National Supervision and Income Smoothing in Banks’ Annual Reports (SpringerBriefs in Accounting) 3030740102, 9783030740108

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SPRINGER BRIEFS IN ACCOUNTING

Costanza Di Fabio

National Supervision and Income Smoothing in Banks’ Annual Reports

SpringerBriefs in Accounting Series Editors Peter Schuster, Fakultät Wirtschaftswissenschaften, Hochschule Schmalkalden, Schmalkalden, Thüringen, Germany Robert Luther, Department of Accounting, University of the West of England, Bristol, UK

SpringerBriefs in Accounting provide a forum for the publication of short books from all areas of accounting, showcasing emerging theory, empirical research, and practical application. They cover a broad range of issues across management accounting, management control, financial and tax accounting, and auditing.

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Costanza Di Fabio

National Supervision and Income Smoothing in Banks’ Annual Reports

Costanza Di Fabio Department of Economics & Business Studies University of Genoa Genoa, Italy

ISSN 2196-7873 ISSN 2196-7881 (electronic) SpringerBriefs in Accounting ISBN 978-3-030-74010-8 ISBN 978-3-030-74011-5 (eBook) https://doi.org/10.1007/978-3-030-74011-5 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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 Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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After the Crisis: New Approaches in Accounting Standards Applied by Banks and the New Framework for Banking Supervision . . . . . . 2.1 The Revision of Accounting Rules Disciplining Bank Annual Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 The Introduction of the Business Model Approach to Financial Instrument Reporting . . . . . . . . . . . . . . . . . . . 2.1.2 The New Standard and the Approach to Classification and Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 The Introduction of the Expected Loss Approach to Impairment Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 The New Framework for Banking Supervision . . . . . . . . . . . . . . . 2.2.1 Supervisors’ Activity in the Scope of the Supervisory Review and Evaluation Process . . . . . . . . . . . . . . . . . . . . 2.2.2 Supervisors Assessing Banks’ Asset Quality: Information at the Entity Level and at the Country Level from the First Comprehensive Assessment . . . . . . . . . . . . . . . . . 2.2.3 Stress Tests Carried Out by the European Banking Authority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 The Business Model Assessment in the Scope of Supervisory Activities . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Financial Supervision and Bank Accounting Numbers: State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 The Role of Financial Supervisors and the Effects on Banks’ Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Supervisory Characteristics Banks’ Accounting Behaviour . . . . . . 3.2.1 Supervisory Strictness . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

3.2.2 Independence of the Supervisory Authority . . . . . . . . . . . . 3.2.3 Strength of External Auditing . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Market Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Supervisory Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Business Model and Accounting Behaviours . . . . . . . . . . . . . . . . 3.3.1 The Concept of Business Model and the Theoretical Debate in the Accounting Literature . . . . . . . . . . . . . . . . . 3.3.2 Empirical Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Business Model and Banks’ Accounting Behaviours . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

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Supervisory Characteristics and Income Smoothing: The Case of European Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Facets of National Supervision and Bank Income Smoothing: The Key Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Sample and Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Results of the Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exploring the Role of Business Models . . . . . . . . . . . . . . . . . . . . . . . 5.1 Linking the Business Model to Bank Smoothing Strategies . . . . . . 5.2 Methodological Approaches to the Identification of Bank Business Models: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Cluster Analysis and Emerging Business Models . . . . . . . . . . . . . 5.4 Models and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Results of the Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Chapter 1

Introduction

This book tackles the relationship between the characteristics of national supervisory systems and manipulative practices in banks’ annual reports, with a specific focus on income smoothing. Since the 2008 financial crisis, governmental bodies and regulators have remarked the need that supervision improves transparency of information bank diffuse on their business, thus implying that stronger supervision should also increase the quality of bank annual reports.1 Nevertheless, the effects of supervision on banks’ accounting opacity are still highly debated and research discusses the unintended consequences of monitoring mechanisms on the actual compliance of bank accounting choices with financial reporting standards (Quagli et al., 2021). Overall, a complex picture emerges, and monitoring systems appear as a multifaceted object of enquiry2 whose varied facets could produce no obvious responses in accounting strategies chosen by supervised banks. This volume looks at this issue by focussing on the relationship between the varied facets of banking supervision and income smoothing through loan loss provisions.

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This book specifically focusses on banks’ annual reports and accounting discretion in the banking industry. However, it is worth recalling here a rich literature not specifically focussing on financial firms and discussing transparency of accounting and disclosure practices. The debate spams from discretion in firms’ accounting choices (Lai & Stacchezzini, 2009; Quagli & Avallone, 2010; Avallone et al., 2015; Avallone & Quagli, 2015; Greco, 2015; Leoni & Florio, 2015; Marchini et al., 2018) to discretion in disclosure (Bini et al., 2010; Dainelli et al., 2013; Marchini et al., 2019), and flourished as a consequence of the introduction of the International Financial Reporting Standards (IFRS) in the European context (Pizzo, 2000; Giunta, 2003; Lionzo, 2005; Andrei, 2006; Cordazzo, 2008; Quagli, 2009; Corbella & Florio, 2010; Azzali et al., 2011; Florio, 2011; Pavan & Paglietti, 2011; Marchi & Potito, 2012; Corbella et al., 2013; Teodori & Veneziani, 2013; Moscariello et al., 2014), also due to complexity and subjectivity in interpreting and applying principle-based standards as well as in enforcing them (Quagli & Ramassa, 2018a; Quagli et al., 2020, 2021; Ramassa, 2020). 2 The combination of different features of the national supervisory system has been referred to by Carretta et al. (2015) as ‘supervisory style’. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 C. Di Fabio, National Supervision and Income Smoothing in Banks’ Annual Reports, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-74011-5_1

1

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1 Introduction

Understanding how supervisory features influence bank manipulative practices is of particular interest as, since the 2014 publication of IFRS 9—Financial Instruments, banking regulators have stressed the direct link between ‘high-quality’ supervision and the quality of bank accounting numbers (BCBS, 2015), and this idea was in theory certainly inherent the logic of supervisory-system revision after the 2008 financial crisis (Di Martino et al., 2017). In addition, supervisors have thus far supported a forward-looking approach in estimating loan loss provisions to enable banks to cover expected losses (Gaston & Song, 2014); this approach, finally adopted with IFRS 9, could implicitly leave more room for opportunistic bank income smoothing. At first sight, smoothing can be, for sure, considered aligned with the financial stability objective because it allows covering credit risk by creating hidden reserves against expected losses in boom periods of the economic cycle, when this risk arises, and to exploit such reserves to cover losses in downturns, when risks materialise.3 Nonetheless, studies cast doubt on the effectiveness of conservative loan loss accounting in reducing systemic effects (Andreou et al., 2017) and underscore that the main effect of this accounting strategy is reducing perceived risk (Beaver, 1970; Goel & Thakor, 2003; Kanagaretnam et al., 2004). Bank managers are indeed particularly used to exploiting income smoothing to avoid the close eye of supervisors and signal the stability of bank business models. This objective is achieved opportunistically adjusting loan loss provisions, which constitute the banks’ main accrual, to hide the risk attributes of complex loan portfolios (Kanagaretnam et al., 2004; Fonseca & González, 2008; Bushman & Williams, 2012; Bushman, 2014, 2016; Di Martino et al., 2017).4 From this perspective, the high aversion of supervisors to risky businesses could produce undesirable side effects by enabling banks to run unstable businesses whose effects on earnings can be masked using smoothing strategies. In other words, supervision implemented to pursue the supervisory aim of financial stability could itself undermine transparency and, in turn, even stability itself. Prior research provides evidence that financial supervision—and, more generally, the institutional environment—plays a significant role in explaining bank propensity to smooth income, with patterns varying across countries due to their institutional characteristics (Fonseca & González, 2008). Overall, studies suggest that the many supervisory facets produce distinct incentives for bank managers to engage in income smoothing. For instance—at least in the European context—the strictness

3

With specific reference to this research setting, studies support bank opacity, and remark the relevance of the opportunistic perspective to explain bank accounting choices (Morgan, 2002; Flannery et al., 2004; Andreou et al., 2017). 4 The book employs loan loss provisions in line with other studies on banks’ earnings management. Indeed, loan loss provisions have thus far been widely explored by a rich literature focussing on the banking industry (see also Wahlen, 1994; Liu & Ryan, 1995; Kim & Kross, 1998; Kanagaretnam et al., 2003, 2004; Fonseca & González, 2008; Gebhardt & Novotny-Farkas, 2011; Jin et al., 2018; García-Osma et al., 2019). As noted by Curcio et al. (2017), accounting research usually distinguishes between a non-discretionary component of loan loss provisions and the discretionary one, whereas the practice considers specific and general provisions.

1 Introduction

3

of national supervisors seems to increase banks’ recourse to earnings management (Gebhardt & Novotny-Farkas, 2011; García-Osma et al., 2019), and that this sideeffect can be constrained only by high independence of supervisors from the banking industry and by market monitoring (Fonseca & González, 2008; García-Osma et al., 2019; Di Fabio et al., 2021). Other two supervisory features, namely the involvement of external auditors and supervisory effectiveness, have been far less investigated; however, recent research suggests that the supervisory reliance on the audit function is positively related to smoothing in market-oriented banks, and that effectiveness seems to influence how other monitoring mechanisms reduce smoothing (Di Fabio, 2019; Di Fabio et al., 2021). Against this background, the book investigates whether a broad range of characteristics of national supervisors affects bank propensity to smooth income, also considering the potential role of the bank business model, whose influence of bank income smoothing has been recently highlighted (Di Fabio, 2019). To address these issues, the focus is specifically on European banks observed from 2003 to 2015. In particular, the empirical analysis employs a single rich dataset including data from listed and non-listed bank consolidated financial statements, country-level variables that proxy for several features of the supervisory style taken from the database by Barth et al. (2008a, 2008b, 2013a, 2013b), data from the Asset Quality Review conducted by the European Central Bank (ECB) in 2013, and data on GDP of European Countries from 2003 to 2015 are taken from the World Bank databases. In contrast with the previous studies providing results on different supervisory features but exploiting different datasets, this book provides empirical evidence on a single dataset, thus enabling comparison between the effects of different supervisory features and gaining a broad understanding of the phenomenon. Specifically, the contents of this volume are organised as follows. Chapter 2 introduces the main novelties affecting accounting rules applied by banks and the debate surrounding their introduction in the aftermath of the 2008 financial crisis. It also outlines the process of revision of the European supervisory architecture and the changes introduced with the emergence of the Single Supervisory Mechanism. Chapter 3 reviews literature identifying supervisory features as crucial to explain bank accounting behaviours and provides an overview of empirical evidence on the topic, overall suggesting that the varied supervisory facets produce distinct incentives for bank managers to engage in income smoothing and then different effects on this accounting strategy. Given the increasing academic interest in the business model as explanatory factor for banks’ risk-taking behaviours and their accounting behaviours, this chapter also provides an overview of theoretical and empirical contributions on the matter with a specific focus on financial reporting literature. Chapters 4 and 5 develop the key issues emerging from the literature by carrying out two empirical analyses on European banks. In detail, Chap. 4 discusses the association between the different features of the supervisory system and bank income smoothing, while Chap. 5 complements this discussion by introducing the business model as further explanatory variable for bank income smoothing. This chapter also provides a review of methodologies used in banking research to identify bank business models. Based on the empirical findings, the concluding chapter provides

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1 Introduction

the policy implications of this work and some directions for further research in the field. Acknowledgements I gratefully thank Prof. Alberto Quagli, Prof. Francesco Avallone and Prof. Paola Ramassa for their generous support during my research work and their valuable suggestions. I also sincerely thank Prof. Francesco Giunta, Prof. Claudio Teodori, Prof. Stefano Zambon, Prof. Cristina Florio and Prof. Araceli Mora for their insightful comments at various stages of this work. Finally, I gratefully acknowledge helpful comments from Luc Paugam in the initial phase of this research.

References Andrei, P. (Ed.). (2006). L’adozione degli IAS/IFRS in Italia: impatti contabili e profili gestionali. Giappichelli. Andreou, P. C., Cooper, I., Louca, C., & Philip, D. (2017). Bank loan loss accounting treatments, credit cycles and crash risk. The British Accounting Review, 49(5), 474–492. Avallone, F., & Quagli, A. (2015). Insight into the variables used to manage the goodwill impairment test under IAS 36. Advances in Accounting, 31(1), 107–114. Avallone, F., Gabbioneta, C., Ramassa, P., & Sorrentino, M. (2015). Why do firms write off their goodwill? A comparison of different accounting systems. Financial reporting. Azzali, S., Fornaciari, L., & Pesci, C. (2011). The value relevance of the performance of listed Italian companies following the introduction of the IAS/IFRS. Analele Stiintifice ale Universitatii “Alexandru Ioan Cuza” Iasi, pp. 3–18. Barth, J. R., Caprio, G., & Levine, R. (2008a). Bank regulations are changing: For better or worse? Comparative Economic Studies, 50(4), 537–563. Barth, M. E., Landsman, W. R., & Lang, M. H. (2008b). International accounting standards and accounting quality. Journal of Accounting Research, 46(3), 467–498. Barth, J. R., Caprio, G., & Levine, R. (2013a). Bank regulation and supervision in 180 countries from 1999 to 2011. Journal of Financial Economic Policy, 5(2), 112–219. Barth, J. R., Lin, C., Ma, Y., Seade, J., & Song, F. M. (2013b). Do bank regulation, supervision and monitoring enhance or impede bank efficiency? Journal of Banking and Finance, 37(8), 2879–2892. Basel Committee on Banking Supervision – BCBS. (2015). Guidance on credit risk and accounting for expected credit losses. Available on the internet at: https://www.bis.org/bcbs/publ/d350.htm Beaver, W. H. (1970). The time series behavior of earnings. Journal of Accounting Research, 8, 62–99. Bini, L., Giunta, F., & Dainelli, F. (2010). Signalling theory and voluntary disclosure to the financial market-evidence from the profitability indicators published in the annual report. Available at SSRN 1930177. Bushman, R. M. (2014). Thoughts on financial accounting and the banking industry. Journal of Accounting and Economics, 58(2), 384–395. Bushman, R. M. (2016). Transparency, accounting discretion, and bank stability. Economic Policy Review, 22(1), 129, vi. Bushman, R. M., & Williams, C. D. (2012). Accounting discretion, loan loss provisioning, and discipline of banks’ risk-taking. Journal of Accounting and Economics, 54(1), 1–18. Carretta, A., Farina, V., Fiordelisi, F., Schwizer, P., & Lopes, F. S. S. (2015). Don’t stand so close to me: The role of supervisory style in banking stability. Journal of Banking and Finance, 52, 180–188. Corbella, S., & Florio, C. (2010, September). Issues arising for accounting harmonization: The case of stock options in Italy. Accounting Forum, 34(3–4), 184–195.

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Corbella, S., Florio, C., & Rossignoli, F. (2013). IFRS adoption in Italy: Which effects on accounting figures and subjectivity? Accounting and Finance Research, 2(4), 130–148. Cordazzo, M. (2008). Principi contabili internazionali e risultati economici delle quotate italiane (pp. 340–350). FrancoAngeli. Curcio, D., De Simone, A., & Gallo, A. (2017). Financial crisis and international supervision: New evidence on the discretionary use of loan loss provisions at Euro Area commercial banks. The British Accounting Review, 49(2), 181–193. Dainelli, F., Bini, L., & Giunta, F. (2013). Signaling strategies in annual reports: Evidence from the disclosure of performance indicators. Advances in Accounting, 29(2), 267–277. Di Fabio, C. (2019). Does the business model influence income smoothing? Evidence from European banks. Journal of Applied Accounting Research, 20(3), 311–330. Di Fabio, C., Ramassa, P., & Quagli, A. (2021). Income smoothing in European banks: The contrasting effects of monitoring mechanisms. Journal of International Accounting, Auditing and Taxation, 43, 100385. Di Martino, G., Dicuonzo, G., Galeone, G., & Dell’Atti, V. (2017). Does the new European banking regulation discourage earnings management. International Business Research, 10(10), 45–56. Flannery, M. J., Kwan, S. H., & Nimalendran, M. (2004). Market evidence on the opaqueness of banking firms’ assets. Journal of Financial Economics, 71(3), 419–460. Florio, C. (2011). La verifica di impairment nella prospettiva delle politiche di earnings management: profili teorici ed evidenze empiriche (Vol. 896). FrancoAngeli. Fonseca, A. R., & González, F. (2008). Cross-country determinants of bank income smoothing by managing loan-loss provisions. Journal of Banking and Finance, 32(2), 217–228. García-Osma, B., Mora, A., & Porcuna, L. (2019). Prudential supervisors’ independence and income smoothing in European banks. Journal of Banking and Finance, 102, 156–176. Gaston, E., & Song, M. I. (2014). Supervisory roles in loan loss provisioning in countries implementing IFRS. IMF working paper 14/170, pp. 1–41. Gebhardt, G. U., & Novotny-Farkas, Z. (2011). Mandatory IFRS adoption and accounting quality of European banks. Journal of Business Finance and Accounting, 38(3–4), 289–333. Giunta, F. (2003). Competenza contro prudenza: arbitra il “fair value”. Competenza contro prudenza: arbitra il “fair value”, pp. 1000–1003. Goel, A. M., & Thakor, A. V. (2003). Why do firms smooth earnings? The Journal of Business, 76 (1), 151–192. Greco, G. (2015). Le politiche di bilancio aziendali. Metodi di ricerca e analisi delle determinanti. FrancoAngeli. Jin, J. Y., Kanagaretnam, K., & Liu, Y. (2018). Banks’ funding structure and earnings quality. International Review of Financial Analysis, 59, 163–178. Kanagaretnam, K., Lobo, G. J., & Mathieu, R. (2003). Managerial incentives for income smoothing through bank loan loss provisions. Review of Quantitative Finance and Accounting, 20(1), 63–80. Kanagaretnam, K., Lobo, G. J., & Yang, D. H. (2004). Joint tests of signaling and income smoothing through bank loan loss provisions. Contemporary Accounting Research, 21(4), 843–884. Kim, M. S., & Kross, W. (1998). The impact of the 1989 change in bank capital standards on loan loss provisions and loan write-offs. Journal of Accounting and Economics, 25(1), 69–99. Lai, A., & Stacchezzini, R. (2009). Managers’ discretion in purchase price allocation: A comparison between UK and Italian insurers. International Review of Business Research Papers, 5(6), 161–171. Leoni, G., & Florio, C. (2015). A comparative history of earnings management literature from Italy and the US. Accounting History, 20(4), 490–517. Lionzo, A. (2005). Il sistema dei valori di bilancio nella prospettiva dei principi contabili internazionali (Vol. 294). FrancoAngeli.

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Liu, C. C., & Ryan, S. G. (1995). The effect of bank loan portfolio composition on the market reaction to and anticipation of loan loss provisions. Journal of Accounting Research, 33(1), 77–94. Marchi, L., & Potito, L. (Eds.). (2012). L’impatto dell’adozione degli IAS/IFRS sui bilanci delle imprese italiane quotate (Vol. 943). FrancoAngeli. Marchini, P. L., Mazza, T., & Medioli, A. (2018). The impact of related party transactions on earnings management: Some insights from the Italian context. Journal of Management and Governance, 22(4), 981–1014. Marchini, P. L., Andrei, P., & Medioli, A. (2019). Related party transactions disclosure and procedures: A critical analysis in business groups. Corporate Governance: The International Journal of Business in Society, 19(6), 1253–1273. Morgan, D. (2002). Rating banks: Risk and uncertainty in an opaque industry. The American Economic Review, 92(4), 874–888. Moscariello, N., Skerratt, L., & Pizzo, M. (2014). Mandatory IFRS adoption and the cost of debt in Italy and UK. Accounting and Business Research, 44(1), 63–82. Pavan, A., & Paglietti, P. (2011). La Value Relevance dell’informativa di bilancio: dai principi contabili italiani agli standard contabili internazionali. Rivista Italiana di Ragioneria e di Economia Aziendale, 111, 19–32. Pizzo, M. (2000). Il “fair value” nel bilancio d’esercizio. Cedam. Quagli, A. (2009). Dal fair value al fairy value: coerenza concettuale e condizioni di impiego del fair value negli IFRS. Financial Reporting, 1, 94–120. Quagli, A., & Avallone, F. (2010). Fair value or cost model? Drivers of choice for IAS 40 in the real estate industry. European Accounting Review, 19(3), 461–493. Quagli, A., & Ramassa, P. (2018a). L’enforcement dell’informativa contabile (Vol. 19). G Giappichelli Editore. Quagli, A., Lagazio, C., & Ramassa, P. (2020). From enforcement to financial reporting controls (FRCs): A country-level composite indicator. Journal of Management and Governance, 1–31. Quagli, A., Avallone, F., Ramassa, P., & Di Fabio, C. (2021). Someone else’s problem? The IFRS enforcement field in Europe. Accounting and Business Research, 51(3), 246–270. Ramassa, P. (2020). L’interpretazione delle regole contabili: Analisi del ruolo dell’IFRS Interpretations Committee. FrancoAngeli. Teodori, C., & Veneziani, M. (Eds.). (2013). L’evoluzione della disclosure nella sezione narrativa: L’impatto dei principi contabili internazionali e del processo di armonizzazione (Vol. 14). G Giappichelli Editore. Wahlen, J. M. (1994). The nature of information in commercial bank loan loss disclosures. The Accounting Review, 69, 455–478.

Chapter 2

After the Crisis: New Approaches in Accounting Standards Applied by Banks and the New Framework for Banking Supervision

2.1

The Revision of Accounting Rules Disciplining Bank Annual Reports

Following the introduction of Regulation (EC) No 1606/2002, listed banks in Europe must prepare their annual reports under International Financial Reporting Standards (IFRS) (Quagli, 2019). In addition, in many European countries also unlisted banks apply IFRS instead of national GAAP, depending upon national requirements (Pacter, 2017). For instance, in Italy banks are required to prepare their financial statements under IFRS and this applies whether or not the company’s securities are traded on a regulated exchange (Di Pietra, 2008). Anyway, the mandatory application of IFRS for banks is quite diffused; this happens also in Belgium, Bulgaria, Croatia, Cyprus, Estonia, Finland, Greece, Latvia, Lithuania, Malta, Poland, Portugal, Romania, Slovakia and Sweden. With reference to the bank’s annual report, accounting standards with the most pervasive effects are those concerning classification and measurement of financial assets and liabilities. After 12 years under IAS 39—Financial Instruments: Recognition and Measurement, from 2018 IFRS 9—Financial Instruments (issued by the International Accounting Standards Board—IASB on 24 July 2014) is mandatorily effective in Europe. It took more than 10 years to the new standard to come into place, as the reconsideration of accounting rules for financial instruments already started in the wake of the 2008 financial crisis. Already in March 20081 the IASB issued the Discussion Paper Reducing Complexity in Reporting Financial Instruments, which was part of a joint project with the

1 Following the FASB’s initiative (November 2007), in February 2008 the IASB published a discussion paper including a part focussing on financial instruments with characteristics of equity.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 C. Di Fabio, National Supervision and Income Smoothing in Banks’ Annual Reports, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-74011-5_2

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After the Crisis: New Approaches in Accounting Standards Applied by Banks. . .

