22(2), 139–191 (1954). https://doi.org/10.1111/j.1467-9957.1954.tb00021.x 9. Harris, J.R., Todaro, M.P.: Migration, unemployment and development: a two-sector analysis. Am. Econ. Rev. 60(1), 126–142 (1970). https://www.jstor.org/stable/1807860. Accessed 28 Aug 2022 10. Lee, E.S.: A theory of migration. Demography 3(1), 47–57 (1966). https://doi.org/10.2307/ 2060063 11. Bureau of Education of Hangzhou Municipality: Implementation Measures for Enrolling Children of Migrants in Hangzhou City. Bureau of Education of Hangzhou Municipality, 29 November 2019. http://edu.hangzhou.gov.cn/art/2019/11/29/art_1229424902_1656784. html. Accessed 28 Aug 2022 12. Hangzhou Housing Security and Real Estate Administration Bureau: Hangzhou Occupational Skills Enhancement Action Implementation Plan. Hangzhou Housing Security and Real Estate Administration Bureau, 05 June 2020. http://hrss.hangzhou.gov.cn/art/2020/6/5/art_122911 3731_1157500.html. Accessed 28 Aug 2022 13. Hangzhou Housing Security and Real Estate Administration Bureau: Suggestions on accelerating the constructions of temporary rental housing,” Hangzhou Housing Security and Real Estate Administration Bureau, 15 December 2017. http://fgj.hangzhou.gov.cn/art/2017/12/ 15/art_1229265366_1228263.html. Accessed 28 Aug 2022 14. Mao, F.: A research on the housing conditions and inclinations of the floating population——a case study of the migrant workers in Hangzhou. J. Hangzhou Univ. Comm. 6, 90–95 (2009). https://doi.org/10.3969/j.issn.1009-1505.2009.06.015 15. Zou, J., Deng, X.: Residential neighbourhood choices, subjective cognition and migrants’ urban identity. Popul. Dev. 27(3), 2–17 (2021) http://wfdata.hznet.com.cn/D/Periodical_scyr kfx202103001.aspx. Accessed 28 Aug 2022 16. Wong, F., Li, C., Song, H.: Rural migrant workers in urban China: living a marginalised life. Int. J. Soc. Welf. 16(1), 32–40 (2006). https://doi.org/10.1111/j.1468-2397.2007.00475.x 17. Jiang, L.: Living conditions of the floating population in urban China. Hous. Stud. 21(5), 719–744 (2006). https://doi.org/10.1080/02673030600807431 18. Shi, Z., Xue, W.: Research on housing choice for floating population and its influential factors -analysis of dynamic data based on the survey on the floating population in Hubei Province in 2012. J. Chongq. Technol. Bus. Univ. (West Forum) 2, 25–33 (2014). https://doi.org/10. 3969/j.issn.1674-8131.2014.02.03
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19. Meng, X.: A study on housing affordability and residence decision of migrant populationbased on micro-evidence of migrant population. MSc thesis, Northwest A&F University (2020). http://wfdata.hznet.com.cn/D/Thesis_D02225215.aspx. Accessed 28 Aug 2022 20. Zeng, H., Yu, X., Zhang, J.: Urban village demolition, migrant workers’ rental costs and housing choices: Evidence from Hangzhou, China. Cities 94, 70–79 (2019). https://doi.org/ 10.1016/j.cities.2019.05.029
Research on United States Core CPI Forecast Based on Exponential Smoothing and ARIMA Model Li Eric(B) Raffles Institution (Graduate), Singapore, Singapore [email protected]
Abstract. Core inflation in the United States is a very important issue as it directly affects the day-to-day lives of hundreds of millions of Americans. Given the importance of forecasting future values and the lack of recent research, this article aims to forecast future year-on-year Core CPI via exponential smoothing and the ARIMA model method. Seasonally adjusted data is collected from the Federal Reserve Economic Data website, the start of the data in January 1955 is set to the baseline level of 100, and the above models are fitted. It is observed that the ARIMA (3, 2, 2) model has the best fit and best forecasts future values, followed by the HoltWinters method, which has a good fit but also a very large confidence interval, and lastly the Simple Exponential Smoothing method, which has a modest fit. The results are that the upward trend of core inflation will continue for many years within a narrow confidence interval, with a predicted core inflation rate of 5.64% from Jan 2023 to Jan 2024. This is likely due to excessive demand caused by expansionary fiscal policies enacted during the COVID-19 pandemic. It is recommended that policymakers continue to adopt a contractionary, or “tight”, monetary policy to fight the undesirable effects of inflation. Keywords: ARIMA · Inflation · Forecast · Holt-Winters · Exponential
1 Introduction 1.1 Research Background Core inflation - a “steady and sustained increase in the general price level” in the United States has been running at a 40-year high for multiple months, eroding the savings of hundreds of millions of Americans [1]. Hyperinflation - which is a possibility if inflation is not controlled - can destabilize political systems and prevent businesses from planning [2]. This research paper aims to forecast future values of core inflation in the United States, to better aid policymakers in formulating appropriate policies to combat it. Several methods, including simple exponential smoothing, the Holt-Winters Method, and ARIMA models, are employed to forecast future values of Core CPI. Given that the most recent research on ARIMA forecasts of United States inflation was conducted more than 3.5 years ago - in February 2019 - it is timely to conduct additional research incorporating updated values of core inflation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 702–714, 2023. https://doi.org/10.1007/978-981-99-6441-3_64
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1.2 Literature Review Kharimah et al. (2015) employed the ARIMA(1, 1, 0) model to forecast CPI data in Malaysia from 2009 to 2013, and also concluded that inflation had a large upward trend [3]. Xiao Manjun and Xia Rongyao’s (2008) results show that inflation in China is predicted well by the ARIMA (p, d, q) model [4]. Junttila (2001) evaluated the effects of structural changes on the inflation forecasts in Finland and forecasted the rate of future inflation since the beginning of 1987 using an ARIMA model [5]. Adam et. al. modeled the inflation rate in Nigeria from 1980 to 2010 using the ARIMA (1, 2, 1) model and forecasted an average increase of 2.4% from 2011 to 2015 [6]. Stanley Jere et al. used exponential smoothing and ARIMA ((12), 1, 0) models to predict the future CPI in Zambia and predicted that inflation in April and May 2015 would be 7.0% and 6.6% respectively [7]. Etri Pujiati et al. used double exponential smoothing to forecast the CPI in Samarinda City [8]. 1.3 Research Motivation It is critical to forecast the Core CPI for Urban Consumers, less Food and Energy. This is because the Core CPI is a good indicator of the level of living expenses in urban areas in the United States. Given that food and energy prices are very volatile and highly seasonally dependent, they have been stripped out to allow better and more accurate forecasting [9]. This topic was chosen because recently there has been a wave of inflation, with core inflation rising to 6.42% in Feb 2022 - the highest since 1982. Moreover, there have been few time series analysis conducted on Core CPI over the past 3–5 years, so it is useful to conduct innovative and up-to-date research. Inflation is an especially important issue as it causes money to lose value, can increase financial inequality in society, and can increase the cost of living. High levels of sustained inflation can even foment social unrest. For these reasons, this topic is especially crucial.
2 Methodology 2.1 Data Sources The data in Fig. 1 shows the monthly “aggregate of prices paid by urban consumers for a typical basket of goods, excluding food and energy”. The Core CPI was picked and is indexed at 100 for the beginning of the data in 1955, to better allow for comparisons with historical data. Because this article conducts forecasting for the long-term (e.g. multiple years), the data is not viewed as a year-on-year change but rather considered the general upward trend over the past few decades. The graph is from the U.S. Bureau of Labor Statistics. The R libraries ggplot2, tidyverse, forecast, tseries were used. 2.2 ARIMA It is first important to introduce the concept of an ARIMA (p, d, q) model. The AR, I, and MA individually represent Autoregressive, Integrated, and Moving Average respectively. The model ignores independent values and assumes that past values and error terms are
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Fig. 1. Monthly Core CPI from Jan 1955 to Jul 2022.
sufficient to forecast future values [10]. This paper assumes familiarity with the model, but it can be summarized as such: p is the number of lags to be used as predictors, d is the number of times differencing is conducted on the data to make the time series stationary (meaning that it has constant mean & variance, and therefore does not have a trend or seasonality), and q refers to the number of lagged errors that are in the ARIMA model. Note that since the data provided has already been seasonally adjusted, there is no need for the ARIMA model to have a seasonal component. First, this paper test whether the data is stationary using ACF, where CPICORE refers to the time series data from January 1955 to July 2022 (Fig. 2).
Fig. 2. Core CPI ACF.
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As observed above, the autocorrelation exceeds the critical values for each lag, so the data is not stationary, and the data significantly correlates with the lagged values (Fig. 3).
Fig. 3. Core CPI Time Series Partial ACF.
Considering partial autocorrelation, the graph has a very large spike at the first lag, and afterwards is negligible in magnitude for other lags, implying that autocorrelations at larger lags are solely due to the outbreak of autocorrelation at the first lag. This implies that differencing is required. Differencing is conducted once, and the resultant ACF is as follows (Fig. 4):
Fig. 4. ACF of differenced Core CPI data.
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Unfortunately, the lags exceed the critical values for all lags, so the data is differenced again (Fig. 5):
Fig. 5. ACF of twice-differenced Core CPI data.
Now, much better results are achieved. Significant autocorrelation at lags = 1 and 3 is noted. To test whether the twice-differentiated data is stationary, the Augmented Dickey-Fuller Test is conducted [11]. Since the p-value is lower than 0.01, which does not exceed the critical value of 0.05, the null hypothesis that the data is non-stationary is rejected, and therefore the data is stationary. The results are shown in Table 1 below: Table 1. Results of Augmented Dickey-Fuller Test. Dickey-Fuller
Lag order
p-value
Alternative hypothesis
−13.57
9
0.01
Stationary
3 Empirical Results Analysis First, an autoregressive integrated moving average (ARIMA(p, d, q)) model was used. Here, all the possible ARIMA models for p ≤ 3, q ≤ 3, and d = 2 have been listed out (Table 2).
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Table 2. ARIMA Models and results. Model
Log-likelihood
AIC
AICc
ARIMA(0, 2, 0) ARIMA(1, 2, 0)
BIC
−908.8
1819.59
1819.6
1824.26
−871.49
1746.97
1746.99
1756.31
ARIMA(2, 2, 0)
−866.59
1739.18
1739.21
1753.18
ARIMA(3, 2, 0)
−839.88
1687.77
1687.82
1706.43
ARIMA(0, 2, 1)
−832.94
1669.88
1669.89
1679.21
ARIMA(1, 2, 1)
−800.88
1607.77
1607.8
1621.77
ARIMA(2, 2, 1)
−800.61
1609.22
1609.27
1627.88
ARIMA(3, 2, 1)
−781.34
1572.68
1572.76
1596.01
ARIMA(0, 2, 2)
−805.63
1617.27
1617.3
1631.26
ARIMA(1, 2, 2)
−800.77
1609.54
1609.59
1628.2
ARIMA(2, 2, 2)
−798.94
1607.88
1607.96
1631.21
ARIMA(3, 2, 2)
−770.53
1553.07
1553.18
1581.06
ARIMA(0, 2, 3)
−792.86
1593.73
1593.78
1612.39
ARIMA(1, 2, 3)
−792.73
1595.47
1595.54
1618.79
ARIMA(2, 2, 3)
−782.15
1576.29
1576.4
1604.28
ARIMA(3, 2, 3)
−770.42
1554.84
1554.98
1587.5
Here all the possible ARIMA models for p ≤ 3, q ≤ 3, and d = 2 have been listed out. Higher order terms were not considered, as the effect is negligible, and we follow the parsimony principle which prioritizes the smallest number of parameters possible in the models - more parameters result in an increased risk in overfitting [12]. Note that models with the greatest log-likelihood and lowest AIC, AICc and BIC are generally better [13, 14]. A few candidates are considered, first filtering by Log-likelihood: ARIMA (3, 2, 3) with log-likelihood -770.42, ARIMA (3, 2, 2) with log-likelihood -770.42, ARIMA (3, 2, 1) with log-likelihood -781.34, and lastly ARIMA (2, 2, 3) with log-likelihood -782.15. All other models have log-likelihoods that are too small to be considered alongside these four candidate models. Now, the models are filtered by AIC, AICc, and BIC. The ARIMA (3, 2, 2) has values that are much less than the others’, except ARIMA (3, 2, 3). Here, we consider that that ARIMA (3, 2, 2) has a log-likelihood that is only very slightly lower than ARIMA (3, 2, 3)’s (the absolute percentage difference is approximately ~ 0.02%), yet the AIC AICc and BIC are much less than ARIMA (3, 2, 3)’s (indeed, 0.4% lower than ARIMA (3, 2, 3) for the BIC, which shows it is a better model). The ARIMA (3, 2, 2) model is selected for the Core CPI time series data. Moreover, it also has one fewer parameter than ARIMA (3, 2, 3), which is thus beneficial (Table 3).
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L. Eric Table 3. Coefficients of the ARIMA (3, 2, 2) model.
Coefficients
AR(1) 0.9420
S.E.
0.0759
AR(2)
AR(3)
MA(1)
MA(2)
−0.2851
−0.2435
−1.5129
0.6911
0.0570
0.0405
0.0691
0.0701
Calculating, the data is presented above, where AR and MA refer to the autoregressive and moving-average terms respectively, and S.E. refers to the standard error. The Box-Ljung test (which tests for autocorrelation at multiple lags) on the residuals [15] (Table 4). Table 4. Box-Ljung test and results. Q*
df
p-value
Model df
Total lags used
28.992
15
0.01612
5
20
As observed, the p-value is 0.01612 which also does not exceed the critical value of 0.05, implying that there is indeed an auto-regressive component to the data. The accuracy of the data is also validated (Table 5): Table 5. Training and Test Errors. ME
RMSE
MAE
MPE
MAPE
MA SE
ACF1
Theil’s U
Training set
0.0070
0.5052
0.3667
0.0066
0.1257
0.0286
0.0037
—
Test set
3.5721
8.2005
6.2005
0.3916
0.7268
0.4829
0.9687
5.7442
The results are shown above. As can be seen, the low MPE and MAPE (Mean Average Percentage Error) for both the training and test set imply that the model has a very good fit, and thus has high predictive power. The following results are also achieved, which implies that there is significant autocorrelation at lags 7, 12 and 25. However, given that the ACFs just fall outside the critical regions, it is assumed that it is due to randomness. It is noted that the residuals from this ARIMA (3, 2, 2) are largely distributed normally, as shown in Table 6:
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Table 6. ARIMA (3, 2, 2) forecasts of future Core CPI values. Point Forecast
Lo 80
Hi 80
Lo 95
Hi 9 5
Aug 2022
1040.331
1039.501
1041.160
1039.062
1041.599
Sep 2022
1044.633
1043.186
1046.080
1042.420
1046.846
Oct 2022
1049.636
1047.600
1051.672
1046.522
1052.749
Nov 2022
1055.038
1052.504
1057.572
1051.163
1058.913
Dec 2022
1060.610
1057.628
1063.592
1056.050
1065.171
Jan 2023
1066.058
1062.627
1069.489
1060.810
1071.306
Feb 2023
1071.243
1067.308
1075.178
1065.225
1077.261
Mar 2023
1076.175
1071.647
1080.703
1069.250
1083.100
Apr 2023
1080.973
1075.759
1086.187
1072.999
1088.948
May 2023
1085.782
1079.808
1091.756
1076.646
1094.918
Jun 2023
1090.700
1083.924
1097.476
1080.337
1101.063
Jul 2023
1095.751
1088.154
1103.348
1084.133
1107.369
Aug 2023
1100.893
1092.468
1109.319
1088.008
1113.779
Sep 2023
1106.057
1096.792
1115.322
1091.888
1120.226
Oct 2023
1111.183
1101.059
1121.306
1095.700
1126.665
Nov 2023
1116.244
1105.233
1127.256
1099.403
1133.085
Dec 2023
1121.250
1109.316
1133.185
1102.999
1139.502
Jan 2024
1126.233
1113.341
1139.124
1106.517
1145.949
Feb 2024
1131.224
1117.344
1145.103
1109.997
1152.451
Mar 2024
1136.243
1121.350
1151.136
1113.466
1159.020
Listed above are the forecasts for the next 20 months starting from August and listed below are forecasts for the next 100 months. It can see that there is an upwards trend, and core inflation is forecasted to be 5.64% from Jan 2023 to Jan 2024 (Figs. 6 and 7). This implies that there will likely be sustained increases in the core CPI, and policymakers may wish to consider continuing with the policy of contract fiscal policy for the time being - at least the next 6 months. Now, another method of predicting the future Core CPI values is introduced: exponential smoothing. First, simple exponential smoothing is considered. The results of the errors are as follows (Table 7):
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Fig. 6. Forecasts of Core CPI data from the ARIMA (3, 2, 2) model.
Fig. 7. Residuals from ARIMA (3, 2, 2).
As the values of alpha increase, the errors decrease. However, even when alpha equal to 0.9, the errors, especially the ME, RMSE and MAE, are very high and exceed 1. For this reason, this article opts not to consider simple exponential smoothing but instead consider more advanced methods, such as the Holt-Winters method. Moreover, simple exponential smoothing is best suited for data without trends, and it is clear there is an upwards trend in the Core CPI data as inflation is always positive during periods of recession. The Holt-Winters method is now used, and familiarity with exponential smoothing and the Holt-Winters method is assumed. Nonetheless, the above concepts are briefly summarized as follows: exponential smoothing uses coefficients for past terms that decrease exponentially for increasingly earlier terms. The Holt-Winters method involves
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Table 7. Errors from simple exponential smoothing. Alpha
ME
RMSE
MAE
MPE
MAPE
MASE
0.2
5.811228
6.94165
5.815461
1.438282
1.442508
0.4147168
0.3
3.910981
4.748928
3.920238
0.9703286
0.9732948
0.2795632
0.4
2.947998
3.626589
2.962129
0.7323564
0.7357729
0.2112377
0.5
2.365614
2.944897
2.382382
0.5881449
0.5920528
0.1698944
0.6
1.975342
2.486974
1.99417
0.4914023
0.495713
0.1422098
0.7
1.695501
2.158159
1.715645
0.4219674
0.4266419
0.1223474
0.8
1.485003
1.910775
1.505451
0.3697125
0.3745601
0.1073579
0.9
1.320928
1.718357
1.342722
0.3289938
0.3342493
0.09575326
the forecast equation and three smoothing equations — one for the level t , one for the trend bt , and one for the seasonal component st , with corresponding smoothing parameters α, β, and γ [16]. The seasonal component may be omitted from our analysis as the data provided is already seasonally adjusted. ETS stands for Error, Trend, and Seasonal. Given that there is no seasonal component, as well as the error should be multiplicative, and the trend should be additive, the ETS (M, A, N) model is picked as a candidate (Fig. 8). The data from the above model is thus generated as follows (Table 8): Table 8. ETS (M, A, N) results. alpha
0.9999
beta
0.227
Initial state: I
99.7189
Initial state: b
0.2817
Sigma
0.0016
AIC
4444.345
AICc
4444.422
BIC
4467.687
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Fig. 8. Components of ETS (M, A, N) models.
