Financial Machina: Machine Learning For Finance: The Quintessential Compendium for Python Machine Learning For 2024 & Beyond
Reactive Publishing "Step beyond the horizon of traditional finance with "Financial Machina: The Quintessenti
142 33 6MB
English Pages 769 Year 2024
Table of contents :
Title Page
Dedication
Epigraph
Contents
Introduction
Chapter 1: Foundations of Machine Learning in Finance
1.1 The Evolution of Quantitative Finance
1.2 Key Financial Concepts for Data Scientists
1.3 Statistical Foundations
1.4 Essentials of Machine Learning Algorithms
1.5 Data Management in Finance
Chapter 2: Machine Learning Tools and Technologies
2.1 Computational Environments for Financial Analysis
2.2 Data Exploration and Visualization Tools
2.3 Feature Selection and Model Building
2.4 Machine Learning Frameworks and Libraries
2.5 Model Deployment and Monitoring
Chapter 3: Deep Learning for Financial Analysis
3.1 Neural Networks and Finance
3.2 Convolutional Neural Networks (CNNs)
3.3 Recurrent Neural Networks (RNNs) and LSTMs
3.4 Reinforcement Learning for Trading
3.5 Generative Models and Anomaly Detection
Chapter 4: Time Series Analysis and Forecasting
4.1 Fundamental Time Series Concepts
4.2 Advanced Time Series Methods
4.3 Machine Learning for Time Series Data
4.4 Forecasting for Financial Decision Making
4.5 Evaluation and Validation of Forecasting Models
Chapter 5: Risk Management with Machine Learning
5.1 Credit Risk Modeling
5.2 Market Risk Analysis
5.3 Liquidity Risk and Algorithmic Trading
5.4 Operational Risk Management
Chapter 6: Portfolio Optimization with Machine Learning
6.1 Review of Modern Portfolio Theory
6.2 Advanced Portfolio Construction Techniques
6.3 Machine Learning for Asset Allocation
6.4 Quantitative Trading Strategies
6.5 Portfolio Management and Performance Analysis
Chapter 7: Algorithmic Trading and High-Frequency Finance
7.1 Introduction to Algorithmic Trading
7.2 Strategy Design and Backtesting
7.3 High-Frequency Trading Algorithms
Chapter 8: Alternative Data
8.1 Structured and Unstructured Data Fusion
8.2 Alternative Data in Portfolio Management
Chapter 9: Financial Fraud Detection and Prevention with Machine Learning
9.1 Understanding Financial Fraud
9.2 Feature Engineering for Fraud Detection
9.3 Machine Learning Models for Fraud Detection
9.4 Real-Time Fraud Detection Systems
Conclusion
Epilogue: Navigating Future Frontiers from Berlin
Additional Resources
Glossary of Terms
Afterword
Title Page
Dedication
Epigraph
Contents
Introduction
Chapter 1: Foundations of Machine Learning in Finance
1.1 The Evolution of Quantitative Finance
1.2 Key Financial Concepts for Data Scientists
1.3 Statistical Foundations
1.4 Essentials of Machine Learning Algorithms
1.5 Data Management in Finance
Chapter 2: Machine Learning Tools and Technologies
2.1 Computational Environments for Financial Analysis
2.2 Data Exploration and Visualization Tools
2.3 Feature Selection and Model Building
2.4 Machine Learning Frameworks and Libraries
2.5 Model Deployment and Monitoring
Chapter 3: Deep Learning for Financial Analysis
3.1 Neural Networks and Finance
3.2 Convolutional Neural Networks (CNNs)
3.3 Recurrent Neural Networks (RNNs) and LSTMs
3.4 Reinforcement Learning for Trading
3.5 Generative Models and Anomaly Detection
Chapter 4: Time Series Analysis and Forecasting
4.1 Fundamental Time Series Concepts
4.2 Advanced Time Series Methods
4.3 Machine Learning for Time Series Data
4.4 Forecasting for Financial Decision Making
4.5 Evaluation and Validation of Forecasting Models
Chapter 5: Risk Management with Machine Learning
5.1 Credit Risk Modeling
5.2 Market Risk Analysis
5.3 Liquidity Risk and Algorithmic Trading
5.4 Operational Risk Management
Chapter 6: Portfolio Optimization with Machine Learning
6.1 Review of Modern Portfolio Theory
6.2 Advanced Portfolio Construction Techniques
6.3 Machine Learning for Asset Allocation
6.4 Quantitative Trading Strategies
6.5 Portfolio Management and Performance Analysis
Chapter 7: Algorithmic Trading and High-Frequency Finance
7.1 Introduction to Algorithmic Trading
7.2 Strategy Design and Backtesting
7.3 High-Frequency Trading Algorithms
Chapter 8: Alternative Data
8.1 Structured and Unstructured Data Fusion
8.2 Alternative Data in Portfolio Management
Chapter 9: Financial Fraud Detection and Prevention with Machine Learning
9.1 Understanding Financial Fraud
9.2 Feature Engineering for Fraud Detection
9.3 Machine Learning Models for Fraud Detection
9.4 Real-Time Fraud Detection Systems
Conclusion
Epilogue: Navigating Future Frontiers from Berlin
Additional Resources
Glossary of Terms
Afterword
- Author / Uploaded
- Sampson
- Josh; Strauss
- Johann; Bisette
- Vincent; Van Der Post
- Hayden
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