FASB2 (IASB, 2008). The issue at hand was the need for reducing complexity3 in accounting for financial instruments and increasing flexibility of the existing standard: these were at those times the requests from preparers, auditors and users claiming for standards to be more easily applied and interpreted.4 The first step in this direction should have been the revision of the measurement criteria, deemed too many and too articulated. The long-term solution presented in this first document was a single measurement attribute, and the criterion proposed was fair value.5 At those times, the Board foresaw fair value as an exit strategy from difficulties caused by the ‘patchwork of measurement methods for financial instruments’ (IASB, 2008, p. 18) and enhance the comparability across reporting entities and over time. Of course, the Board admitted that fair value appropriateness could vary across instruments. For instance, for instruments with high expected variability of future cash flows (as derivatives) and uncorrelated with the initial cash flows, cost is not desirable as it is not useful to provide information about future cash flows and fair value seems the most appropriate measurement basis. By contrast, for instruments with fixed or slightly variable future cash flows cost could be considered a better alternative to fair value; nonetheless, even in these cases, a single measurement criterion as fair value would allow to avoid criticism related to impairment related issues and to avoid issues concerning the definition of boundaries between the categories of fixed and variable cash flows.6 Acknowledging the potential issues arising from the introduction on fair value as a single measurement attribute (see Giunta, 2003; Quagli, 2009), the Board indicates

2 The document reported the history of the project started with the FASB. Indeed, both the standard setters started to deal with the issues relating to financial instruments’ reporting since, respectively, 1986 the FASB and 1988 the IASB. 3 The Exposure draft provides a definition of complexity, borrowing it from the draft decision memorandum published in January 2008 by the Advisory Committee on Improvements to Financial Reporting (chartered by the US Securities and Exchange Commission). According to this definition, complexity rests the difficulty of understanding and applying, and firstly it refers to the difficulty for (1) users to identify the economic substance of a transaction/event and the actual financial position of the entity, (2) preparers to be able to apply appropriately accounting standards and to communicate the economic substance of a transaction/event involving the entity and to represent the effective position of the entity, and, finally, complexity concerns the difficulty for other subjects to be able to audit, monitor and regulate financial reporting. 4 To address issues relating to financial instruments’ reporting, from 2004 the IASB coordinated a working group (Financial Instruments Working Group) involving users, preparers and auditors and representatives of regulatory bodies and financial institutions. 5 Presenting the hypothesis of a single measurement criterion, the document provided only a working definition of fair value. 6 In its comment letter, the Basel Committee maintains that a full fair value measurement system would be appropriated only if four conditions were met: ‘(i) the conceptual and practical issues associated with fair value are resolved, (ii) active markets develop for major aspects of banking book positions, (iii) bank risk management evolves to rely on fair value measurements, and (iv) a broad range of users of financial statements, including depositors and other creditors of banks, find fair value to be the best measure in the primary financial statements’ (Basel Committee, CL 94, page 4).

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intermediate approaches to ‘reduce today’s complexity more quickly’. It suggested the reduction of the number of measurement categories by either eliminating the held to maturity category (and presumably moving those financial instruments to the available for sale category) or eliminating the available for sale category, simply accounting as fair value through profit and loss those instruments which were previously included in the available for sale category (this possibility could run the risk to raise oppositions due to an increase in earnings’ volatility). Alternatively, it proposed to measure at fair value all the instruments traded in active markets,7 and use the existing measurement categories to report the other instruments. Another possibility (a sort of compromise between the single fair value measurement to be achieved in the long term and a more acceptable solution for the short/ medium term) was to introduce a fair value measurement with some exceptions, consisting of financial instruments allowed to be measured at cost (for instance, depending on considerations about cash flow variability8). Under this approach, however, entities could also decide not to apply the exception. The comment period for the Discussion Paper closed on September 19th of the same year, and a first analysis of answers was presented in occasion of the October 21 joint IASB/FASB meeting. It seemed that the Respondents’ opinions seemed to suffer from economic contingency, as they did not support the adoption of a full fair value measurement system in the long term. Several motivations were opposed to the IASB’s view from a multiplicity of respondents. For instance, albeit strongly supporting the objective of reducing complexity in reporting, the Association of Chartered Certified Accountants (ACCA) maintained that a ‘mixed model’ of measurement bases would be better at reflecting ‘the various risk strategies management employ’ (ACCA, CL 114, p.1). Particularly, the banking industry vehemently disagreed on the proposal of a full fair value measurement system in the long term,9 in the light of the procyclical consequences of the application of fair values to financial instruments and of the increased market volatility. Citigroup remarked that, if the objective of financial

In its comment letter, the Associazione Bancaria Italiana argues that ‘such solution would have the benefit to address some of the issues concerning reliability of fair value measurement as such criteria would employed only for those instruments that have a published price quotation that is representative of actual market transaction’ (Associazione Bancaria Italiana CL 81, page 5). 8 The Associazione Bancaria Italiana comments that: ‘Such solution would simplify accounting for financial instruments as long as instruments features are easily acknowledgeable. However due to continuing financial innovation and difficulties in providing definition of instruments (such as distinction between equity and liabilities), makes it difficult to achieve such solution’ (Associazione Bancaria Italiana, CL 81, page 5). 9 ‘The recent crises, and the application of such a model to some categories of financial instruments already, have demonstrated the drawbacks of such a model. The use of fair value measurements amplifies crises and has procyclical effects. Besides, markets have become more volatile and the market measurements at a single point in time are less meaningful than in the past: the informative content of a very volatile measurement is dubious and difficult to interpret. Consequently, it would be inappropriate to extend the use of fair market value beyond those activities where a fair value measurement is consistent with the business model followed’ (BNP Paribas, CL 100, page 2). 7

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reporting is to provide information ‘that is useful to a wide range of users in making economic decisions’, reporting as fair value through profit or loss those instruments which are not managed on a fair value basis or held for trading, would worsen the information’s usefulness. As many other respondents, Citigroup heavily stressed the need of a different approach for reducing complexity of reporting. This different approach should start from the need for ‘reporting profit or cash flows that are actually likely to be realized by the businesses (. . .) Financial statements are presented on a going-concern basis, demonstrating the value of the continued use of the assets to the business, which must reflect management’s intended use of those assets’. Additionally, Citigroup stressed the fact that, especially for certain business models as retail banking, the requirement of reporting instruments on a full fair value basis could seriously threaten both the usefulness of information and the possibility of entities to provide timely and reliable information. Indeed, several banks characterised by traditional business models do not have available internal processes and controls able to continuatively assess fair value of certain portfolios (especially in case of retail banking), and the collection of inputs needed to continuously update fair values could represent a relevant cost without benefits (e.g. internal use of the information) for the reporting entity. As result, users could be sceptic about the reflection of the entity use of unobservable inputs on measurements’ uncertainty. As alternative to a full fair value measurement, entities could be required to provide disclosures on fair values of instruments not carried at fair value; this could actually enable users ‘to understand the impact of using an alternative measurement basis on the financial results’ (Citigroup, CL 105, page 9).

2.1.1

The Introduction of the Business Model Approach to Financial Instrument Reporting

One year after the issuing of the Discussion Paper, in March 2009 the IASB took the stock completing a full analysis of the 162 comment letters received.10 The complete analysis confirmed again the view that preparers and auditors were not supporting the long-term solution of full fair value measurement for all financial instruments and preferred a mixed-attribute model able to better reflect the business purposes inspiring the instrument’s management. Several reasons underpinned the arguments against the full fair value measurement. A rich number of respondents (including even users supportive of full fair value) raised concerns on the use of fair value in absence of reliable estimates. In line with these positions, many preparers argued that in case of either illiquid markets

10

Financial institutions represented 14.8% of total respondents, approximately the same participation came from accountancy bodies, while preparers represented 19.8% of respondents. Only 4 companies (2.5%) sent comment letters in this phase.

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(this was the scenario brought by the financial crisis) or not fully developed markets (this could be the case of developing countries) the unreliability of fair values could significantly affect dividends, taxes and other cash flow items. Given the interest of parties and the G-20 leaders’ recommendations for improving and simplifying accounting requirements for financial instruments, the Board operationalised the project dividing it into three stages; clearly, the first one concerned requirements for the classification and measurement of financial instruments. Additionally, the measurement issue was at the centre of the debate on the links between accounting and the financial crisis. In July 2009, considering the issues raised by interested parties in the comment letters to the Discussion Study, the IASB issued the Exposure Draft Financial Instruments: Classification and Measurement (IASB, 2009). The approach proposed individuates fair value as the main criterion to measure financial instruments, unless these meet two conditions, namely (1) the instrument has only basic loan features and (2) it is managed on a contractual yield basis (paragraphs 3–5). Gains and losses on instruments measured at fair values would be presented in P&L, unless the financial asset is an investment in an equity instrument not held for trading purposes and the entity choose to present them in the other comprehensive income with an irrevocable election at the initial recognition (paragraphs 19–21).11 These six paragraphs were extensively discussed by respondents during the period for comment letters received by 14 September 2009, summarised by the Agenda Study 7. Overall, respondents supported the two measurement bases model, based on both contractual terms and on how the entity manages the instrument, sometimes questioning the interaction between the two conditions provided by paragraph 4 and the supposed primacy of one of them.12 Three are the notable (and interconnected) remarks made by respondents. The main observation concerns the relevance of how the entity manages the instruments.13 Respondents supported the necessity to categorise financial instruments according to the reason why they are held by the entity, as entities can acquire financial instruments to recognise profits deriving from variations of market prices or 11 According to paragraph 22, if the entity irrevocably decides to present gains and losses in the other comprehensive income, then it should also recognise in other comprehensive income dividends from those investments ‘when the entity’s right to receive payment is established’. 12 Among the others, the position expressed by BNP Paribas was bitterly critical, maintaining that the Exposure Draft was not addressing the issues ‘expressed by the G20 to “improve standards for the valuation of financial instruments based on their liquidity and investors” holding horizons and “or valuation uncertainty”’ (BNP Paribas, CL 176, p. 1). Additionally, this letter remarked that the Draft was not consistent with issues raised by the Basel Committee on Banking Supervision, which expressed concerns on the effectiveness of fair values in case of either dislocated or illiquid markets. 13 We support the introduction of a business model criterion in addition to the basic loan features criterion. We also agree that if the entity’s business model is focused on receiving or paying the contractual cash flows of the instrument, rather than focused on the cash flows from selling an asset (or payments to transfer or settle a liability) that the instrument should be eligible for amortised cost measurement.

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from cash flow streams’ collection until the instrument maturity. This criterion would allow financial statements to reflect the actual performance of the entity achieved pursuing the business model. In addition to the basic loan features criterion, the perspective of business model gained favours of respondents. While some respondents agreed with the IASB direction that both the nature of the instrument and the business model should be determinative in classification (Deutsche Bank, CL 42), others saw business model as a criterion that should prevail over the characteristics of the instruments to determine the appropriate measurement basis (BNP Paribas, CL 176). Especially in response to Question 2 (Do you believe that the exposure draft proposes sufficient, operational guidance on the application of whether an instrument has ‘basic loan features’ and ‘is managed on a contractual yield basis’? If not, why? What additional guidance would you propose and why?), comments supported the idea that fair value measurement should not be defined as a measurement basis by default and that its use should be limited to instruments which actually could realise gains and losses already reported, i.e. to instruments managed within a trading business model. A second remark concerned the wording used by the Exposure Draft for the measurement. Indeed, respondents suggested that the Board avoid the expression ‘managed on a contractual yield basis’. Indeed, some argued that the term ‘managed’ could indicate an active management of the contractual yield although this might not be the case; for instance, in case of short-term receivables, entities neither consider the time value of money nor actively manage cash flows. Comments suggested to refer to a clearer wording as ‘predominantly held for the collection of contractual cash flows or issued for funding purposes resulting in the payment of contractual cash flows’. Suggested improvements were more tied linked to the rationale of business model, and this approach could avoid certain difficulties in measurement by resorting to the role of sales within the business model.14 The third observation made is tightly linked to the previous, as it concerns the level at which the conditions stated at paragraph 4 should be assessed; in most opinions, the most appropriate level of assessment should be superior to the instrument-by-instrument level, but inferior to the entity level. Indeed, business models could be set at a granular level. The rationale of business model was also stressed with reference to reclassifications.

“Application of the ED’s principle to some portfolios such as liquidity portfolios that are neither managed for trading nor managed for on a contractual yield basis is unclear. The portfolios are held for liquidity purposes; they earn a return but are sufficiently liquid that they could be sold to raise funding in the short term in dislocated market conditions. Under the current wording in the ED we do not believe it is clear whether or not these portfolios meet the positive test of managed on a contractual yield basis. We believe that under the wording proposed in Appendix 2 that if sales from the liquidity portfolios were infrequent then these portfolios could qualify as amortised cost (if they have basic loan features)” (Deutsche Bank, CL 42, page 2).

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The Exposure Draft proposed to eliminate the possibility to reclassify financial instruments between fair value and amortised cost. Respondents opposed that this prohibition was substantially inconsistent with a classification approach based on the business model and suggested that (even if infrequent) changes in business model are likely to occur. Respondents suggested that, in those cases, reclassifications due to business model changes should be applied prospectively and providing robust disclosure concerning the reasons for the reclassifications and the amount reclassified to allow users to understand the impact of the reclassification.

2.1.2

The New Standard and the Approach to Classification and Measurement

Since then, the Board included the business-model notion in the project for classification and measurement. In November 2009, the IASB issued the chapters of IFRS 9 concerning the classification and measurement of financial assets. Following these chapters, financial assets should be classified based on the business model within which they are held and based on their contractual cash flows’ characteristics. In October 2010, the IASB added to IFRS 9 the requirements for the classification and measurement of financial liabilities, whose requirements tightly derived from IAS 39, with the exception of those relating to the fair value option for financial liabilities. The final version of requirements for financial assets’ classification and measurement was issued only in July 2014, when the category of fair value through other comprehensive income was introduced. Overall, the approach presented by IFRS 9 to classify financial assets is principlebased and driven by both cash flow characteristics and the business model for managing financial assets. The Standard admits three accounting systems for financial assets, namely (1) amortised cost, (2) fair value through other comprehensive income, or (3) fair value through profit or loss. The entity should classify assets in one of these categories referring to both the entity’s business model for managing the financial assets and the contractual cash flow characteristics of the asset. IFRS 9 directly provides only the main distinction between financial assets to be measured at amortised cost and financial assets to be measured at fair value through other comprehensive income, stating two requirements for each of these categories. Requirements are (1) the assessment of a specific type of business model and (2) the assessment of the characteristics of the contractual cash flows to identify whether these are ‘solely payments of principal and interest on the principal amount outstanding’.

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Table 2.1 Business model approach Business model • Holding financial assets to collect contractual cash flows • Holding financial assets in order to collect contractual cash flows and • Selling financial assets • Other business models

Contractual cash flow characteristics test Passed Failed Amortised cost Fair value through profit or loss Fair value through other Fair value through comprehensive income profit or loss Fair value through profit or loss

Fair value through profit or loss

Financial instruments that do not meet requirements should be classified as fair value through profit and loss (FVTPL). This test seems designed with the implicit aim to exclude from the two measurement systems financial assets characterised by complex application of the effective interest method or by low usefulness of information. As the EFRAG points out in the Endorsement Advice, amortised cost or FVOCI measurement results then only appropriate for simple cash flows characterised by low variability (EFRAG, 2015). Table 2.1 provides a synthesis of the classification and measurement criteria. Particularly, in order to be classified as subsequent measured at amortised cost, the financial asset must be held within a business model whose objective is to hold financial assets in order to collect contractual cash flows, and its contractual terms must provide precise dates in terms of cash flows representing payments of the fair value of the financial asset at initial recognition (namely, ‘principal’) and to interests (intended by the Standard as ‘consideration for the time value of money, for the credit risk associated with the principal amount outstanding during a particular period of time and for other basic lending risks and costs, as well as a profit margin’). Differently, to be measured at fair value through other comprehensive income (FVOCI), the financial asset must be held within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets and its contractual terms must provide precise dates to the payments of principal and to interests. Additionally, the contractual cash flows deriving by these instruments should be solely payments of principal and interest on the principal amount outstanding. Otherwise, financial assets must be classified as fair value through profit or loss (paragraph 4.1.4). As regard to financial liabilities, the overall treatment under IFRS 9 does not differ from rules of IAS 39, as users and preparers taking part in the process of development of IFRS 9 supported the validity of IAS 39 functioning. Therefore, in most cases, financial liabilities should be measured at amortised cost, unless the fair value option is elected. Only in this case, the liability is accounted at FVTPL being required to account for its component parts. The IASB focused only on the revision of the issue of changes in the credit risk of financial liabilities accounted at FVTPL. Indeed, this issue was worth a closer look as the treatment of credit risk under IAS 39 generates an accounting paradox. Particularly, the variations the of entity credit

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risk have impact on the fair value of financial liabilities. Accordingly, counterintuitively, if the credit quality decreases, the value of liabilities diminishes too, and the entity should recognise a gain in its P&L. Conversely, when the entity’s credit quality increases, the fair value of its own liabilities will go up and the entity will recognise a loss in its P&L. To address this issue, IFRS 9 maintains the measurement of these liabilities at fair value (deemed useful by users), but it introduces the recognition in the other comprehensive income of changes in changes of liabilities’ fair value when they are due to upgrade or downgrade of credit riskiness of the entity itself (paragraph 5.7.1, letter c). Consequently, following paragraph 5.7.7, gains or losses on financial liabilities accounted at FVTPL due to the election of the fair value option should be presented in other comprehensive income for the amount due to the change in credit risk, and in P&L for the amount due to change in the fair value. However, if such a treatment causes or amplifies an accounting mismatch, gains and losses can be recognised in P&L (paragraph 5.7.8), without separating the effects of variations in credit risk. Concerning initial measurement, paragraph 5.1.1 rules that, in general, financial instruments should be measured using their fair value. If the instrument is not accounted as FVTPL, then at the initial measurement the entity should exploit its fair value adding or subtracting transaction costs directly attributable to the instrument’s acquisition or issue.15 Paragraph B5.1.1 explains the intended meaning for ‘fair value of a financial instrument at initial recognition’. Remarking the link with IFRS 13, this paragraph states that it is normally the transaction price, in absence of other elements than the financial instrument (otherwise, the entity should calculate the fair value only referring to the financial instrument). The standard provides the example of a longterm loan or receivable without interests; in this case, fair value can be measured through the method of discounted cash flows, exploiting the prevailing market interest rate for a similar instrument, characterised by an aligned credit rating. It may happen that, although normally fair value at initial recognition is the transaction price, fair value differs from the transaction price (paragraph B5.1.2A). In these cases, if level 1 inputs of fair value hierarchy are available (quoted prices or valuation methods exploiting observable inputs) to identify fair value, then the entity should use this fair value in the balance sheet and recognise the difference between this fair value and the transaction price in the P&L, as a gain (if the price paid is higher than the fair value) or a loss (if the price paid is lower than the fair value). If market inputs are not available, the entity should adjust the price deferring the difference between the fair value at initial recognition and the transaction price. After the recognition, the deferred difference should be recognised as a gain or loss only consistently with the information that market participants would have derived from a change in factors (including time) when pricing the instrument.

15 Following paragraph 5.1.3, however in absence of a relevant financing component the entity should measure trade receivables at their transaction price according to IFRS 15.

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Differently from the Exposure Draft, IFRS 9 requires reclassifications of financial assets between the categories of fair value and amortised cost if the entity changes its business model for managing financial assets (paragraph 4.4.1). In this case, reclassification must be applied prospectively from the reclassification date. Consequently, previous gains, losses (also deriving from impairments) cannot be restated. Paragraphs from 5.6.2 to 5.6.7 set out the requirements for reclassifications, dealing with the different kinds of reclassification. For reclassifications from the amortised cost to either FVTPL category or FVOCI category, the asset’s fair value is measured at the reclassification date. Any gain or loss arising from the comparison between the previous cost and fair value is recognised in the P&L, for reclassification to FVTPL, and to other comprehensive income, for reclassifications to FVOCI.16 If reclassification is out of either FVTPL or FVOCI into the amortised cost category, the asset’s fair value at the reclassification date becomes the new gross carrying amount. For assets previously accounted as FVOCI, gains or losses which have been previously recognised in other comprehensive income are removed from equity and adjusted (without impact on P&L) against the fair value of the financial asset at the reclassification date. Finally, if the entity reclassifies a financial asset out of FVTPL into FVOCI, the asset continues to be measured at fair value. The same happens for reclassifications from FVOCI to FVTPL, but, in this case, there is a reclassification adjustment of gains or losses previously recognised in the other comprehensive income.

2.1.3

The Introduction of the Expected Loss Approach to Impairment Model

The final version of IFRS 9 includes results of the other two projects undertaken by the IASB, namely the revision of the impairment model and the rules for hedge accounting. With regard to impairment, IFRS 9 has introduced an expected loss impairment model, requiring more timely recognition of credit losses. As before mentioned, a relevant issue addressed by the standard setter during the issuing of IFRS 9 concerned the need to overcome the delayed recognition of loan losses, as one of the major weaknesses of IAS 39 has been the incurred loss approach applied to loan evaluation. Under this approach, entities should provide for credit risk only in case there is an objective evidence that impairment has effectively occurred. This happens if trigger events (as events listed by paragraph 59 of IAS 39) can be identified and, therefore, only when the probability of default has become certain.

16 The effective interest rate and the measurement of expected credit losses are not adjusted in consequence of the reclassification.

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As remarked by Novotny-Farkas (2016), this approach to loan loss accounting had been harshly criticised by international bodies in charge of the stability of the financial system (BCBS, 2009; FCAG, 2009; FSF, 2009; G20, 2009). Although—as previously mentioned—the incurred loss approach has the ‘attractive feature that it restricts the use of loss provisioning as an earnings-management device’ (Hashim et al., 2016, p. 229), this model brought the unintended consequence of a tardive recognition of loan impairments and of an exacerbation of the 2008 financial crisis. Conversely, IFRS 9 allows a prompter recognition of loan losses by introducing the expected loss approach, which considers information from available ‘without undue cost or effort at the reporting date about past events, current conditions and forecasts of future economic conditions’ (IFRS 9, paragraph 5.5.17, letter c) and does not require the occurrence of a trigger event. Particularly, according to paragraph B5.5.28, expected credit losses are ‘a probability-weighted estimate of credit losses (i.e. the present value of all cash shortfalls) over the expected life of the financial instrument’. The standard specifies that a cash shortfall occurs not only when there is a mismatch between the total amount of cash flows contractually due and cash flows that the entity reasonably expects to receive, but also when there is a delay of the payment in terms of timing. To assess the expected loss amount, the entity requires reasonable and supportable information which is available without undue cost or effort (IFRS 9, paragraph B5.5.49) and the extent to which it should exercise judgement depends on the availability of information; indeed, the longer is the period covered by the forecast, the more the entity will exert its judgement. However, concerning periods which are far in the future, the entity will not be able to provide detailed estimates and should therefore extrapolate reasonable projections from ‘available, detailed information’ (IFRS 9, paragraph B5.5.50). The criteria used to implement the process of evaluation of expected losses should match the actual stage of credit risk characterising the asset, with the aim to bring the accounting representation nearer the economic valuation. The standard contemplates three stage of credit risk, which are illustrated in Table 2.2. The first stage (Stage 1) includes performing assets. Performing assets are both instruments characterised by no significant increase in credit risk since the initial recognition and those instruments having low credit risk at the reporting date. Expected credit losses for Stage 1 should be estimated over a 12-month period. In Table 2.2 Three-stage impairment model introduced by IFRS 9 Asset credit quality Stage 1 Performing Stage 2 Under performing Stage 3 Non-performing

Criterion No significant increase in credit risk Significant increase in credit risk No evidence of impairment Significant increase in credit risk Evidence of impairment

Horizon expected loss 12 months

Revenues from interests Gross carrying amount

Lifetime

Gross carrying amount

Lifetime

Net carrying amount

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this case, a lifetime losses approach would cause a distortion in the value of loan book and would confuse investors, as performing loans would be treated as loans characterised by higher credit risk. The second stage (Stage 2) groups under-performing assets. Therefore, it comprises instruments with significant increase in credit risk but without any objective evidence of impairment. Paragraph 5.5.11. introduces a rebuttable presumption concerning the increase in credit risk. Particularly, the entity should presume that the credit risk on a financial asset has increased significantly since initial recognition when contractual payments are more than 30 days past due.17 For assets belonging to this stage, expected credit losses should be estimated over the entire lifetime characterising the asset, with the aim to reflect in the financial statements the economic loss arising when the expected credit loss critically exceeds expectations. The third stage (Stage 3) includes non-performing instruments, namely instruments characterised by significant increase in credit risk and with objective evidence of impairment. Again in this case, expected credit losses should be estimated over the lifetime. It is worth recalling that IFRS 9 introduces a rebuttable presumption for the identification of default. Particularly, following paragraph B.5.5.37, default should occur in case the asset is more than 90 days past due. Overall, the new accounting rules concerning impairment could have a significant impact on the numbers reported in banks’ financial statements as loan loss provisions are frequently the biggest expense in the P&L. As revealed by the Fourth Global IFRS Banking Survey performed by Deloitte (2014) at the time of IFRS 9 publication, many banks believed that loan loss allowances will have increased in the future by 50%. Requiring larger loan loss allowances, the new approach is more aligned with supervisory requirements concerning provisioning. In fact, understated loan loss allowances produce an overestimated regulatory capital, with a consequent increase in the likelihood of bank failure (Benston & Wall, 2005; Novotny-Farkas, 2016); thus, supervisors appreciate and suggest prudent approaches to evaluation of loan losses. The new accounting approach brings therefore evaluation of loan losses nearer to the regulatory measure for expected losses under the Internal Ratings-based (IRB) Approach.18 IRB banks evaluate the expected loss concerning performing exposures

17 However, it is possible for the entity to rebut the presumption in case there is reasonable and supportable information available ‘without undue cost or effort’, in order to show that the risk of credit has not grown significantly since recognition although payments are past due more than a month. 18 The treatment of loans varies depending on the approach used by the bank to calculate capital requirements for credit risk. Particularly, there are two possible approaches, namely the Standardised Approach (SA) and the Internal Rating-based (IRB) Approach. While banks adopting the SA exploit external credit ratings to define the amount of capital for credit risk, under the IRB approach banks exploit their own estimated credit risk parameters.