The accuracy and fit of the model are tested as follows (Table 9): Table 9. Training and Test Errors. ME
RMSE MAE
MPE
Training set 0.0052 0.5087 0.3662 0.0052 Test set
MAPE MASE ACF1
Theil’s U
0.1249 0.0286 0.0246 —
4.5187 9.1625 6.9739 0.502373096 0.8159 0.5431 0.9694 6.4052
Again, the low MPE and MAPE signify that the model is accurate and a good fit. Thus, it is now possible to forecast Core CPI in the upcoming years with this model (Fig. 9): It is observed that there is a very large spread in the forecast for values a few years in the future. For this reason, it is inadvisable to use the forecast from this Holt-Winters method for long-term policy planning, as both the 80% and 95% confidence intervals are extremely large, and the light blue area even suggests that there is a risk of prolonged deflation in the next few years, which is extremely unlikely. Comparing the two ARIMA (3, 2, 2) and the ETS (M, A, N) models, it is observed that for the same test window, the ETS (M, A, N) model has a significantly lower MPE and MAPE. It is thus advisable to select that as the preferred model to use.
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Fig. 9. Forecasts from ETS (M, A, N).
4 Conclusion This study found that inflation will have an upward trend in the upcoming years and is likely to continue at a rate that is like that of the last few years. This is likely the result of heightened consumer demand as interest rates were slashed to zero at the beginning of the COVID-19 pandemic, to revive the economy. This implies that policymakers should adopt a contractionary fiscal policy to curb spending and reduce demand-pull inflation, as inflation erodes savings and earning power, and increases the cost of living for hundreds of millions of Americans. This can be achieved by the Federal Reserve raising interest rates in an appropriate manner and achieving a “soft landing” that achieves a 2% inflation target without causing a recession. A 5.64% core inflation rate is predicted from Jan 2023 to Jan 2024. The results are also that the Core CPI time series data must be differenced twice to achieve stationarity, and only then can an ARIMA model be used. It has been calculated that the ARIMA (3, 2, 2) model is the best ARIMA model, as it results in the least Log-likelihood, and among the lowest AIC, AICc, and BIC. The respective training and test errors are very low, including the MAE and MAPE, therefore the model is a good fit for the Core CPI. Meanwhile, it is observed that simple exponential smoothing is less useful as a method when forecasting, as errors are compounded. This research report has helped to fill in the gap of recent forecasting based on recent data, as other research reports using the ARIMA model focus on the CPI of other countries, or the CPI instead of the Core CPI in the United States or are several years old. Further improvements to the research can be conducted by considering global macroeconomic factors when forecasting, or by making accurate forecasts via the TBATS method. The moving average method can also be employed as another method for forecasting.
References 1. Friedman, M.: Inflation. Causes and Consequences, Asia Publishing House: New York (1963)
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2. The New York Times Homepage. https://www.nytimes.com/. Accessed 20 Sept 2022 3. Kharimah, F., Usman, M., Elfaki, W., Elfaki, F.A.M.: Time series modelling and forecasting of the consumer price Bandar Lampung. Sci. Int (Lahore) 27(5), 4119–4624 (2015) 4. Xiao, M.J., Xia, R.Y.: Inflation forecasting in China, An empirical analysis based on ARIMA model. Shanghai Finance 8, 38–42 (2008) 5. Junttila, J.: Structural breaks, ARIMA model and Finnish inflation forecasts. Int. J. Forecast. 17, 203–230 (2001) 6. Adam, S.O., Awujola, A., Alumgudu, A.I.: Modeling Nigeria’s CPI using ARIMA model. Int. J. Dev. Econ. Sustain. 2(2), 37–47 (2014) 7. Jere, S., Siyanga, M.: Forecasting inflation rate of Zambia using holt’s exponential smoothing. Open J. Stat. 6, 363–372 (2016) 8. Pujiati, E., Yuniarti, D., Goejantoro, R., Peramalan, D., Menggunakan, M.: Double exponential smoothing Dari Brown. EKSPONENSIAL 7(1), 33–40 (2017) 9. Kalkuhl, M., von Braun, J., Torero, M.: Volatile and extreme food prices, food security, and policy: an overview. In: Kalkuhl, M., von Braun, J., Torero, M. (eds.) Food Price Volatility and Its Implications for Food Security and Policy, pp. 3–31. Springer, Cham (2016). https:// doi.org/10.1007/978-3-319-28201-5_1 10. Mohamed, J.: Time series modeling and forecasting of Somaliland consumer price index: a comparison of ARIMA and Regression with ARIMA Errors. Am. J. Theor. Appl. Stat. 9(4), 143–153 (2020) 11. Leybourne, S., Kim, T.-H., Newbold, P.: Examination of some more powerful modifications of the Dickey-Fuller test. J. Time Ser. Anal. 26, 355–369 (2005) 12. Hyndman, R.: ARIMA processes (2007) 13. McElreath, R.: Statistical Rethinking: A Bayesian Course with Examples in R and Stan, vol. 189. CRC Press. (2016) 14. Lee, H., Ghosh, S.K.: Performance of information criteria for spatial models. J. Stat. Comput. Simul. 79(1), 93–106 (2009) 15. Hossein, H., Mohammad Reza, Y,: Selecting optimal lag order in Ljung–Box test. Phys. A: Stat. Mech. Appl. 541 (2020) 16. Chatfield, C.: The Holt-winters forecasting procedure. J. R. Stat. Soc.: Ser. C (Appl. Stat.) 27, 264–279 (1978)
The Differences Between Longping High-Tech’s Company Value and Stock Prices of Agricultural and Forestry Industry Based on Factor Analysis and Structural Equation Model Heying Xu and Weizhe Feng(B) International College Beijing, China Agricultural University, Beijing 100083, China [email protected]
Abstract. Aiming to research the differences between Long Ping High-Tech’s company value and stock prices of the agricultural and forestry industry, this study separately researched factors that affect the value of Longping High-Tech and stock prices of the agricultural and forestry industry on A shared stock market. When researching the factors that affect the value of Longping High-Tech, the study made a questionnaire for securities analysts who majored in the agricultural and forestry industry and used factor analysis to find the answer. When researching the factors that affect the industry’s stock prices, the research builds a System Equation Model (SEM) and uses AMOS to find the significant influence factor. After finding these factors, the study compared the differences and found that the value of Long Ping High-Tech is not fully reflected in its stock prices. This paper used such a method to prove that there might be no relationship between the company value and the price of the stock for Long-Ping High Tech, so as to offer some references for future research. Keywords: Longping High-Tech · Company Value · Stock Price · Factor Analysis · System Equation Model
1 Introduction The constant fluctuation of price is the movement of recession to the intrinsic value; in other words, value is distinct from price, and price moves around the axle of value [1]. It is particularly reflected in the stock market. In the stock market, the price of a stock fluctuates around the value of the company. On the one hand, the stock price is relevant to the company’s value, for stock represents the company’s ownership; on the other hand, stock prices move around or deviate company’s value, which happens due to some outside factors. Longping Hi-Tech is a very typical example. As China’s agricultural policies continue to deepen in 2021, the value of this company is rising. However, in the stock market, the price of its stock almost remains the same. Based on this, this paper uses Longping High-Tech as an example to research the differences between value and price. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 715–727, 2023. https://doi.org/10.1007/978-981-99-6441-3_65
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The paper used such a method to prove that there might be no relationship between the company value and the price of the stock for Long-Ping High Tech, hoping to offer some references for future research.
2 Literature Review 2.1 The Value of a Company When making the questionnaire to find the factors that affect the value of the company, the research referenced PEST analysis, Porter’s five forces model, and SWOT analysis. PEST Analysis. One technique for examining the macroeconomic aspects affecting the company is the PEST (Political, Economic, Social, and Technological) method (analysis) [2]. In this case, most often can be specified as (Fig. 1).
Fig. 1. PEST analysis [3]. Source: online.visual-paradigm.com.
Porter’s Five Forces Model. Michael Porter’s 5 forces model, the tool created by M. Porter in 1979, is used to help figure out how five key competitive forces are affecting an industry [4]. Figure 2 shows the factors given by Michael Porter (Fig. 2). SWOT Analysis. The SWOT (Strength, Weakness, Opportunity, Threats) method (analysis) is one of the methods to examine the internal and external factors of the company. In this case, most often can be specified as (Fig. 3).
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Fig. 2. Porter’s five forces model [4]. Source: Pest analysis template with reference to online.visual-paradigm.com
Fig. 3. SWOT analysis. Note: original by the author
2.2 Stock Price of the Industry Stock prices are primarily affected by four factors: macroeconomic factors, industrial factors, organization factors, and stock market factors. However, because one of the goals of this research is to find factors that affect the stock price of the industry where Longping High-Tech belongs, we do not need to focus on the industrial factors. Macroeconomic Factors. Macroeconomic factors can affect the stock price; among them, GDP and currency flows can be two important factors. In 2019, Vychytilová et al. found that GDP can explain stock volatility [5]. As for currency flow, it might seem a bit complicated. According to Franck and Young’s study from 1972, there is no discernible relationship between the exchange rate and stock prices [6]. Using the Nonlinear Least Square approach, Ong and Izan analyzed the relationship between stock prices and exchange rates in 1999 and discovered a very weak correlation between the US stock
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market and currency rates [7]. While Soenen and Henniger discovered a considerably negative association between the value of the US dollar and stock prices in 1998 by collecting monthly data on stock prices and effective exchange rates for the years 1980 through 1986 [8]. Therefore, stock prices might be affected both by GDP and currency flows. Stock Market Factors. In 2017, Li found that the stock market can also influence the price of the stock. Therefore, stock market factors are also important to the price of the stock [9]. Organizational Factors. In 2007, through Albadvi’s research on the Hedland stock market, the stock price has a significant connection with the company’s fundamental information [10]. Therefore, the stock price can be evaluated based on the four following organizational factors: Turnover rate of total assets, roe (weighted), cash flow from operating activities, and liability/assets ratio.
3 Methodology 3.1 Research Hypotheses This study assumes that macroeconomic factors, stock market factors, and organizational factors all have positive impact on the price of the stock in the agricultural and forestry industry: H1: macroeconomic economic factors have a positive impact on the price of the stock. H2: stock market factors have a positive impact on the price of the stock. H3: organizational factors have a positive impact on the price of the stock. 3.2 Research Model Based on the hypothesis, the model is shown in Fig. 4.
Fig. 4. Research model. Note: original by the author.
After adding all the latent variables, the model is shown in Fig. 5.
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Fig. 5. Model with the latent variables. Note: original by the author.
3.3 Research Design As for the research design, this study mainly had two steps. The first step is to find the factors affecting the value of the company. In this step, this study first designed a Likert scale questionnaire covering prime factors affecting the company’s value, then sent them to stock analysts who majored in the agricultural and forestry industries. Finally, after receiving the answer, by using factor analysis, the study would receive some prime factors that affect the company’s value. The second step is to find factors affecting the price of the stock. In this step, the study first found some main factors, then made three hypotheses, and finally built the preliminary model. According to the objective of finding factors affecting the company’s value and based on the literature review, the study divided the questionnaire questions into two parts. The first part is about Macro-environment. In this part, there are 20 questions covering factors of the political environment, economic environment, social environment, and technological environment. The second part is about competitive analysis. There are 18 questions covering factors of strengths, weaknesses, opportunities, and threats in this part. The questionnaire has many draft versions, and Professor Feng helped provide suggestions. Compared with the draft version, the final version of the questionnaire is more detailed in the first part to learn about respondents’ information and left out many repeated expressions. 3.4 Selection of Variables Macroeconomic Factors. Macroeconomic factors can affect stock prices. Among them, this study selected GDP and Ten-year Treasury Bond Yield. The reason to choose
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GDP is that it can reflect the country’s economic level, affecting the decision in the stock market. Besides, the study selected Ten-year Treasury Bond Yield because treasury bonds and stocks are substitutes; if the Ten-year Treasury Bond Yield rise, then fewer investors will buy stocks. Stock Market Factors. Stock market factors can also affect stock prices. For example, when stocks in a stock market are generally well-formed, consumer confidence will generally increase, and the price of each stock will change. Two factors can significantly reflect the environment of the stock market; they are the Average Trading Volume of Stocks and The Shanghai Composite Index. Organizational Factors. Besides, organizational factors can affect the stock price of a company. It happens because investors usually buy stocks from promising companies. And whether a company has a bright future can usually be decided based on the financial statements.
3.5 Data Collection After designing the Likert Scale questionnaire, this study handed out the questionnaire to stock analysts who majored in the agricultural and forestry industries. After sending the questionnaires, the study collected 56 samples. After collecting samples, the study did the factor analysis. Based on the model previously built, this study also collected data from companies in the agricultural and forestry industry of the A-shares stock market in the recent 5 years. Eliminating those companies that have been listed for less than two years, there are total of 19 companies and we can get 94 sets of data. In each data, there are 9 variables (x1, x2, x3, x4, x5, x6, x7, x8, x9). Each variable represents a latent variable. After collecting the data, because some variables are too different (for example, the number of GDP and Liability/Asset Ratio is more than ten million times, the study did data standardization to eliminate these differences. The formula of data standardization is as follows in formula (1): xi =
xi − ximax (i = 1, 2, 3, 4, 5, 6, 7, 8, 9) ximax − ximin
(1)
3.6 Factor Analysis Reliability Test. Reliability refers to the stability of research results and Cronbach proposed a method using Cronbach’s coefficient α. The reliability of the questionnaire is deemed to be very good when the Cronbach’s coefficient (α) is more than 0.9. When α is between 0.8 to 0.9, it is regarded as good. The reliability is considered acceptable when 0.7 < α < 0.8. However, the reliability is deemed to be insufficient when α < 0.7. According to the data analysis of SPSS, the Cronbach’s coefficient of all variables in this questionnaire is 0.940, which is above 0.9, so the reliability is excellent.
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Validity Test. A validity test is about whether the questionnaire can match what researchers aim to test. Factor analysis is regarded to be inapplicable when the KMO (Kaiser-Meyer-Olkin) value is less than 0.5. The questionnaire’s validity is high when the KMO value is greater than 0.5 and the Bartlett spherical test’s significant probability is 0 (less than 0.01). According to SPSS data analysis, the KMO value is 0.653 and the spherical test is 0 (Table 1). It shows that there is a correlation and validity among different variables.
Table 1. The result of KMO and Bartlett’s test. KMO Measure of Sampling Adequacy
.653
Bartlett’s Test
.000
Significance
Note: Original by the author
4 Empirical Analysis and Result Analysis 4.1 Factor Analysis Results Based on the factor analysis, the study finds the rotated factor loading matrix that is shown in appendix 2. Based on the matrix, the study got 8 prime factors that affect the value of the Longping High-Tech. They are: (1) Alternative Products; (2) Food genetically modified policy of China; (3) Production and marketing model; (4) Manpower Management; (5) National Policy; (6) International Environment; (7) Forecast of Future Market; (8) Grain Demand. 4.2 AMOS Analysis AMOS Used for Further Analysis. P value shows whether the influence between two variables is significant enough and an asterisk indicates a significant correlation. If there is nothing wrong with the model, the estimate value should be −1–1 (Table 2).
Table 2. The result of correlation between different variables analyzed by AMOS. Estimate
S.E
C.R
P
Stock_Prices
F
R-squared
Ln firm size
appearance**
0.074
0.03
2.51
0.012
[0.02,0.13]
0.01
0.0018
*** p < 0.01, ** p < 0.05, * p < 0.1
average wage of their employees; and the higher the score of appearance, the more likely that the employees would go to a larger size firm. So, in short, appearance has a significant influence on employees’ wages through the mechanism of the different sizes of the firms they go. Whether the Employee Have Signed Labor Contract. Figure 7 demonstrates the different average appearance scores for the groups with and without labor contracts. Apparently, the group with labor contracts have a higher average appearance score. The t-test statistics for mean value difference is 6.74, and the P-value is 0.00, so it’s significant at the 1% level.
Fig. 7. Relationship between appearance and having labor contract.
Overall, in Sect. 4.3, we conclude that physically attractive employees more often enter higher-paying positions, and this is one of the main reasons that physically attractive employees earn a beauty premium in their wages/incomes. 4.4 Would Physically Attractive Employees Have Higher Likelihood in Promotion? Figure 8 shows the different average appearance scores for the group with and without administrative ranks. Apparently, the group with administrative ranks has a higher
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Fig. 8. Relationship between appearance and administrative ranks.
average appearance score. The t-test statistics for mean value difference is 3.14, and the P-value is 0.00, so it’s significant at the 1% level.
Fig. 9. Relationship between appearance and having subordinate.
Similarly, Fig. 9 shows the different average appearance scores for the group with and without subordinates. Apparently, the group with subordinates has a higher average appearance score. The t-test statistics for mean value difference is 2.36, and the P-value is 0.02, so it’s significant at the 5% level.
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One suspicious argument is whether the promotions are highly related to the ‘positions’ we discussed in Sect. 4.3. To address this, we control the four types of factors we discussed in Sect. 4.3, and the regression result using Eq. (3) in Table 7 suggests that appearance would still contribute to promotions after controlling the ‘position’ relevant variables. Table 7. Regression of promotion on appearance. Dependent
Independent
Coefficient
administrative ranks
Industry dummy
Controlled
SE
T-statistics
P-value
[95% CI]
Ownership dummy
Controlled
Ln firm size***
0.01
0.004
2.95
0.00
[0.004,0.018]
Labor contract***
0.04
0.01
2.72
0.01
[0.01,0.07]
appearance**
0.01
0.005
1.99
0.047
[0.0002,0.0202]
Prob > F
R-squared
0.00
0.04
*** p < 0.01, ** p < 0.05, * p < 0.1
Overall, in Sect. 4.4, we conclude that physically attractive employees have higher likelihoods of promotion on average, and this is another main reason that physically attractive employees would have a premium in their wages/incomes. 4.5 Would Physically Attractive Employees Have Higher Human Capital? In this section, we use the education to measure human capital, and therefore, we would like to investigate whether physically attractive employees are more likely to be educated after controlling what we discussed in Sect. 4.3 and 4.4. Table 8. Regression of education on appearance. Dependent
Independent
Coefficient
education
Industry dummy
Controlled
Ownership dummy
Controlled
SE
T-statistics
P-value
[95% CI]
Ln firm size***
0.065
0.017
3.95
0.00
[0.033,0.098]
Labor contract***
0.64
0.07
9.65
0.00
[0.51,0.77]
administrative***
0.51
0.10
5.28
0.00
[0.32,0.70]
subordinate
0.27
0.18
1.47
0.14
[-0.09,0.62]
appearance***
0.13
0.02
5.4
0.00
[0.08,0.18]
Prob > F
R-squared
0.00
0.33
*** p < 0.01, ** p < 0.05, * p < 0.1
Table 8 runs a regression of Eq. (4), suggesting that appearance contributes to level of education after controlling the ‘position’ and ‘promotion’ relevant variables. Overall, in Sect. 4.5 we conclude that physically attractive employees would have higher levels of education, and this is another main reason that physically attractive employees have a premium in their wages/income.
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4.6 Does the Special Treatment of Attractive Employees Still Exist After Controlling These Variables? In this section, we answer the key question of our research paper, which was as follows: After considering all these mechanisms we mentioned, does the beauty premium still exist for physically attractive employees? To answer this question, we run regressions of Eq. (5) and (6), and we added the control variables we discussed group-by-group in Sect. 4.3 to 4.5. In regression (1), we ran a regression of wage on appearances and controls. The control variables include age, sex, marriage status, height, weight, the health condition of the surveyed individual, and cosmetology expenditure. In regression (2), we ran a regression of wage on appearances, controls and ‘position’ relevant variables. In regression (3), we ran a regression of wage on appearances, controls, ‘position’ relevant variables and ‘promotion’ relevant variables. In regression (4), we ran a regression of wage on appearances, controls, ‘position’ relevant variables, ‘promotion’ relevant variables and educations. In regressions (5) through (8), we ran similar regressions, but instead used income as the dependent variable. Since the controls and the fix effects of dummy variables are not what we are focusing on, we do not report all coefficients here. Table 9. OLS Regression of wage or income on appearance. wage(1)
wage(2)
wage(3)
wage(4)
income(5)
income(6)
income(7)
income(8)
Appearance
3.571***
2.989***
2.895***
2.308***
4.059***
3.363***
3.266***
2.588***
(0.957)
(0.941)
(0.901)
(0.884)
(1.051)
(1.044)
(1.008)
(0.988)
Controls
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Industry dummy
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Ownership dummy
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Ln firm size
2.016***
1.577***
1.339***
2.288***
1.853***
1.578***
(0.419)
(0.402)
(0.395)
(0.465)
(0.450)
(0.441)
11.70***
9.804***
6.847***
10.92***
9.047***
5.631***
(1.866)
Labor contract
(1.682) administrative
subordinate
(1.616)
(1.611)
(1.809)
(1.801)
5.390**
3.012
6.621**
3.874
(2.325)
(2.290)
(2.603)
(2.560)
43.50***
41.54***
41.32***
39.05***
(4.320)
(4.934)
(4.829)
(4.407) Educations
5.166***
5.967***
(0.535)
(0.598)
F-test
35.97***
14.39***
19.87***
22.50***
33.34***
12.97***
17.09***
19.87***
Observations
2,303
2,187
2,187
2,187
2,303
2,187
2,187
2,187
R-squared
0.111
0.203
0.270
0.301
0.104
0.187
0.242
0.275
Table 9 shows the results of the regression, wherein we can see that the results are very consistent. Generally speaking, after controlling basic characteristics, a one point
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score increase in appearance (scale from 1 to 7 as we mentioned) would increase annual wage by ¥3571 on average. If we take the mechanisms we discussed in Sect. 4.3 to 4.5 into consideration, the effect would reduce to ¥2308, which is about 60% of the original value. The very close result would be drawn if we consider income. Therefore, in conclusion, physically attractive employees have higher earnings, and approximately 40% of this income is attributable to the ‘position’, ‘promotion’ and ‘human capital’ mechanisms. The remaining 60% is attributable to the traditional favoritism mechanism.
5 Robustness Test Though the results from Table 9 are consistent with our expectation, we nevertheless tested the robustness of our results. In this test, we use following three methods. The first method involves using multiple measure of income. In previous discussions, we simultaneously use two different measures of income. In all conditions, we used variable wage and income, where wage represents the monetary and non-monetary benefits provided by a primary employer and income represents income from all sources. The result for using wage and income are very similar and consistent with one another. The second method involved using the whole sample test. As we discussed above, we sought to ensure consistency across elements between individuals, and therefore selected urban households for the sample. In the robustness test, we then used the whole sample (both urban and rural samples together) to redo the same thing through Sect. 4.1 to Sect. 4.6. We yielded a less significant yet consistent result. The third method involved conducting an instrument variable. As discussed in Hamermesh et al. (2002), reverse causation may exist between appearance and income. Specifically, when income increases, the expenditure on dressing and cosmetology may increase. A way to deal with endogeneity is to use instrument variables. Here we would use neatness as the instruments for appearance. It’s obvious that whether people dress neat and clean would affect their appearance. And the connection between income and neatness would be much weaker, as people could be neat and clean regardless of income. We report the first stage regression results in Table 10, and we post the results of second stage IV regression in Table 11 of Eq. (5) and (6); we could see a consistent and robust result similar to that of Table 9. We could also use IV family average score of appearance, and get a less significant but nevertheless consistent result. Table 10. First stage IV Regression of appearance on neatness. Dependent
Independent
Coefficient
SE
T-statistics
P-value
[95% CI]
Prob > F
R-squared
appearance
neatness***
0.899
0.008
112.14
0.000
[0.883,0.915]
0.00
0.76
*** p < 0.01, ** p < 0.05, * p < 0.1
Table 10 reports the first stage regression of IV regression, we could see that the instrument neatness could be valid. It has impact on appearance variable.
890
X. Zhang Table 11. IV Regression of wage or income on appearance. wage(1)
wage(2)
wage(3)
wage(4)
income(5)
income(6)
income(7)
income(8)
Appearance
4.056***
3.176**
3.078**
2.226*
3.852**
2.912*
2.816*
2.510**
(1.404)
(1.382)
(1.323)
(1.301)
(1.541)
(1.533)
(1.481)
(1.025)
Controls
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Industry dummy
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Ownership dummy
Controlled
Controlled
Controlled
Controlled
Controlled
Controlled
Ln firm size
2.017***
1.578***
1.339***
2.287***
1.852***
2.088***
(0.419)
(0.402)
(0.395)
(0.465)
(0.450)
(0.334)
11.69***
9.794***
6.850***
10.95***
9.073***
6.255***
(1.867)
Labor contract
(1.683) administrative
subordinate
(1.617)
(1.612)
(1.810)
(1.306)
5.385**
3.012
6.633**
8.547***
(2.326)
(2.290)
(2.604)
(1.991)
43.50***
41.54***
41.31***
37.69***
(4.320)
(4.934)
(3.713)
(4.407) Educations
5.169***
0.975***
(0.536)
(0.142)
F-test
35.27***
14.26***
19.75***
22.41***
32.25***
12.79***
16.91***
27.04***
Observations
2,303
2,187
2,187
2,187
2,303
2,187
2,187
3,443
R-squared
0.111
0.203
0.270
0.301
0.104
0.187
0.241
0.248
Table 11 suggests the result of regression; we see that the results are very consistent with those of Table 9. Physically attractive employees have higher wages, approximately 40% of which can be attributed to the ‘position’, ‘promotion’ and ‘human capital’ mechanisms. The remaining 60% can be attributed to the traditional favoritism mechanism.
6 Conclusion While the beauty premium and its evaluations are widely discussed, this paper supplements a new mechanism as to how physical attractiveness can influence employees’ wages, going beyond the existing discrimination and favoritism frameworks. We observe that physically attractive employees would have higher wages, and there are other mechanisms than personal confidence and favoritism. We discover that physically attractive employees are more likely to find desirable positions, have higher prospects of advancement, and are more educated. In addition, they are more likely to receive special treatment. Overall, approximately 40% of the beauty premium is attributable to the ‘position’, ‘promotion’ and ‘human capital’ mechanisms, while the remaining 60% is attributable to the traditional favoritism mechanism. Due to the data limitation, measurement error of appearance is inevitable as we use the investigators’ subjective evaluation, which would be influenced by many other factors. A improved method could be using artificial intelligence classification rating, to guarantee objective and precise data. Further more, panel data could be applied in dealing with unobserved factors. And our further research would focus on these aspect to supplement current literature.
A Empirical Research on the Relationship Between Appearance and Income
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References 1. Hamermesh, D.S., Biddle, J.: Beauty and the labor market (1993) 2. Roszell, P., Kennedy, D., Grabb, E.: Physical attractiveness and income attainment among Canadians. J. Psychol. 123(6), 547–559 (1989) 3. Frieze, I.H., Olson, J.E., Russell, J.: Attractiveness and income for men and women in management 1. J. Appl. Soc. Psychol. 21(13), 1039–1057 (1991) 4. Mulford, M., Orbell, J., Shatto, C., Stockard, J.: Physical attractiveness, opportunity, and success in everyday exchange. Am. J. Sociol. 103(6), 1565–1592 (1998) 5. Loh, E.S.: The economic effects of physical appearance. Soc. Sci. Q. 7, 420–438 (1993) 6. Averett, S., Korenman, S.: The economic reality of the beauty myth (1993) 7. Mobius, M.M., Rosenblat, T.S.: Why beauty matters. Am. Econ. Rev. 96(1), 222–235 (2006) 8. Krebs, D., Adinolfi, A.A.: Physical attractiveness, social relations, and personality style. J. Pers. Soc. Psychol. 31(2), 245 (1975) 9. Eagly, A.H., Ashmore, R.D., Makhijani, M.G., Longo, L.C.: What is beautiful is good, but…: a meta-analytic review of research on the physical attractiveness stereotype. Psychol. Bull. 110(1), 109 (1991) 10. Andreoni, J., Petrie, R.: Beauty, gender and stereotypes: evidence from laboratory experiments. J. Econ. Psychol. 29(1), 73–93 (2008) 11. Agthe, M., Spörrle, M., Maner, J.K.: Does being attractive always help? positive and negative effects of attractiveness on social decision making. Pers. Soc. Psychol. Bull. 37(8), 1042–1054 (2011) 12. Johnson, S.K., Podratz, K.E., Dipboye, R.L., Gibbons, E.: Physical attractiveness biases in ratings of employment suitability: tracking down the “beauty is beastly” effect. J. Soc. Psychol. 150(3), 301–318 (2010) 13. Johnson, S.K., Sitzmann, T., Nguyen, A.T.: Don’t hate me because I’m beautiful: acknowledging appearance mitigates the “beauty is beastly” effect. Organ. Behav. Human Decis. Processes 125(2), 184–192 (2014) 14. Doorley, K., Sierminska, E.: Myth or fact? the beauty premium across the wage distribution in Germany. Econ. Lett. 129, 29–34 (2015) 15. Dechter, E.K.: Physical appearance and earnings, hair color matters. Labour Econ. 32, 15–26 (2015) 16. Hamermesh, D.S., Meng, X., Zhang, J.: Dress for success—does primping pay? Labour Econ. 9(3), 361–373 (2002) 17. Feingold, A.: Good-looking people are not what we think. Psychol. Bull. 111(2), 304 (1992) 18. Hatfield, E., Sprecher, S.: Mirror, Mirror: The Importance of Looks in Everyday Life. Suny Press, Albany (1986) 19. Institute of Social Science Survey, Peking University. China Family Panel Studies (CFPS). Peking University Open Research Data Platform, V42 (2015). https://doi.org/10.18170/DVN/ 45LCSO
Option Mispricing and Maturity Date: Evidence from China Yaqi Tu1 , Yidi Zhai2(B) , and Moan Lu3 1 School of Economics, Fudan University, Shanghai 200433, China 2 Business School, Beijing Institute of Technology, Zhuhai 519088, China
[email protected] 3 School of Interpreting and Translation Studies, Guangdong University of Foreign Studies,
Guangzhou 510420, China
Abstract. This paper examines whether short-term options are mispriced during 4-week expiration cycles or 5-week expiration cycles in the Chinese option market. Using data of 50ETF and 50ETF options on the Shanghai Stock Exchange (SSE) from 2015 to 2022, the paper finds a significant difference in options IV-HV in a 4-week cycle and 5-week cycle, suggesting a hint of options mispricing. This paper then conducts further examination by building up option portfolios including straddles, delta-hedged calls, and delta-hedged puts. The regression result shows that options with 5-week expiration cycles are significantly underpriced, which verifies the existence of mispricing in the Chinese option market. Keywords: Option Mispricing · Calendar Effect · Time Value · Option Strategy
1 Introduction Options prices are composed of their intrinsic value and time value, or their extrinsic value. As a probabilistic approach to assigning a value to an options contract, pricing options has been a prevailing topic with variegated practices, including the Black-Scholes model, the binomial options pricing model initiated by William Sharpe and formalized by Cox, Ross, and Rubinstein, and Monte-Carlo simulation method [1–3]. Ever since pricing models’ appearance, manifold studies to test their effectiveness have long been burgeoning since the existence of deviation of observed and theoretical options prices from these models. The previous studies calculate an implied volatility smile and illustrate the probability distribution of the future price of the underlying asset may be a mixture rather than a unimodal [4, 5]. Numerous scholars have delved into studying the factors driven options mispriced. They have investigated volatility-driven options mispricing [6–8] and time-driven options mispricing in different markets [9–11]. Following the existing literature, the paper extends the evidence on time-driven mispricing to include the Chinese option market, using 50ETF options with its underlying SSE 50ETF as the sample since they provide enough samples based on their large trading volume and earliest listing time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 892–898, 2023. https://doi.org/10.1007/978-981-99-6441-3_81
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By replicating Assaf Eisdorfer, Ronnie Sadka, and Alexei Zhdanov’s methods [9] in the US market, the paper finds that Chinese investors possibly ignore the option mispricing effect led by different maturity week cycles. To be specific, despite the underlying deficits of the pricing models, neophyte investors have a strong proclivity to treat 4-week options and 5-week options as the same, ignoring the extra time value of 5-week options, resulting in the mispricing of options. This paper first summarizes the options trading information, including Delta, Vega, Gamma, IV, HV, skewness, kurtosis, etc. and then selects options that satisfy both at-themoney and will mature next month to form three portfolios: delta-hedged calls, deltahedged puts, and straddles [9], subsequently computes and compares the weekly returns of these portfolios built up with options with 4 weeks between expiration date or 5 weeks between expiration date. Since options with distinct maturity are fundamentally different, straightly comparing the expiration-to-expiration return is not convincing enough. Thus, this paper also constructs two portfolios with fixed maturities by either step one week before or one week after the expiration date and calculates their monthly returns. All of the results point to the fact that mispricing that arises from the inattention to the exact expiration week-cycle exists in the Chinese option market. The rest of the paper is organized as follows: Sect. 2 describes the main variables and provides descriptive statistics; Sect. 3 presents the empirical tests and results; Sect. 4 concludes.
2 Data and Variables The data of SSE 50ETF covering the period from 2015 to 2022 are primarily obtained from Wind Database and Choice Database. Wind Database provides strike prices, daily opening prices and closing prices, daily trading volume and open interests, and Greeks. Choice Database provides SSE 50ETF Options implied volatility (IV) and underlying assets daily trading data. We compute option portfolio returns using closing prices. To obtain precise results, the paper filters the original data by the following process. Firstly, since the observations are 50ETF options that mature on the fourth Wednesday of each month and the time between expiration days is 4 or 5 weeks, it only includes Wednesday data of each option when the options market wasn’t closed. As well, the paper removed the samples with missing values and negative IV at the previous expiration date. The paper then matches the 50ETF option data with its underlying’s trading volume, market turnover, and net asset value obtained from the Choice database and exclude the options without underlying’s data since investment strategies can’t be formed without underlying assets. After data pre-processing, there are 2,337 options qualified (including 1,089 call and 1,248 put). The resulting sample data are summarized in Table 1. To capture the impact of option mispricing due to 4-week or 5-week maturities, the paper follows the literature [6, 9] to evaluate option’s mispricing in the cross-section by IV and IV-HV. The latter is a strong indicator of option mispricing [6]. This paper computes historical volatilities (HV) as the standard deviation of logarithmic returns based on daily closing prices over the past month and then annualized HV. For the other control variables in the regression model, the paper measures options liquidity by volume/open interest (i.e., the ratio of options volume at the end of the
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Y. Tu et al. Table 1. Summary Statistics. Calls
Puts
4-Week Cycle
5-Week Cycle
Obs = 711 Mean Volume/open interest
Sdv
0.73 0.03
4-Week Cycle
378 Median Mean 0.54
5-Week Cycle
813 Sdv
0.51 0.02
Median Mean 0.40
435 Sdv
0.67 0.02
Median Mean 0.44
Sdv
0.44 0.02
Median 0.33
Implied volatility
0.25 0.00
0.23
0.23 0.01
0.21
0.27 0.00
0.25
0.31 0.01
0.23
IV-HV
0.04 0.00
0.03
0.03 0.01
0.02
0.06 0.00
0.04
0.10 0.01
0.05
log(volume)
−0.74 0.04 −0.62
log(market-to-book)
0.00 0.00
0.00
Past stock return
0.05 0.01
0.05
4.64 0.12
4.29
Liquidity Skewness
−0.08 0.03 −0.04
−1.28 0.07 −0.93
0.00 0.00
−0.91 0.04 −0.81
−1.50 0.07 −1.10
0.00
0.00 0.00
0.00
0.00 0.00
0.00
−0.02 0.01 −0.02
0.05 0.01
0.05
0.00 0.01
0.00
4.46 0.11
4.26
4.10 0.16
3.22
4.14 0.18
3.18
−0.09 0.04 −0.08
−0.04 0.03 −0.04
−0.10 0.03 −0.08
Kurtosis
0.92 0.07
0.29
0.76 0.07
0.63
0.92 0.06
0.38
0.80 0.06
Gamma
1.43 0.04
1.25
1.36 0.06
1.18
1.29 0.03
1.07
1.24 0.04
0.63 1.01
Vega
0.17 0.00
0.17
0.19 0.01
0.18
0.17 0.00
0.17
0.21 0.00
0.21
trading day to the open interest in the same contract). The paper uses options Greeks (Gamma and Vega) as proxy variables for option riskiness, and also includes log marketto-book, the past return, liquidity, skewness and kurtosis of return as the characteristics of the options’ underlying 50ETF [9]. From Table 1, this paper finds that options with 4-week maturity are more liquid than those with a 5-week maturity. But for the mispricing indicators, IV and IV-HV, the result is mixed: 4-week call options’ IV and IV-HV are higher than 5-week call options, while the result is the opposite for puts. These differences suggest that options with 4-week cycles and 5-week cycles are priced differently and are potentially mispriced in some way and this possibly results from traders’ unawareness of the exact duration to maturity.
3 Empirical Test (1) A general analysis using portfolio returns In order to minimize the effect of the underlying asset, the paper builds portfolios of straddles, delta-hedged calls, and delta-hedged puts [9]. Considering that options with distinct maturities are fundamentally different, the paper equalizes the time value of option portfolios by dividing the data into two maturity groups, 4-week maturity and 5-week maturity, both containing 4-week cycle and 5-week cycle options. To be specific, for options with 4-week expiration cycle, the paper forms the portfolios at both the penultimate expiration date and the day one week before. For options with 5-week expiration cycle, the paper forms the portfolios at the penultimate expiration date and the day one week after. Then this paper separately computed the weekly returns of option portfolios with 4- and 5-week maturity.