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exploiting their own estimates for the probability of default and the loss given default. The Capital Requirement Regulation suggests that, to estimate the probability of default, banks adopt only through the cycle rating methodologies, which aim at neutralising the impact of cyclical contingencies to obtain more stable results. However, these methods are not consistent with the IFRS 9 approach, as they include a range of possible economic outcomes instead of those actually expected at the reporting date. Concerning the estimate of the loss given default, under IFRS 9 it should encompass actual expectations of the future at the reporting date, contrasting the more conservative regulatory perspective. Moreover, while IFRS 9 mandates the estimation of the expected loss over a lifetime period for performing loans characterised by significant increase of credit risk, regulators mandate its calculation over a 12-month period for the performing book as a whole. Since the effective application of IFRS, supervisors have been playing thus a crucial role in exerting their function in the scope of Pillar 2 of the Basel framework; they are responsible for evaluating the adequateness of credit risk management practices implemented by the entities and the outputs of these processes (especially, data provided by the internal credit risk models) should be used in turn as inputs to estimate the expected loss ex IFRS 9. As remarked by the Guidance on credit risk and accounting for expected credit losses issued by the Basel Committee in 2015, high-quality supervision should be able to significantly improve the quality of bank accounting data by contributing to the accounting enforcement function (Novotny-Farkas, 2016), and dealing with bank auditors to adequately review entity credit risk assessment. This idea was certainly inherent the logics behind reframing the supervisory system, whose process has its roots within regulators’ reflections on the 2008 financial crisis. At those times, the Financial Stability Forum (FSF)19 developed high-level recommendations for managing the financial crisis highlighting significant gaps in the extant framework of regulation and supervision. The vigorous debate that followed made clear that actions were needed in designing financial regulation, assessing systemic risk, and increasing effectiveness of supervision’s mechanisms (Blanchard, 2008).

2.2

The New Framework for Banking Supervision

The G-20 Declaration of 2 April 2009 on Strengthening of the Financial System called for internationally consistent efforts to improve transparency, accountability and regulation by improving the quantity and quality of capital in the banking

Founded in 1999, the FSF was a group formed by national financial authorities to promote the financial stability at the international level. In occasion of the 2009 G20 London summit, the Financial Stability Board was established as successor to the FSF.

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system. In September 2009, the Group of Central Bank Governors and Heads of Supervision (GHOS) agreed on several of provisions aimed at improving the regulation of financial sector, later endorsed by the G-20 leaders at the Pittsburgh Summit (24–25 September 2009) and then detailed in December. The same year, the High-Level Group on Financial Supervision in the European Union, chaired by Jacques de Larosière urged the European Union to implement a more harmonised framework for financial regulation. The European Council of 18 and 19 June 2009 also remarked the priority to adopt a European single rule book for the whole financial sector in the internal market. In July and September 2010, the GHOS emanated two pronouncements on how to implement the new measures, and in December the Basel Committee on Banking Supervision (BCBS), which is the global standard setter for banks’ prudential regulation,20 issued the Basel III prudential framework. Aiming at reacting to the major vulnerabilities of the banking institutions, this framework set out higher capital requirements for banks and, from its adoption (1 January 2014), it required banks to maintain sufficient reserves of capital and liquidity. In the view of the Committee, ‘this new framework will make EU banks more solid and will strengthen their capacity to adequately manage the risks linked to their activities, and absorb any losses they may incur in doing business’.21 Against this background, one of the main innovations was the establishment of the European Banking Authority (EBA) in 2011, together with the European Insurance and Occupational Pensions Authority (EIOPA) and with the European Securities and Markets Authority (ESMA). These authorities were conceived as supervisors of three distinct sectors of financial markets. Particularly, concerning the banking sector, the EBA has the mandate to contribute to a consistent application of European law and to a convergence of national supervisory practices. It has no power toward the single institution, as consequence of the system of directives concerning banks, which should be implemented at the national level, by the national supervisory authority. However, the EBA has the mandate to issue binding technical standards as provisions of secondary legislation becoming part of European banking regulation. Standards issued by the EBA contribute to the ‘single rulebook’ projected to harmonise regulatory discipline across the European Union. This project has also been implemented by the Directive 2013/36/EU on access to the activity of credit institutions and the prudential supervision of credit institutions and investment firms (CRD IV) and the Regulation (EU) No 575/2013 on prudential requirements for credit institutions and investment firms (CRR), which came into force on 1 January 2014. The ‘CRD IV package’ primarily transposes the Basel III agreement framework into European law; this package allowed to merge the previous discipline

20

The BCBS provides also a forum for cooperation on the issues concerning the supervision of the banking sector. It has the responsibility to enhance the regulation, supervision and practices of banks worldwide with the objective of preserving and strengthening financial stability. 21 Press release held on 16/07/2013, available at: http://europa.eu/rapid/press-release.

2.2 The New Framework for Banking Supervision

21

forming the supervision and prudential requirements for credit institutions and investment firms. However, the activity of enforcement of compliance with regulatory requirements was the responsibility of national supervisors, in charge of the ongoing monitoring of banks. Being the supervisory system deemed not sufficient, in October 2013 a package underpinning the creation of a Single Supervisory Mechanism (SSM)22 entered to force.23 This package was formed by two regulations: the first conferred supervisory responsibilities to the ECB, while the second served to amend the EBA Regulation to adapt it to the new supervisory framework.24 The ECB is responsible for the effective functioning of the SSM and can ‘conduct supervisory reviews, on-site inspections and investigations, grant or withdraw banking licences, assess banks’ acquisition and disposal of qualifying holdings, ensure compliance with EU prudential rules and set higher capital requirements (“buffers”) to counter any financial risks’.25 In accordance with art. 97 CRD IV and SSM Regulation, the SSM is responsible for conducting the Supervisory Review and Evaluation Process (SREP) of all credit institutions in the participating Member States. To the purpose of organising supervisory activity and ascribing responsibilities to either European or national authorities, European banks are divided into banks of ‘significant relevance’ and banks of ‘less significant relevance’. The criteria for identifying significant banks have been defined by the SSM Regulation and the SSM Framework Regulation. To be deemed significant, banks must satisfy at least one of the following criteria: (1) the total value of the bank’s assets exceeds 30 billion euros; (2) the bank is of economic importance for the specific country or the EU economy as a whole; (3) the total value of the bank’s assets exceeds 5 billion euros and the ratio of its cross-border assets/liabilities in more than one other participating Member State to its total assets/liabilities is above 20%; (4) the bank has requested or received funding from either the European Stability Mechanism or the European Financial Stability Facility. These requirements should be evaluated at the consolidated level (Article 6(4), subparagraph 1). The distinction between the two groups of banks differentiates the authority being responsible for supervision on the specific institution. Particularly, in

22

Council Regulations (EU) No 1024/2013, [2013] OJ L 287/63. On 9 July 2013, Jorg Asmussen, Member of the Executive Board of the ECB, stated: ‘Technical preparations have been ongoing since September 2012, involving officials from the ECB and the national supervisory authorities. (. . .). The aim is to create, for each banking group we supervise, a joint supervisory team that vertically integrates supervisors in Frankfurt with those in the national competent authorities (. . .). Central bankers in Europe had been cooperating for decades (. . .) however, cooperation has historically been looser, and as a result there are several different supervisory traditions and philosophies that we need to unite into a single system. In other words, we are working together to create a single supervisory culture and find our leitmotif, but it will take time’. 24 Council Regulations (EU) No 1022/2013, [2013] OJ L 287/5. 25 Information retrieved from: https://www.bankingsupervision.europa.eu/about/thessm. 23

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After the Crisis: New Approaches in Accounting Standards Applied by Banks. . .

2014 the ECB was directly supervising 126 European banks (which hold almost 82% of banking assets in the euro area) judged significant.26 After the creation of the SSM, the role of national supervisors has changed. Previously, the everyday supervisory tasks were within their ambit. Accordingly, they had to collect and analyse data concerning the banks’ exposure to risks, to conduct on-site examinations, and to adopt compulsory measures and to apply sanctions addressed to banks resulting not compliant with regulatory requirements. Once the SSM was established, the national supervisors still oversee only less significant banks and in close cooperation with the ECB.27 Therefore, national supervisors maintain powers enshrined in the national law only on banks deemed as less significant in the scope of SSM; on these institutions, national supervisors can directly exercise the powers connected to official supervisory functions, such as the power to address binding recommendations to supervised banks. However, these powers must be exerted within the framework adopted by the ECB, which monitor them and can decide anytime to directly supervise any of the banks supervised at the national level. Additionally, if the status of a less significant bank changes due to ‘normal business activity or due to one-off events such as mergers or acquisitions’, then the ECB and the national supervisors organise the transfer of supervisory responsibilities. However, the national supervisors still play a key role, as they ensure a longestablished knowledge of national specificities in terms of economic, jurisdictional, organisational and cultural characters. Additionally, they maintain powers connected to the macro-prudential supervision, namely supervision whose objective concerns the stability of the financial system as a whole. In this field, the national competent authorities establish requirements, and the ECB has only the possibility to ask for tightening them. De facto, the ECB has new far-reaching power of banking supervision, which now includes also the field of authorisation for banking licenses and their revocation, regardless of whether the financial institution is deemed significant or less significant. The national authorities maintain only the right of proposal and consultation. National supervisors’ functions include also actively assisting the ECB in supervising significant financial institution by participating to Joint Supervisory Teams (JSTs), which comprise staff from both national supervisory authorities and the ECB. These teams conduct the day-to-day supervisory work, the activity of data collection and of data analysis. The frequency and the intensity of the review and evaluation are established having regard to the size, systemic importance, nature, scale and complexity of the institution concerned. Additionally, the review and evaluation shall be updated at least on an annual basis for institutions covered by the supervisory examination programme referred to in Article 99(2) CRD IV.

26

Banks directly supervised by the ECB are currently 115. The ECB is subjected to technical standards issued by the European Banking Authority (EBA) and to the EBA’s European Supervisory Handbook. 27

2.2 The New Framework for Banking Supervision

2.2.1

23

Supervisors’ Activity in the Scope of the Supervisory Review and Evaluation Process

The SREP requires the review of arrangements, strategies, processes and mechanism implemented by banks and evaluates the risks28 to which the institutions are exposed. The outputs of this process include the supervisor’s findings of a single year about the assessment of both the level of each bank in terms of capital requirements and the way the bank is able to deal with risks. A decision follows the assessment; this decision presents to the bank the objectives individuated by the supervisor in order to address the identified issues.29 Prior to the establishment of the SSM, the regular assessment and measurement of the risks for each bank were performed by the national supervisory authorities. Indeed, this approach to supervision (i.e. the SREP) was introduced in 2004 with the Basel II accords. Since then, rules were implemented across the European Union and have been followed by national supervisors across the Member States. As a result, the process presented relevant differences across countries, depending on the choices of the national supervisor. In December 2014, the EBA issued the Guidelines for common procedures and methodologies for the supervisory review and evaluation process, which has been applied since 1 January 2016 (EBA, 2014). Under the SSM, there are one shared methodology and one common timeline applied to all significant banks in the euro area, but national differences could persist concerning the methodology applied to less significant banks. The Guidelines define the scope of application of the SREP framework30 and have been conceived just to harmonise the framework applied across the European Union and to provide a common framework for the work of supervisors to assess the risks characterising banks’ business models, their solvency and liquidity. Although the guidelines should not limit the supervisory judgement and initiative, additional procedures applied by national authorities should be adopted under the superior perspective of harmonisation.

In this context, the term ‘risks’ refers to risks that an institution poses to the financial system in general and the risks revealed by stress testing, taking into account the nature, scale and complexity of an institution’s activities. Supervisors indeed consider if the bank is a parent entity, is a subsidiary of an individual entity and can then better appreciate the riskiness associated to the institution. Therefore, the process can be tailored (changing scope, intensity and frequency) depending on the characteristics and the risk profile of the institution supervised. 29 Indeed, in addition to the compliance with the minimum capital level (Pillar 1), a bank could be also required to be compliant with the supervisory request to hold additional capital and/or satisfy certain qualitative requirements individuated by the supervisor (Pillar 2); for instance, it could be likely that the supervisor highlights the necessity of changing the business mix to reach profitability objectives. 30 The Guidelines recall the general framework and principles defined in Regulation (EU) 575/2013 and Directive 2013/36/EU. 28

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After the Crisis: New Approaches in Accounting Standards Applied by Banks. . .

The Guidelines present four building blocks as key-constituent of the SREP. Building blocks are as follows: 1. 2. 3. 4.

Business model analysis; Assessment of internal governance; Assessment of risks to capital and adequacy of capital; Assessment of risks to liquidity and adequacy of liquidity.

These building blocks allow supervisors to analyse the bank’s risk profile under different but complementary perspectives. From the business model’s perspective, the supervisor can evaluate the level of sustainability of the bank’s activity, generally considering focalisation on certain business lines as a factor of potential risk. Adopting a perspective focused on governance and risk management, the supervisor should explore the bank’s organisational structure and monitoring its management bodies, to verify whether risks are managed adequately. The perspective of risk to capital supports an analysis of whether a bank could be able to absorb potential losses. In terms of check on the risk to liquidity and funding, the supervisor should verify the bank’s ability to cover cash needs and uncertainty.

2.2.2

Supervisors Assessing Banks’ Asset Quality: Information at the Entity Level and at the Country Level from the First Comprehensive Assessment

Jointly with the national supervisors, the ECB performs assessments on the quality of assets of financial institutions directly supervised, to ensure that the banks are adequately capitalised and can resist financial shocks. Results of these assessments are considered as part of the SREP. National supervisors are responsible for projecting activity at the national level and reporting to the ECB; they also select a team formed by both part of their staff and third parties (external auditors, property appraiser and valuation advisers) to assure expertise and independence to evaluations. These assessments are conducted either regularly (this is the case of the first assessment on entities recently defined significant) or on an ad hoc basis (this is the case of assessments motivated by recurring of exceptional circumstances), and they are based on two main pillars. The first concerns an Asset Quality Review (AQR) designed aiming at enhancing the transparency of bank exposures, the adequacy of asset and provisions. The objective of the AQR is to detect whether banks hold overvalued assets on the balance sheet. The second pillar draws on a stress test performed together with the EBA, designed with the purpose to verify the resilience of balance sheets to shocks.

2.2 The New Framework for Banking Supervision

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Before assuming the banking supervision tasks in November 2014, between November 2013 and October 2014, the ECB implemented the first comprehensive assessment31 on 130 European banks in the euro area, which represented 81.6% of total assets of financial institutions. Only a subsample of 76 banks was included in the program of review of fair value exposure. Reviewed bans were divided into three groups, according to the balance sheet assumption adopted to perform the stress test. Particularly, banks were divided into non-restructuring banks, restructuring banks approved before 31 December 2013, and restructuring banks approved after 31 December 2013.32 The assessment was based on Capital Requirements Regulation and Directive (CRR/CRD IV) and therefore banks were required to have a minimum Common Equity Tier 1 (CET1) ratio of 8%. The stress test was aimed at providing a forward-looking examination of the banks’ resilience to stress. Consequently, the procedure used two scenarios. Under the baseline scenario33 (characterised by the development of European economy develops in line with the European Commission’s projections), banks should have a minimum CET1 ratio of 8%. Under the adverse scenario34 (characterised instead by macroeconomic developments clearly deteriorate), banks should be able to rely on a minimum CET1 of 5.5%. To the purpose of the comprehensive assessment, the CET1 was calculated applying existing requirements and arrangements, except the removal of the prudential filter on unrealised gains and losses on sovereign exposures classified as available for sale, for which was computed only a percentage determined by the EBA. Results of the stress test showed a decrease of the capital of participating banks by 22% and an increase in risk weighted assets of 860 billion euros, under the adverse scenario. The methodology comprises two phases. The first phase concerns the selection of portfolios which are most likely to present a material misstatement on the balance sheet; particularly, the methodology selects portfolios whose AQR adjustment is likely to materially impact CET1. The second phase includes several steps. The first step consists of the review of processes, policies and accounting practices, with focus on fair value hierarchy, accounting classifications, provisioning, non-performing exposures and

The methodology exploited for this first review has also been implemented in the following years. Non-restructuring banks are reviewed under the assumption of static balance sheet (i.e. applying a zero-growth rate of the total balance sheet and under the assumption of invariance of business mix). Restructuring banks—approved before 12 December 2013—are banks implementing a restructuring plan and have been analysed applying a dynamic balance sheet assumption which reflects changes in business model. Restructuring banks—approved after 12 December 2013—are banks implementing a restructuring plan authorised during 2014. These banks have been analysed applying a static balance sheet assumption and results of this analysis are compared to results obtained applying a dynamic balance sheet assumption. 33 The baseline scenario was planned by the European Commission. 34 The adverse scenario was elaborated by the European Systemic Risk Board. 31 32

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After the Crisis: New Approaches in Accounting Standards Applied by Banks. . .

forbearance.35 Collected data are tested to check data integrity (second step) and sample is generated (third step). Then, teams organised by national supervisors review the credit file36 to verify information released and their classification (fourth and fifth steps). Findings from the credit file review (e.g. impairment provisions and non-performing exposures reclassifications) are projected on the un-sampled part of the portfolios (sixth step). Collective provisioning is checked comparing the degree of provisioning to the proper provisioning individuated by applying accounting standards (seventh step). Then, fair value exposures are reviewed if they are material (namely, significant); in these cases, the review is carried out on a selective basis. In case of banks characterised by material level 3 exposures, the procedure provides a revaluation of most relevant securities. For banks with material trading book, a double review on both processes involving the trading book and the derivative pricing models for instruments classified as level 3 of fair value hierarchy is performed. Particularly, review of fair value exposures is organised into two phases. During the first, a review of the policies exploited to identify the levels of fair value hierarchy is performed to assess which is the banks’ definition for an ‘active market’ and for level 3 inputs. The second phase is aimed at check for exposures wrongly classified. Adjustments are conducted based on IFRS 13. To provide an overview of this first AQR finding, it is worth mentioning that the application of the uniform EBA definition for non-performing exposures (NPE) led to an increase in NPE of 54.6 billion euros. After the credit file review and the projection of findings, the additional increase in NPE was of 81.3 billion euros, leading a total increase in NPE of 135.9 billion euros. Individually assessed provisions were adjusted of 26.8 billion euros, while collectively assessed provisions were increased by 16.2 billion euros. Concerning the review of fair value exposures, only 67% of banks results to have a clear policy to define an ‘active market’ and only 72% of institutions have a definite policy defining level 3 inputs. Adjustments led to an increase of level 3 exposures by 4.6 billion euros. Results of the assessment performed provide several significant outputs, both at the entity level and at the country level.

35

As the definition of non-performing exposures (NPE) is not homogenous across Europe, the AQR applied a simplified definition provided by the EBA and published on 21 October 2013. According to the EBA approach, can be reported as non-performing every material exposure which is 90 days past due, every impaired exposure and every exposure in default according to CRR. The overall percentage of entities in line with the AQR approach, which did not require any adjustment, was 57%. 36 Loan files reviewed in the first assessment were more than 119,000. Participating banks provided more than 6,000,000 data items reported in a standardised template form. Banks provided also information on debtors, loan terms and characteristics, business logic to extend the credit, historical financial data for debtors.

2.2 The New Framework for Banking Supervision

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At the entity level, the AQR output was a template filled in by the team set up by the national supervisor, including remedial actions that the bank must complete addressing the issues emerging from the assessment and under the supervision of the JSTs. Recommendations might concern the proper application of fair value hierarchy, the revision of the treatment and the identification of forborne exposures and the provisioning approach. Additionally, a relevant outcome of the AQR at the entity level was the discovery of an imperative need to revise and improve their data systems. At the country level, this first AQR provides aggregate information on the degree to which the bank system is resilient within the single country, allowing to discuss the effectiveness of the supervisory system in the country. For instance, results from the AQR performed in 2013–2014 show that the impact (in terms of billion euros) of the stress test on CET1 capital is between 0 and 1 for countries like Slovakia (0.0 billion), Latvia (0.0 billion), Lithuania (0.0 billion), Malta (0.0 billion), Luxembourg (0.7 billion) and Slovenia (1.1 billion). Conversely, the impact is relevant for Germany (27.0 billion), France (30.8 billion) and Italy (35.5 billion). It is to say that, in 2018, the ECB updated the guidance on the AQR procedure to adapt the prior guidelines to IFRS 9.

2.2.3

Stress Tests Carried Out by the European Banking Authority

Biannually the EBA carries out a process of stress testing which involves national authorities. In 2014, this process was performed in conjunction with the first AQR, but it has been projected to work as a stand-alone protocol. To this purpose, the EBA has developed a stress testing methodology to be applied by all the banks examined. As the EBA explains in the Methodological Note, this stress test is restrictively designed with the aim to assess the resilience of both the European banking sector and more relevant European banks to adverse conditions although it does not represent a forecasting exercise and pass fail thresholds are not provided. In 2016, stress testing has been conducted on a sample of 51 banks representing 70% of the European banking sector and having at least 30 billion euros of total assets, with the complete exclusion of insurance activities. The process has assessed the resilience under a common baseline and adverse scenarios, for the period ranging from 2016 to 2018, and its outputs have been reported in terms of CET1 capital (on both a transitional and on a fully loaded basis), with some adjustments. For instance, in case of sovereign exposures classified as available for sale, the Methodological Note requires the adoption of a common approach to overcome the national differences. Indeed, in case of computation of fully loaded capital ratios, the entire gains/losses from the sovereign exposures classified as available for sale

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After the Crisis: New Approaches in Accounting Standards Applied by Banks. . .

have been required.37 Differently, in case of gains and losses deriving from the measurement of other instruments included in the AFS portfolio, the Note allows the application of prudential filters as provided by the national rules, with disclosure of their impact on the stress test results. Results provided by the EBA show that the credit losses (impairment or reversal of impairment on financial assets not accounted as fair value through profit or loss) have the highest impact on economic results that have been quantified as a decrease in results of 349 billion euros. Additionally, significant negative impact on results is due to market risk shock; losses on financial assets and liabilities held for trading, on financial assets and liabilities designated at fair value through profit and loss, and accumulated other comprehensive income accounts for a negative impact of 83 billion euros. According to results’ elaborations performed by De Groen (2016), the main driver of results appears to be non-performing exposure, which showed a significant positive relation with the total impact of the adverse scenario on CET1. Particularly, non-performing exposures seem to broadly explain the amounts of impairments and have a significant impact on profitability, even excluding impairment from the profitability’s calculations. Across Europe, a large share of non-performing exposures is represented by customer loans, and especially by loans to small and mediumsize enterprises which are more affected by failures than households.38 Another relevant driver for results has government exposures, which present a significantly positive relation with the impact of adverse scenario. Overall, the difference among countries and among banks within the same country are large, with the worst results reported by the Italian bank Monte dei Paschi di Siena (whose CET1 capital ratio under the adverse 2018 scenario would be negative—2.44%—showing a reduction of 14.5% of its CET1) and by the Allied Irish Bank (whose CET1 capital ratio under the adverse 2018 scenario amounts to 4.31%, being the minimum requirement 4.5%).