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895
In this way, the time value difference of 4- and 5-week cycle options is removed from possible factors affecting portfolio return. For the next step, the paper forms three types of option portfolios to capture the difference in return which implies mispricing. For straddles, the paper picks single calls and puts with the same expiration date and the same strike price, the strike price of which is closest to the underlying price at the portfolio formation date. For delta-hedged calls, the paper fixed the position into long one call and short delta shares of the underlying asset, so that the more expensive the calls are, the lower the portfolio returns will be. For delta-hedged puts, the paper fixed the position into long one put and short delta (the put delta is negative) shares of the underlying asset for the same reason. The return of the portfolio is calculated as follows: the payoff at the option expiration date is subtracted by the initial investment (continuously compounding at the risk-free interest rate, with the risk-free rate SHIBOR 1M), and the result is divided by the absolute value of the initial investment to remove the scale effect. For each group (4-week verses 5-week maturity group), the paper normalizes the returns of the whole duration to weekly returns with the divider of week number. In the very first test, we roughly calculated the weekly returns of each portfolio with t-statistics, using two-sample t-test. The result is shown in Table 2. Table 2. Option Portfolio Returns for 4- and 5-Week Cycles 4-week maturity weekly return
5-week maturity weekly return
staddles
DH calls
DH puts
staddles
DH calls
DH puts
4-week cycle
−0.004
−0.005
−0.005
−0.022
−0.002
−0.003
5-week cycle
0.016
0.001
0.001
0.026
0.002
0
difference
−0.00103
−0.006
−0.006
−0.048
−0.004
-0.003
t-statistic
−0.372
−5.086
−6.339
−1.113
−4.246
−4.145
The results provide several implications. First and foremost, for DH calls and DH puts, options portfolios with 5-week expiration cycle always outperform the 4-week cycle ones, with very significant t-statistic. This is strong evidence that option mispricing exists in SSE market, with traders underpricing the 5-week cycle options and overpricing the 4week ones, leading to a deviation from the theoretical option prices, which is consistent with our initial assumption. Secondly, the difference of 4-week cycle returns and 5week ones is not significant in straddle portfolios. To find out the reason, the paper calculates the return of single calls and puts, both weighed by the corresponding closing price. The result is listed in Table 3. From the result, it’s clear that 4-week cycle single calls outperform 5-week ones, while put returns are the opposite. Therefore, the returns of components of straddles offset each other, leading to the non-significant difference between the return of 4-week and 5-week cycle portfolios. Thirdly, the absolute values of straddle returns are usually much larger than the deltahedged ones. This is due to the distinction in the scale of payoff(numerator) and initial
896
Y. Tu et al. Table 3. Single Option Returns for 4- and 5-Week Cycles.
4-week maturity
4w cycle call
4w cycle put
5w cycle call
5w cycle put
3.79%
−6.59%
−3.68%
8.29%
5-week maturity
4w cycle call
4w cycle put
5w cycle call
5w cycle put
4.98%
−7.63%
−1.55%
10.91%
investment(denominator). For the straddles, the level of payoff is equal to the first order quantity of the underlying asset price change. While for the delta-hedged portfolios, the level of payoff is equal to the second order quantity of underlying price change, which can be much smaller (the price change is usually below 1, for the price change is limited to 10% every day). Meanwhile, the initial investment of straddles is option price, while the initial investment of delta-hedged portfolios includes stock price, which is much larger in scale. In test 1, the paper roughly inspects the effect of mispricing on portfolio returns by comparing the sample means. And the difference in returns remains significant with more variables controlled. (2) Regression based test The paper conducts the OLS regression with weekly return as the dependent variable. Since straddles are insignificant in difference of return, this paper focuses more on delta hedged portfolios. The 5-week dummy is the explanatory variable of main concern. For the paper presumes that options with 5-week expiration cycle have higher returns (as the options are underpriced), we expect that the coefficient of the 5-week dummy is significantly positive. The result of regressions is presented in Table 4. Firstly, the paper simply regresses the weekly return to the 5-week dummy (Panel A). Consistent with results in Table 2, all the coefficients are significant with positive signs. Then the paper introduces the control variables in Panel B. consistent with Goyal and Saretto [6], IV-HV is negatively related to the weekly return of the portfolio. As is expected, coefficients of 5-week dummy are still significantly positive, which is another evidence for the existence of mispricing.
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Table 4. Regression of Option Returns on 4–5 Week Cycle. Panel A 4-week maturity
5-week maturity
DH calls
DH puts
DH calls
VARIABLES
Weekly return
Weekly return
Weekly return
Weekly return
5w DUMMY
0.00701***
0.00631***
0.00464***
0.00346***
(0.00124)
(0.001000)
(0.00114)
(0.000843)
−0.00525***
−0.00517***
−0.00265***
−0.00300***
(0.000674)
(0.000532)
(0.000672)
(0.000504)
Option controls
No
No
No
No
Underlying controls
No
No
No
No
Observations
1,061
1,174
967
1,060
R-squared
0.029
0.033
0.017
0.016
Constant
DH puts
Panel B 4-week maturity DH calls
5-week maturity DH puts
DH calls
DH puts
VARIABLES
Weekly return
Weekly return
Weekly return
Weekly return
IV-HV
−0.0360***
−0.0145***
−0.0431***
−0.0105***
(0.00478)
(0.00309)
(0.00537)
(0.00387)
0.00236**
0.00458***
0.00372***
0.00356***
(0.00117)
(0.00104)
(0.00123)
(0.000919)
Option controls
Yes
Yes
Yes
Yes
Underlying controls
Yes
Yes
Yes
Yes
Observations
1,061
1,126
919
1,012
R-squared
0.249
0.141
0.105
0.075
5w DUMMY
Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
4 Conclusion Option expiration day can lead to different expiration cycles (i.e. different numbers of weeks between expiration days) of options. Investors trading on penultimate expiration day may simply recognize them as ‘maturing next month’, with inattention to the exact expiration dates. This can result in option mispricing. Options with 4-week expiration cycle tend to be overpriced, for investors will overestimate their time value. 5-week ones may be underpriced for the same reason. In this research, the paper extends the evidence on time-driven mispricing to include the Chinese option market. This paper conducted portfolios of straddles, delta-hedged calls, and delta-hedged puts to find out whether this form of mispricing appears in the Chinese option market. Judging from the results, this paper concludes that, consistent with our initial assumption, 5-week cycle options are underpriced, with delta-hedged portfolios displaying significantly higher weekly return rates compared to portfolios containing their counterparts. Straddles, however, show less significancy in difference, since without hedging strategy, single calls and puts offset each other in return.
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Meanwhile, some problems remain to improve. First of all, the paper only looks into the options with the underlying SSE 50ETF. This is partly due to the fact that the derivative market is underdeveloped in China, only ETF options are being traded on a relatively large amount. But still, there are other options left to be investigated. Secondly, for the regression selection, the factors hidden in error terms (factors excluded from the listed variables which can affect weekly returns) might be correlated with each other (for example, a negative shock affecting a whole year), which means that the error term might not be independent. To resolve this problem, this paper can introduce time dummies to drag out factors that contribute to the correlation of error terms. Acknowledgment. Yaqi Tu and Yidi Zhai contributed equally to this work and should be considered co-first authors.
References 1. Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973) 2. Sharpe, W.F.: Capital asset prices with and without negative holdings. J. Finan. 46(2), 489–509 (1991) 3. Cox, J.C., Ross, S.A., Rubinstein, M.: The Cox—Ross—rubinstein model. In: Mathematical Finance and Probability, pp. 201–19. Birkhäuser Basel, Basel (2003). Accessed 29 Sep 2022. https://doi.org/10.1007/978-3-0348-8041-1_12 4. Chiras, D.P., Manaster, S.: The information content of option prices and a test of market efficiency. J. Finan. Econ. 6(2), 213–234 (1978) 5. Cox, J.C., Ingersoll, J.E., Ross, S.A.: An intertemporal general equilibrium model of asset prices. Econometrica 53(2), 363 (1985) 6. Goyal, A., Saretto, A.: Option Returns and Volatility Mispricing. SSRN J. (2007). Accessed 29 Sept 2022. http://www.ssrn.com/abstract=889947 7. Feng, H.: Indication with Further Analysis of Mispricing and Barriers in Arbitrage in Chinese Option Market A Five-Month Study on Sample Option. All Graduate Plan B and other Reports. Accessed 1 May 2013. https://digitalcommons.usu.edu/gradreports/313 8. Cao, Z., Chelikani, S., Kilic, O., Wang, X.: Implied volatility spread and stock mispricing. J. Behav. Finan. 9, 1–13 (2022) 9. Eisdorfer, A., Sadka, R., Zhdanov, A.: Maturity driven mispricing of options. J. Finan. Quant. Anal. 57(2), 514–542 (2022) 10. Hu, G., Liu, Y.: The pricing of volatility and jump risks in the cross-section of index option returns. J. Finan. Quant. Anal. 57(6), 2385–2411 (2022) 11. Kling, G., Gao, L.: Calendar effects in Chinese stock market. Ann. Econ. Financ. 6(1), 75–88 (2005)
Applicability of Fama-French Six-Factor Improved Model to Explain Stock Returns in Chinese Rare Metal Industry Fenyu Chen1 , Jiuan Jiang2(B) , and Yuting Jiang3 1 Renmin University of China, Beijing 100872, China 2 City University of Hong Kong, Hong Kong 999077, China
[email protected] 3 Fudan University, Shanghai 200433, China
Abstract. The paper mainly focuses on exploring the applicability of FamaFrench Six-Factor Improved Model to explicate the stock returns in China’s rare metals industry. The classic Fama-French Five-Factor Model has been widely used in various industries such as media, household electrical appliances in China and China’s GEM market. Nevertheless, few papers mention the applicability of the model in the rare metal industry. Since the new energy sector will play an important role in the progress of achieving carbon peaking dioxide emissions and carbon neutrality, the rare metal industry appealed public’s attention in security market. This paper adds liquidity premium factors to form a Six-Factor Model. After collecting data, constructing factors and empirical analysis, it is concluded that there is a positive correlation between the trend of China’s rare metals sector and the trend of the general stock market. Based on the analysis, the small-scale and high book-to-market ratios companies acquire higher stock returns. In addition, smallscale companies with conservative investment styles obtain higher stock returns than small-scale aggressive companies in Chinese rare metal stocks. Liquid companies will earn higher stock returns than illiquid companies. The study validates the conjecture that the Fama-French Six-Factor Improved Model is suitable to explain Chinese rare metal stocks, which brings investment suggestions toward the stock market. Keywords: Fama-French Six-Factor Improved Model · Stock Return · Liquidity Factor · Rare Metal
1 Introduction Capital asset pricing is a practical research area in the stock market, and many related theories emerge one after another. Since the emergence of asset pricing model, many scholars have been pursuing the influencing factors of capital asset prices, aiming to build a universal asset pricing model. Initially, Fama-French Three-Factor Model was accepted F. Chen, J. Jiang and Y. Jiang—These authors contributed equally. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 899–908, 2023. https://doi.org/10.1007/978-981-99-6441-3_82
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and influential in the financing market [1]. Later on, Carhat added the momentum factor, forming a four-factor model [2]. Then, a profit factor and an investment factor were utilized to construct a Fama-French Five Factor Model. The profit factor is robust minus weak (RMW), and the investment factor is conservative minus weak (CMA) [3]. Reviewing the evolution process of asset pricing, Haugen et al. found earnings anomalies different from Three-Factor Model, and high-profit companies showed higher expected returns than low-profit companies [4]. Fama et al. established the profit index based on net profit and confirmed the positive relationship between earnings and stock expected return rate. However, under the control of scale and book-to-market ratio, earnings did not make any new contribution to predicted returns [5]. A few years later, Novy-marx pointed out that gross profit is the most authentic indicator to measure profitability, and the ability to explain stock returns based on gross profit is no less than the book-to-market ratio [6]. Fama et al. formally put forward the five-factor model by adding profit and investment factor [3, 7]. At the same time, scholars illustrated stock market pricing from different perspectives. There were two main explanations including the investment Q theory under rational expectations from scholars like Hou et al., and the mispricing theory from the perspective of behavioral finance, such as Lam et al. [8, 9]. Later, Xu et al. considered adding liquidity as a factor to improve the model [10]. Chinese scholars have carried out many studies on the performance of the stocks in different factors. Fan et al. carried out a Fama-Macbech regression analysis and dynamic combination analysis, and concluded that the three-factor model was applicable in Chinese stocks [11]. Then, Yang et al. compared the Fama-French multi-factor model with the CAPM model. They found that multiple factors may have more explanatory power, but they may also have redundancy. [12] Based on the five-factor model, Cui et al. improved the model and made use of the unit valuation profitability factor, so that the improved model better explained the stock market [13]. In the international market, the adaptability of the five-factor model has been tested, while in China, there is less research on industry-specific factor models. In 2020, China pledged to strive for carbon peaking dioxide emissions by 2030 and achieving carbon neutrality by 2060. In addition, the new energy sector will play a significant role to help achieve the goal. As important raw materials in the upstream industry chain of new energy, rare metals have been supporting the new energy circuit in China. As a result, rare metal stocks are in growth potential and possess great investment value. Due to the strong profitability of Chinese rare metal enterprises, the ROE of many stocks is more than 20% in 2022. The investment in the rare metal sector is in the stage of price-value mismatch, leading to a new investment opportunity in the rare metal market. Then the model mentioned is worthy to research on. In the essay, the Fama-French Six-Factor Improved Model tests the factor effects of rare metal industry stocks in conjunction with the actual situation of the rare metal industry.
2 Methodology The study puts forward the classic model with a liquidity factor, and analyzes the applicability of the new model in the Chinese rare metal industry. The six factors and stock returns data range from January 1, 2010 to July 1, 2022. The overall period is 151 months. The data source is from the CSMAR database.
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2.1 Data Source In the empirical analysis, the sample includes monthly returns of individual stocks, risk-free return, market capitalization outstanding, book-to-market, operating profit and Amihud index. The parameter of the risk-free rate of return is the one-month Chinese Treasury bond yield. Among them, the return of individual stock and market return are considered cash dividend reinvestment. In addition, the research takes 49 rare metal stocks in China as the research object, group the sample stocks according to the size of the enterprises, the book-to-market ratio, earnings factor, investment style and liquidity, and conduct empirical research based on Fama-French Six-Factor Improved Model. Moreover, this research analyzes the correlations between six factors, containing the market factor, scale factor, book-to-market ratio factor, profit level factor, investment level factor, and liquidity factor. Research object is selected from the rare metals industry, and there are 49 listed companies based on the classification criteria given by Flush iFind. Data of ST and *ST class stocks are filtered out. ST class stocks are stocks with abnormal financial conditions. Their trading rules are different from normal stocks, and cannot represent the market performance of a normal stock market. Companies with missing data on operating profit, owner’s equity, total assets, market capitalization outstanding, and total company market capitalization are excluded. 2.2 Stocks Grouping Firstly, all the sample stocks are divided into two groups in term of the median stock market value, with small (S) and large (B) market value. All stocks are split into two categories according to the median book-to-market ratio, with high (H) and low (L). Second, all stocks can be split into four combinations using the two indices of market value and book-to-market ratio: SH, SL, BH, and BL. Thirdly, the book-to-market ratio is replaced with the profit rate, investment style, and liquidity. The methods above are repeated to form 12 combinations, including SR, SW, BR, BW, SC, SA, BC, BA, SQ, and ST. Additionally, the above portfolios’ market capitalization-weighted average return rates for each period of time are calculated. Among them, a high book-to-market ratio is represented by letter H, and a low book-to-market ratio by letter L. W denotes a weak profit whereas R denotes a strong profit. Additionally, C denotes a conservative approach to investing whereas A suggests an aggressive approach. Moreover, Q represents quick liquidity while T represents tarry liquidity. Finally, the difference in the returns of each portfolio is constructed into five factors respectively to form a six-factor improved model. 2.3 Factors Construction The model with six factors is used for empirical research. The model expression is as follows: Rit − Rft = αit + βi Rmt − Rft + si SMBt + hi HMLt + ri RMW t + ci CMAt + qi QMT t + eit
(1)
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MKT is a market factor, represented as a market excess return. In the market factor, Rft adopts the one-month interest rate of national bonds, and the market return Rmt is obtained by the weighted average method of total market value. MKT = Rmt − Rft
(2)
Except for MKT, the other five factors were calculated by mean. SMB is a size factor, represented as small minus big. The difference in portfolios are formed as SMBB/M , SMBOP and SMBINV , depending on the stock grouping. The calculation method is as below. SH + SL BH + BL − 2 2
(3)
SMBOP =
BR + BW SR + SW − 2 2
(4)
SMBINV =
SC + SA BC + BA − 2 2
(5)
SMBB/M =
SMB =
SMBB/M + SMBOP + SMBINV 3
(6)
HML is a value factor, represented as high minus low, grouped by size. Then, Bookto-market ratio is an index. The calculation is based on the book owner’s equity in the prior year and the total market value of shares. HML =
SH + BH SL + BL − 2 2
(7)
RMW is a profit factor, represented as robust minus weak. The group classification is based on scale, and profitability is an index. The profitability index is calculated by operating profit at end of last year and owner’s equity at last year. RMW =
SR + BR SW + BW − 2 2
(8)
CMA is an investment factor, represented as conservative minus aggressive. Investment level is an index, and it is derived by total new assets in last year and total assets in the year before last year. CMA =
SA + BA SC + BC − 2 2
(9)
QMT is a liquidity factor, represented as quick minus tarry. Amihud index is used as corresponding index, and it is derived by the yield, daily trading volume and trading days. QMT =
SQ + BQ ST + BT − 2 2
(10)
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3 Results and Discussion The essay analyzes the descriptive statistics of each factor, the unit root test, and the test of the correlation coefficient between factors. Then, the Regression result is shown to conclude the relationship between six factors and stock market return in Chinese rare metal stocks. 3.1 Factor Analysis The paper contains a total of six explanatory variables. These variables are dispersed in different ways.
Fig. 1. Boxplot of six factors.
Figure 1 is a boxplot of six factors which visually shows the data dispersion of each factor. It can be seen from the figure that the shortest box is RMW. As a result, the data of RMW is the most centralized, while the data of MKT and SMB are scattered. Because the median lines of the six factors are close to the middle, it can be inferred that the data of the six factors are normally distributed. It can also be inferred from the position of the box that the mean values of the other five factors are close to each other except that the mean value of MKT is relatively less than that of others. What’s more, data of MKT has the most outlier value and the largest outlier distance. To be more specific, the quantitative indicators are shown in Table 1. From Table 1, the average value of MKT is −0.019, and the standard deviation is 0.064. Therefore, the market rate of return is lower than the risk-free rate of return with little fluctuation, and the difference is significantly different from 0 at a 1% confidence level. The mean value of SMB is 0.010, and the standard deviation of SMB is 0.058. In addition, the average rate of return of small-scale portfolios is higher than large-scale
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F. Chen et al. Table 1. Descriptive statistics of each factor. CMA
HML
QMT
RMW
MKT
SMB
Mean
0.001
−0.001
0.001
0.002
−0.019
0.010
Std
0.040
0.047
0.050
0.037
0.064
0.058
−0.176
−0.168
Min Max Skew
−0.125 0.099 −0.210
−0.195 0.110
0.138
0.103
−0.563
0.091
−0.318
−0.277 0.144 −0.223
−0.183 0.178 −0.054
Kurt
0.359
1.228
1.136
3.021
1.994
0.682
T-Statistic
0.334
−0.290
0.160
0.632
−3.416***
1.975*
Note: *** represents the significance level of 1% and * represents the significance level of 10%.
portfolios. It is significantly different from 0 at a 5% confidence level. As a result, the rare metal industry has a certain scale effect. The mean value of HML is −0.001, and the standard deviation is 0.047. Therefore, the assumption that it is not remarkably distinct from zero is acceptable. RMW is positive and not significantly different from 0 at a 10% confidence level. The mean value of CMA is 0.001, and the standard deviation is 0.040. Moreover, a conservative or radical investment style has little impact on stock return. It is also acceptable that liquidity factor is not significantly different from zero. Table 2. Augmented Dickey-Fuller (ADF) unit root test. CMA
HML
QMT
RMW
MKT
SMB
−10.120
−4.009
−11.897
−5.721
−8.652
−12.432
1%level
−3.479
−3.481
−3.479
−3.480
−3.479
−3.479
5%level
−2.883
−2.884
−2.883
−2.883
−2.883
−2.883
10%level
−2.578
−2.579
−2.578
−2.578
−2.578
−2.578
0.000
0.001
0.000
0.000
0.000
0.000
T-Statistic
Prob.*
Table 2 shows a unit root test, and it displays test result of six factors in monthly data. The P value is less than 5% significance level, so the time series of all factors are stable. Then the correlation test among factors can be conducted. The correlation coefficients among six factors is shown in Table 3. The highest correlation coefficient is 0.518, so that the subsequent analysis is reasonable. MKT is negatively correlated with HML and QMT. The corresponding correlation coefficients are −0.130 and −0.046. MKT is positively correlated with SMB, RMW, and CMA. The correlation coefficients are 0.115, 0.043, and 0.060 respectively. RMW has the weakest correlation. In addition, SMB is negatively correlated with HML, RMW, and CMA, reflecting that the investment rate of large market capitalization companies is relatively low. However, SMB is positively correlated with QMT. Furthermore, HML is positively correlated with RMW. It is negatively correlated with CMA and QMT. The
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Table 3. Test of the correlation coefficient between factors. CMA
HML
QMT
RMW
MKT
SMB
CMA
1.000
−0.027
−0.315
0.518
0.060
−0.010
HML
−0.027
1.000
−0.151
0.062
−0.130
−0.212
QMT
−0.315
−0.151
1.000
−0.247
−0.046
0.226
RMW
0.518
0.062
−0.247
1.000
0.043
−0.090
MKT
0.060
−0.130
−0.046
0.043
1.000
0.115
SMB
−0.010
−0.212
0.226
−0.090
0.115
1.000
negative correlation coefficients are −0.027 and −0.151. The RMW is positively correlated with CMA. The investment style of profitable companies is relatively aggressive with a correlation coefficient of 0.518, which is not a strong correlation. 3.2 Factors Analysis After a series of calculations and tests, the model shows the regression result based on 16 stock groups. Table 4 shows the coefficients of six factors and goodness of fit. The proxy variable for the size factor is SMB. SMB coefficient of the SL portfolio is 0.563, and the SMB coefficient of the SH portfolio is 0.501. The values of the two coefficients are greater than 0, indicating a positive correlation. The SMB coefficient of the BL portfolio is −0.497, and that of the BH portfolio is −0.435. The values of the two coefficients are less than 0, showing a negative correlation. The results show that in the A-share rare metal industry, the large-size enterprise tend to obtain lower stock return, while the small-size enterprise obtain higher stock return. HML regression result is displayed. It shows that SL and BL combined HML factors are significant. However, SH combined HML factor has no significant relationship. The book-to-market coefficients of BH and BL portfolios are −0.233 and −1.290, which are negatively correlated with the HML factor. The small-scale enterprises stock may obtain higher stock return rate. In sum, enterprises’ stocks returns are uncorrelated with book-to market ratio value. RMW regression result is displayed. The results illustrate that the RMW factor coefficients of SR and BR portfolios are positive, that is, the return rate of SR and BR portfolios is positively correlated with the profitability level. The RMW factor coefficient of SR is 0.657, which shows significant results. Therefore, the robust profitability of rare metal stock obtains higher stock returns. The proxy variable for investment style is CMA. The regression result shows that the CMA factor coefficients of the combination of SA and BA are negative. It is concluded that the aggressive investment style’s rare metal stocks obtain lower stock returns. The proxy variable for liquidity factor is QMT. The regression result shows that the QMT factor coefficients of the combinations of BQ and SQ are positive, and the result is significant under 10% significance level. Therefore, the more liquid rare metal stocks obtain higher stock returns.
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F. Chen et al. Table 4. Regression result. HML
QMT
RMW
MKT
SMB
P>F
R2
Groups
CMA
SH
−0.229
−0.079
0.209*
−0.141
1.101*
0.501*
0.000
0.704
BH
−0.454*
−0.233*
0.383*
−0.114
1.148*
−0.435*
0.000
0.772
SL
−0.492*
−1.021*
0.407*
−0.212*
1.163*
0.563*
0.000
0.852
BL
−0.190
−1.290*
0.185*
−0.043
1.085*
−0.497*
0.000
0.767
SR
−0.513*
−0.577*
0.290*
0.657*
1.195*
0.506*
0.000
0.794
SW
−0.358*
−0.759*
0.354*
−0.737*
1.081*
0.582*
0.000
0.794
BR
−0.306*
−0.631*
0.364*
0.084
1.091*
−0.429*
0.000
0.747
BW
−0.461*
−0.449*
0.299*
−0.521*
1.205*
−0.504*
0.000
0.786
SC
0.210*
−0.513*
0.306*
−0.001
1.176*
0.548*
0.000
0.812
SA
−0.922*
−0.743*
0.328*
−0.298*
1.097*
0.548*
0.000
0.801
BC
−0.013
−0.660*
0.343*
−0.217
1.102*
−0.444*
0.000
0.746
BA
−0.880*
−0.429*
0.321*
0.079
1.182*
−0.443*
0.000
0.793
BQ
−0.369*
−0.528*
0.425*
−0.095
1.123*
−0.484*
0.000
0.777
BT
−0.278
−0.887*
−0.718*
−0.262
1.099*
−0.199*
0.000
0.660
SQ
−0.336*
−0.927*
1.008*
−0.333*
1.112*
0.750*
0.000
0.751
ST
−0.427*
−0.567*
0.150*
−0.165
1.135*
0.464*
0.000
0.807
Note: * represents the significance level of 10% (two-sided).
From the goodness-of-fit index, the R-squared value reach more than 66% in all portfolios, indicating a high degree of fit. The combination of SL has the highest degree of fit, indicating that the six factors in the model have the strongest explanatory power in small market value rare metal stocks containing low book-to market ratio.
4 Conclusion The following conclusions are based on empirical analysis of 16 portfolios of 49 rare metal stocks, under Fama-French Six-Factor Improved Model. After the ADF test and factor correlation analysis, it can be concluded that all six factors are in smooth series. There is no serious correlation between the factors, indicating that the stock return results made by the model are reliable. In addition, the goodness of fit of all 16 portfolios is above 66%. In summary, FamaFrench Six-Factor Improved Model has high applicability in Chinese rare metal stocks. The model possesses some explanatory power, which can be used to explain the excess returns of rare metals companies listed in China’s A-shares. There is a positive correlation between the trend of China’s rare metals sector and the trend of the general stock market. The reason may be that the rapid development of China’s economy leads to increased investment. Then, it leads to rapid growth in demand for the rare metals industry, irritating the rapid growth of profits in the rare metal industry.
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Although there is great potential to invest in rare metal industry stocks, investors need to pay special attention to the overall economic environment and the A-share trend. When the A-share market trend is upward and the macroeconomic boom, investment in the rare metals sector will bring relatively reliable returns. The small-scale effect exists in China’s listed rare metals stocks, where small-scale enterprises have higher stock returns. There are a few reasons for small-scale effects. First, small-scale stocks are easy to be sought by investors, with frequent transactions. Meanwhile, small-scale companies develop faster, with greater growth potential. Secondly, rare metal enterprises with larger scale may lead to greater losses, compared to small-scale listed companies in the rare metal market. Among the rare metal stocks, a small-scale and high book-to-market ratio portfolio has a higher yield, which reflects that the P/N ratio indicator of rare metal enterprises has a high investment reference. Thus, these stocks can be regarded as high-quality stocks in the industry. Based on test results, the returns on investment of small-scale and highly profitable enterprises are higher than that of large-scale and highly profitable enterprises. Choosing a small-scale enterprise with high operating profits may bring a higher probability of increasing stock returns. However, large-scale enterprises with low operating profits should have a smaller probability of increasing stock returns. Rare metal companies with conservative investment styles have higher ROI than companies with aggressive investment styles, which also shows a small-scale effect. Choosing small-scale companies with a conservative investment style may lead to a higher probability of high stock returns. At the same time, large-scale companies with aggressive investment styles bring lower probabilities of stock return increases. Using liquidity indicators to group portfolios by liquidity, descriptive statistics and empirical analysis find that A-share rare metal stock firms may have a certain liquidity premium phenomenon, and the more liquid the stock is, the higher excess return it tends to obtain.
References 1. Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. J. Econ. 33(1), 3–56 (1993) 2. Carhart, M.M.: On persistence in mutual fund performances. J. Finance 52, 57–82 (1997) 3. Fama, E.F., French, K.R.: A five-factor asset pricing model. J. Financ. Econ. 1661(1), 1–22 (2015) 4. Haugen, R.A., Baker, N.L.: Commonality in the determinants of expected stock returns. J. Financ. Econ. 41, 401–439 (1996) 5. Fama, E.F., French, K.R.: Profitability, investment, and average returns. J. Financ. Econ. 82, 491–518 (2006) 6. Novy-Marx, R.: The other side of value: the gross profitability premium. J. Financ. Econ. 108, 1–28 (2013) 7. Fama, E.F., French, K.R.: International tests of a five-factor asset pricing model. J. Financ. Econ. 123(3), 441–463 (2016) 8. Hou, K., Xue, C., Zhang, L.: Digesting anomalies: an investment approach. Rev. Financ. Stud. 28, 650–705 (2015)
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F. Chen et al.
9. Lam, F.E.C., Wang, S.J., Wei, K.J.: The profitability premium: macroeconomic risks or expectation errors. SSRN Electron. J. (2) (2016) 10. Fan, L., Yu, S.: The three-factor model of Chinese stock market. Chin. J. Eng. Syst. 6, 537–546 (2002) 11. Yang, Q., Yang, J., Lyu, L.: Fama-French multifactor model and CAPM model comparison: evidence from the Chinese market. SME Manag. Sci. Technol. 6, 86–87 (2020) 12. Cui, L., Chen, X.: Empirical study on the stock market of China’s home appliance industry: analysis based on Fama-French five-factor model. Region. Finance Res. 1, 49–54 (2021) 13. Xu, J.Y.: The applicability of Fama-French three-factor model and its liquidity correction model in Chinese scientific innovation board. China Collect. Econ. 89–93 (2022)
Impact of COVID-19 on China’s Real Estate Industry - An Empirical Study Based on the Stock Market Zilun Chen1(B) , Pengyao Gao2 , and Kaiqing Liang3 1 Applied Mathematics, College of Science Hohai University, Jiangsu 211100, China
[email protected]
2 Applied Statistics, Stony Brook Institute at Anhui University, Anhui 230601, China 3 Accounting and Finance, University of Exeter, Exeter EX4 4SB, UK
Abstract. In the process of economic operation, the real estate industry plays an indispensable function as a pillar of the national economy. The outbreak of the COVID-19 has greatly influenced China’s real estate industry. Since people need to invest in the real estate industry through the capital market, this paper focuses on the analysis of real estate stocks to explore the impact of the COVID-19 on China’s real estate industry. This paper analyzes and forecasts Vanke stock using ARIMA model, and the model obtained is slightly different from the actual situation. The main fluctuation points occur in January 2020 and May 2021. In January 2020, the new epidemic has just started. The change of the actual stock price trend and the forecasted trend shows that the new epidemic has impacted the real estate market, while the change in May 2021 is due to the continued influence of macro policies. Finally, we propose a strategy for combining real estate sales with the Internet. Keywords: Real Estate Stocks · COVID-19 · ARIMA Model
1 Introduction The real estate industry is a significant part of economy, a pillar industry and one of the pioneer industries in China’s cities. It has an important pulling effect on the development of the national economy. In 1998, China entered the stage of full marketization of real estate. Then in 2003, China implemented the housing system reform, and the real estate industry ushered in a period of rapid development. In 2008, the U.S. financial crisis broke out and China was hit hard, the government enacted encouraging housing policies and the real estate market rebounded rapidly. With a lot of speculation in the market, housing prices rose too fast. Since 2010, localities have introduced regulatory measures such as “purchase restriction orders”, “price restriction orders” and higher purchase business taxes. Against the background of inflation and abundant funds for developers, the increase in house prices was controlled, but house prices did not fall and even rose [1]. From 2016 to the first half Z. Chen, P. Gao and K. Liang—These authors contributed equally. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 909–917, 2023. https://doi.org/10.1007/978-981-99-6441-3_83
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of 2019, against the background of the gradual tightening of domestic financing channels, the aggressiveness of overseas bond issuance by real estate enterprises increased significantly. From June 2019, in order to prevent people from malicious speculation and a series of speculation in the real estate industry, real estate financing continued to tighten. The capital chain of real estate development enterprises has been in a tight running state for a long time. However, under the keynote of the real estate policy of “stable land price, stable house price and stable expectation”, the growth rate of national real estate development investment in 2019 is relatively stable. From the end of 2019 to the beginning of 2020, the COVID-19 epidemic ravaged the world, causing a huge impact on the international economic environment. The suspension of property sales further aggravated the capital chain tension of real estate enterprises. Many factors implicitly affected the share prices and real estate prices of real estate enterprises, and the growth rate of national real estate development investment declined significantly. Relevant data show that in January–February 2020, the total amount invested in national real estate development projects was 101.1542 billion yuan, a 16.3 percent decrease from the previous year. Among them, residential investment fell 16% year-on-year, office investment fell 17.8% year-on-year, and investment in commercial premises fell 25.6% year-on-year. With the effective control of the new domestic epidemic, the growth rate of real estate development investment gradually narrowed. The national investment in real estate development up through May came to 4,592 billion yuan, a decrease of 0.3% from the previous year, compared with the decline in April narrowed by 3 percentage points, of which, residential investment was flat, office investment fell 1.2% year-on-year, and commercial business investment fell 6.9% year-on-year [2]. From 2021 to 2022, the quantity of real estate enterprises in China remained stable, with a significant decrease in growth rate, and the overall sales and average sales maintained a stable growth trend. However, the market as a whole was under certain pressure to dematerialize [3]. The recovery of the real estate industry has been interrupted by a new round of outbreaks caused by the more contagious variant of the Coronavirus, and the real estate industry is again facing several serious challenges. Epidemic-type emergencies have affected many industries, among which the impact of the epidemic on the financial market has been enthusiastically discussed by many scholars. From a macro perspective, Song, using data mining models and regression analysis, found that firms with comparable financial status and the same kind of bond issue were able to accomplish bond financing more cheaply. Were able to complete bond financing at a lower cost after the epidemic compared to the bond financing environment before the epidemic [4], in terms of corporate financing theory, the impact of the epidemic on China’s economy, and information asymmetry theory. Zhou predicted a loss value of 2.3–2.6 trillion yuan in 2020 for minor service enterprises in China through regression algorithm optimization model, and proposed a series of countermeasures for macro-risk prevention and control [5]. Chen analyzed the impact effect of the epidemic on China’s stock market using the event study method and analyzed the heterogeneous performance of enterprises facing the impact of the epidemic based on different indicators such as financial indicators, type indicators, and geographical indicators. Results showed that stocks of enterprises with small asset size, non-state, foreign trade category, and non-Wuhan region were more negatively affected [6].
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Further, the epidemic has a significant impact on the real estate market as well as on real estate stocks. Zhang analyzed several influencing variables of the real estate market and the real estate stock market to investigate the correlation between them. He constructed a theoretical model by theoretically analyzing the influencing factors of money supply M2 and real estate development investment amount. Then he conducted a heteroskedasticity test analysis to find that money supply M2 and real estate development investment amount affect both the real estate market and the real estate. The heteroskedasticity analysis showed that money supply M2 and real estate development investment affect both the real estate market and the stock market. The deposit and loan interest rates are negatively correlated with real estate market and real estate stock market, and the three homogeneous variables do not affect the two markets to the same extent [7]. Geng analyzed the relationship between house prices and the scale of social financing, price index, real estate stock, real estate investment, and social consumption. He conducted OLS regression by establishing a multiple linear model and gradually eliminating variables with insignificant effects in the regression to study the economic impact of the new crown pneumonia epidemic on real estate prices. He concluded that an increase in the scale of social financing makes house prices rise, and the scale of real estate completed area increase in the scale of social financing makes house prices fall [8]. Among them, in the analysis of stocks, Zhang selected eight main financial indicators and chose to conduct cluster analysis and factor analysis on 50 stocks in the real estate industry listed on the Shanghai Stock Exchange. Both analysis methods were able to distinguish listed companies into four categories, which were consistent with the actual situation of the companies. In addition, the companies were comprehensively ranked by factor analysis, and some opinions and suggestions for stock investment were derived from the analysis and research of listed companies [9]. Wang applied the CAPM model (Capital Asset Pricing Model, CAPM) to 30 real estate stocks to conduct an empirical analysis. He concluded that there is no strict linear relationship between the returns of real estate stocks in Shenzhen A-shares and market returns, systematic risk, and unsystematic risk. As a result, the CAPM model is not applicable to the valuation of real estate stocks in Shenzhen A-shares [10]. Zhang studied from the stock market perspective, based on panel data of manufacturing companies listed in Shanghai and Shenzhen, using a fixed effects model. Results showed that the COVID19 had a marginal impact on the development of China’s manufacturing industry that first strengthened and then gradually weakened [11]. In summary, empirical analysis through stocks is beneficial to reflect the real situation of the industry. This paper adopts the ARIMA model to explore the impact of the epidemic on the real estate industry through empirical analysis of the stock market and to make predictions on the expected future trend.
2 Methodology ARIMA model is the abbreviation of differential autoregressive moving average model. It is a time series forecasting method proposed by Box and Jenkins in the early 70’s. ARIMA model contains three parts, AR (autoregressive model), MA (moving average
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model), and I (difference). ARIMA converts non-stationary time series into stationary time series using difference, then determines the model order by ACF and PACF plots to determine the model order, and finally use the established model to forecast. According to the smoothing process and the identify the ranking of the models, the ARIMA model is built on the basis of a smooth time series, the smoothness of the time series occupies an important position. First of all, whether the time series has obvious fluctuations need to be determined, because the original series shows a similar linear trend, so the first-order difference is performed. After further determining the smoothness of the differenced series, the autocorrelation of the differenced series is examined in the graph. Relying on the sample data, the series after smoothing is analyzed according to the autocorrelation function ACF and partial autocorrelation function PACF (Fig. 1).
Fig. 1. Introduction of ARIMA model.
By analyzing the corresponding autocorrelation and partial autocorrelation plots, the optimal stratum p and order q can be obtained, and the three parameters can be determined [8]. Relying on the sample data to derive the sample autocorrelation coefficients. T (xt − x)(xt−s − x) rs = ρs = t=s+1 (1) T 2 t=1 (xt − x)
Determine the PACF partial autocorrelation function. xt = ϕs1 xt−1 + ϕs2 xt−2 + ... + ϕss xt−s Establish the ARIMA (d, b, q) model as follows. p q y t = a0 + ai y t−1 + i=1
i−1
βt εt−1 + εt
(2)
(3)
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3 Results and Discussion Vanke A (000002) (Stock) was selected as the benchmark stock of real estate companies. The share price dataset from 1st January of 2018 to 30th June of 2022 was collected for ARIMA time series analysis as a way to analyze the impact of the epidemic and real estate macro-regulation on the share value of real estate companies. The data were imported to create the time series (the vertical coordinate is the stock price, and the horizontal coordinate is the amount of data from 1st January of 2018 to 30th June of 2022) (Fig. 2).
Fig. 2. ARIMA time series analysis of vanke A.
Due to the large dataset, the first 900 figures are chosen for training and forecast. The figure of ACF and PACF plotted to test the stationarity of dataset was shown below. The line chart in Fig. 3 shown that the stock price of Vanke A is roughly up, but more volatile, especially reflected in the beginning of 2020 and May 2021. After the outbreak of the epidemic in 2020, the stock price of the real estate company is still very volatile because of the impact of the epidemic on the form of the global economy, and there is a precipitous drop in the stock price. The dataset was divided into two parts for further research: training and prediction. Based on the ACF values are above the critical value, the selected data is not a white noise model and is a continuing model. Based on the slow decline of ACF to 0 and the sudden drop of PACF from lag1 to 0 or based on the p-value which is 0.09625 larger than 0.05, the dataset is a non-stationary series. A differencing is necessary.