2.2.4

The Business Model Assessment in the Scope of Supervisory Activities

Supervisors have become aware that the diversity of European Banks in terms of business models can significantly affect their behaviour and their risk profile in a context characterised by increased competition, tighter regulation and a prolonged

For the transitional capital, the Note requires the EU-wide application of prudential filters for gains and losses arising from these assets consisting of the inclusion of 60% of unrealised gains and losses in 2016, 80% in 2017, and 100% in 2018. 38 However, significant differences persist among European countries in terms of economic structure, landing policies of credit institutions, policies to deal with distressed debt, and legal systems: these differences can explain the different impact of non-performing loans. 37

2.2 The New Framework for Banking Supervision

29

period of low interest rates. Therefore, supervisors need to deeply understand the diversity and the implication of this diversity for financial supervision and financial stability. Consequently, the business model analysis has lately become a supervisory priority and is an integral part of the SREP. The EBA’s Guidelines for common procedures and methodologies for the supervisory review and evaluation process remark the need for accurate quantitative analysis of the institution’s ‘current’ business model, to understand the financial performance and the level of the bank’s risk appetite (EBA, 2014). Overall, the crucial elements of the assessment of business models for supervisory purposes draw on the identification of the main activities of the banks and of the business environment; then it is necessary to analyse the forward-looking strategy and financial plans of the entity, in order to define the viability in the short term and the sustainability both in the medium term and in the long term referring to the key vulnerabilities of the business. More in details, the Guidelines suggest identifying firstly materiality of business areas, building on any relevant internal or external information that may be useful to individuate the contribution of each area of business to revenues, profits and risks. Therefore, a first phase aims at understanding trends and amount of key-areas representing the income composition and derived ratios (e.g. net interest margin, cost/income, loan impairment), the asset-liability mix (in terms of funding structure, main activity), relevant concentrations emerging from the profit or loss and from the balance sheet related to customers, sectors and geographies, and the risk profile of the entity. Additionally, a relevant role in the identification of business model is played by qualitative analysis of the current business models, to identify its success drivers and dependencies (Table 2.3). The above-described actions should assist the supervisor in evaluating the banks’ capacity to generate profits and should be corroborated by the assessment of financial projections and strategic plan prepared by the banks. Indeed, the Guidelines stress the necessity of complementary quantitative and qualitative forward-looking information, assessing the overall strategy and related management objectives, the projected financial performance (exploiting metrics close to those covered in the quantitative analysis of the current business model),

Table 2.3 Areas to be monitored • Key external dependencies The main exogenous factors that influence the success of the business model (e.g. third-party providers, intermediaries, specific regulatory drivers) • Key internal dependencies The main endogenous factors affecting the success of the business model (e.g. the quality of IT platforms and operational and resource capacity) • Franchise Strength of relationships with customers, suppliers and partners (e.g. corporate reputation, the branches’ effectiveness, the loyalty of customers and the effectiveness of partnerships) • Areas of competitive advantage over peers

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success drivers of both the strategy and financial plan, assumptions (especially concerning macroeconomic metrics, market dynamics, volume and margin growth in key products, segments and geographies), and execution capabilities (referring to the management’s track record in adhering to previous strategies and forecasts). Exploiting collected information should provide the necessary information for supervisory judgement, which should be based on the analysis of ratios and scores derived by the prior analysis to facilitate comparisons between banks. Results of the 2015 assessment show that banks in the scope of SSM rely on average on net interest income for 56% of total income, while 28% of income is generated by fees and commissions, and 16% by trading activity. Particularly, Greece is the country where banks rely most on income from interests (86%), while banks in Luxembourg derive their income mainly from fees and commissions (56%). Banks in Finland show the highest share of revenues from trading activities (36%). It is worth recalling that the kind of activities conducted by financial institution are also tightly linked to the banking regulation on the topic. National authorities can, indeed, restrict the range of activities that a bank could run, therefore defining what the national system acknowledges to be a bank, and define the extent to which banks and non-banks institutions can combine into conglomerates. National authorities have broad regulatory power in this respect,39 even if their freedom should be exerted in the scope of Directive 2013/36/EU (the above-mentioned Capital Requirements Directive IV—CRD IV), that determines the conditions for access to the banking activity, the possibility for banks to establish the European Union and the extent to which they are allowed to provide services.

References Basel Committee on Banking Supervision – BCBS. (2009). Guiding principles for the replacement of IAS 39. Available on the internet at: https://www.bis.org/publ/bcbs161.htm Benston, G. J., & Wall, L. D. (2005). How should banks account for loan losses. Journal of Accounting and Public Policy, 24(2), 81–100.

39 For instance, in France the French Code monétaire et financier—Monetary and Financial Code (‘MFC’) provides the principal rules and regulations concerning the banking sector. It then rules for banking and financial monopolies (indeed, only licensed entities, or entities acting under an EU/EEA ‘Passport’, may carry out banking activities in France; in this sense, the ‘banking monopoly’ comprises deposit-taking activities and credit activities, while other regulated financial activities are also restricted to duly licensed or EU/EEA ‘passported’ entities), for the regulation of banking transactions, investment services, payment services, electronic money issuance and management, and banking and financial solicitation, for licensing, registration and access requirements to regulated status of banking and financial services providers. In Italy, the Consolidated Law on Banking and the Consolidated Law on Finance authorise the Bank of Italy to regulate banking and financial activity. Therefore, the Bank of Italy is competent in issuing regulations.

References

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Blanchard, O. J. (2008). The tasks ahead. IMF working paper 8(262). Available on the internet at: https://www.imf.org/en/Publications/WP/Issues/2016/12/31/The-Tasks-Ahead-22515 De Groen, W. P. (2016). The EBA EU-wide stress test 2016: Deciphering the black box. CEPS Policy Brief, 346(August), 1–12. Deloitte. (2014). Fourth global IFRS banking survey. Deloitte. Available on the internet at: https:// www2.deloitte.com/au/en/pages/financial-services/articles Di Pietra, R. (2008). Bilanci consolidati dei gruppi bancari: le principali conseguenze delle valutazioni al fair value. Rirea. European Banking Authority – EBA. (2014). Guidelines for common procedures and methodologies for the supervisory review and evaluation process. Available at the internet: https://www. eba.europa.eu/regulation-and-policy European Financial Reporting Advisory Group – EFRAG. (2015). Endorsement advice on IFRS 9 financial instruments. European Financial Reporting Advisory Group. Financial Crisis Advisory Group – FCAG. (2009, April). Report of the Financial Crisis Advisory Group. Available on the internet at: http://www.ifrs.org/Features/Documents Financial Stability Forum – FSF. (2009). Report of the Financial Stability Forum on addressing procyclicality. Available on the internet at: http://www.fsb.org/wp-content/uploads/r_0904a.pdf G20. (2009). London summit – Leaders’ statement 2 April 2009. Available on the internet at: https:// www.imf.org/external/np/sec/pr/2009/pdf/g20_040209.pdf Giunta, F. (2003). Competenza contro prudenza: arbitra il “fair value”. Competenza contro prudenza: arbitra il “fair value”, pp. 1000–1003. Hashim, N., Li, W., & O’Hanlon, J. (2016). Expected-loss-based accounting for impairment of financial instruments: The FASB and IASB proposals 2009–2016. Accounting in Europe, 13(2), 229–267. International Accounting Standards Board – IASB. (2008). Reducing complexity in reporting financial instruments. Discussion paper. London: International Accounting Standards Committee Foundation. International Accounting Standards Board – IASB. (2009). Financial instruments: Classification and measurement. Exposure Draft ED/2009/7. London: International Accounting Standards Committee Foundation. Novotny-Farkas, Z. (2016). The interaction of the IFRS 9 expected loss approach with supervisory rules and implications for financial stability. Accounting in Europe, 13(2), 197–227. Pacter, P. (2017). Pocket guide to IFRS® standards: The global financial reporting language. IFRS Foundation. Quagli, A. (2009). Dal fair value al fairy value: coerenza concettuale e condizioni di impiego del fair value negli IFRS. Financial Reporting, 1, 94–120. Quagli, A. (2019). Gli standard dello IASB nel sistema contabile italiano (Seconda edizione). Giappichelli.

Chapter 3

Financial Supervision and Bank Accounting Numbers: State of the Art

3.1

The Role of Financial Supervisors and the Effects on Banks’ Performance

Given banks’ crucial role of financial intermediation between depositors, one of the main goals of the revision of the supervisory system that followed the 2008 financial crisis was increasing bank transparency. From this angle, ‘high-quality’ supervision should not only increase the overall sustainability of banks’ activities and bank ability to manage risks, but also contribute to improve the quality of bank accounting data (BCBS, 2015). From a theoretical perspective, this role can be better understood considering the agency dynamics affecting the banking industry. In particular, agency issues affect the relationship between managers and fund providers—i.e. shareholders (ownermanager agency problem) and depositors (firm-creditor agency problem)—because bank management tends to engage in activities connoted by excessive risk taking at the expense of fund providers (Bushman, 2016). Two main sources of information asymmetry induce these dynamics. The first is the inherent opacity of bank businesses and the dispersion of fund providers, which makes them not effective at controlling managers’ behaviours (Flannery et al., 2004; Beatty & Liao, 2014). Thus, the rationale underpinning banking supervision relies on the scarcity of monitoring due to significant costs, finally leading to inefficiency in terms of bank performance and stability. From this angle, supervisory bodies should improve the efficiency of financial system and discourage banks from engaging in risk-taking habits therefore (1) leading to an overall improvement of financial institutions’ performance and stability,1 (2) limiting

1 Regulation and supervision constitute separate rationales. Generally, regulation embraces formal rules adopted by an official public authority (the so called “law-on-the-books”), while supervision embraces the ongoing process of monitoring the financial performance and operations of banks to ensure that they are operating following regulations. Particularly, banking regulation derives from

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 C. Di Fabio, National Supervision and Income Smoothing in Banks’ Annual Reports, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-74011-5_3

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bank failures, (3) preventing systemic crises and, finally, (4) safeguarding financial stability (Rochet, 1992; Barth et al., 2004; Flannery et al., 2004). The second source of information asymmetry is the scarce information available to disperse funds providers on the real risks associated to bank business models and those to lose their savings. Information provided by bank annual reports could play an essential part in mitigating the agency problem since it should limit the information asymmetry among the subjects (Wagenhofer, 2015). Nonetheless, in an agency setting, managers behave opportunistically; they cook the numbers to conceal the effects of risky decisions on accounting numbers, aiming at altering fund providers’ perception on banks’ economic performance and managerial behaviours (Schipper, 1989; Healy & Wahlen, 1999; Morgan, 2002; Flannery et al., 2004). In the end, manipulation of accounting figures impairs the informativeness of accounting information provided by financial statements and reduce outsiders’ ability to monitor the bank (Fan & Wong, 2002; Leuz et al., 2003). In such a setting, regulation is conceived to mitigate the negative outcomes of agency through different requirements of information disclosure, capital adequacy and monitoring mechanisms exerted by public authorities and external subjects, such as private investors. The last decade saw a growing academic interest in the effects of financial supervision on banks’ behaviours to understand which features ‘high-quality’ supervision should incorporate. Research in the banking field overall supports a positive association between forms of private monitoring—namely the extent to which private investors are able to exert effective governance on banks (Barth et al., 2008a, 2008b)—and bank performance. Differently, the power of the national authority to undertake prompt measures to change bank behaviours and to impose penalties on bank managers— also referred to as national authority’s strictness (Di Fabio et al., 2021)—seems to affect negatively bank performance. Concerning influence of private monitoring, exploiting their new database on bank regulation and supervision, Barth et al. (2004) assess the link between

microeconomic concerns over the ability of depositors to monitor the risks originating on the lending side and from micro and macroeconomic concerns over the financial stability if a bank crisis occurs. Regulatory provisions include restrictions on entry and on branching, on activities and business’ lines, restrictions on pricing (limits to interest rate and other restrictions on prices or fees), on assets, compulsory deposit insurance, regulations on both ownership linkages among financial institutions and mergers, compulsory deposit insurance, requirements to direct credit to favoured sectors or enterprises (either formal rules, or informal government pressure), reserve requirements, capital-adequacy requirements. Particularly, capital regulation can be explained by the information asymmetry between banks and depositors (Beatty & Liao, 2014). Flannery et al. (2004) maintain that market failure in controlling managers and shareholders due to outsiders’ impossibility to obtain timely and accurate information is a direct reason for the need of capital regulation. Other motivations might be due to a more indirect link between information asymmetry and the necessity of capital regulation (Beatty & Liao, 2014). In this respect, Diamond and Dybvig (1983) maintain that banks are exposed to depositors’ runs as assets’ liquidation values are lower than the value of liquid deposits due to information asymmetry; thanks to regulation’s intervention, bank runs are likely to decrease.

3.1 The Role of Financial Supervisors and the Effects on Banks’ Performance

35

regulatory and supervisory aspects and development, efficiency, and fragility of banks. They show that policies based on the enhancement of accuracy in disclosure are better at stimulating bank development, performance and stability. Their results are complemented by Nier and Baumann (2006), whose findings indicate that market discipline plays a crucial role in the mitigation of the risk of insolvency as it contributes limiting banks’ risk-taking, and by Beck et al. (2006), who suggest that supervisors relying on private monitoring and forcing banks to disclose accurate information decrease obstacles to finance raising due to corruption and that regulations stressing private monitoring improve the integrity of bank lending in countries with sound legal institutions. Further, their evidence shows that traditional approaches to supervision, drawing on the empowerment of official supervisory agencies to monitor banks directly, does not improve the integrity of bank lending (Beck et al., 2006). Among studies finding negative effects produced by strict supervisors, some argue that strict supervisors may use their powers to favour constituents, appeal more donations, and eventually gain grafts (Quintyn & Taylor, 2003; Djankov et al., 2008). Then, supervisory strictness could be positively associated to corruption, therefore contrasting bank development, performance and stability. Focussing on the Asian context, Masciandaro et al. (2011) find that attempts to strengthen financial supervision through changes in the architecture and governance of supervisory system are negatively related to economic resilience. A negative association between supervision inspired by low flexibility and severe controls and bank stability is documented also by Carretta et al. (2015), who study the role of national supervision from the perspective of culture informing supervisory systems across European Union. They develop a measure for defining the culture informing the national supervisory authority of European Member States2 and classify the national supervisory culture as Power Distance, Collectivism, Masculinity, Uncertainty Avoidance, Normative and Indulgence. Their evidence shows that, while a culture oriented to Collectivism (typical of authorities whose propensity is to focus on the overall stability of the banking system) or to Uncertainty Avoidance (typical of supervisors whose propensity is to define regulation, answer to clarification questions, clarify and update rules) is positively related to the banks’ distance to default, a supervisory authority characterised by Power Distance (indicating a strict supervision without flexibility) or Normative (depicting a strict empowerment of norms and the tendency to foresee consequences of unexpected banks’ behaviour) has a negative relation with bank stability.3 This would suggest

2

Particularly, following Fiordelisi and Ricci (2014), they analyse official discourses held from 1999 to 2011 by the head of each national supervisory authority in the European Union. Obviously, they assume that words and expressions used by the deans of the national authority reveal information on the adopted style of national supervision. 3 To measure bank stability they use the Z-score, extensively used in this literature (e.g. Laeven & Levine, 2009; Demirgüç-Kunt & Huizinga, 2010; Houston et al., 2010; Fiordelisi & Mare, 2014); Z-score indicates the distance of a bank to default and is calculate as the sum of leverage (i.e. share of total equity in total assets—E/TA) and the return on assets (ROA) scaled by the standard

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3 Financial Supervision and Bank Accounting Numbers: State of the Art

that supervision characterised by low flexibility and/or by a strict empowerment of norms has a negative relation with bank stability.4 Focussing on the effects of banking activities’ regulation, Beltratti and Stulz (2012) exploit the variation of stock returns’ cross-section of large banks to evaluate the role of some factors presented as having played a crucial role in affecting the banks’ poor performance during the crisis. Cross-country diversity among banking regulations seems uncorrelated with the performance of banks during that period, with the exception for performances of large banks, that were generally better (and with a lower decrease in loans) in countries with more restrictions on bank activities. However, they find no evidence that restrictions made banks less risky before the crisis, suggesting that banks in countries characterised by stricter regulation for bank activities suffered less because they were less relying on non-traditional bank activities. Moreover, their results show that stricter capital requirements were associated with less risk before the crisis.

3.2

Supervisory Characteristics Banks’ Accounting Behaviour

Accounting research widely agrees that country-level institutional structures shape the reporting environment (Ball et al., 2000, 2003; Fan & Wong, 2002; Burgstahler et al., 2006; Bushman & Piotroski, 2006) and generate incentives for firms to manipulate earnings (Bhattacharya et al., 2003; Leuz et al., 2003; Barth et al., 2008a, 2008b; Jeanjean & Stolowy, 2008; Ahmed et al., 2013). Studies on banks further stress this argument: institutional structures would be key determinants for earnings management as the banking industry is a highly regulated sector whose accounting numbers are strictly monitored by many private and public entities due to banks’ critical role in financial stability (Fonseca & González, 2008; Barth & Landsman, 2010; Chortareas et al., 2012; Acharya & Ryan, 2016). As it happens for non-financial firms, banks’ annual reports are under the scrutiny of accounting enforcers.5 Studies overall find a positive association of accounting enforcement and transparency of bank accounting numbers; enforcement has a positive effect on the informativeness of banks’ financial statements (Duru et al., deviation of ROA, so it indicates ‘the number of standard deviations by which the bank’s profitability has to fall to devour the entire capital buffer’ (Carretta et al., 2015, p. 182). 4 It is worth recalling the paper by Delis and Staikouras (2011), who study the role of banking supervision in controlling bank risk. Interestingly, their evidence shows an inverted U-shaped relationship between on-site audits and bank risk, being however the relationship between sanctions and risk linear and negative. 5 Their activity is aimed at detecting irregularities in companies’ financial reporting (UNCTAD, 2017; Quagli & Ramassa, 2018a, 2018b) and they impose penalties in the case of infringement (CESR, 2003; Brown et al., 2014).

3.2 Supervisory Characteristics Banks’ Accounting Behaviour

37

2018), constrains bank earnings manipulation to avoid losses and management discretion in estimating loan loss provisions (Dal Maso et al., 2018), and reduces income smoothing under strict supervisors (Di Fabio et al., 2021), mainly in cases in which national enforcement agencies ensure both formal and substantive compliance (Quagli et al., 2020a). However, given their institutional responsibilities with reference to the industry, national supervisors are those who closely monitor bank accounting numbers to assess whether they are adequately covering credit risk and to what extent they are implementing risky strategies. The supervisory interest in the stability of banks’ businesses represents a relevant motivation for banks to engage in income smoothing, which is one of the most frequent manipulative behaviours across the industry. Typically, bank managers manipulate accounting figures to smooth earnings over time as a smooth path hides excessive risk-taking by reducing the business perceived risk. For example, a stable earnings stream can support higher dividends compared to a variable one, thus producing favourable effects in terms of share value and investors’ perceptions (Beidleman, 1973). Managers can easily reduce the fluctuation of earnings relative to fundamental performance by manipulating the main bank accrual, namely loan loss provisions (Barnea et al., 1976; Herrmann & Inoue, 1996).6 They can exert considerable discretion on loan loss provisions given the complexity of bank loan loss accounting, which is due to the illiquidity of loans’ markets and the inherent intricacy of loan portfolios (Becht et al., 2011; Acharya & Ryan, 2016). Prior studies provide evidence that bank managers opportunistically smooth income via loan loss provisions to dampen discipline over risk-taking by obscuring the underlying risk attributes of loan portfolios (Bushman & Williams, 2012; Bushman, 2014).

3.2.1

Supervisory Strictness

The adjustment of loan loss provisions upwards due to high pre-managed earnings and downwards due to low pre-managed earnings is substantially in line with supervisors’ prudential approach to provisioning (Gebhardt & Novotny-Farkas, 2011). Indeed, this forward-looking policy enables covering credit risk by creating hidden reserves against expected losses in boom periods of the economic cycle when this risk arises. It also allows exploiting such reserves to cover losses when risks materialise. The alignment of income smoothing with supervisors’ preference for a forward-looking provisioning approach is supported by recent studies on European banks (Gebhardt & Novotny-Farkas, 2011; García-Osma et al., 2019). Gebhardt and

6 Instead, from an information enhancement perspective, smoothing constitutes an efficient signal used by managers to communicate private information to the market as it is easier for investors to predict future earnings from smoother earnings (Warfield et al., 1995; Beatty & Harris, 1999; Kanagaretnam et al., 2005; Dechow et al., 2010).

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3 Financial Supervision and Bank Accounting Numbers: State of the Art

Novotny-Farkas (2011) focussing on 12 European countries from 2000 to 2007 provide evidence that banks in stricter supervisory systems engage more in income smoothing even after the IFRS adoption, suggesting that the reduction in discretionary estimation of loan loss provisions induced by the IAS 39 incurred-loss model has been weaker in European countries characterised by stricter supervision. Differently, Fonseca and González (2008) work on a worldwide sample from 1995 to 2002 and find that overall stricter supervisory regimes reduce the use of loan loss provisions to smooth earnings. Although they individuate different smoothing patterns among the countries, they provide no specific evidence for the European Union. This book focuses instead on the European setting as it seems highly relevant for policy implications; in fact, a direct link between the effect of supervisory features and bank earnings management could work against the supervisors’ objective of financial stability (Jeanneau, 2014). In the medium term, opacity resulting from income smoothing could contribute to jeopardise financial stability by enabling banks to run risky businesses without incurring supervisory restrictions. The paradoxical link between strict supervision and decreased banking stability in Europe seems supported by Carretta et al. (2015), who note that the national supervisory style significantly influences banks’ decisions on their risk profile and stability (Masciandaro et al., 2011; Bushman & Williams, 2012; Cihak et al., 2012). Building on these studies, here it is argued that banks under stricter supervisory regimes have incentives to conceal the underlying economic performance by reducing the business’ perceived risk, in line with the penalty hypothesis (Shen & Chih, 2005). Thus, banks could resort to income smoothing to avoid attracting supervisors’ attention to avoid corrections and sanctions. Indeed, particularly high/low earnings are likely to attract attention, so banks could cook the numbers to show a lower risk using a provisioning strategy apparently aligned to supervisors’ preferences. In this perspective, strict supervisory authorities could induce banks to adopt accounting behaviours substantially aligned with the prudential approach to provisioning. This relationship has been recently supported by findings by Di Fabio et al. (2021), who find that propensity of European banks to smooth income is higher under strict supervisors.