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Fig. 3. ACF&PACF of vanke A.
First-order differencing is performed below, and the figure told that the dataset after first-order differencing is smooth. The test result in Fig. 4 shows the first-order differencing of the data set fluctuates up and down around 0. The ACF result shows lag16 exceeds the critical value and p-value is far smaller than 0.01 which means the new series is not white noise, and the modeling continues. After series of processing, the automatically fit of ARIMA model is used to do the prediction. The appropriate p, q is found, and residual test of the model is necessary. The p-value was 0.05283 over 0.05, which means the residual test is passed. Finally, the ARIMA (4,1,3) (2,0,2) model was used to predict. The result in Fig. 5 shows that the model is a statistically significant fit. Due to the downturn in the economic situation caused by the COVID-19, a precipitous fall in the share price as a result of the pause in the property market was foreseen (blue shaded part of the graph). Because a persistent fall in Vanke’s share price after May 2021 through the time series graph, forecast the analysis of the data after May 2021 is done. By comparing the predicted trend with the actual observed values, the predicted trend is on a downward trend, clearly the model fit anticipates the subsequent fall in share price, but the trend is flat and not as sharp as the actual observed fall (Fig. 6).
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Fig. 4. Test after first-order differencing.
Fig. 5. Residual test from ARIMA (4,1,3) (2,0,2) [7].
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Fig. 6. Forecast from ARIMA (4,1,3) (2,0,2) [7].
Next, using program to analyze the reasons for the deviations between the actual and forecasted stock prices: The problem of not fitting the observed and forecasted trends well, it occurred in early 2020 and after May 2021. In early 2020 was during the outbreak of the COVID-19 epidemic, when there was no change in real estate related policies and other factors had little impact on Vanke’s stock price. The epidemic led to a decline in real estate stocks led by Vanke, which shows that the COVID-19 epidemic had a significant impact on real estate. For after May 2021, the real situation is that Vanke and other real estate companies mostly appear all the way down. Take Vanke for example, originally not high PE fell to about 1/5, but about the amounts of sales and contracts show that prices are still rising. Therefore, it can be seen in the normalization of the epidemic, high-pressure policies and monetary easing to protect the blessing of the real estate industry caused by the impact was turned small, but the challenge to the company further intensified.
4 Conclusion According to the analysis of this paper, the epidemic has indeed caused terrible impact on the real estate industry. The COVID-19 poses a challenge to the real estate industry, so the real estate industry should follow the trend of normalizing the epidemic and innovate marketing models in an effort to reduce the impact of the epidemic. Therefore, the idea is to change the fixed mode of sales and develop a new sales model-sales with the help of the Internet. Online shopping model-sales gradually has become very common in China, especially after the outbreak of the COVID-19, and online shopping, online entertainment, online meetings and offices, online games, and online courses have become commonplace and popular. However, real estate is not easy to achieve, as it has large value, non-standardized products that require more field surveys, complicated procedures with other special features, which make it very unlikely for buyers to make decisions based on online real estate displays alone.
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Besides, the epidemic has prompted consumers to become a habit of online consumption, online home purchase is not unacceptable. The 5G era of information technology to “online sales office” “VR cloud viewing” “no contact” online signatures and other real estate online model-sales business support is also more mature. It’s necessary to improve relevant laws and regulations to secure the safety of online purchase in the future. Also, it’s a brand-new way to explore the combination of online and offline sales of real estate. The innovation of information technology of online real estate sales will become the first choice of home buyers to seize the market. Therefore, real estate enterprises should prepare as soon as possible to improve the technology of online sales, to explore the combination of online and offline sales in order to seize the opportunities in the near future. As the research progresses, it is crucial to understanding the situation and promoting the healthy and stable development of the real estate industry. Based on the current state of development of the real estate industry under the influence of the epidemic, this paper analyzes the short-term and long-term effects of the epidemic on the real estate industry, with the resulting impact mainly reflected in stock volatility. The paper focuses on the timing of the volatility and the reasons for the volatility in terms of the policies or public events implemented at the time. In addition, the paper further proposes countermeasures for the development of the real estate industry in the era of epidemic normalization at the level of real estate companies, with a view to providing references for relevant decision-making authorities.
References 1. Cao, Y.: The history of real estate development in China. Enterprise Technol. Dev. 33(11), 110–111 (2014) 2. Zhou, X.H.: Study on the impact of the COVID-19 on China’s real estate industry and countermeasures. Natl. Circulat. Econ. 32, 127–131 (2020) 3. Liu, W.: Development situation of Chinese real estate enterprises in 2021 and outlook for 2022. Banker 03, 26–29 (2022) 4. Zong, M.W.: Research on the impact of the epidemic on the cost of corporate bond financing. Shandong University (2021) 5. Li, F., Li, Y.Z., Zhou, X.H.: Study on the impact assessment and countermeasures of the COVID-19 on small and medium-sized service-oriented enterprises–an analytical prediction based on regression algorithm optimization model. Econ. Rev. 03, 101–117 (2020) 6. Chen, F.K.: The heterogeneous impact of the new crown pneumonia epidemic on Chinese firms-an empirical study based on the perspective of stock price volatility. Ind. Technol. Econ. 39(10), 3–14 (2020) 7. Zhang, Y.: Research on the correlation between China’s real estate market and real estate stock market. China University of Mining and Technology (2021) 8. Geng, Y., Xu, X.L.: Research on the economic impact mechanism of the new crown pneumonia epidemic on real estate prices. Mod. Trade Ind. 43(17), 13–14 (2022) 9. Zhang, W.Q.: An empirical analysis report on the application of cluster analysis and factor analysis in real estate stock market. Times Finance 23, 155–157 (2014) 10. Wang, X.R.: Study on the adaptability of CAPM model–an empirical analysis based on 30 real estate stocks in A-shares of Shenzhen market. China Collect. Econ. 20, 70–71 (2020) 11. Xue, W.X., Yang, L., Zhang, Y.H.: The impact of the COVID-19 on China’s manufacturing industry–an empirical study based on stock market returns. J. Xi’an Univ. Technol. 37(04), 460–467 (2021)
Prediction and Analysis of Commodity House Price Based on ARIMA Model Xiaohu Liang(B) School of Mathematics and Statistics, Ningbo University, Ningbo 315211, China [email protected]
Abstract. In recent years, the rapid rise of urban housing prices has attracted people’s attention. As China’s economic and financial center, Shanghai’s real estate industry is developing rapidly. The study of Shanghai real estate market is of great significance to ensure the steady growth of Shanghai economy. Because there are few predictions and analyses of house prices in Shanghai. This paper will study the housing price in Shanghai. This paper selects the average transaction price of commercial housing in Shanghai (yuan/square meter) as the research object, and the research time range is from February 2001 to June 2022. Firstly, descriptive statistical analysis is carried out according to the drawn time series diagram. Then Autoregressive Integrated Moving Average model (ARIMA) is established for the data from February 2009 to June 2022. Finally, it forecasts and analyzes the housing price of Shanghai in the next 12 months. The results show that the overall house price in Shanghai will have a slight downward trend in the next year. Keywords: ARIMA Model · Real Estate Industry · Housing Price · Time Series · Forecast
1 Introduction Since 2000, due to China’s rapid economic development, people’s living ability has been significantly improved. At the same time, people’s production capacity has also improved, leading to the progress of the real estate industry. According to the data from 2016 to 2021 released by the National Bureau of statistics of China. China’s housing prices rose by more than 40%, giving the impression that they only rose but did not fall. The prices of the real estate industry can display a lot of information. The study of housing prices can make real estate developers have a more accurate analysis of business prospects and people’s consumption patterns. At the same time, it can also provide reference for rational buyers. Shanghai is the most representative and commercially developed city in China. It is meaningful to study and predict its house price. Therefore, this paper hopes to study and predict the housing price in Shanghai from the perspective of time series analysis. At present, the prediction of house prices is mainly realized by econometric models and machine learning models. Zhang and Zhu used the method of multiple linear regression analysis to analyze and predict the short-term fluctuations of urban house prices [1]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 918–929, 2023. https://doi.org/10.1007/978-981-99-6441-3_84
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For the sales price index, Shao used cluster analysis and factor analysis to statistically analyze the influencing factors of housing prices. And the nonparametric autoregressive model is used to model and predict the housing sales price index [2]. Xu can accurately predict the house price of the houses with planned Metro under appropriate conditions by establishing BP neural network improved based on genetic algorithm [3]. Ding used ARMA-GARCH model to study and forecast the fluctuation of house price index in Jinan [4]. Among them, ARIMA model has been widely used in the field of time series analysis since it was proposed in 1970. The forecasting method of ARIMA model has strict mathematical guarantee. It has unique advantages in short-term forecasting [5]. Fu et al. established ARIMA model for the average price data of commercial housing in Hengyang City and analyzed and predicted the house price with the help of Eviews and SPSS data analysis software [6]. Huang et al. built the LASSO-ARIMA integrated model based on the data of house prices in Beijing. Innovatively introduced the concept of “Lag” into the model and used the influencing factor data of the current month to compare the house price of the next month, rather than the house price of the current month, for LASSO regression. The effect that future house prices can be predicted based on known data is realized [7]. In view of the problem that traditional house price forecasting methods lack the ability to analyze a large amount of data. Wang et al. Proposed a house price prediction method based on deep learning and ARIMA model [8]. Hou and Qiao took the house price data of Taiyuan, Shanxi Province as the research object. They combined the wavelet analysis theory with ARMA model to forecast the house price of Taiyuan [9]. Hu used ARIMA model to predict and analyze the housing price in Hefei. The short-term prediction of house prices is realized [10]. In view of the problem that the data prediction accuracy is not high when using a single prediction model, You and Chen proposed a real estate price prediction method based on ARIMA-BP combination model. It trained the best weight combination by using the error variance weighted average training method and established a combination model to make an empirical analysis on the real estate price and trend prediction in an urban area [11]. In view of this, this paper will select the housing prices in Shanghai from 2008 to 2022 and use ARIMA model to predict and analyze the housing prices. Because the prediction method of ARIMA model is based on the concept of time series prediction, with strict mathematical guarantee. It has unique advantages in short-term prediction.
2 Method Autoregressive Integrated Moving Average model (ARIMA) is a prediction model that uses difference method to transform non-stationary time series into stationary time series, and then regresses the lag value of the series with dependent variable and random error term. If the non-stationary series {Xt } has a d-order difference. After d times of difference operation, the series ∇ d Xt is a stationary time series. The following ARIMA (p, d, q) model can be established: ⎧ d ⎪ = θ (B)εt ⎨ λ(B) ∇ Xt
p λ(B) = 1 − i=1 λi Bi (1)
⎪ ⎩ θ (B) = 1 − q θj Bj j=1
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where B is the Lag operator. {εt } is the white noise series and Eεt = 0. The steps of establishing ARIMA model mainly include the following 5 steps. The first step is to obtain data. The second step is to preprocess the time series. The preprocessing of time series includes the stationarity test and the white noise test. Because the time series that can be analyzed and predicted by ARIMA model must be non-white noise series and meet the condition of stationarity. If the data simply does not satisfy the stationarity condition, it is differentiated (d times) until the data after the difference satisfies the stationarity. The third step is to determine p and q. It is possible to determine p and q from the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) maps. Further, or to use Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC). The fourth step is to fit the data and estimate the parameters λi , θj in formula (1). The fifth step is to evaluate the model and predict the time series. Finally, the corresponding analysis is carried out through prediction.
3 Results and Discussion Through the National Bureau of Statistics of China, this paper obtains the average transaction price of commercial housing in Shanghai every month (yuan/square meter). From 2001 to 2022, there were 236 pieces of data. Since the Bureau of statistics has not released the house price data in January every year. Therefore, the 236 samples lack the average house price in January each year. This is not conducive to Arima modeling. So here, the simplest linear interpolation method is used to fill (That is, take the average value of December of last year and February of the same year as the value of January of the same year). This filling can reduce the interference to the sample population and facilitate the establishment of the ARIMA model. The following is a time series diagram made using software (missing values have been filled in).
Fig. 1. The average transaction price of commercial housing in Shanghai.
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It can be seen from Fig. 1 that the housing price in Shanghai is generally rising, and there is no obvious seasonal change in the curve. Therefore, in the following analysis, we can weaken the influence factors brought by seasonality. House prices in Shanghai fluctuate greatly. Not only that, the price fluctuations in different periods are also different. As shown in Fig. 2, it can be divided into three parts according to different fluctuations of house prices.
Fig. 2. Division of house price change period.
In the first stage, house prices only show a slight upward trend, and the fluctuation range is small. The decline was also relatively small, with only one large decline. This is favorable for investors to invest in this period because the risk is small. However, in the second stage, although the overall house price shows an obvious upward trend, the fluctuation range is relatively large, and there are many large drops. This requires investors to take a lot of risks. Of course, most real estate investments are long-term investments. The risk taken in the second part is not as big as expected. In the third stage, the fluctuation of housing prices is relatively large. First there was a big drop, then a sudden sharp rise. This is not a good omen for investors and buyers. So, it is meaningful for investors and buyers to predict and analyze the housing prices in the third part. To sum up, the risks faced by investors and home buyers in the first stage are different from those in the second and third stages. And the first stage is earlier. So, this paper considers it unnecessary to fit the data in the first stage. Therefore, the next step of this paper is to predict and analyze through ARIMA model. And model the data from February 2009 to June 2022 (the second and third stages). The prediction is given from the perspective of the time series analysis. 3.1 Test of Stationarity Combined with the fact that the Chinese housing prices rose earlier and the curve rising state in Fig. 1, it can be preliminarily considered that this time series is non-stationary.
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However, in order to be more rigorous, this paper continues to use Augmented DickeyFuller test (ADF) for further verification. Table 1. ADF test. ADF test
T-statistic
P-value
Output
1.165
0.996
The ADF test was performed on the data of the housing prices (Table 1). The Pvalue is 0.996, which is much higher than 0.05. Therefore, this makes us believe that the housing price is non-stationary. 3.2 Perform Differential Operation Since the original data is non-stationary. It is necessary to perform differential operation on it and until it is stationary. The following is the result of one difference:
Fig. 3. Differential operation.
After a differential operation, it can be seen from Fig. 3 that the data has become relatively stationary. For the sake of preciseness, the next step is to check whether the data is stable through ADF test. Table 2. ADF test for the data after a differential operation. ADF test
T-statistic
P-value
Output
−5.773
0
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The stationarity test of the data after the first-order difference (Table 2) shows that the P-value is almost close to 0, far less than 0.05. It can be considered that the data after the first-order difference satisfies the stationarity condition. Then the model parameter d = 1. 3.3 Test of the White Noise The data after the first-order difference has met the stationarity condition. Then white noise test shall be performed next. Table 3. Test of white noise. Ljung-Box test
LB-statistic
P-value
Output
18.701
0
According to the operation results (Table 3), the P-value is almost close to 0, which far less than 0.05. It can be considered that the data after the first-order difference is a non-white noise series. 3.4 Determine the Orders P and Q According to the above discussion, the data after the first-order difference satisfies the conditions of the stationarity and the non-white noise series. Thus, ARIMA modeling can be performed on the data. The determination of P and Q mainly depends on the analysis of autocorrelation and partial autocorrelation. The following are diagrams drawn by software.
Fig. 4. Autocorrelation.
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Fig. 5. Partial Autocorrelation.
Figure 4 shows tailing and basically falls within the range of 2 times standard deviation after the third order, so q = 3. Figure 5 also shows tailing and basically falls within the range of 2 times standard deviation after the second order, so p = 2. In order to ensure that the selection of p and q values is more accurate and appropriate. Next, the software is used to simulate and output AIC and BIC to ARIMA (p, 1, q). (In order to ensure that the model will not over fit the data. The order P and Q are limited here: P + Q ≤ 5). Table 4. Find P and Q by AIC or BIC. P
Q
AIC
BIC
0
0
3284.466
3290.617
0
1
3219.980
3229.205
0
2
3214.579
3226.88
0
3
3226.356
3241.732
0
4
3235.087
3253.538
0
5
3237.511
3259.037
1
0
3267.081
3276.307
1
1
3217.737
3230.038
1
2
3222.666
3238.042
1
3
Inf
Inf
1
4
3207.586
3229.112
2
0
3217.624
3229.924 (continued)
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Table 4. (continued) P
Q
AIC
BIC
2
1
3219.135
3234.511
2
2
3213.203
3231.654
2
3
3194.216
3215.742
3
0
3219.557
3234.933
3
1
3218.734
3237.185
3
2
3215.162
3236.688
4
0
3208.206
3226.657
4
1
3207.389
3228.915
5
0
3204.350
3225.877
According to the results in Table 4, we are more convinced that ARIMA (2, 1, 3) is the best. Because AIC and BIC reached the minimum value respectively when p = 2 and q = 3. 3.5 Evaluation of the Model and Prediction of the Time Series The data were fitted by computer. Establish ARIMA (2,1,3) model and estimate parameters λi and θj . The output is as follows: Table 5. Results of parameters estimation. Coefficients
Standard Error
z
P > |z|
λ1
−1.102
0.081
−13.576
0
λ2
−0.821
0.056
−14.601
0
θ1
0.580
0.099
5.873
0
θ2
−0.172
0.094
−1.836
0.066
θ3
−0.552
0.105
−5.251
0
In Table 5, the second column is the estimation result of parameters, and the third column is the standard error of estimation. The fourth and fifth columns are the significance tests of the parameters in the model. In the significance test, except for the parameter θ2 , the P-values of all other parameters tend to be 0, far less than 0.5 (In column 5). Considering that the model itself is not complicated, and the P-value of parameter θ2 is also close to 0.05. Therefore, this paper believes that the significance of retaining it is far greater than deleting it.
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The ARIMA (2, 1, 3) model can be written as follows: ∇Xt = −1.102∇Xt−1 − 0.821∇Xt−2 + εt − 0.580εt−1 + 0.172εt−2 + 0.552εt−3 (2)
where: Var(εt ) = 2.626 × 107 . After the ARIMA (2, 1, 3) model is established, the residuals need to be analyzed.
Fig. 6. Standardized residual.
The standardized residual (Fig. 6) is also near the 0 axis, without obvious trend and seasonal characteristics.
Fig. 7. Histogram plus estimated density.
We can see that the kernel density estimation (KDE) line is close to N (0,1) in Fig. 7. The residual approximation conforms to the standard normal distribution.
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Fig. 8. Q-Q map.
In the Q-Q map (Fig. 8), all points are also basically on the diagonal. This also shows that the residual approximates to the normal distribution.
Fig. 9. Correlogram.
The data in Fig. 9 are basically within the 95% confidence interval. This shows that the time series residual has a relatively low correlation with its own lag term. To sum up, the model is suitable. The data were fitted with ARIMA (2,1,3). Due to the defects of ARIMA model, the effect of long-term prediction is not very good. So here is the forecast for the next 12 months. The dotted line in Fig. 10 is the forecast of the average house price in Shanghai in the next 12 months. The predicted trend is consistent with the original trend. House prices fluctuated greatly and showed a slight downward trend as a whole.
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Fig. 10. Prediction.
4 Conclusion This paper first introduces the basic situation of Shanghai real estate market. Then it draws the time series chart of Shanghai’s house price dynamic data. The time series is divided into stages according to the fluctuation of house price. Through descriptive statistical analysis, reasonably select the stage that needs to be fitted with the model. And ARIMA model is used to fit the divided time series data. Through the time series analysis and prediction of Shanghai’s housing prices from 2009 to 2022 (the second and third stages), it can be concluded that from 2022 to 2023: Shanghai housing prices will still continue to fluctuate substantially commonly and show a small downward trend overall. This also shows that in the short term, there is little probability that the overall house price in Shanghai will rise by a large margin. For home buyers, buying a house now may devalue the house in the future. For investors, short - and medium-term investment is of little value. In addition, the house price in the third stage fluctuates greatly. Combined with the prediction of the model, if the short-term real estate investment is made, the risk faced by investors will be greater than the income. Of course, this is based on the prediction that there is no major change in China’s current policy. Therefore, if China’s policy changes in the future will have a great impact on the real estate industry, it needs further analysis.
References 1. Zhang, K., Zhu, X.: Analysis of Z city house price forecast based on application of multiple linear regression. In: Proceedings of the First International Symposium on Economics, Management, and Sustainable Development 2019, EMSD, pp. 74–77. Clausius Scientific Press, Canada (2019) 2. Shao, H.: Analysis on the influence factors of real estate price and prediction on the real estate price in China. MA thesis,Xi’an University of Technology (2010) 3. Xu, R.: Research on prediction of subwaylines housing transaction price on BP neural network. MA thesis,Xinjiang University (2021)
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4. Ding, Y.: House price forecasting and fluctuation analysis based on time series model. MA thesis, Shandong University (2018) 5. Wang, L., Liu, J.: Research on the price forecast of commercial housing in Baoding based on ARIMA model. Estate Sci. Tribune 18(09), 96–98 (2019) 6. Fu, Q., Yi, Y., Zhang, S., Ding, J., Tang, M.: Prediction and analysis of house price in Hengyang city based on ARIMA model. Technol. Innov. Appl. 11(31), 47–50 (2021) 7. Huang, T., Yang, L., Niu, C., Zhao, L.: LASSO-ARIMA house price forecast based on Beijing urban development. In: (The Seventh) National College Student Statistical Modeling Competition Award Winning Papers (II) 2021, pp. 390–432. Statistical Education Society of China, Tianjin in China (2021) 8. Wang, F., Zou, Y., Zhang, H., Shi, H.: House price prediction approach based on deep learning and ARIMA model. In: Proceedings of IEEE 7th International Conference on Computer Science and Network Technology 2019, CCSNT, pp. 314–318. Harbin Huilian Education Technology Company Limited, Dalian in China (2019) 9. Hou, P., Qiao, Z.: Research on house price prediction based on wavelet analysis and ARMA model. Stat. Decis. 15, 20–23 (2014) 10. Hu, X.: Research on real estate price prediction based on ARIMA model—take Hefei City as an example. China Manag. Information. 25(05), 163–166 (2022) 11. You, Y., Chen, J.: Research on real estate price forecasting method based on ARIMA-BP combination model. Comput. Knowl. Technol. 16(09), 264–269+273 (2020) 12. Wang, Y.: Time Series Analysis with R, 2nd edn. China Renmin University Press, Beijing (2020)
The Prospect of Chinese Internet Companies’ Strategy Investment Take Tencent, ByteDance, Alibaba and Baidu as Examples Lin Zicheng(B) Business School, Hohai University, Nanjing 211100, China [email protected]
Abstract. Recent years, Chinese internet companies appear to no longer creating companies benefits merely rely on their main business. Big internet companies actually commonly have advantage on internet flow, which is appropriate for nowadays Chinese investment circumstance. The companies can use tremendous amount of internet flow and cash flow to enlarge their business layout by investing internet flow and cash on other new companies. This paper researched the four famous companies in China called Tencent, Alibaba, ByteDance and Baidu. Data from each company up to 2022/9/29, such as numbers of strategy investment events, were analyzed. Analyzing the data, it could preliminarily find that the four companies’ investment thoughts were similar and exist an apparent trend on each four companies that they all pay attention to high-technology industries and follow the government policy to make sure the long-term investment are worthy. It could be meaningful to give a prospect on next few years’ investment of the four companies, especially, during the Covid-19 and other different situations, some suggestions on strategy investment can be refined from the four of the largest internet companies in China. Keywords: Strategy Investment · Internet Company · Trend of Investment
1 Introduction 1.1 Background Recent years, listed internet companies are all trying to increase their revenue and market share to promise the stock price keep increasing. However, unfortunately, some big companies like Tencent, the main product QQ and WeChat have been nearly reaching to the peak of numbers of users and benefits, so that those companies have to find another way to increase profits. Strategy investment is a second chance for internet companies. Strategy investment is not only important for companies’ growth and enlargement, but also can be a stable part of income in the future. What’s more, a swiftly update exist in internet industries, that means if an internet company do not have tremendous and constant users, it will be replaced by other new companies easily. It’s important for internet companies to choose which industries to invest and when to invest it. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 930–940, 2023. https://doi.org/10.1007/978-981-99-6441-3_85
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1.2 Related Research Strategy investment is gradually increased in China in the last 20 years. Wang summarized the development process of Chinese corporate venture capital (CVC). Statistic data showed that CVC is highly increased from 2013, and nowadays, internet companies has played the most significant role in this industry [1]. Vrande et al. find that when companies are used in combination with other technology sourcing modes, corporate venture capital investments are particularly beneficial for the innovative performance of companies. It may not only partly explain why a lot of companies choose to make CVC, but also guide other domestic internet companies to do the same thing [2]. Tang reveals how did an internet company creating value when mergers and acquisitions (M&A) other companies. More than that, she selected some cases which are representative and across enough time so that the results can provide effective proposals: 1. Internet companies should put their resource in a significant position when they owe other companies’ equity, especially for flow; 2. When internet companies owe a little equity from other companies, companies may keep changing the allocation of resource to suit the change of market, and others [3]. Huang analyzed the internet companies’ investment in China, summarized a few strategies that a commonly used in them like self-incubation strategy, decentralization strategy, overseas investment strategy, etc. [4]. Luc et al. Researches display the difference between the U.S. and West Europe’s CVC plan which is mainly caused by the companies’ size to their GDP. Results shows that they behaving the opposite relationship, which could give a reference to Chinese internet companies [5]. Considering the micro vision of CVC, Chen took Tencent as an example indicated that the strategic investment is not only helpful for company marching to new industries which have huge barriers to entry, but also convenient for it to build up a nationwide multiple circumstance even satisfied almost every aspect of Tencent users’ demands [6]. Liu noticed that Tencent’s investment strategy make itself to set up a huge value net so the investment efficiency could be improved apparently under the financial management circumstance. He pointed out that if the benefits between the core company and knot companies can be balanced, and it figure out the whole management model, an internet company could maximize the goal of value net [7]. Dai took Alibaba as a sample to figure out that technology, development of digital, youth population play a significant role on Alibaba’s region choice. Dai counted the China internet companies’ foreign investment event from 2009 to 2019.Jun and draw a conclusion that investment on foreign regions keeps a highly increasing speed and comes to rationalization gradually [8]. Quan found that its necessary for an internet company to do some cross-border investment then illustrated about how should an investment to be successful compared with Tencent and Alibaba [9]. 1.3 Objective This paper aims to explore the development trend of the top internet companies in China, and concludes their future investment strategies. In part II, it will be shown the general situation about strategy investment in Chinese internet companies. What’s more, some basic information about the investment feature and idea of the four companies are also
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introduced. In part III, the research will discuss and compare the detail include the number of investment events, the fields of investment and investment changes about the four companies. In part IV, some explanations on those statistic results will be given base on policy influence, and make an analysis from vision of their main products, then give the suggestions and a prospect about the investment in the next few years. The last part is conclusion.
2 Description of Statistics 2.1 Domestic Industry Circumstance The strategy investments were mainly dealt by head companies, especially the four of most famous internet companies in China, which were known as Tencent, Alibaba, ByteDance and Baidu. With the influence of Covid-19 and adjustments of internet company policies, the cumulate rate of BOCOM CSI Overseas China Internet Index (QDII-LOF,164906) was generally decreased recent 2 years (Fig. 1). 200.00%
CSI 300
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150.00% 100.00% 50.00% 0.00% -50.00% -100.00%
Fig. 1. Changes of Cumulative rate of CSI 300, Shanghai Stock Exchange Index (SSEC) and QDII-LOF (from 2012/5/29 to 2022/8/17) [10].
In one hand, the consists of income for internet companies are far different from past, attribute to the development of short video, artificial intelligence, block chain, IOT (Internet of Things), cloud computing and big data, etc. Not only head companies want to into those aeras, but some new internet companies like ByteDance, start with short video, as it to a main business, which enhanced the competition. On the other hand, except the contend in high technology field, Chinese internet companies have been trying to into most of things in daily life to increase their benefits, some internet corporations can even into an area which are totally different from their main business. Rely on the feature of light asset, huge liquidity and advantage of network flow, they have enough resources to invest a related company, furthermore, the cooperation between these companies is a win-win strategy that the invested company can earn cash, and more important, network flow, which could provide a large quantity of exposure, more convenient than common advertisement. As for investment company, giving some network flow is also a bargain to
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use fewer cash to exchange the same equity. It is precisely because this kind of investment could easily enlarge the internet companies’ business area, companies could eventually form an enormous integration. 2.2 Features of Chinese Internet Companies’ Strategy Investment Wide Scope. Chinese internet companies are keeping their gravity into kinds of track, according to statistics, from 2013 to 2020, the network media, daily consumption, internet finance, online education and entertainment are the five major sources of benefits to internet companies. 8 years past, the benefits on daily consumption have still the highest in the four field, but internet finance is gradually exceeding entertainment and become the second major market. However, to segment the market, some fields are weak in profits but still important for strategic arrangement, like online medical, enterprise service, traffic service, etc. Nowadays, with the improvement of bottom technology like 5G, satellite network, biology technology and so on, an experienced internet company like Tencent and Alibaba has already invested about 30 different industries to enrich their strategic layout, contains domestic companies and foreign companies [11]. Different Investment Idea. The strategy investment is kind of different from the common investment. Common investment takes return on investment as the main principal, while strategy investment considering to integrate the invested company and trying to accomplish a complete company ecology. Under this idea, the internet companies can invest to build their own subsidiary to enlarge the marketing boundary. More commonly, they could invest some companies, through giving the capital and network flow, so the customers in the internet companies are actually binding to the invested companies. This idea made internet company, who own a lot hundred million users, became more stable and unreplaceable.
3 Investment Analysis 3.1 Investment Numbers Tencent strategy investment can trace back to 2001, it has participated in over 1300 investment events through 21 years (before 8/17/2022). The sum trends of the investment events are climbing slowly in 2014 to 2018. In the next 3 years, the numbers were increased significantly, increased from less than 150 in 2019 to over 250 events in 2021 (Fig. 3). It could be observed that ByteDance has a blowout growth in 2015, nearly fourfold than 2014, then kept a stable rising speed in the next six years except 2017 (Fig. 4). Alibaba started strategy investment at 2012, 2 years earlier than ByteDance, but the increasing stability is worse than ByteDance, the numbers of investment are fluctuated between 2012 to 2022 (Fig. 5). Baidu is the youngest in the four internet companies who started strategy investment just from 2017. Baidu’s investment is also fluctuated (Fig. 2), but has a decrease trend in total 10 years (Fig. 6).
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numbers of strategy investment increaseing speed of strategy investment in numbers 400 300 200 100 0
300.00% 200.00% 100.00% 59.07% 30.94% 0.00% 2.92% 31.82%-8.19%34.74%-36.93% -100.00%
222.64% -50.47%
Fig. 2. Changes of Tencent’s investment quantity [12].
numbers of strategy investment increaseing speed of strategy investment in numbers 100
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Fig. 3. Changes of ByteDance’s investment quantity [12].
numbers of strategy investment increaseing speed of strategy investment in numbers 150
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54.76% 50.00% 54.55% 18.82% 7.41% 20.69% 0.00% -19.80% -21.43% -31.43% -41.67% -50.00%
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Fig. 4. Changes of Alibaba’s investment quantity [12].
3.2 Investment Fields Tencent join in the 36 different industries, which is the most in the four companies, higher than, ByteDance, Baidu and Alibaba, at 25, 26 and 30 respectively. The four companies have extremely high repetition on invest fields with each other, even if, for instance, take the “real estate service” and “real estate and household” as two different fields, which actually are the nearly same, the four companies are still only have 2 or 3 unique invest
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numbers of strategy investment increaseing speed of strategy investment in numbers 60
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50.00% 48.39% 32.43% 0.00% 0.00% -3.13% -6.82%-21.95% -10.20% -21.28% -43.48% -50.00%
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Fig. 5. Changes of Baidu’s investment quantity [12].
fields on average that other three companies don’t join in. ByteDance are even totally have the same invest fields with others. They all invest some industries like enterprise service, advanced manufacturing, transportation, medical healthy, finance, logistics, etc. However, the high reputation doesn’t mean there are same invest focus with each other. Despite they participated in a lot of industries, they all chose few industries as the core invest objects. The enterprise service and entertainment are both the top 3 invest object of the four companies, illustrated that they attached importance to this industry. Tencent and ByteDance did the most frequently investment on entertainment, which occupies a very important proportion, account for about 15.5% and 18.3%, above any other industries while Alibaba and Baidu invest enterprise service more than other two companies, account for 14.9% and 21.1% respectively (Fig. 6, Fig. 7, Fig. 8 and Fig. 9). Compared with the four companies, considering the difference of the investment fields, it could be found that the owing to the different type of main business, for instance, Tencent started from network community while Alibaba began with E-commerce, and the various management idea, the enlargement of layout is different too.
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Fig. 6. Tencent’s strategy investment distribution [12].
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Entertainment Education Tool software others
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Fig. 7. ByteDance’s strategy investment distribution [12].
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Fig. 8. Alibaba’s strategy investment distribution [12].
3.3 Investment Changes Though the four companies are all plummet in 2022, unlike Tencent, ByteDance and Baidu, Alibaba is the only company whose quantity of investment decreased during the 2019 to 2021, which is known as the most difficult 3 years because of Covid-19. Discuss about each company’s investment changing, it could be found that [12]: The top four investment industries about Tencent are entertainment, game, enterprise service and E-commerce retail. Entertainment was kept a steady increasing from 2014 to 2018, but go down to less than halved in 2019. Investment on game is keep growing from 2016. In 2020, it became three times as much as the previous year, and the data doubled in 2021. As for enterprise service, it also maintains a stable rising from less than ten times per year became nearly thirty times per year in 2020, and suddenly doubled in 2021. E-commerce retail was fluctuated, but it has an increasing trend in total.
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Fig. 9. Baidu’s strategy investment distribution [12].
Entertainment, business services, games, and education are the four sectors in which ByteDance invests most frequently. ByteDance has a fluctuated changing on entertainment and reach to the peak at 10 in 2020. Enterprise service and game industry are similar with each other, both at a small quantity before and after 2021, which means only in 2021, they have an upheaval on the investment number. However, the investment on enterprise service started at 2016, while the game industry was beginning at 2018. ByteDance constantly decrease the investment on education and it comes to zero in the first half year of 2022. Alibaba most favored four investing industries are enterprise service, electronic commerce, entertainment and transportation. Enterprise service and entertainment both have a peak at 2015, while they maintain a normal level in other years. Electronic commerce keeps ten to twenty during 2015 to 2018, but plummet at next year and keeps a low quantity until 2022. Transportation investment is fluctuated but the investment capital was nearly unchanged. Enterprise service, entertainment, automobile & transportation and education were the four most investing industries for Baidu. From 2015 to 2017, Enterprise service were going up gradually while it dropped quickly in the next year and remain the small scale until 2022. Entertainment industry were stable but have a small falling trend in general. Automobile & transportation was sightly undulating in the past 9 years, peak to six in 2016. However, until 8/17/2022, Baidu hasn’t done any investment on automobile & transportation in 2022. The investment on education is similar to Alibaba that both decreased in the past 9 years.
4 Discussion 4.1 Policy Intervention by Government The government policy can make great influence on investment. For instance, after the publishment of Ease the burden of excessive homework and off-campus tutoring for students undergoing compulsory education published by Chinese government in 2021/5/21
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[13], which kept mentioned and requested about entirely prohibiting the commercial extracurricular training for students in compulsory education, all the four companies’ investment on education were plummet. When it comes to finance, under the formula of preventing disordered capital expansion, especially for loan industry. Ant financial is a classic representative, which almost to quoted on the Shanghai stock exchange, but failed because it collided with the Interim Measures for the Administration of Online Small Loan Business [14]. 4.2 Main Products and Strategic Expansion It’s known in 3.3 that the four companies are all liked to invest entertainment and enterprise service industries. Some products made by the four companies are well known in entertainment such as Tencent Video, Alibaba Pictures, TikTok and iQIYI. Products about enterprise service, for instance, Cloud service Tencent Cloud, Aliyun and so on, are all made great results. Tencent started its business from an instant message software called QQ. Until 6/30/2022, QQ and WeChat were both already have about hundreds of millions of monthly active users (MAU). Nowadays QQ and WeChat both became the essential foundation of Tencent’s entertainment and game business. Pushing the development of Tencent Video, Glory of Kings, Peacekeeper Elite, etc., which have become the most profitable products among the entertainment and game industry. Alibaba began its business with electronic commerce. In 2003, Alibaba set up the Taobao, which now has more than a billion users. During 19 years, Alibaba not only keeps developing the scale of Taobao, but creating a lot of other electronic commerce platform including domestic and overseas as well, like Tmall, XianYu, Lazada, etc. Apart from this, Alibaba also made an enormous achievement in cloud computing. For instance, Aliyun, which birthed in 2009, now has become the largest supplier in Chinese cloud service (contains PaaS and IaaS service). TikTok was released in 2016 by ByteDance, and now has hundreds of millions of domestic daily active users in estimate in China. ByteDance also have other product near to short video and media industry, like TouTiao, Ixigua, etc., which were also a significant part of income. Baidu’s business started with search engines. The income from advertisement were decrease those years, while the searching, clouding service and intelligence driving are increase a lot. It shows that Baidu has a well development and maintain a strong competitiveness on high technology industry, but it didn’t ignore life related industries as well. 4.3 Prospects and Suggestions To estimate the next few years of investment trend, in one hand, internet companies should keep following to invest the industries that supported by Chinese government, such as advanced manufacturing, new energy and so on. Not only these industries have a great allowance by government, but there is a good prospect in the next few years. On the other hand, companies should also notice the change of policy of Chinese government. For instance, the real estate and education were both hot in the past, but with the change
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of policy and government management, these industries are now no longer flourishing as before. What’s more, industries that close to our daily life is also cannot be ignored, because those industries have a huge basement on numbers of masses. Like entertainment and games, they are some industries that very hard to vanish for rest is an essential part of daily life. To sum up, the four internet companies can keep investing the entertainment and game industries, and their adventure industries. Notice about the influence of Covid19, which means offline industry is still hard to invest, and care about the publish of new policy, avoid to on the contrary of government.