3.2.2

Independence of the Supervisory Authority

A second supervisory feature that has recently attracted researchers’ interest is the independence of supervisors. This is a key characteristic to the achievement of supervisory objectives. As remarked by Garcìa-Osma, Mora and Porcuna (2019), the private or self-interest theory suggests that supervisors do not focus on overcoming market failures (as predicted instead by the public interest view—Beck et al., 2006) because they can be guided by their own interests. Clearly, incentives to pursue private interests can be enhanced and shaped by powerful groups eventually linked to the supervisor. From this angle, supervisory independence is critical in ensuring that supervisors work in the public interest, thus implementing strategies

3.2 Supervisory Characteristics Banks’ Accounting Behaviour

39

consistent with their institutional mandate (Dincer & Eichengreen, 2014). Nonetheless, the political sphere and the industry seek to exert influence within the private process of dialogue between the authority and supervised banks. It is to note that, as highlighted by Garcìa-Osma et al. (2019), some conflicted arguments could suggest a certain trade-off between political and industry independence. According to the theories of political self-interest, in fact, politicians work to maintain the control over activities connoted by redistributive effects or producing rents (Alesina & Tabellini, 2004). According to the regulatory capture theory, however, in absence of adequate political monitoring, entities in charge of regulations succumb to the interests of the industry. Nevertheless, political independence can also be seen as directly linked to independence from industry because it makes supervisor able to resist business interests (Garcìa-Osma et al., 2019). The absence of independence ultimately jeopardises supervisors’ independence and the achievement of their institutional goals (Quintyn & Taylor, 2003; Quintyn et al., 2007). Supervisory independence has been seen as a key prerequisite to financial stability because it avoids that political interference from governments supports insolvent banks producing unfair competition and higher costs for taxpayers (Quintyn & Taylor, 2003; Garcìa-Osma et al., 2019). Empirical contributions reveal political independence is associated to higher capital ratios and lower nonperforming loans and improves solvency (Doumpos et al., 2015). Looking at the interaction between supervisory strictness and independence Barth, Lin, Ma, Seade, and Song (2013b) find that only if the supervisory is independent increasing powers of supervisors is associated to higher bank efficiency. Focussing on the European context, García-Osma et al. (2019) find that supervisors’ independence mitigates income smoothing under powerful supervisors suggesting a positive association between independence and transparency of accounting behaviours.

3.2.3

Strength of External Auditing

Although the national supervisory authority is the key actor in charge of supervision at the national level, regulation also shapes the role of other subjects exerting supervisory activities (Barth et al., 2004, 2008a, 2008b), which are characterised by distinct approaches in monitoring bank financial statements (Dahl et al., 1998). In particular, the market monitoring and involvement of external auditors in supervisory activities complement the supervisory function at the national level.7 Supervisors place great reliance on information from audited financial statements and use it to determine banks’ conditions and assess capital adequacy (BCBS, 2008). The rules on supervisory functions and on audit activity shape the actual possibility of external

On the effects of auditor’s independence on value relevance in the context of financial entities, see the paper by Cimini et al. (2020).

7

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3 Financial Supervision and Bank Accounting Numbers: State of the Art

auditors to act autonomously from the national competent authority. Based on the key function of the auditor for supervisory purposes, national regulation can provide for high involvement of external auditors in supervisory activities (Barth et al., 2008a, 2008b; Marton & Runesson, 2017), allowing the relationship between the auditor and the national supervisor to become tighter. The effect of a significant auditor role within the supervisory system on bank income smoothing is not clear a priori. On the one hand, evidence from non-financial firms reveals that the accuracy of the audit process (Becker et al., 1998; DeFond & Subramanyam, 1998; Francis et al., 1999; Kim et al., 2003; Krishnan, 2003; Caramanis & Lennox, 2008) and the auditor’s specialisation in the industry (Gramling & Stone, 2001; Balsam et al., 2003; Krishnan, 2003) increase the quality of accounting numbers. Additionally, evidence on the effects of audit quality shows that firms audited by a Big Four firm present lower earnings management (Becker et al., 1998). In line with this research, studies on banks provide evidence that a lower audit quality is associated with greater income smoothing in the presence of a higher ownership concentration (Bouvatier et al., 2014) and that auditor reputation is negatively related to earnings management (Kanagaretnam et al., 2010). In particular, auditors have incentives to provide high-quality audits to avoid threatening their reputational capital (Kanagaretnam et al., 2009), because a reputation loss could cause a loss of status (Kanagaretnam et al., 2010). Accordingly, high auditor involvement in supervisory activities should decrease bank earnings-management practices. Specifically, in strict supervisory regimes, in which penalties for banks are higher, auditors’ involvement in the supervisory process should decrease bank income smoothing. On the other hand, auditors’ reputational concerns could produce the opposite effect. Indeed, consider that bank auditors are aware of the peculiarities characterising the banking industry compared with non-financial ones and of their role complementarity to supervisory functions (BCBS, 2002). From this perspective, such tight links with the national supervisor can increase the auditors’ concerns for banks’ stability due to reputational issues. Indeed, when auditors have an influential role in the scope of supervisory activities, negative results of the supervisory checks on banks’ resilience could lead to actions against the auditors—if the audit is judged as inadequate—and threaten auditors’ reputations. This issue might be particularly relevant in strict supervisory regimes, in which penalties are potentially higher for banks not complying with regulatory requirements. In such contexts, auditors could have reputational incentives for accommodating income smoothing in line with prudential provisioning and with managers’ desire to signal stability to avoid the national authorities’ measures.8

8 Furthermore, auditors could accommodate income smoothing via loan loss provisions because this prudent behaviour avoids the risk of identifying in the future a relevant amount of loans to be impaired. Indeed, research suggests that detecting troubled loans might be considered a negative outcome for the audit firm (see Jeffrey, 1992).

3.2 Supervisory Characteristics Banks’ Accounting Behaviour

3.2.4

41

Market Monitoring

Regulation can encourage market monitoring through different measures indirectly affecting the possibility of private investors to exert supervision on banks and governance (Barth et al., 2013a, 2013b). For instance, it can require ratings from international rating agencies, disclosure of full risk-management implemented strategies, and make managers legally liable for the diffusion of poor-quality and unreliable information (Barth et al., 2004). It is to note that the effect of market monitoring on income smoothing is not straightforward. From an opportunistic perspective, entities exploit ‘artificial’ smoothing to alter ‘true’ performance and mislead the market (Healy & Wahlen, 1999). From this perspective, income smoothing reduces accounting quality (Leuz et al., 2003; Ahmed et al., 2013) and conflicts with the information needs of investors, who need transparent financial statements to choose investments in line with their risk profile. Based on the information enhancement perspective (Kanagaretnam et al., 2005), the market could expect—and not object to—a certain level of income smoothing as it could make reported earnings reflective of future performance (Warfield et al., 1995; Beatty & Harris, 1999). Differently, noisy earnings could amplify information asymmetries between insiders and outsiders (Kanagaretnam et al., 2004). Thus, income smoothing motivated by informative choices can improve the decision usefulness of financial information (Dechow et al., 2010).

3.2.5

Supervisory Effectiveness

Even when research in the banking field has worked on the definition of effectiveness (see Delis & Staikouras, 2011), this feature has not however been considered in the accounting literature. Nevertheless, the notion of the national supervisor’s official power (Barth et al., 2008a, 2008b) is markedly different from the notion of effectiveness. Indeed, the former is based on the legal power of the national authority to undertake measures and to impose penalties; in other words, it is defined and measured by resorting to the law-on-the-books. In contrast, the notion of effectiveness should be defined by referring to the actual results of supervisory activity because it expresses the extent to which supervisory objectives (e.g., banks’ stability and their resilience) are achieved due to supervisory enforcement of regulatory requirements. Under powerful supervisors, high effectiveness of these authorities weakens managers’ incentives to engage in earnings management because doing so is more likely to incur sanctions and penalties. Accordingly, managers have lower opportunities to hide the ‘true’ performance (Shen & Chih, 2005) as high effectiveness of supervisory activities is achieved through frequent on-site audits (Delis & Staikouras, 2011) and the actual application of sanctions (BCBS, 2006). On-site

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3 Financial Supervision and Bank Accounting Numbers: State of the Art

inspections are particularly aimed at verifying the reliability of information produced by banks (BCBS, 2002), with specific attention devoted to data on banks’ exposure to risks and compliance in loan evaluation. Supervisory effectiveness increases the likelihood that supervisors detect impaired transparency in bank accounting numbers, thus increasing the likelihood for managers to incur sanctions and penalties by powerful supervisors. Consistent with these arguments, Di Fabio et al. (2021) find that accounting enforcement limits banks’ income smoothing in countries characterised by limited supervisory corrections due to the effective supervisory activity; their results suggest that strict supervisors can diffuse ex ante a supervisory culture informed by transparency. In addition, they find that enforcement again reduces income smoothing when European supervisors effectively complement weak national supervision through making ex-post corrections to bank regulatory capital. Taken together, these findings indicate that accounting enforcers intervene particularly in countries already characterised by effective banking supervision leaving, instead, broader scope for prudential supervisors’ interventions in other cases. The literature reviewed shows that the supervisory features are important factors to explain bank accounting choices, and particularly income smoothing, although these features produce distinct incentives for bank managers to engage in income smoothing. More recently, as consequence of bank regulators’ efforts towards the business model analysis and—more generally—of a growing regulatory attention for business model-related issues also outside the banking industry (Di Fabio & Avallone, 2018), research has started to discuss the role of business models in determining banks’ risk-taking behaviours and accounting behaviours. Focussing on financial reporting literature, the following section provides an overview of theoretical and empirical contributions on the matter; then, it discusses the role of the business-model notion with reference to banks’ accounting behaviours.

3.3

Business Model and Accounting Behaviours

In the last years, the accounting community has devoted growing attention to the notion of business model, whose ‘journey’ from the strategy field seems answering the need to enhance financial reporting and its—questioned—relevance (Nielsen & Roslender, 2015; Lev & Gu, 2016; Girella et al., 2019). Standard setters, regulators and professional bodies have increased efforts to encompass this notion within their provisions. As introduced in the second chapter, in the financial reporting field the IASB has presented a business model approach for classification and measurement purposes in the scope of the IFRS 9. Although the underlying assumption to this approach is that the business model is the primary determinant of the actual management of financial assets and of their use for cash flows’ generation purposes, the IASB has not issued an official definition of business model so that in some quarters the new provision seemed only an (undesirable) attempt to extend intentbased accounting (Leisenring et al., 2012). Nevertheless, the standard setter left an

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43

open door to a different perspective, as the Staff Paper 6A quotes Lee and Cole (2003), defining the business model as ‘a statement of how a firm will make money and sustain its profit stream over time’. Additionally, the IASB stressed the importance of narratives focussing on business model within the 2010 Practice Statement Management Commentary, clarifying that management commentary should be consistent with the need of integrating the data provided by financial statements with information providing a picture of the management’s view of the entity’s performance, position and prospects. Furthermore, although since 1990s many bodies have supported the enhancement of annual reports’ narrative sections to illustrate the processes through which companies create value (see Bagnoli & Redigolo, 2016), after the financial crisis these positions have intensified (EFRAG, 2013). From an academic perspective, there is a lively debate among accounting scholars on the business model, but scholars themselves acknowledge that the business model research is at the initial stage (see Beattie & Smith, 2013; Bini et al., 2016, 2018; Di Fabio & Avallone, 2018). This section specifically looks inside the literature on business model within the field of financial reporting although most of the research has been developing outside it.9

3.3.1

The Concept of Business Model and the Theoretical Debate in the Accounting Literature

Scholars’ interest on the topic dates back to the debate on the opposition between the income statement orientation of financial reporting and the balance sheet one. In this respect, already Dichev discusses it while supporting the higher usefulness of an income statement orientation compared to the balance sheet one supported by the FASB (Dichev, 2008). In this respect, the Author states: The main problem with the balance sheet approach is that it is largely silent about the notion of business model and business performance that are central to a firms’ success and value creation. The balance sheet approach [. . .] diverts attention from operations, which are the key to firm success and value. In contrast, the income statement model by its nature focuses the attention [. . .] on the fact that firm value arises [. . .] from continuously using and putting

9

Specifically, this research has developed with reference to intellectual capital (Cuozzo et al., 2017) and Integrated Reporting. In 2013, an agreed definition of this concept was issued by the International Integrated Reporting Council (IIRC), which defined the BM as ‘the chosen system of inputs, business activities, outputs and outcomes that aims to create value over the short, medium and long term’ (IIRC, 2013) and pose it as a central element to Integrated Reporting. Integrated reporting has been centred on the BM notion. From this perspective, the whole process of value creation and value delivery determines both the narratives and the numbers included within the report, and the report should be able to illustrate the extent to which the process of value creation and delivery will be sustainable in the future. See, in this respect, Bozzolan et al. (2003), Giuliani et al. (2016), Melloni et al. (2016), Zambon (2016), Silvestri et al. (2017), Zambon et al. (2019), and Marasca et al. (2020).

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3 Financial Supervision and Bank Accounting Numbers: State of the Art these resources at risk in executing a business model [. . .]. The firm is an ongoing process of business operations and not a collection of “things” [. . .]. (Dichev, 2008, p. 460)

However, attention to the business model has developed in financial reporting quarters specifically with reference to issues concerning the classification and measurement of financial assets since, as above mentioned, IFRS 9 mandates the use of the business model approach to account for financial assets. Although a recent survey carried out by Yong et al. (2016) shows that respondents consider the business model as a proper basis for determining whether assets and liabilities should be reported at fair values or cost, accounting scholars do not agree on the point. Specifically, opponents claim that it is not only not significant and unnecessary due to its [supposed] substantial identity with the approach based on the management intent (Leisenring et al., 2012), but also that business model approach, and—more generally—intent-based accounting could impair comparability (Leisenring et al., 2012; CFA Institute, 2014; Jílek, 2016).10 Criticism arises as the adoption of a full business model approach would imply that the managerial view of the company affects the way how transactions are accounted for. Consequently, the use, transfer, or disposition of assets or liabilities as foreseen by the entity’s business model will influence its financial reporting by changing criteria underpinning classification and measurement of items depending on their function. From this angle, the rationales driving classification and measurement should not dependent on the business model. Ronen (2014) argues that the ‘need for fair values (specifically, exit values) and historical costs arises with equal force [. . .] How do business models enter into this picture? What is their role in financial reporting? It should be apparent by now that the formation of expectations about future cash flows depends on the business model. In essence, the business model is a set of strategies and tactical moves that management devices in order to meet its objective. Thus [. . .] recognition and measurement principles [. . .] should not depend on the business model. Rather, it is the quantum of expected cash flows that naturally depends on managerial plans embedded in the business model’ (Ronen, 2014). Supporters of the business model approach consider instead that it would provide users with more relevant information concerning the resources of the entity, claims against the entity, and how the entity’s management and governing board have discharged their responsibilities in the use of the entity’s resources eventually even increasing the comparability between entities and allowing to represent more reliably how entities generate value (Brougham, 2012). Some remark that, since the business model would be a ‘simplified version of reality’ (Page, 2014) of what firms do and how they do, financial reporting itself has been conceived to communicate to users if the business model has actually worked (Singleton-Green, 2014); indeed, at

10 Leisenring et al. (2012) argue that comparability can be ensured and relevance not impaired if reporting is based on the recognition and measurement for an item or arrangement on the rights and obligations in that item or arrangement and management intent concerning an item are reflected by differences in classification and display.

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the balance sheet date, accounting effects of past transactions are matters of fact that can be checked and compared across years, to assess the credibility of management. From this angle, knowing accounting standards without being aware of the differences between entities’ business models may lead to difficulties in the preparation of financial statements and to comparability issues when such differences are ‘necessary’, namely they arise due to the fact that distinct business model draw upon different assets and distinct transactions (ICAEW, 2010; Singleton-Green, 2014). Differences are instead ‘avoidable’ when the same item is accounted differently depending on its function within the business model configuration. These differences (as those concerning tangible assets accounted as inventory or PPE, or financial instrument classified as FVTPL or AFS) are those arising in the scope of accounting standards and they have been accepted with reference to the ‘management intent’. Only in IFRS 9 there is explicit reference to business model in the requirements for deciding whether certain items should be measured at fair value or amortised cost. Although it is undeniable that there is continuity between the approach of management intent and the business model one as they both refer in some way to how the firm makes money and how managers expect to generate future cash flows, some consider these two as synonyms (Leisenring et al., 2012) as it is unfeasible for a firm to have a business model inconsistent with the management’s intention, as the business model is articulated to achieve goals decided by the management. Specifically, the difference between these two rationales is limited to the fact that business model could refer to the entity level, while management’s intent could refer to the single item; indeed, as the rationale driving management behaviour is profit-seeking, the managerial intent and the action undertaken will be aimed at achieving profits. This point of view seems implicitly shared by other scholars; for instance, Yong et al. (2016) refer to the business model as ‘management’s intent with respect to the use, transfer, or disposition of assets or liabilities’ (Yong et al., 2016, p. 68). Others argue that the business model cannot be reduced to a matter of management intent for the following reasons. First, management intent is intrinsically depending on managers’ subjectivity and can therefore become a tool for manipulation (Singleton-Green, 2014). Second, while business model the first refers to groups of items (i.e. to the aggregate level), management’s intent could refer to either the aggregate level or to single items (Brougham, 2012). Third, it is unlikely that the unique rationale for the business model is profit-seeking and that its configuration is inspired only by contingent objectives characterising the management intent (Brougham, 2012). A way forward is proposed by Nielsen and Roslender (2015). Their paper does not tackle directly the issue of the extent to which the business model approach should be used to classify and measure assets, as they remark that ‘rather than seeking to incorporate the business model concept [. . .] the objective should be to explore how these two traditions might most fruitfully be combined’ (Nielsen & Roslender, 2015, p. 271). Indeed, their paper clarifies that per se the business model focus is on value creation and value delivery, not limiting the perspective to value realisation, which is instead the focus of financial reporting. To represent business model, indeed, an interdisciplinary attitude is required as well

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3 Financial Supervision and Bank Accounting Numbers: State of the Art

as emphasis on narratives, which are outside the scope of formalisation, and on forward-looking perspectives, rather than historical as it happens in financial statements. Indeed, the focus of information aimed at representing business models is intrinsically on how the firm will be able to sustain in the future the process of value creation and delivery. They consider that a possible way forward to ‘envisage the union of business models and financial reporting’ in order to meet information needs of a wider range of users is enhancing financial reporting through more prospective information (e.g. details of managerial plans, opportunities, risks and uncertainties affecting measurement), a broader focus on the factors that may affect value creation in the long run (including non-financial indicators for the key business processes), and an increased consistency between the information management utilises to manage their business and that which is presently reported externally.

3.3.2

Empirical Evidence

Empirical evidence provided on the part played by the business model as an explicative variable for entities’ accounting choices are Lassini et al. (2016) and Pinto et al. (2015), who provide opposite findings on the validity of the business model argument. Focussing on the European context, Lassini et al. (2016) classify 103 companies into different business model-related clusters based on different dimensions of the business model, using variables of (1) ownership, (2) size, (3) supply chain relation, (4) internationalisation; (5) R&D commitment, (6) economic performance, (7) operative growth performance, (8) structural growth, (9) financial profile, (10) liquidity profile. Then they examine accounting choices by companies in different clusters distinguishing among choices on accounting measurement, choices of accounting treatment and choices in terms of disclosure. Overall, their analysis of relations between business models and accounting choices reveals no significant associations.11 In contrast, results of the qualitative study by Pinto et al. (2015) suggest that measurement choices on investment properties are explained by the firm business model. The business model seems here a key explanatory factor to understand decisions of publicly traded real estate management companies in Brazil. Despite the in-depth approach adopted (i.e. documentary research and interviews with the main agents), the analysis encompasses only a few Brazilian companies; thus, it is not clear the extent to which results are generalisable.

11 Bischof et al. (2011) investigate to what extent banking industry’s BMs could explain differences in the usage and reporting of fair values versus historical costs and investigates if heterogeneity among them has consequences for the relevance of income measures to investors. Their results cast doubts on the validity of the business model in the revision of accounting for financial instruments. Indeed, they show that, in case of instruments included in the banking book with the aim to collect cash flows, only fair value measures of income show an association with stock returns.

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Di Carlo et al. (2016) deal with the boundaries of the business model in the context of the consolidated Italian financial statements, specifically exploring the way in which how the degree of business-model independence is measured within the group. They find that the financial statements expresses: (1) the full effect generated by the reporting entity’s business model, as this entity is independent of the other affiliated companies; (2) only a partial effect of the business model of the entire group, as its activity presents interrelations with those of other affiliated entities of the group to which it belongs, and (3) the effect of the business models of multiple entities, as the consolidated financial statements contain the results of multiple independent entities, (4) misleading information, as consolidated financial statements of a group operating in different businesses are affected by consolidating principles requiring homogeneous accounting. Another investigated issue is the relation between the business model and operating segments under IFRS; the boundaries of these elements could not coincide and this often depends on the angle of observation (Di Carlo et al., 2016). Indeed, a single business model may encompass various operating segments and vice versa. The study by Barneto and Ouvrard (2015) explores the association between disclosure of segment-related information ex IAS 14 and IFRS 8 and the business model in a sample of 101 companies listed on the Euronext. Their proxy for the business model is based on the assumption that the business model is determined by firms’ choices represented by cash flows. Accordingly, they individuated four types of business models, namely withdrawal, restructuring, launch, growth. No association between information communicated in segment reporting disclosures and the business model is however found. André et al. (2016) focus on the usefulness of segment reporting quantity and quality for financial analysts. They show that analysts do not always understand the quality of segment disclosures, remarking potential suggesting that a business-model type of standard creates difficulties even for sophisticated users. In contrast, the interest of markets for business model-related information is supported by the study by Mechelli et al. (2017). By studying a sample of 124 European financial entities over the period 2010–2013, they reveal that the level of voluntary disclosure of the non-mandatory IASB (2010) macro-components of business model enhances the value relevance of accounting amounts.