5 Conclusion This paper discussed about the changes of strategy investment of Tencent, Alibaba, ByteDance and Baidu until 2022/9/29, and analyzed the investment idea and experience of the four companies. The results indicate that entertainment, game, enterprise service and high-technology industries are favored by internet companies. However, policy and domestic circumstance have great influence on strategy investment, some industries were favored now nearly disappear like education and loan industry. Internet companies should be careful about investing entity industry for the Covid-19 is still didn’t vanished, but advanced manufacturing seems doesn’t influenced. High-technology industry is still important for internet companies’ long-term layout, while entertainment and game industry can provide a constant highly benefits to investment companies. Besides, companies should focus on government policy, so that can avoid some unnecessary injections.
References 1. Xiaoqing, W.: Discussion on the development mode of domestic venture capital enterprises (CVC). China Internet 04, 22–27 (2021) 2. Vrande, V., Vanhaverbeke, W., Duysters, G.: Additivity and complementarity in external technology sourcing: the added value of corporate venture capital investments. MPRA Paper 58(3), 483–496 (2010) 3. Yuchen, T.: Research on the value creation mechanism of multi step mergers & acquisitions in internet enterprises——based on company a’s acquisition of company B. (Master’s thesis, Beijing Jiaotong University) (2021) 4. Shijin, H.: Analysis of internet enterprise investment strategy. Accounting Study 16, 214– 215.5 (2019) 5. Luc, A.G., Da, G., Benoit, et al.: International analysis of venture capital programs of large corporations and financial institutions. Entrepreneurship Theor. Pract. 39(5) 1213–1246 (2015) 6. Yu, C., Cong G.: The relationships between internet companies’ strategic investment and business development- Take Tecent company as an example. Comput. Telecommun. 3 (2012) 7. Chang, L.: Case study on improving investment efficiency under financial management environment of Tencent value net, (Master’s thesis, Harbin University of Commerce), Harbin (2022) 8. Xueke, D.: Analysis on location selection and influencing factors of Chinese internet companies’ overseas investment, (Master’s Thesis, Jilin University), Jilin (2022) 9. Jian, Q.: An analysis of cross-border investment by internet enterprises in the internet era: A case study of Tencent, (Master’s thesis, Jinan University), Jinan (2020)
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10. Investing Homepage. https://cn.investing.com/. Accessed 17 Aug 2022 11. IResearch: Runwu Yousheng III China’s Internet Development in 2019, p. 8,30,42,51,58–60 (2019) 12. QMP Homepage. https://www.qimingpian.cn/. Accessed 29 Sept 2022 13. Ministry of Education of the People’s Republc of China. http://www.moe.gov.cn/jyb_xxgk/ moe_1777/moe_1778/202107/t20210724_546576.html. Accessed 17 Aug 2022 14. The People’s Republic of China, http://www.gov.cn/xinwen/2020-11/03/content_5556884. htm. Accessed 17 Aug 2022
Rough Volatility Lida Zeng(B) Ulster University, London EC1R 4TF, UK [email protected]
Abstract. Investors are increasingly concerned about the security of their investment in light of the fact that volatility could lead to an increase or decline in the value of assets. As a result, predicting volatility has been an important subject for investors over the years. Volatility modelling is one of the main areas of interest that the researchers have been interested in uncovering. As a result, this research paper aims at reviewing the smoothness and volatility process by taking into consideration the influence of continuity and changing partial time scale, logvolatility. The Hurst component (H), which assesses the features of a time series has been referred to throughout the text to calculate the log-volatility of an asset. Subsequently, a model to predict the smoothness of the volatility process has been proposed. Finally, a model to assess the long-term volatility of an assess, as well as to forecast the figure has also been developed in the hope that future predictions concerning the value of an asset can be made. Keywords: Rough Volatility · Smoothness · Hurst Exponent · RFSV Model · Log-Volatility · Brownian Motion
1 Introduction Investors are increasingly concerned with protecting their investments, and therefore, they make the attempt to take various steps to protect their interests. One of the biggest risks to such investment is volatility. Volatility, in this case, is defined as “the degree of fluctuation of asset prices,” including the share and stock prices [1]. Therefore, this refers to the variability and randomness of asset prices owned by the investor. This explains why volatility can also be described as the rate and magnitude of changes in the pricing of various assets. In the last few years, changes in the volatility of returns to stock has been a major concern for investors and scholars, considering that they are used to measure financial risks. Volatility modelling, therefore, is an important measure that continues to evolve as more buyers want to be certain that their investment is secure [2]. By observing theoretical and empirical high-frequency data available in finance, it is quite evident there are many random drifts resulting in uncertainty in asset pricing and capacity to secure investment. As a result, this research paper will aim at reviewing the smoothness and volatility process by taking into consideration the influence of continuity and changing partial time scale, log-volatility [1]. The hurst component, in this case, will be used to assess the fractal change and slight chaos scale deviation, which can stimulate the fractional stochastic volatility building development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. T. Dang et al. (Eds.): CONF-BPS 2023, AEPS, pp. 941–950, 2023. https://doi.org/10.1007/978-981-99-6441-3_86
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2 Volatility Modeling Log-prices within the derivatives world is exhibited as a continuous semi-martingale. More or less, this means that for a given asset with log price expressed as Yt , the process of modeling its log-price is as shown below: dYt = μt dt + βt dWt
(1)
In this case, W_t is a Brownian motion with one dimension whereas μt is the drift term and βt denotes the volatility process. This volatility process is quite important and is requisite to most of the models used to predict volatility, for instance, within the BlackScholes framework, this ingredient is considered to either be a deterministic function of time or a constant [3]. According to Dupire’s local volatility model also assessing the volatility process, a deterministic volatility function denoted as σ (Yt , t), which he referred to as local volatility is used [4]. It is a deterministic function of price and time, which matches the exact European option prices. Such a model is highly unrealistic since it is time-inhomogeneous, meaning that it could lead to future volatility surfaces. The implication of these additional volatility surfaces is that the prices of exotic options observed under local volatility could be off-market, and thus, not representative of the actual situation.
3 The Hurst Index The Hurst exponent, H, is a statistical approach used to infer the properties of a time series by ignoring any presumptions of stationarity. H is a function of the time span of a time series defined using the asymptomatic behaviour of its rescaled range and is defined as shown below. R(n) = CnH as n → ∞ (2) E S(n) where: • refers to expected value • R(n) is the range of the mean’s cumulative deviation from the first n • S(n) standard deviation’s sum series of the first n • n is the observation’s times span or simply the number of data points within the time series. • C is a constant Generally, H is a ration identified the expected range of first n cumulative deviation from its mean. The result is further subdivided by the sum of the standard deviation from n. n refers to the number of observations. Notably, C is the constant under the formula whereas n tends towards infinity. The specific Hurst exponent is derived from taking the logarithm function for each side and then rearranging it [3]. The method described above involves fitting the power-law to deduce H; however, the method is often criticised since it produces a biased estimate of the power-law exponent.
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This means that for a small data set, there is a deviation from the slope/straight line, which is known as white-noise [3]. According to the Ani-Lloyd’s estimate of the white-noise’ is as shown below: ⎧ n−1 ⎪ n−1
n−i 2 ⎪ ⎪ ⎨ √π ( n ) i , for n ≤ 340 2 i=1 (3) E[R(n)/S(n)] = n−1
n−i ⎪ ⎪ 1 ⎪ √ , for n > 340 ⎩ π i n 2 i=1
Where is the Euler Gamma Function The Hurst index utilizes a martingale ratio ranging between 0 and 1 where statistical research and the FSV model finds that if H is less than 0.5, then its volatility is rough. This means that the time series has a single high value followed by a low value, meaning it has negative correlation/anti-persistence. When the value of H is above 0.5 then there is positive correlation where in the time series a high value is followed by another higher value. Nonetheless, an intermediate time series where H = 0.5 demonstrates that the values in the time series are completely unrelated. The use of the Hurst exponent to assess the volatility has been supported by various proponents over the years. This mainly because time is unlimited, and H is a nondeterministic figure, meaning that is determined through examining observed data, for example, the biggest shift or change in the stock market index exhibited on a particular date can always be exceeded on a later date [3]. Basically, the exponent is described as a function of the expected size of changes between observations in a time series measured by the following mathematical formula; H(q) = E(\Xt + T − Xt|2)
(4)
From the above formula, H is directly related to D, a fractal dimension given by 1 < D < 2, meaning that 2 – H = D. Since H varies between 0 and 1 where lower figures indicate rough trend, more roughness, and more volatility, and higher figures are vice versa. The mathematical formula to estimate H (q) as shown above communicates information concerning the averaged generalized volatilities at scale r where the only figures used to define volatility are q = 1, 2. More specifically, the H1 exponent communicates persistence when H1 < 0.5 it represents behaviour of trend and when H1 > 0.5 it represents anti-persistent. In regards to the brown noise (BRW) which is equivalent to (1/f 2 ). As a result, H q = 1/2 As for pink noise, it is given by (1/f) Therefore, pink noise gives Hq = 0 The H exponent for white noise is dependent on dimension. As for 1D and 2D, it is given by the following formula. H1D q = −1/2, On the other hand, H2D q = –1. By introducing a different parameter α to the popular truncated Levy process and Levy stable processes, the following discovery was made.
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Hq = q/α and Hq = 1 for q < α and q ≥ α. One of the most important methods used in the estimation of the Hurst exponent (H (q)) is the multi-fractal detrended fluctuation analysis since it also uses the nonstationary time series. When the exponent is a non-linear function of the time series, then it becomes a multi-fractal system [3]. When used effectively, the model described can predict current and future volatility with more accuracy. Though the RFSV model described fails to decay volatility with increase in financial time, yet it can be used to effectively measure the decay rate through long-range random data sets. Apart from that, the long memories are also measured using the power function since it cannot grow as much as the exponential.
4 Option Price Dealing and Microstructure Related to the Roughness of Volatility Stock markets are quite dynamic and whereas they are characterised by discrete jumps in value, in other cases they are continuous and with random drift. Derivatives and martingales defining the sum of expectation are used to assess the small-scale changes exhibited over time. The log of the volatility of price assets could be integrated with drift time change that can be differentiated and the one-dimensional Brownian motion volatility process described above [5]. Combining these two elements could create a better-defined volatility process method that is more accurate. For instance, implied volatility provides metric to assess and predict the likelihood of certain changes to take place. The future volatility surface as shown in the Fig. 1 demonstrates a connection between historical volatility corresponding to different price strikes and expirations [3].
Fig. 1. The S&P volatility surface June 20, 2013.
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In this case, both asset price volatility and drift can be construed as a semi-martingale. In this case, the asset prices of stock are a random space that measures its probability and contains drifts in local martingale stochastic processes, as well as bounded variation. Since the log-prices are modeled as continuous semi-martingales, then the log-price can be given as shown below: dYt , = μt dt + βt dWt
(5)
This foundational measure is key to the construction and conceptualization of the various theories, including Hull and White’s model used to assess future interest rates. Hull and White’s model is a non-arbitrage model that uses lognormal and probability density functions, depending on the factors, as well as the Monte-Carlo simulations [3]. Its market application is also largely dependent n the local-stochastic volatility model which is developed through predictable dynamic volatility. Both the fractional Brown Motion and fractional Volatility are determined using two co-variance relationships between two sets of data identified randomly with the unique Gaussian process and a mean zero auto-covariance function. The derivatives determined are then assessed under log-volatility used to ensure that the decaying rate among them has a long-range dependence [3].
5 Smoothness of the Log-Volatility Process This section of the report explores the smoothness of the log-volatility process using Mat lab. That is, Mat lab acts as a mesh grid and assesses the discrete data set and calculates the expected logarithm function and differentiating it to the power q. Based on the knowledge of partial differentiation and the Holder property for function analysis, the characteristic function identified can be referred to as the Besov Smoothness Space because of the distinct boundary identified. A sequential assessment of the above function demonstrates the existence of a negative relationship between the variables. Some of the leading financial markets, including NASDAQ and S&P in developing the stock market indices., which means that the different assets in the financial market exhibit the same behaviour irrespective of their q and Hurst exponent. Below is a simple model used to assess/estimate the smoothness of the volatility process as proposed by Gatheral et al. [3]. According to Gatheral et al. [3], start by assuming that we have made discrete observations of the volatility process a time with a mesh on [0, T]: σ0, σ,…,σk,…, k ∈ {0, [T/]}, Set N = [T/], then for q ≥ 0, we define, q 1
log(σk ) − log σ(k−1) N N
m(q, ) =
(6)
k=1
The main assumption, in this case is sq > 0 and bq, as moves towards 0, Nqsq m(q, ) → bq
(7)
From equation Eq. (7), and the technical conditions known about the volatility process, it is believed to belong to the Besov smoothness space. As a result, Sq is a regularity to the volatility when measured using the lq rule.
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5.1 Volatility Smoothness This third part of the report intends to develop a simple model to assess the smoothness of volatility. As a result, the model developed here will be as a result of the demonstrated shown in the previous section of the paper, which showed that the incremental log volatility’s assets difference matched to a scaling property with a factor of constant smoothness parameter, and all their distribution tends to a Gaussian distribution [6]. As a result, the RFSV model will first be specified. 5.2 Specification of the RFSV Model The incremental log volatility’s assets difference matched to a scaling property with a factor of constant smoothness parameter, and all their distribution tends to a Gaussian distribution. This suggests the following model. H Log σ t + − log σ t = v (WH t+ − Wt )
(8)
From Eq. (8), WH is a fractional Brownian motion with the Hurst parameter used to measure volatility’s smoothness, v, in this case is a positive constant, which will be denoting as b. this can be further expounded as σt = σ exp v WH (9) t just like v, which is a positive constant, is also one though being stationary is desirable due to mathematical tractability, as well as ensuring a model is reasonable in most circumstances, this model is not stationary. As a result, to make the above the model proposed by Eq. (9) better applicable, there is need to ensure stationarity by exhibiting the log-volatility as a fractional Ornstein-Uhlenbeck (fOU) process with a longer reversion time-scale [3]. The Ornstein-Uhlenbleck process can be defined as shown below. Dxt = −θ xt dt + σ dWt
(10)
where whereas Wt denotes the Wiener process. An additional drift term is often added to the above stochastic differentiation equation as shown. Dxt = θ(μ − xt)dt + σ dWt
(11)
In this case μ is a constant. Evidently, the above proposition demonstrates that the difference in asset logvolatility is proportional to the difference of the Brownian motion multiplied by the scale factor, and then utilizing the Hurst exponent to compare between the RFSV and FSV models. If the Hurst exponent exceeds 0.5, then the skew functions increase over time, whereas if it is less than 0.5, then the decay is constant [3]. By introducing an auto-covariance function to compare random data sets during a certain period (t), the log-volatility function is noted to be linear with the variance former state positions minus the other quadratic function with a coefficient of 0.5. This
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demonstrates that the variance of the first asset position is higher than the left quadratic equation since the increase in the constant is only slight. Additionally, the exact formulae among two log-volatility using the Hurst exponent is used to generate the exponential partial integration with Gamma factorial function of the variance.
L, in this case is the Gamma function. The actual volatility, on the other hand, can be generated using the Gaussian distribution model defined previously as Eq b. Suppose the expected outcome from the logvolatility is identified, then the sum of exponential power index can be added suing the Gaussian distribution process. The previous volatility expected could be used to deduce the index power of the exponential function by adding in two sums for an expectation of two increasing intervals adding two sums of its variance multiplied by 0.5, combined with an auto-covariance for these two sets, such a linear homogeneous exponential factor index could also taking limit when a is small enough, a log-volatility expectation of two states multiplication is also linear with delta^2*H, and forms in a linear counterpart relationship as the following graph slop is downturn as dela^2*H increasing, for more incompatibility of the classical long range dependence FSV model with volatility data test, they adjust the quantity to two and observe the linear relationship by taking logarithm function find that the result is still logarithm function of delta scaled by a coefficient and constant term adding in the end, for both FSV and RFSV, they also taking the expectation of two continuous increasing log-volatility’ tie difference and then square it could leads to the gap among variance for former log-volatility minus the covariance of two log-volatility time interval set with a scale factor multiplied outside the bracket, by arrange different number data testing, they could more approximate the graph with precisely simulation. 5.3 Spurious Long-Term Volatility This section of the report makes an attempt to counterproof the proposition that volatility is rough. It also starts from an assessment of the covariance of two log-volatility data sets and finds that the autocovariance function decay is determined using the exponential power law. More or less, long memory of volatility demonstrates that the autocovariance function tends towards zero whereas tends towards infinity as shown in the model below [7].
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From the formula, it is evident that for neither the data nor the model does the auto covariance function decay as a power law. In fact, both the model and the data do not exhibit long memory. In fact, to demonstrate long memory in the volatility involves demonstrating that the process is a product of the fractional differentiation of the log-volatility, where d is presumed to be equivalent to 0.4 and L is a lag operator. The function operates as white noise [3]. This can be confirmed using an autocorrelation function of (Fig. 2).
Fig. 2. Autocorrelation function.
5.4 Forecasting Log-Volatility Using the RFSV Model Forecasting is one of the main applications of the RFSV model. We can see from the formula proof it still exists a transformation from a brown fractal motion into the expectation of log volatility to calculating the growth rate more precisely. The Taylor series is also an excellent way to simulate, as it could transfer into logarithm function quickly, the expectation of brown motion contains a cosine function which demonstrates the fluctuation of stock volatility growth kind of Fourier coefficient over a reasonable interest rate when Hurst index number is less than 0.5, then taking logarithm with each side through a Riemann sum integration, by taking number substitute into both AR and HAR formula which given above, the expectation denotes the empirical mean of the log-volatility variance over the whole period [7]. The prediction for the log formula is as shown below.
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In fact, the approximation shown above, which uses the Riemann sum is used to make predictions of the log-volatility, 1, 5, 20 days ahead. That is, = 1, 5, 20. From the above, we can obtain a formula to predict the RFSV variance as shown below.
In this case, the log function is the estimator whereas V2 is an estimation of the exponential of the intercept in the linear regression Log(m(2,)) on Log(). The Table 1 shows the RFSV forecast and compares it with the AR and HAR forecasts. Table 1. Table captions should be placed above the tables. AR(5)
AR(10)
HAR(3)
RFSV
SPX2.rv = 1
0.520
0.566
0.489
0.475
SPX2.n- = 5
0.750
0.745
0.723
0.672
SPX2.rv A = 20
1.070
1.010
1.036
0.903
FTSE2.rv = 1
0.G12
0.621
0.582
0.567
FTSE2.it A = 5
0.797
0.770
0.756
0.707
FTSE2.rv = 20
LOW
0.984
0.935
0.874
N2252.rv = 1
0.554
0.579
0.504
0.505
N2252.rv A = 5
0.857
0.807
0.761
0.729
N2252.rv = 20
1.097
1.016
1.011
0.96–1
GDAXI2.rv A = 1
0.439
0.448
0.399
0.386
GDAXI2.rv A = 5
0.675
0.650
0.616
0.566
GDAXI2.rv A = 20
0.931
0.850
0.81G
0.746
FCHI2.rv = 1
0.533
0.542
0.470
0.465
FCHI2.rv A = 5
0.705
0.707
0.691
0.631
The log-volatility predictions shown mainly demonstrate the convergence limit of the predictor when the Hurst index tends towards zero.
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One of the key questions that one must ponder upon as they apply the concept of volatility roughness regards the reason volatility is usually irregular. This can be deduced from the Hawkes process where the mathematical values are collected and located within the Euclidean space such that the time interval is identified and differentiated into minimum levels the integrated fractional process adjusted using the Hurst index is as shown below. φ(t − Ji ) λt = μ + 0