3.3.3

Business Model and Banks’ Accounting Behaviours

More or less explicitly, many studies published in the last three decades provide support to the idea that the differences in terms of business models affect firms’ accounting manipulation. Already seminal accounting papers provide evidence on the part played by the firms’ funding choices, even if without explicitly mentioning the ‘business model’. For instance, empirical results indicate that leverage is positively associated with earnings management strategies based on income increasing (Sweeney, 1994; Chan

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et al., 2008), specifically in troubled companies (Christie & Zimmerman, 1994; Easterwood et al., 1997), while other research finds a negative association between leverage and firm recourse to earnings management (Jensen, 1986; Denis & Denis, 1993; Jelinek, 2007). Besides, literature widely employs the positive relation between fixed capital intensity and accruals to model total accruals (Jones, 1991; Dechow et al., 1995; Francis et al., 2005; Kothari et al., 2005). Further, research implicitly acknowledges the diversity among entities in terms of specific features of their business models, such as working capital intensity (Ettredge et al., 2010) and provides evidence that firms use working capital as an earnings management tool more than other elements of net operating assets (Kreutzfeldt & Wallace, 1986; DeFond & Jiambalvo, 1994). Studies on the banking industry highlight that several business model’s features are associated with earnings manipulation; for instance, the composition of loans seems to affect managers’ ability to exercise discretion over loan loss provisions. Discretion available to managers is broader when dealing with commercial loans because such loans are characterised by higher heterogeneity than other loan types and provisioning is mainly based on managerial judgement, thus allowing de facto managers to manipulate easily loan loss provisions (Liu & Ryan, 1995, 2006). For consumer loans, instead, discretion available to managers is considerably lower; loan loss provisions are individuated on a statistical basis as these loans are normally homogeneous. Recent research documents that the ratio of core deposits to total liabilities is associated with lower earnings manipulation through discretionary loan loss provisions, weaker likelihood of meeting-or-beating earnings targets, and decreased banks’ propensity to income smoothing (Jin et al., 2018). As remarked by Di Fabio (2019), although prior studies suggest that the business model would play its part in affecting earnings management, available evidence mostly concerns specific features of the bank asset-liability mix. This is not aligned with the very nature of the concept of business model, which is far more articulated as it involves ‘the logic, the data (. . .) that support a value proposition for the customer, and a viable structure of revenues and costs for the enterprise delivering that value’ (Teece, 2010, p. 179). Not only single features of the asset-liability mix, but the whole mix of asset and liability itself reflects the managerial hypothesis concerning how the firm generates value to customers (Massa et al., 2017), is connected to the bank structure of costs and revenues and to the stakeholders interacting with the bank (Zott et al., 2011). Stakeholder encompasses retail depositors and wholesale financiers holding different interests in monitoring bank financial statements and assessing bank stability (Barth & Landsman, 2010; Acharya & Ryan, 2016). Thus, the combination of (1) earnings variability and riskiness characterising business models and (2) stakeholders’ expectations could generate different managerial incentives to smooth earnings. Observing a sample of European banks from 204 to 2015, Di Fabio (2019) explores the role of the business model as a whole in influencing smoothing propensity. Employing a methodology used in banking research (Ayadi et al., 2012, 2016; Ayadi & de Groen, 2014; Roengpitya et al., 2014), the study classifies banks into different business models resorting to ratios built based on the balance sheet. This allows to broaden the enquiry from the liability

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side to embrace also the bank asset composition, which reflects the managerial view about the way in which the firm generates value to customers (Teece, 2010; Massa et al., 2017). The paper finds that the business model affects smoothing; specifically, banks characterised by risk-averse business models, such as those mainly relying on deposits and diversifying the asset side to balance interest and non-interest income (Stiroh, 2004; Köhler, 2012), smooth income for informative reasons. From an information enhancement perspective (Kanagaretnam et al., 2005), income smoothing enables managers to diffuse private information on firms’ prospects because it makes easier for users to predict future earnings by smoothing out noise (Warfield et al., 1995; Subramanyam, 1996; Beatty & Harris, 1999; Tucker & Zarowin, 2006; Dechow et al., 2010). In turn, predictable earnings generate advantages to smoothers; the cost of capital decreases as well as trading costs for the bank securities (Affleck-Graves et al., 2002; Kanagaretnam et al., 2004), whereas market liquidity and value of securities improve (Callahan et al., 1997). Results are overall supportive of Jin et al. (2018), who argue that retail-funded banks show higher earnings quality than banks relying mainly on wholesale funds. In fact, although retail depositors have typically limited financial skills and resources (Macey & Miller, 1988; Calomiris & Kahn, 1991; Demirgüç-Kunt & Huizinga, 2004), retailfunded banks have weaker incentives to manipulate earnings; these banks are usually closer to depositors, which can easily assess riskiness (Loutskina & Strahan, 2011) and get private information. In this research context, only limited attention has been devoted to the combined effect of institutional factors and the business model on bank income smoothing. Again, the focus is here on the institutional environment more relevant to banks, namely the supervisory and regulatory one. So far, research has explored the interplay between the business model and two main features of the monitoring system, namely (1) the involvement of auditors in national supervision and (2) the relevance ascribed to private investors. Empirical findings on the European context find that the supervisor’s reliance on the audit function is positively related to bank income smoothing in market-oriented business models. This would indicate that banks connoted by market-oriented business models smooth earnings to produce an impression of alignment with the objectives of prudential supervisors (Peterson & Arun, 2018) in countries characterised by considerable complementarity between auditing and national supervision (BCBS, 2002, 2008, 2015), since the accounting effects of income smoothing are perceived by the authorities as in line to prudential provisioning (Gaston & Song, 2014). In these national settings, auditors can have major concerns for bank stability especially in case of business models that suffer from market dynamics and higher variability of income, namely market-oriented banks, and can look at income smoothing as a tool to align with supervisory objectives (Peterson & Arun, 2018) and avoid the close eye of the authorities. Further, consistent with evidence concerning non-financial firms (Leuz et al., 2003), findings indicate that private monitoring reduces bank incentives to engage in smoothing behaviours, especially for banks characterised by business models highly reliant on wholesale markets. This suggests that, in the case of diversifiedretail business models, the high reliance on wholesale markets could make banks

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more sensitive to external governance. The intensity of external governance exerted by private investors at the country-level might reduce managerial incentives to smooth income.

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Chapter 4

Supervisory Characteristics and Income Smoothing: The Case of European Banks

4.1

Facets of National Supervision and Bank Income Smoothing: The Key Points

The features of the supervisory system are key to explain bank accounting choices; however, research suggests that the supervisory facets produce distinct incentives for bank managers to engage in income smoothing. The previous chapter has outlined empirical evidence provided by studies on the topic, highlighting that—at least in the European context—the strictness of national supervisors seems to increase income smoothing, as this strategy is substantially in line with supervisors’ prudential approach to provisioning (Gebhardt & Novotny-Farkas, 2011; García-Osma et al., 2019; Di Fabio et al., 2021). Its effect can be constrained by high independence of supervisors from the banking industry (García-Osma et al., 2019) and by private investors’ monitoring (Fonseca & González, 2008). Other two supervisory features, namely the involvement of external auditors and supervisory effectiveness, have been less investigated; however, recent research suggests that the supervisor’s reliance on the audit function is positively related to smoothing in market-oriented banks (Di Fabio, 2019) and that effectiveness seems to influence how other monitoring mechanisms reduce smoothing (Di Fabio et al., 2021). In this chapter, the association between the different features of the multifaceted supervisory system is investigated by exploiting a single setting, which enables to compare results and to gain a broad understanding of the phenomenon.

4.2

Research Design

The first step to investigate the effect of supervisory features on income smoothing is assessing the existence of income smoothing. In line with prior research on income smoothing practices across the banking industry, here the empirical focus is on loan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 C. Di Fabio, National Supervision and Income Smoothing in Banks’ Annual Reports, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-74011-5_4

59

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4 Supervisory Characteristics and Income Smoothing: The Case of European Banks

loss provisions, as these are the main accrual in banks’ annual report and are able to explain much of the variability in total accruals (Beatty & Liao, 2014). The multivariate regression model used in the current analysis to detect banks’ income smoothing has been obtained adapting prior studies that model the magnitude of loan loss provisions as a function of a set of explanatory variables (Kanagaretnam et al., 2003, 2004). Thus, the basic model employed is as follows: LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 CLoansi,t þ β3 LLAi,t1 þ β4 CAPi,t X T t þ εt þ β5 GDP GRt þ β6

ð4:1Þ

In this model, LLPi, t is loan loss provisions reported by the bank i in year t divided by total assets at the beginning of the same year. The variable EBTPi, t expresses pre-managed earnings, namely earnings before taxes and provisions at year t scaled by total assets at the beginning of the same year. Bank smoothing propensity is the coefficient of pre-managed earnings—β1; thus, a positive coefficient of pre-managed earnings indicates the propensity to income smoothing. The mechanism behind the sign of β1 is the following: banks that smooth income rise loan loss provisions when pre-managed earnings are high and restrict provisions when these are low. The model encompasses accounting variables that indicate the non-discretionary component of loan loss provisions (Wahlen, 1994; Beatty et al., 1995; Beaver & Engel, 1996; Kim & Kross, 1998), i.e. CLoansi, t and LLAi, t  1. The first, CLoansi, t, is the change of loans from year t  1 to year t, scaled by total assets at the beginning of year t; the second, LLAi, t  1, is the total loan loss allowance at the beginning of the year t scaled by total assets at the beginning of the same year. Besides, the model encompasses capital management as further determinant of loan loss provisions magnitude (Ahmed et al., 1999; Pinto & Picoto, 2018) by including CAPi, t, calculated as the bank capital scaled by risk weighted assets, namely TIER 1, and controls for the procyclical effect of provisioning by including the growth in national GDP (GDP_GROWTHt) (Laeven & Majnoni, 2003; Bikker & Metzemakers, 2005; Fonseca & González, 2008; Pérez et al., 2008). During the boom periods, rising incomes mirrors improved firms’ health and would limit loan defaults, so banks would decrease provisioning levels. By contrast, defaults are more likely to happen during recessions, in which banks would expand provisions. Thus, a negative relationship between GDP_GROWTHt and loan loss provisions is expected. The model includes a set of dummy time variables ∑Tt controlling for time effects that are bank invariant controls for banks’ fixed effects.1 In addition, the same model is run to consider that a major determinant for banks to smooth income could be the bank listing. In this respect, literature argues that

1 In this way, the estimates of coefficients are corrected so that they are not correlated with the error term.

4.2 Research Design

61

listed entities engage more in income smoothing; possible theoretical reasons are several. Listed banks are more exposed to outsiders’ attention and their economic results have greater signaling effect (Beatty et al., 2002). Additionally, the reduction in earnings variability brought by income smoothing favours the reduction of perceived information asymmetries, thus lowering trading costs, augmenting liquidity and the value of securities (Callahan et al., 1997). Empirical studies in a U.S. setting, as Beatty and Harris (1999) and Beatty et al. (2002), support these considerations showing that U.S. publicly listed banks engage in income smoothing more than non-listed ones; nevertheless, as noticed by Fonseca and González (2008), there is paucity of studies outside U.S.A. on the topic concerning differences between listed and non-listed firms in exploiting smoothing across the banking industry. Research on non-financial firms shows that listed firms typically smooth income (Moses, 1987; Chaney et al., 1998; Young, 1998; Buckmaster, 2001), as this practice moderates income fluctuations over the years diminishing income volatility (Copeland, 1968). To explore the effect of listing, a second model is defined: LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 LISTi,t þ β3 EBTPi,t  LISTi,t þ β4 CLoansi,t þ β5 LLAi,t1 þ β6 CAPi,t þ β7 GDP GRt X T t þ εt þ β8

ð4:2Þ

where all the variables have been previously defined, except the variable LISTi, t, which is a dummy variable which equals 1 if observed bank is listed in year t, and 0 otherwise. The coefficient of interest in model (4.2) is β3, namely the coefficient of the interaction term EBTPi, t * LISTi, t; this coefficient expresses the strength of income smoothing propensity for publicly traded banks if compared to non-publicly traded banks. It is expected to be positive and significant. Model (4.2) includes a set of dummy time variables ∑Tt controlling for time effects that are bank invariant. Additionally, it controls for banks’ fixed effects. After these first two basic models, other two models are developed to test the effect of the different features of national supervision and the propensity of banks to smooth earnings. The first model (model 4.3) includes the supervisory variables and the second (model 4.4) also includes the listing effect. LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 < HIGH SUPERVISION > t þ β3 EBTPi,t  < HIGH SUPERVISION > t þ β4 CLoansi,t þ β5 LLAi,t1 þ β6 CAPi,t þ β7 GDP GRt X T t þ εt þ β8

ð4:3Þ

In model (4.3), supervisory variables have been sequentially incorporated, so that the coefficient of each interaction term measures the influence of every aspect of

62

4 Supervisory Characteristics and Income Smoothing: The Case of European Banks

supervision on income smoothing. Specifically, the term t represents dummy variables built from data of Barth et al. (2008a, 2008b, 2013a, 2013b), that equal 1 if the value of the corresponding index is above median and 0 otherwise. The first feature investigated is supervisory strictness; the variable that will be tested in this respect is HIGH_OFF that equals 1 if the country presents a level of Official Supervisory Power above median and 0 otherwise. Official Supervisory Power indicates whether the national supervisory authority has the authority to undertake specific actions to prevent and correct problems, thus it is a proxy of supervisory strictness. The coefficient of interest in model (4.3) is the coefficient of interaction between HIGH_OFFt and EBTi, t; the coefficient has not a definite prediction about its sign and could be legitimate from previous literature. The second feature investigated is supervisory independence, represented by variable HIGH_IND that equals 1 if the country presents a level of Independence of Supervisory Authority-Bank above median and 0 otherwise. Independence of Supervisory Authority-Bank represents the degree to which the national supervisory authority is protected by the legal system from the banking industry. The coefficient of interest in model (4.3) is the coefficient of interaction between HIGH_INDt and EBTPi, t; this coefficient is expected to be negative. Indeed, the higher will be the legal independence of the national supervisory authority from the banking industry, the lower are expected to be the incentives for banks to engage in income smoothing. The third feature examined is the strength of external auditing, namely the level to which regulation facilitates external governance by debt and equity holders through the effectiveness of external auditing, the lower are expected the incentives for banks to engage in income smoothing. The associated variable is HIGH_AUD, which equals 1 if the country shows a level of Strength of External Audit above median and 0 otherwise; Strength of External Audit is the effectiveness of external audits of banks in the country. The coefficient of interest in model (4.3) is the coefficient of interaction between HIGH_AUDt and EBTPi, t; this coefficient is expected to be negative. Then, market monitoring is investigated; the related variable is HIGH_MON, which equals 1 if the country presents a level of Private Monitoring Index above median and 0 otherwise. The Private Monitoring Index expresses the country level of incentives and opportunities for the private sector to monitor banking industry. To test the effect of private monitoring on bank income smoothing, the coefficient of interest in model (4.3) is the coefficient of interaction between HIGH_MONt and EBTPi, t. It is expected to be negative, since, building on previous literature, the higher are the incentives and the ability of private investors to monitor and exert effective governance of banks, the lower are expected to be incentives for banks to engage in income smoothing. For sought of completeness, in line with other studies, the effect of the restrictiveness of the capital stringency for the country is checked. The fifth variable is then HIGH_REG, which equals 1 if the country presents a level of Capital Regulatory Index above median, and 0 otherwise. The coefficient of interest in model (4.3) is the

4.2 Research Design

63

coefficient of interaction between HIGH_REGt and EBTPi, t; this coefficient is expected to be positive. Model (4.3) includes a set of dummy time variables ∑Tt controlling for time effects that are bank invariant, and it controls for banks’ fixed effects. The second model employed to test the effect of the different features of national supervision and the propensity of banks to smooth is model (4.4), which includes also the listing effect. Thus, the model tests the effect of listing on the relationship between the level of supervision and the propensity to smooth earnings. LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 < HIGH SUPERVISION > t þ β3 LISTi,t þ β4 EBTPi,t  < HIGH SUPERVISION > t þ β5 EBTPi,t  LISTi,t þ β6 < HIGH SUPERVISION > t  LISTi,t þ β7 EBTPi,t  < HIGH SUPERVISION > t  LISTi,t þ β8 CLoansi,t þ β9 LLAi,t1 þ β10 CAPi,t X T t þ εt þ β11 GDP GRt þ β12

ð4:4Þ

The coefficients of interest in model (4.4) is β7 that shows the effect of banks’ listing in moderating the degree of income smoothing in countries characterised by high levels of supervision. This model (4.4) considers a set of dummy time variables ∑Tt controlling for time effects that are bank invariant, also controlling for banks’ fixed effects. Model (4.5) enables testing the moderation effect of a measure of the effectiveness of the supervisory system, namely the banking system resilience: LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 HIGH OFFt þ β3 GOOD PERFt þ β4 EBTPi,t  HIGH OFFt þ β5 EBTPi,t  HIGH PERFt þ β6 HIGH OFFt  GOOD PERFt þ β7 EBTPi,t  HIGH OFFt  GOOD PERFt þ β8 CLoansi,t þ β9 LLAi,t1 þ β10 CAPi,t X T t þ εt þ β11 GDP GRt þ β12

ð4:5Þ

where all the variables have been previously defined, except GOOD_PERF. GOOD_PERF is a dummy variable that equals 1 if the country has reported a Performance above the median value during the ECB stress tests and 0 otherwise. The index of Performance has been calculated building on the results of the 2013–2014 Asset Quality Review, as follows. At first, a measure of the degree to which banks of the country have been compliant with rules of regulation is AQR_corr ¼ 1 + (TIER1revised  TIER1reported). Particularly, the difference ‘TIER1revised  TIER1reported’ is the difference between the average value for the country of TIER1 capital after the ECB revisions and the average value for the same country of TIER1 as it was reported by banks; the more is the downward revision of TIER1, the higher will be the absolute value of this difference. Consequently, the index AQR_corr assumes higher values for countries

64

4 Supervisory Characteristics and Income Smoothing: The Case of European Banks

where the correction of TIER1 has been lower. Then, a measure of how a country has performed during the stress tests is defined as Performance ¼ AQR_corr + Average_Scenario, where AQR_corr is calculated as above explained and Average_Scenario is the country average value of the 2016 baseline scenario and the 2016 worst scenario. Performance is an index of the effectiveness of the supervisory system in the country, since it expresses a degree to which the system is resilient. As GOOD_PERF is determined as time invariant using 2014 inputs, this model is run only for the period 2011–2015. All the variables in the models have been winsorised at the 1 and 99% levels. Moreover, all the estimated models pass the VIF test.

4.3

Sample and Data Collection

As the studies focus on the European setting, the sample used in empirical analyses initially consists of 1103 listed and non-listed banks from the European Union (EU28) (14,339 bank-year observations) observed from 2003 to 2015. Financial data on consolidated financial statements of sampled banks obtained from the BankScope database. Data on the country-level variables that proxy for several features of the supervisory style are taken from the database built by Barth et al. (2008a, 2008b, 2013a, 2013b) (see Table A.1 in the Appendix). Specifically, for period 2003–2008, this study uses data from Survey III (which provides information on bank regulation of 142 countries); from 2009 to 2015, the analysis uses data from Survey IV (which provides data on banking policies just in 125 countries). The analysis also employs the data taken from results of the Asset Quality Review conducted by the ECB in 2013. Particularly, a single measure is developed as elaboration of country-level results for TIER 1 reported, TIER 1 adjusted, baseline scenario for 2016 and worst scenario for 2016 to obtain a proxy for the effectiveness of supervision. Finally, data on GDP of European Countries from 2003 to 2015 are taken from the World Bank databases. Table 4.1 reports descriptive statistics for variables used in the models and Table 4.2 provides correlation analysis.

4.4

Results of the Empirical Analysis

Before analysing the impact of national supervisory features on banks’ income smoothing, model (4.1) is run to assess whether sampled banks smooth income (Table 4.3, second column) and model (4.2) is run to assess the effect of listing on bank’s smoothing propensity (Table 4.3, third column). Looking at the second column of Table 4.3, it is possible to note that overall banks in the sample smooth income over the timeframe observed; in fact, the coefficient of EBTP is positive and statistically significant at the 1% level. The coefficients of the proxy variable for the non-discretionary part of loan loss

4.4 Results of the Empirical Analysis

65

Table 4.1 Summary statistics Panel A: Statistics by country Median Median Country LLP EBTP AT 0.0035 0.0079 BE 0.0005 0.0011 BG 0.0116 0.0229 CY 0.0089 0.0308 CZ 0.0053 0.0061 DE 0.0019 0.0054 DK 0.0064 0.0118 EE 0.0028 0.0139 ES 0.0041 0.0129 FI 0.0007 0.0054 FR 0.0019 0.0083 GB 0.0020 0.0122 GR 0.0148 0.0243 HR 0.0073 0.0139 HU 0.0108 0.0242 IE 0.0025 0.0091 IT 0.0053 0.0127 LT 0.0044 0.0190 LU 0.0006 0.0066 LV 0.0063 0.1153 MT 0.0022 0.0198 NL 0.0015 0.0048 PL 0.0046 0.0110 PT 0.0048 0.0127 RO 0.0124 0.0325 SE 0.0006 0.0028 SI 0.0092 0.0331 SK 0.0046 0.0278 Panel B: Sample’s statistics Mean 0.0070 St. Dev. 0.0119 Q1 0.0008 Median 0.0028 Q3 0.0441 # Observations 7316

Median CLoans 0.0169 0.2266 0.0410 0.0623 0.0469 0.0097 0.0157 0.0688 0.0067 0.0353 0.0253 0.0185 0.0384 0.0340 0.0006 0.0160 0.0240 0.0529 0.0163 0.1715 0.0241 0.0128 0.0590 0.0031 0.0367 0.0404 0.0093 0.0599 0.1335 0.6764 0.0026 0.0110 0.0441 5419

Median LLA 0.0174 0.0050 0.0303 0.0478 0.0198 0.0071 0.0181 0.0121 0.0167 0.0036 0.0178 0.0052 0.0352 0.0419 0.0270 0.0058 0.0201 0.1982 0.0027 0.0166 0.0104 0.0049 0.0254 0.0179 0.3664 0.0029 0.0465 0.0202

0.0442 0.1228 0.0124 0.0210 0.0745 7807

Median CAP 0.1500 0.1650 0.1396 0.1198 0.1187 0.1380 0.1650 0.1333 0.1502 0.1283 0.1372 0.1471 0.1263 0.1397 0.1146 0.1562 0.1451 0.1294 0.1392 0.1555 0.1614 0.1380 0.1663 0.1523 0.1541 0.1350 0.1542 0.1551

0.0237 0.0310 0.0047 0.0142 0.0282 6515

0.1684 0.9771 0.1180 0.1431 0.1790 5374

Median GR_GDP 0.0508 0.0459 0.0893 0.0254 0.0536 0.0411 0.0479 0.0896 0.0306 0.0453 0.0424 0.0456 0.0208 0.0329 0.0431 0.0566 0.0290 0.1183 0.0862 0.1153 0.0626 0.0428 0.1006 0.0352 0.1096 0.0589 0.0463 0.0726 0.0349 0.1035 0.0450 0.0477 0.1025 13,227

66

4 Supervisory Characteristics and Income Smoothing: The Case of European Banks

Table 4.2 Correlations Variables LLP EBTP CLoans LLA CAP GR_GDP

LLP 1.0000 0.1234 0.0059 0.5344 0.1168 0.0026

Table 4.3 Income smoothing in sampled banks and the effect of listing

EBTP

CLoans

LLA

CAP

GR_GDP

1.0000 0.0757 0.0546 0.0302 0.0098

1.0000 0.1942 0.2459 0.0513

1.0000 0.0784 0.0083

1.0000 0.0771

1.0000

LLP EBTP LIST EBTP * LIST CLoans LLA CAP GDP_GR Year Dummies Intercept Adjusted R-squared # Observations

Is hypothesis 0.0013***

0.0039* 0.0531*** 0.0023 0.0267*** Yes 0.0048*** 0.6508 2359

Listing 0.0015*** 0.0049* 0.0007 0.0049** 0.0545*** 0.0021 0.0267 Yes 0.0035* 0.651 2359

*, **, *** denote, respectively, significance at the 10%, 5%, and 1% level

provisions are as expected in case of LLA (positive and statistically significant at the 1% level), while has a negative and statistically significant coefficient (at the 10% level). Consistent with the capital-management hypothesis, capital has a negative coefficient, even if not statistically significant. The growth of GDP presents a negative coefficient, statistically significant at the 1%; this confirms the procyclical effect CLoans of loan loss provisions. Concerning the effect of the listing status on bank’s smoothing propensity (Table 4.3, third column), results indicate that unlisted banks are likely to smooth income more than listed ones; there is no statistically significant increase/decrease in the income smoothing propensity for listed banks. To test the effects of supervisory features on banks’ income smoothing, model (4.3) is run on the sample sequentially incorporating the supervisory variable of interest and an interaction term including pre-managed earnings and the supervisory variable. Table 4.4 illustrates the association between supervisory strictness, the independence of supervisory authority and the strength of the external audit and income smoothing. The second column reveals a statistically significant (5% level) increase of the bank’s propensity to smooth income in case of high supervisory power, then supporting the idea that supervisory strictness increases bank income smoothing. Indeed, the higher is the authority of supervisors both to obtain information from banks and to undertake prompt measures to change banks’ behaviours, the higher are

0.0074*** 0.0429*** 0.0033 0.0271*** Yes 0.0067*** 0.6510 2093

0.0027*** 0.0015**

Strictness 0.0004

0.0076*** 0.0443*** 0.0031 0.0271*** Yes 0.0054*** 0.6510 2093

0.0004 0.0042 0.0032 0.0026** 0.0019** 0.0001 0.0044

*, **, *** denote, respectively, significance at the 10%, 5%, and 1% level

LLP EBTP LIST EBTP * LIST HIGH_OFF EBTP * HIGH_OFF LIST * HIGH_OFF EBTP * LIST * HIGH_OFF HIGH_IND EBTP * HIGH_IND LIST * HIGH_IND EBTP * LIST * HIGH_IND HIGH_AUDIT EBTP * HIGH_AUDIT LIST * HIGH_AUDIT EBTP * LIST * HIGH_AUDIT CLoans LLA CAP GDP_GR Year Dummies Intercept Adjusted R-squared # Observations 0.0044** 0.0531*** 0.0019 0.0273*** Yes 0.0043** 0.6503 2286

0.0007 0.0020*

Independence 0.0031**

0.0044** 0.0511*** 0.0015 0.0267*** Yes 0.0051** 0.6522 2286

0.0025 0.0017 0.0102** 0.0466*

0.0030135** 0.0013 0.0460*

0.0046** 0.0531*** 0.0024 0.0275*** Yes 0.0047*** 0.6503 2286

0.0002 0.0014*

External audit 0.0018***

Table 4.4 The association between supervisory strictness, independence and strength of the external auditor on banks’ income smoothing

0.0010 0.0020* 0.0021 0.0019 0.0048** 0.0549*** 0.0023 0.0274*** Yes 0.0032** 0.6506 2286

0.0019** 0.0056*

4.4 Results of the Empirical Analysis 67

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4 Supervisory Characteristics and Income Smoothing: The Case of European Banks

banks’ incentives to engage in income smoothing via loan loss provisions. This is consistent with the penalty hypothesis (Leuz et al., 2003; Shen & Chih, 2005) and with evidence provided by García-Osma et al. (2019) and Di Fabio et al. (2021). The third column shows that the coefficient of EBTP * HIGH_IND is negative and statistically significant, suggesting that there is a decrease in the income smoothing behaviour in case of a strong legal independence of the supervisors from the industry. Therefore, evidence confirms that the higher is legal independence of the national supervisory authority from the banking industry, the lower the incentives for banks to engage in income smoothing, in line with findings of García-Osma et al. (2019). Evidence provided in the fourth column suggests that auditors’ influential role in the scope of supervisory activities works as an external control able to increase the transparency in annual reports; in fact, the coefficient of the interaction term EBTP * HIGH_AUDIT is negative and statistically significant at the 10% level. Looking at the combined effect of listing and supervisory features, there is no statistically significant effect of moderation of the listing status on the association between supervisory strictness and income smoothing. Differently, listing seems to exert a negative effect on income smoothing of banks when the supervisor is highly independent from the industry. Indeed, the coefficient of the interaction term EBTP * HIGH_IND * LIST is negative and statistically significant at the 10% level. Results in Table 4.5 report the estimation of model (4.3), particularly focussing on the association between the private monitoring and stringency of rules on regulatory capital and income smoothing behaviour. As indicated in the second column, the coefficient of EBTP * HIGH_MON is negative and statistically significant at the 5% level; this indicates that the higher the degree to which regulatory and supervisory policies stimulate private investors’ monitoring on banks, the weaker is income smoothing. Besides, the third column shows that the interaction term EBTP * HIGH_REG is not statistically significant. Therefore, in contrast with Gebhardt and Novotny-Farkas (2011), evidence does not support an effect of stringency of rules on regulatory capital on bank income smoothing. Table 4.5 also provides results on the moderation effect of listing on the association between supervisory features and banks’ income smoothing. Being the coefficients of the interaction terms EBTP * LIST * HIGH_MON and EBTP * LIST * HIGH_REG not statistically significant, it is to conclude that the listing status has no effect of moderation on the association between private monitoring or stringency of rules on regulatory capital and income smoothing. Table 4.6 provides results for the estimation of model (4.5), which has been run only for the period 2011–2015. The first column of the table provides a re-estimation of the effect of supervisory strictness and banks’ income smoothing over this timeframe. No significant association is found between the level of official supervisory power and the banks’ attitude for earnings smoothing. The second column provides estimation of model (4.5). Interestingly, the coefficient of the interaction term EBTP * HIGH_OFF * GOOD_PERF is positive and statistically significant,

4.4 Results of the Empirical Analysis

69

Table 4.5 Private monitoring, regulatory capital stringency and bank income smoothing LLP EBTP LIST EBTP * LIST HIGH_MON EBTP * HIGH_MON LIST * HIGH_MON EBTP * LIST * HIGH_MON HIGH_REG EBTP * HIGH_REG LIST * HIGH_REG EBTP * LIST * HIGH_REG CLoans LLA CAP GDP_GR Year Dummies Intercept Adjusted R-squared # Observations

Private monitoring 0.0028*** 0.0019*** 0.0054* 0.0013 0.0010 0.0004 0.0025** 0.0028** 0.0016 0.0004

Regulatory capital 0.0009 0.0011* 0.0056* 0.0034

0.0004 0.0009

0.0042* 0.0499*** 0.0030 0.0261*** Yes 0.0056*** 0.6354 2181

0.0045* 0.0518*** 0.0029 0.0262*** Yes 0.0041** 0.6357 2181

0.0044** 0.0525*** 0.0025 0.0273*** Yes 0.0051*** 0.6500 2286

0.0004 0.0009 0.0025 0.0026 0.0045** 0.0540*** 0.0024 0.0274*** Yes 0.0035** 0.6505 2286

*, **, *** denote, respectively, significance at the 10%, 5%, and 1% level Table 4.6 Supervisory strictness and bank resilience LLP EBTP HIGH_OFF GOOD_PERF EBTP * HIGH_OFF EBTP * GOOD_PERF HIGH_OFF * GOOD_PERF EBTP * HIGH_OFF * GOOD_PERF CLoans LLA CAP GDP_GR Year Dummies Intercept Adjusted R-squared # Observations

Banking system resilience 0.0060 0.0581 (Omitted) (Omitted) (Omitted) 0.0050 0.0588* 0.0585* (Omitted) 0.0605* 0.0054 0.0045 0.0691 0.0778*** 0.0001 0.0001* 0.0024 0.0024 Yes Yes 0.0112*** 0.0112*** 0.7076 0.7447 971 840

*, *** denote, respectively, significance at the 10% and 1% level

indicating that income smoothing is higher in countries characterised by higher resilience of the banking industry. This finding is consistent with the idea that a smooth earning path signals bank stability and that, aiming at stability, supervisors

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4 Supervisory Characteristics and Income Smoothing: The Case of European Banks

welcome provisioning systems that seem in line with smoothing habits. Indeed, they welcome the recognition of larger provisions during boom periods, because this allows creating hidden reserves that can be used as buffers against future credit losses (Benston & Wall, 2005); such a dynamic supports bank in facing recession periods as they can exploit hidden reserves when credit risk materialises thus reducing procyclicality (Gray & Clarke, 2004; Jin et al., 2018).

References Ahmed, A. S., Takeda, D., & Thomas, S. (1999). Bank loan loss provisions: A reexamination of capital management, earnings management and signaling effects. Journal of Accounting and Economics, 28, 1–25. Barth, J. R., Caprio, G., & Levine, R. (2008a). Bank regulations are changing: For better or worse? Comparative Economic Studies, 50(4), 537–563. Barth, M. E., Landsman, W. R., & Lang, M. H. (2008b). International accounting standards and accounting quality. Journal of Accounting Research, 46(3), 467–498. Barth, J. R., Caprio, G., & Levine, R. (2013a). Bank regulation and supervision in 180 countries from 1999 to 2011. Journal of Financial Economic Policy, 5(2), 112–219. Barth, J. R., Lin, C., Ma, Y., Seade, J., & Song, F. M. (2013b). Do bank regulation, supervision and monitoring enhance or impede bank efficiency? Journal of Banking and Finance, 37(8), 2879–2892. Beatty, A., & Harris, D. G. (1999). The effects of taxes, agency costs and information asymmetry on earnings management: A comparison of public and private firms. Review of Accounting Studies, 4(3), 299–326. Beatty, A., & Liao, S. (2014). Financial accounting in the banking industry: A review of the empirical literature. Journal of Accounting and Economics, 58(2), 339–383. Beatty, A., Chamberlain, S. L., & Magliolo, J. (1995). Managing financial reports of commercial banks: The influence of taxes, regulatory capital, and earnings. Journal of Accounting Research, 33(2), 231–261. Beatty, A. L., Ke, B., & Petroni, K. R. (2002). Earnings management to avoid earnings declines across publicly and privately held banks. The Accounting Review, 77(3), 547–570. Beaver, W. H., & Engel, E. E. (1996). Discretionary behavior with respect to allowances for loan losses and the behavior of security prices. Journal of Accounting and Economics, 22(1–3), 177–206. Benston, G. J., & Wall, L. D. (2005). How should banks account for loan losses. Journal of Accounting and Public Policy, 24(2), 81–100. Bikker, J. A., & Metzemakers, P. A. (2005). Bank provisioning behaviour and procyclicality. Journal of International Financial Markets, Institutions and Money, 15(2), 141–157. Buckmaster, D. A. (2001). Development of the income smoothing literature, 1893–1998: A focus on the United States (Vol. 4). Elsevier. Callahan, C., Lee, C. M., & Yohn, T. (1997). Accounting information and bid-ask spreads. Accounting Horizons, 11, 50–60. Chaney, P. K., Jeter, D. C., & Lewis, C. M. (1998). The use of accruals in income smoothing: A permanent earnings hypothesis. Advances in Quantitative Analysis of Finance and Accounting, 6, 103–135. Copeland, R. M. (1968). Income smoothing. Journal of Accounting Research, 6, 101–116. Di Fabio, C. (2019). Does the business model influence income smoothing? Evidence from European banks. Journal of Applied Accounting Research, 20(3), 311–330.

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Di Fabio, C., Ramassa, P., & Quagli, A. (2021). Income smoothing in European banks: The contrasting effects of monitoring mechanisms. Journal of International Accounting, Auditing and Taxation, 43, 100385. Fonseca, A. R., & González, F. (2008). Cross-country determinants of bank income smoothing by managing loan-loss provisions. Journal of Banking and Finance, 32(2), 217–228. García-Osma, B., Mora, A., & Porcuna, L. (2019). Prudential supervisors’ independence and income smoothing in European banks. Journal of Banking and Finance, 102, 156–176. Gebhardt, G. U., & Novotny-Farkas, Z. (2011). Mandatory IFRS adoption and accounting quality of European banks. Journal of Business Finance and Accounting, 38(3–4), 289–333. Gray, R. P., & Clarke, F. L. (2004). A methodology for calculating the allowance for loan losses in commercial banks. Abacus, 40(3), 321–341. Jin, J. Y., Kanagaretnam, K., & Liu, Y. (2018). Banks’ funding structure and earnings quality. International Review of Financial Analysis, 59, 163–178. Kanagaretnam, K., Lobo, G. J., & Mathieu, R. (2003). Managerial incentives for income smoothing through bank loan loss provisions. Review of Quantitative Finance and Accounting, 20(1), 63–80. Kanagaretnam, K., Lobo, G. J., & Yang, D. H. (2004). Joint tests of signaling and income smoothing through bank loan loss provisions. Contemporary Accounting Research, 21(4), 843–884. Kim, M. S., & Kross, W. (1998). The impact of the 1989 change in bank capital standards on loan loss provisions and loan write-offs. Journal of Accounting and Economics, 25(1), 69–99. Laeven, L., & Majnoni, G. (2003). Loan loss provisioning and economic slowdowns: Too much, too late? Journal of Financial Intermediation, 12(2), 178–197. Leuz, C., Nanda, D., & Wysocki, P. D. (2003). Earnings management and investor protection: An international comparison. Journal of Financial Economics, 69(3), 505–527. Moses, O. D. (1987). Income smoothing and incentives: Empirical tests using accounting changes. Accounting Review, 62, 358–377. Pérez, D., Salas-Fumas, V., & Saurina, J. (2008). Earnings and capital management in alternative loan loss provision regulatory regimes. European Accounting Review, 17(3), 423–445. Pinto, I., & Picoto, W. N. (2018). Earnings and capital management in European banks–Combining a multivariate regression with a qualitative comparative analysis. Journal of Business Research, 89, 258–264. Shen, C. H., & Chih, H. L. (2005). Investor protection, prospect theory, and earnings management: An international comparison of the banking industry. Journal of Banking and Finance, 29(10), 2675–2697. Wahlen, J. M. (1994). The nature of information in commercial bank loan loss disclosures. The Accounting Review, 69, 455–478. Young, S. (1998). The determinants of managerial accounting policy choice: Further evidence for the UK. Accounting and Business Research, 28(2), 131–143.

Chapter 5

Exploring the Role of Business Models

5.1

Linking the Business Model to Bank Smoothing Strategies

Recent research supports the idea that banks characterised by business models drawing more on traditional retail funding, namely customer deposits, and income diversification is likely to engage more in income smoothing than other business models (Di Fabio, 2019). Indeed, being income smoothing an accounting behaviour consistent with entities’ aim to show lower riskiness, findings of this research are overall consistent with literature suggesting that traditional funding and income diversification strategies implemented by retail banks are an integral part of a strategy characterised by risk aversion (Köhler, 2015). Additionally, evidence provided in the fourth chapter corroborates prior literature supporting the view that supervisory strictness is associated with income smoothing behaviours (Gebhardt & Novotny-Farkas, 2011; García-Osma et al., 2019; Di Fabio et al., 2021). This chapter aims to understand whether supervisory features have specifically an impact on accounting behaviour of banks characterised by certain business models. As prior evidence remarks the risk aversion of retail-funded business models, it could be argued that these banks would prefer meeting supervisory expectations; therefore, a first issue is whether stricter supervisory regimes enhance the propensity of retail-funded banks to exploit income smoothing. Additionally, prior research shows contrasting positions on the effects of regulation of banking activity. On the one hand, the reasons underpinning banking activity regulation draw on the belief that limiting banking activities to traditional activities (e.g. deposit taking and loans making) allows to limit also the conflicts of interest which may arise in case banks engage in securities underwriting, insurance underwriting, real estate investment, and owning non-financial firms. Restrictions on banking activities could also avoid the growth of large banks which might be difficult to monitor (Camdessus, 1997). On the other hand, literature maintains © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 C. Di Fabio, National Supervision and Income Smoothing in Banks’ Annual Reports, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-74011-5_5

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that a laxer regulation allows the development of more diversified banks and more efficient financial services. Evidence provided by Barth et al. (2004) supports a strong link to the regulatory environment. Indeed, context characterised by higher regulatory restrictions has a substantially higher probability of suffering a banking crisis. Restrictive regulatory environments limiting securities underwriting, brokering, dealing are associated to weaker financial systems. This view is supported by Laeven and Levine (2009), who find that restrictions on activity show a negative association with Z-score values. Consequently, it might be argued that banks operating in environments characterised by elevated restrictions on banking activities are overall less averse to risk than banks operating in context where regulation allows risks’ diversification improving banking system’s stability. Accordingly, a second issue is whether stricter regulatory regimes reduce the propensity of retail-funded banks to exploit income smoothing.

5.2

Methodological Approaches to the Identification of Bank Business Models: A Review

To identify the possible business models, the first step is the identification of possible strategies that could be reflected in the composition of balance sheet and income statement (Amel & Rhoades, 1988). Indeed, as in general assumed by banking literature, even if several non-financial variables, as distribution channels, types of clients and products, provide relevant information concerning the bank’s strategy, this information should ultimately be embedded in the balance sheet compositions. Literature dealing with banks’ business model or with its peculiar features (Demsetz & Strahan, 1997; Stiroh, 2004, 2006, 2010; Baele et al., 2007; Laeven & Levine, 2007; Valverde & Fernández, 2007; Demirgüç-Kunt & Huizinga, 2010; Altunbas et al., 2011; Ayadi et al., 2012; Ayadi & De Groen, 2014; Roengpitya et al., 2014; Köhler, 2015; Mergaerts & Vander Vennet, 2016) adopted basically two separate approaches for the identification of business model. The approach adopted more frequently is the individuation of either a single ratio or a set of ratios (representing singular features of the business model) based on the balance sheet and/or the income statement. A second and more recent approach derives from the need to provide a more comprehensive representation of business models. This approach exploits two distinct methodologies, namely cluster analysis and factor analysis. Cluster analysis is used by Ayadi et al. (2012), Ayadi and de Groen (2014) and Roengpitya et al. (2014). Indeed, based on prior literature, they develop indicators that proxy for the business model’s dimensions and then use cluster analysis to allocate banks of their sample to a specific cluster representing a business model. These studies consider the possibility that business model varies over time, in the medium and long term.

5.2 Methodological Approaches to the Identification of Bank Business Models: A. . .

75

Factor analysis is exploited only by Mergaerts and Vander Vennet (2016). This methodology draws on the assumption that business model is an underlying and latent strategy, whose outcomes are the observed variables (namely, selected indicators), which combines information from all variables and observation resulting in continuous common factor, instead of reaching binary group membership. They propose a static notion of business model, assumed to be invariant over time. The reminder of this paragraph provides a brief review of indicators used by prior literature to identify bank business model’s features, distinguishing between proxies derived from the income statement and proxies derived from the balance sheet. Concerning income-based indicators, the idea underlying their identification is that the income dimension captures the business mix as it is reflected by the income streams. A high level of non-interest income (i.e. income from trading activities, investment banking, brokerage fees and commissions) provides financial institutions with an additional source of revenue (Demirgüç-Kunt & Huizinga, 2010; Stiroh, 2010; Köhler, 2012) and makes banks less dependent on interest income. If, on the one hand, the diversification can improve stability in income, on the other hand, non-interest income tends to be more volatile and may destabilise banks. Indeed, Baele et al. (2007) show that systematic risk is positively related to the non-interest income share. Moreover, since regulators require banks to hold less capital to balance non-interest income activities, the leverage could be higher and raising earnings volatility further (DeYoung & Roland, 2001; Köhler, 2012). Therefore, banks need market stability to be able to benefit from diversifications’ advantages. However, literature has shown that not every type of non-interest income is equally volatile or conduces to distress (DeYoung & Torna, 2013). The most used proxy for the income structure is the non-interest income share, defined as non-interest income/income. Concerning balance sheet based indicators, usually literature considers the ratio of net loans to total earning assets (they include loans, securities and investments) to capture the extent to which a bank focusses on traditional intermediary activities, i.e. the transformation of liquid deposits into illiquid loans reflecting the role of a delegated monitor (Diamond, 1984; Mergaerts & Vander Vennet, 2016). More precisely, as Laeven and Levine (2007) remark, the ratio expresses the degree of specialisation in loan-making. They remark that very low values of these ratios signal that the bank specialises in non-lending activities rather than in loan-making. Mergaerts and Vander Vennet (2016) use the ratio of loan loss provisions to loans, considering it as a forward-looking measure of loan quality and a reflection of a bank’s own opinion of the quality of its loans. Loan loss provisions can, however, be used to smooth income and literature supports the idea that they could be distorted by forbearance, particularly during a financial crisis. Indeed, some studies prefer to consider the ratio of gross loans to total assets, to neutralise the effect of provisioning (Roengpitya et al., 2014). To capture the scale of wholesale and interbank activities, Ayadi and De Groen (2014) use loans to banks to total assets, used also by Roengpitya et al. (2014).

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To indicate investment activities exposed to market and liquidity risk, Ayadi and De Groen (2014) use trading assets to total assets, defined as trading book by Roengpitya et al. (2014). The funding structure captures the way the bank finances its activities. From a broad perspective, the funding structure differentiates between market-based financing and customer related financing through deposits (Demirgüç-Kunt & Huizinga, 2010; Bischof et al., 2011). Retail deposits tend to be more stable in period of crisis (Shleifer & Vishny, 2010; Altunbas et al., 2011); in fact, these deposits are usually protected by deposit insurance (Shleifer & Vishny, 2010; Köhler, 2012) and are usually related to liquidity needs of depositors (Song & Thakor, 2007; Altunbas et al., 2011; Huang & Ratnovski, 2011). On the opposite side, wholesale markets are more flexible in adapting to changing financing needs and opportunities. Moreover, they are usually characterised by a better discipline (Calomiris & Kahn, 1991). However, the 2008 crisis illustrated that market funding is dependent on market perceptions and literature casts doubts on the monitoring role of the wholesale investors (Huang & Ratnovski, 2008); furthermore, more recent research reveals that markets appreciate institutions mostly relying on customer deposits, especially when market funding become unavailable (Demirgüç-Kunt & Huizinga, 2010; Altunbas et al., 2011; Beltratti & Stulz, 2012). The ratio of deposits to liabilities represents the reliance on traditional customer deposits, and is used by Mergaerts and Vander Vennet (2016) and Roengpitya et al. (2014). Similarly, customer deposits to total assets is the indicator used by Ayadi and de Groen (2014) to identify the share of deposits from non-bank and private customers (households or enterprises) indicating a reliance on more traditional funding sources, while interbank borrowing represents the share of interbank liabilities and is measured as deposits from banks plus repos and cash collaterals (Roengpitya et al., 2014).

5.3

Cluster Analysis and Emerging Business Models

This book proposes cluster analysis as a methodology to classify banks into different business models; the procedure here employed shares many aspects with previous research on the topic. The cluster analysis performed in this chapter uses the sample of banks that has been already employed in the empirical analysis presented in the previous chapter. Hence, the sample consists of 1103 banks from the European Union (EU28) over the timeframe 2003–2015 (initial 14,339 bank-year observations). As before mentioned, financial data on consolidated financial statements used as inputs for the models are obtained from the BankScope database. To classify business models, in this chapter indicators built on data from the banks’ balance sheet are used as inputs of the cluster analysis. The unit of analysis is a bank in a given year. Therefore, in this methodological setting, the bank could—in theory—change its business model from 1 year to

5.3 Cluster Analysis and Emerging Business Models

77

Table 5.1 Clustering variables Clustering variables

Name LOANS TRADING BOOK INTERCONNECTION CUSTOMERS DEBT CUSTOMER_DEP DER_LIAB

Description Gross loans to total assets Assets measured at fair value through income scaled by total assets Loans and advance to banks scaled by total assets Loans to customers scaled by total assets Long-term funding scaled by total assets Sum of total customer deposits scaled by total assets Derivative liabilities scaled by total assets

another. This approach is also the one adopted by Roengpitya et al. (2014)1 and Di Fabio (2019). Inputs to the cluster analysis are banks’ features, namely the ratios derived from the balance sheet, as these ratios can be interpreted as reflective of the strategic management decisions. Besides, the procedure does not include variables on income composition and profitability, since it assumes that these are the result of the interaction between the management strategy and the environment in which the bank operates. This study exploits cluster analysis, particularly using partitional clustering.2 This method allows the division of the set of data into distinct clusters (i.e. business models) which are non-overlapping partitions. Observations ascribed to the same cluster share a certain level of similarity, while clusters are sufficiently distinct among themselves. Particularly, the analysis exploits K-means clustering, which requires to specify the number of clusters to extract. Inputs for the analysis are (Table 5.1) as follows: • the importance of traditional activities (calculated as gross loans to total assets); • the size of trading book (assets measured at fair value through income scaled by total assets); • the amount of loans to banks (loans and advance to banks scaled by total assets); • the size of loans to customers (calculated as the sum of retail loans, mortgage loans and residential loans); • the importance of long-term funding (long-term funding scaled by total assets); • the importance of customer deposits (customer deposits scaled by total assets); • derivative liabilities (derivative liabilities scaled by total assets). Table 5.2 shows the descriptive statistics for the indicators used as inputs for the cluster analysis. This analysis uses the Calinski & Harabasz pseudo-F index to determine the appropriate number of clusters. The index is a sample estimate of the ratio of between-cluster variance to within-cluster variance. The number of clusters 1

They use a sample of 222 individual banks from 34 countries over the period 2005–2013. This method differs from hierarchical clustering, which consists of nested clusters organized as a tree. 2

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Table 5.2 Descriptive statistics of clustering variables Clustering variable LOANS TRADING BOOK INTERCONNECTION CUSTOMERS DEBT CUSTOMER_DEP DER_LIAB Table 5.3 Calinski/Harabasz pseudo-F

Mean 0.561 0.089 0.144 0.268 0.153 0.477 0.034

Std. Dev. 0.257 0.147 0.162 0.254 0.185 0.268 0.065

Number of clusters 2 3 4 5 6 7 8 9 10

Min. 0.000 0.000 0.001 0.000 0.000 0.000 0.000

Max. 0.995 0.811 0.774 0.887 0.857 0.923 0.382

Observations 8864 7683 8542 3553 7588 8413 6445

Calinski/Harabasz pseudo-F 737.34 804.98*** 729.68 686.84 715.32 671.45 644.23 603.58 582.71

*** denote significance, respectively, at 1% level

associated to the highest pseudo-F is chosen as the better clustering, as clusters appear more distinct. In this case, the pseudo-F reaches its maximum in case of three clusters (Table 5.3). The cluster analysis individuates three distinct business models. Table 5.4 presents the three business models, providing the average ratios of each clustering indicator for all the banks classified in the same cluster and adding three additional indicators for improving the comprehension of the three clusters’ characteristics. The first business model group is labelled ‘retail-funded’, and it is characterised by a high reliance on customers’ deposits for funding. This is the largest group in the universe and presents 1247 bank/year observations over the period. Customer deposits fund diversified activities. The traditional book is the 63.26% of total assets, 9.66% of total assets consist of loans and advances to banks, while there is a moderate activity in terms of trading and a considerable recourse to off-balance sheet items. Income diversification is relatively high for retail banks (71.51%), as prior literature finds non-interest income to income ranging from 15 to 25% (Köhler, 2015). The second business model is labelled ‘market-oriented’, as it presents the highest share of trading assets (accounting for 17.86% of total assets) and the greatest reliance on derivative liabilities. Accordingly, they show the highest level of income diversification, as the ratio of non-interest income to interest income is 212.36%. These banks present also a good degree of connections with the interbank market, for both funding purposes (19.64%) and activities (13.93%). Overall, this cluster has a significant diversification of funding sources.

5.4 Models and Variables

79

Table 5.4 Three distinct clusters

Variable type Clustering indicator

Indicators LOANS TRADING BOOK INTERCONNECTION CUSTOMERS INTERBANK_LIAB DEBT CUSTOMER_DEP DER_LIAB Others Diversification (Diversification is calculated as non-interest income on interest income) ROE No. of observations % Observations

Business models BM1 BM2 (Retail (Market funded) oriented) % % 63.26 42.77 4.70 17.86 9.66 13.93 16.13 11.67 10.23 19.64 8.18 17.93 63.30 28.15 1.80 8.45 71.51 212.36

42.23 1247 51.92

14.83 626 26.06

BM3 (Retail business) % 77.56 3.53 5.12 60.13 17.78 18.39 47.17 1.75 50.35

6.19 529 22.02

2402 100.00

The third cluster is labelled ‘retail-business’. This cluster corresponds to the cluster of ‘diversified-retail’ banks presented by Ayadi and De Groen (2014). This business model is characterised by the highest share of loans to costumers (60.13% of total assets), but funding is quite diversified among interbank liabilities and customer deposits. It relies only marginally on derivative liabilities. This is the smallest group with 529 bank/year observations (22.02% of total observations). These clusters are exploited as variables in the following analysis.

5.4

Models and Variables

We seek to understand whether supervisory features have an impact especially on accounting behaviour of banks characterised by specific business models by running the models (5.1) and (5.2). Model (5.1) tests the influence of supervisory strictness on retail-funded bank income smoothing:

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LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 D BM1i,t þ β3 HIGH OFFi,t þ β4 EBTPi,t  D BM1i,t þ β5 EBTPi,t  HIGH OFFi,t þ β6 D BM1i,t  HIGH OFFi,t þ β7 EBTPi,t  D BM1i,t  HIGH OFFi,t þ β8 CLoansi,t þ β9 LLAi,t1 þ β10 CAPi,t þ β11 GDP GRt X T t þ εt þ β12 ð5:1Þ where LLPi, t is loan loss provisions reported by the bank i in year t divided by total assets at the beginning of the same year. The term EBTPi, t represents earnings before taxes and provisions at year t scaled by total assets at the beginning of the same year. D_BM1i, t represent the dummy variable indicating banks characterised by a retail-funded business model and it is obtained from the cluster analysis explained in this chapter. HIGH_OFF is a dummy variable (already used in the fourth chapter) which equals 1 if the country is characterised by a degree of Official Supervisory Power above median and 0 otherwise. It is worth recalling that Official Supervisory Power expresses whether the national supervisory authority has the authority to undertake specific actions to prevent and correct problems. The two explanatory variables CLoansi, t and LLAi, t  1 control for non-discretionary part of loan loss provisions. Particularly, CLoansi, t is the change of loans from year t  1 to year t, scaled by total assets at the beginning of year t, and LLAi, t  1 is the total loan loss allowance at the beginning of the year t scaled by total assets at the beginning of the same year. Following previous literature, CAPi, t (calculated as the bank capital scaled by risk weighted assets, namely TIER 1) controls for capital management issue. The growth of GDP from year t  1 to year t(GDP_GRt) is intended to control for procyclical effect of provisioning. The model includes a set of dummy time variables ∑Tt controlling for time effects that are bank invariant. Additionally, the model controls for banks’ fixed effects. The coefficient of interest in Model (4.1) in order to test the first hypothesis is the coefficient on the variable built as the triple interaction between EBTPi, t, D_BM1i, t and HIGH_OFFi, t which expresses the variation of the intensity of association between provisions and pre-managed earnings for retail-funded banks in context characterised by high levels of official supervisory power if compared to the level of income smoothing characterising other business models in contexts of lower supervisory power. This coefficient represents the effect of moderation of the supervisory variable on the association between business model and the banks’ propensity to smooth earnings. Particularly, this coefficient is expected to be positive and significant. Model (5.2) tests the influence of restrictions to banking activities on retailfunded bank income smoothing:

5.4 Models and Variables

81

LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 D BM1i,t þ β3 HIGH RESTRICTi,t þ β4 EBTPi,t  D BM1i,t þ β5 EBTPi,t  HIGH RESTRICTi,t þ β6 D BM1i,t  HIGH RESTRICTi,t þ β7 EBTPi,t  D BM1i,t  HIGH RESTRICTi,t þ β8 CLoansi,t þ β9 LLAi,t1 þ β10 CAPi,t X T t þ εt þ β11 GDP GRt þ β12 ð5:2Þ where all the variables have been previously defined, except HIGH_RESTRICTi, t which is a dummy variable taking the value 1 if the country is characterised by a level of Overall Restrictions on Banking Activities above median and 0 otherwise. The level of Overall Restrictions on Banking Activities expresses the extent to which banks are allowed to engage in underwriting, brokering and dealing in securities, to engage in insurance underwriting and selling and investment, development and management in the real estate sector. The model includes a set of dummy time variables ∑Tt controlling for time effects that are bank invariant; the model also controls for banks’ fixed effects. To test the second hypothesis, the coefficient of interest is the coefficient of the interaction variable between EBTPi, t, D_BM1i, t and HIGH_RESTRICTi, t. This coefficient represents the variation of the intensity of association between provisions and pre-managed earnings for retail-funded banks in context characterised by strict regulation on banking activities if compared to the level of income smoothing characterising other business models in contexts of laxer activities’ regulation. Accordingly, this coefficient represents the effect of moderation of the regulatory variable on the association between business model and banks’ income smoothing attitude. This coefficient is expected to be negative and significant. To assess if results from Model (5.1) are robust, the following model is run: LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 D BM1i,t1 þ β3 HIGH OFFi,t þ β4 EBTPi,t  D BM1i,t1 þ β5 EBTPi,t  HIGH OFFi,t þ β6 D BM1i,t1  HIGH OFFi,t þ β7 EBTPi,t  D BM1i,t1  HIGH OFFi,t þ β8 CLoansi,t þ β9 LLAi,t1 þ β10 CAPi,t X T t þ εt þ β11 GDP GRt þ β12

ð5:3Þ

All the variables are above defined, except D_BM1i, t  1 that is the lag of the variable indicating retail-funded business models. This model serves the purpose to isolate the business model variable from the effect of earnings management in year t. Moreover, Model (5.4) is run to verify robustness of results from model (5.2):

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Table 5.5 Overall descriptive statistics Variable LLP EBTP CLoans LLA CAP GDP_GR Official Supervisory Power HIGH_OFF Overall Restriction on Banking Activities HIGH_RESTR

Mean 0.0070 0.1335 0.0442 0.0237 16.8421 0.0349 10.1446 0.4655 5.8634 0.4827

Std. Dev. 0.0119 0.6764 0.1228 0.0314 9.7705 0.1035 1.9955 0.4988 1.0637 0.4997

Q1 0.0008 0.0026 0.0124 0.0047 11.8 0.0450 8.5 0 5 0

Median 0.0028 0.0110 0.0210 0.0142 14.31 0.0473 10 0 6 0

Q3 0.0076 0.0441 0.0745 0.0282 17.9 0.1025 11 1 6 1

Table 5.6 Correlations Panel A Variables LLP LLP 1.0000 EBTP 0.1234 CLoans 0.0059 LLA 0.5344 CAP 0.1168 GR_GDP 0.0026 Panel B Variables HIGH_OFFi, t HIGH_RESTRi, t

EBTP

CLoans

LLA

CAP

GR_GDP

1.0000 0.0757 0.0546 0.0302 0.0098

1.0000 0.1942 0.2459 0.0513

1.0000 0.0784 0.0083

1.0000 0.0771

1.0000

HIGH_OFFi, t 1.0000 0.1038

HIGH_RESTRi, t 1.0000

LLPi,t ¼ β0 þ β1 EBTPi,t þ β2 D BM1i,t1 þ β3 HIGH RESTRICTi,t þ β4 EBTPi,t  D BM1i,t1 þ β5 EBTPi,t  HIGH RESTRICTi,t þ β6 D BM1i,t1  HIGH RESTRICTi,t þ β7 EBTPi,t  D BM1i,t1  HIGH RESTRICTi,t þ β8 CLoansi,t X T t þ εt þ β9 LLAi,t1 þ β10 CAPi,t þ β11 GDP GRt þ β12

ð5:4Þ

Table 5.5 provides overall descriptive statistics. These statistics include also data on the index of supervision/regulation used to build the dummy variables HIGH_OFF/HIGH_RESTR exploited in the models. Table 5.6 shows correlations between variables used in models.

5.5 Results of the Empirical Analysis

5.5

83

Results of the Empirical Analysis

The second column of Table 5.7 shows results of estimations already presented in the previous chapter. From these results, retail-funded business models reveal a superior attitude to engage in income smoothing if compared to other business models. This is confirmed by the coefficient of the interaction term EBTP*D_BM1, which is positive and statistically significant at the 1% level. In order to test the first hypothesis, predicting that stronger supervisory authorities increase the propensity of retail-funded banks to engage in income smoothing behaviour, Model (5.1) is run and results are provided in the second column of Table 5.7. The coefficient of the variable EBTP*HIGH_OFF*D_BM1 is positive and statistically significant at the 5% level. This indicates a significant positive moderation effect of supervisory strictness on the propensity of retail-funded business models to smooth earnings. Controls variables have the predicted sign, except the variation in the total amounts of loans outstanding, which show a negative and statistically significant coefficient. Finally, the negative and statistically significant coefficient of the term GDP_GR confirms the procyclical effect of provisioning. Results of the estimation of Model (5.1) are fully consistent with prior literature arguing that risk aversion can be a motivation for banks to engage in income Table 5.7 Results of estimations of Model (5.1) and Model (5.2) LLPi, t EBTPi, t D_BM1i, t EBTP * D_BM1i, t HIGH_OFFi, t EBTP * HIGH_OFFi, t HIGH_OFF * D_BM1i, t EBTP*HIGH_OFF * D_BM1i, t HIGH_RESTRi, t EBTP * HIGH_RESTRi, t HIGH_RESTR * D_BM1i, t EBTP * HIGH_RESTR * D_BM1i, t CLoansi, t LLAi, t  1 CAPi, t GDP_GRt Year Dummies Intercept Adjusted R-squared # Observations

Basic model 0.0004 0.0002 0.0355***

0.0021 0.0566** 0.0000 0.0354*** Yes 0.0094* 0.6813 820

Supervisory strictness 0.0073 0.0003 0.0062 0.0045** 0.0089 0.0001 0.0328**

0.0114** 0.0332* 0.0000 0.0407*** Yes 0.0124** 0.6925 714

*, **, *** denote significance, respectively, at 10%, 5%, and 1% level

Activity restriction 0.0024 0.0002 0.0438***

0.0016 0.0097 0.0018 0.0342** 0.0026 0.0359 0.0000 0.0348 Yes 0.0032 0.6779 723

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5 Exploring the Role of Business Models

Table 5.8 Results of estimations of Model (5.3) and Model (5.4) LLPi, t EBTPi, t D_BM1i, t  1 EBTP*D_BM1i, t  1 HIGH_OFFi, t EBTP * HIGH_OFFi, t HIGH_OFF * D_BM1i, t  1 EBTP * HIGH_OFF * D_BM1i, t  1 HIGH_RESTRi, t EBTP * HIGH_RESTRi, t HIGH_RESTR * D_BM1i, t  1 EBTP * HIGH_RESTR * D_BM1i, t  1 CLoansi, t LLAi, t  1 CAPi, t GDP_GRt Year Dummies Intercept Adjusted R-squared # Observations

Basic model 0.0011 0.0004 0.0325***

0.0006 0.0703*** 0.0000 0.0338*** Yes 0.0157* 0.6846 775

Supervisory strictness 0.0049 0.0003 0.0335 0.0037* 0.0070 0.0013 0.0002

0.0113** 0.0544** 0.0000 0.0386*** Yes 0.0169** 0.6940 676

Activity restriction 0.0039 0.0003 0.0406*** 0.0007

0.0007 0.0096 0.0004 0.0318** 0.0015 0.0515** 0.0000 0.0340*** Yes 0.0022 0.6776 684

*, *, *** denote significance, respectively, at 10%, 5%, and 1% level

smoothing and that this motivation can be even more compelling in strict regimes. The results provided in Table 5.8 indicate that Model (5.3), which uses the lagged dummy variable representing the business model as inferable from the prior year’s financial statement, weakly confirms this result. To assess whether regulation of banking activity amplifies retail-funded banks recourse to income smoothing, Model (5.2) is run. Results are presented in the third column of Table 5.7. From these results, there is evidence that stricter regulatory regimes diminish the propensity of retail-funded banks to engage in income smoothing, as the coefficient of the term EBTP*HIGH_RESTR*D_BM1 is negative and statistically significant at the 5% level. This finding supports a negative effect of moderation of the banking activity regulation on the association between retailfunded business models and earnings smoothing behaviours. Findings of Model (5.2) are supported by results of Model (5.4) estimation, presented in Table 5.8. Indeed, the term representing the interaction between pre-managed earnings, retail-funded business models and high level of banking activity regulation reveals a negative and statistically significant coefficient (5% level).

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References Altunbas, Y., Manganelli, S., & Marques-Ibanez, D. (2011). Bank risk during the financial crisis: Do business models matter? ECB working paper 1394, pp. 1–51. Available on the internet at: https://www.econstor.eu/bitstream/10419/153828/1/ecbwp1394.pdf Amel, D. F., & Rhoades, S. A. (1988). Strategic groups in banking. The Review of Economics and Statistics, 70, 685–689. Ayadi, R., & De Groen, W. P. (2014, October 14). Banking business models monitor 2014: Europe. CEPS paperbacks. Ayadi, R., Arbak, E., De Groen, W. P., & Llewellyn, D. T. (2012, June). Regulation of European banks and business models: Towards a new paradigm? CEPS paperbacks. Baele, L., De Jonghe, O., & Vander Vennet, R. (2007). Does the stock market value bank diversification? Journal of Banking and Finance, 31(7), 1999–2023. Barth, J. R., Caprio, G., Jr., & Levine, R. (2004). Bank regulation and supervision: What works best? Journal of Financial Intermediation, 13(2), 205–248. Beltratti, A., & Stulz, R. M. (2012). The credit crisis around the globe: Why did some banks perform better? Journal of Financial Economics, 105(1), 1–17. Bischof, J., Daske, H., & Gebhardt, G. (2011). Fair value accounting and the business model of banks. Working paper. Calomiris, C. W., & Kahn, C. M. (1991). The role of demandable debt in structuring optimal banking arrangements. The American Economic Review, 87, 497–513. Camdessus, M. (1997). The challenges of a sound banking system. In C. Enoch & J. H. Green (Eds.), Banking soundness and monetary policy (pp. 535–539). International Monetary Fund. Demirgüç-Kunt, A., & Huizinga, H. (2010). Bank activity and funding strategies: The impact on risk and returns. Journal of Financial Economics, 98(3), 626–650. Demsetz, R. S., & Strahan, P. E. (1997). Diversification, size, and risk at bank holding companies. Journal of Money, Credit, and Banking, 29, 300–313. DeYoung, R., & Roland, K. P. (2001). Product mix and earnings volatility at commercial banks: Evidence from a degree of total leverage model. Journal of Financial Intermediation, 10(1), 54–84. DeYoung, R., & Torna, G. (2013). Nontraditional banking activities and bank failures during the financial crisis. Journal of Financial Intermediation, 22(3), 397–421. Di Fabio, C. (2019). Does the business model influence income smoothing? Evidence from European banks. Journal of Applied Accounting Research, 20(3), 311–330. Di Fabio, C., Ramassa, P., & Quagli, A. (2021). Income smoothing in European banks: The contrasting effects of monitoring mechanisms. Journal of International Accounting, Auditing and Taxation, 43, 100385. Diamond, D. W. (1984). Financial intermediation and delegated monitoring. The Review of Economic Studies, 51(3), 393–414. García-Osma, B., Mora, A., & Porcuna, L. (2019). Prudential supervisors’ independence and income smoothing in European banks. Journal of Banking and Finance, 102, 156–176. Gebhardt, G. U., & Novotny-Farkas, Z. (2011). Mandatory IFRS adoption and accounting quality of European banks. Journal of Business Finance and Accounting, 38(3–4), 289–333. Huang, R., & Ratnovski, L. (2008). The dark side of wholesale bank funding. Working paper. Federal Reserve Bank of Philadelphia. Huang, R., & Ratnovski, L. (2011). The dark side of bank wholesale funding. Journal of Financial Intermediation, 20(2), 248–263. Köhler, M. (2012). Which banks are more risky? The impact of loan growth and business model on bank risk-taking. Discussion Paper Deutsche Bundesbank No 33/2012, pp. 1–64. Köhler, M. (2015). Which banks are more risky? The impact of business models on bank stability. Journal of Financial Stability, 16, 195–212. Laeven, L., & Levine, R. (2007). Is there a diversification discount in financial conglomerates? Journal of Financial Economics, 85(2), 331–367.

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Laeven, L., & Levine, R. (2009). Bank governance, regulation and risk taking. Journal of Financial Economics, 93(2), 259–275. Mergaerts, F., & Vander Vennet, R. (2016). Business models and bank performance: A long-term perspective. Journal of Financial Stability, 22, 57–75. Roengpitya, R., Tarashev, N., & Tsatsaronis, K. (2014). Bank business models. Available at: https:// papers.ssrn.com/sol3/papers.cfm?abstract_id¼2535972 Shleifer, A., & Vishny, R. W. (2010). Unstable banking. Journal of Financial Economics, 97(3), 306–318. Song, F., & Thakor, A. V. (2007). Relationship banking, fragility, and the asset-liability matching problem. The Review of Financial Studies, 20(6), 2129–2177. Stiroh, K. J. (2004). Do community banks benefit from diversification? Journal of Financial Services Research, 25(2), 135–160. Stiroh, K. J. (2006). A portfolio view of banking with interest and noninterest activities. Journal of Money, Credit and Banking, 38, 1351–1361. Stiroh, K. J. (2010). Diversification in banking. In The Oxford handbook of banking (pp. 90–111). Oxford University Press. Valverde, S. C., & Fernández, F. R. (2007). The determinants of bank margins in European banking. Journal of Banking and Finance, 31(7), 2043–2063.

Chapter 6

Conclusions

This volume focusses on European banks’ annual reports to understand whether bank accounting behaviours, and specifically income smoothing through loan loss provisions, can be explained by the key features of national supervision. In doing so, it also discusses and empirically tests the role of business model. The empirical analyses presented indicate that, during the 2003–2015 timeframe, income smoothing is an accounting behaviour diffused across examined banks and particularly exploited by unlisted banks. Banks exhibit higher smoothing propensity under strict regimes, in which they try to avert supervisors’ eye and avoid supervisory corrections and penalties. Indeed, unusually high/low levels of income can typically attract regulatory attention, and banks can decide to cook the numbers to signal low riskiness using a provisioning strategy apparently in line with supervisors’ preferences. This result is consistent with the evidence found by prior research (Gebhardt & Novotny-Farkas, 2011; García-Osma et al., 2019; Di Fabio, 2019; Di Fabio et al., 2021). Independence of the supervisory authority from the banking industry reduces smoothing, in line with findings of García-Osma et al. (2019). In addition, when the supervisor is highly independent, the listing status has a constraining effect on this accounting behaviour. Further, the findings show that income smoothing is higher in countries characterised by resilience of the banking industry due to higher supervisory effectiveness. This finding is consistent with the idea that a smooth earning path signals bank stability and supervisors welcome provisioning systems that seem in line with smoothing habits (Peterson & Arun, 2018). Concerning the role assigned by regulation to other subjects exerting monitoring activities, evidence indicates that auditors’ influential role in the scope of supervisory activities reduces bank income smoothing, thus positively contributing to transparency in annual reports. External governance exerted by private investors reduces bank income smoothing, suggesting that the involvement of forms of monitoring alternative to the national authority helps favouring accounting transparency (Fonseca & González, 2008).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 C. Di Fabio, National Supervision and Income Smoothing in Banks’ Annual Reports, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-74011-5_6

87

88

6

Conclusions

With specific reference to the role of business models, results show that banks characterised by retail-funded business models exhibit higher smoothing propensity if compared to other business models. This attitude is in line with previous evidence on the topic (Di Fabio, 2019) and seems amplified by supervisory strictness, consistent with the idea that risk aversion of retail-funded banks can represent a motivation to smooth earnings and that this motivation can be even more compelling in strict regimes; under strict authorities, risk-averse banks could be even more concerned about showing a stable earning path to supervisors. Finally, tighter regulatory regimes limit the propensity of these banks to engage in income smoothing, thus supporting a negative moderation effect of the banking activity regulation. The empirical findings presented in this book contribute to the literature in several ways. First, results extend research on the relationship between banking supervision and bank earnings management by providing evidence of the way in which distinct features of the multifaceted supervisory system influence banks’ propensity to smooth income. The analyses employ a common dataset; thus the findings offer a broad overview of the effects of different supervisory features enabling also to compare them. In addition, the study provides evidence on the effect of external auditors’ involvement in supervisory activities on bank income smoothing, which has been so far almost overlooked by prior literature. Besides, the findings extend research on the effects of bank regulations, offering further empirical support to the view that tightening restrictions to banking activities exerts a constraining effect on smoothing behaviours of retail-funded banks. While disentangling the effect of different supervisory features, this study seeks to go beyond the use of variables based on the law-on-the-books and questionnaires and develops a measure based on the results of the supervisory activity using data of the 2013–2014 Comprehensive Assessment performed by the ECB. This measure is employed in the empirical analyses to identify countries characterised by resilience of the banking industry due to higher effectiveness of supervisors. Finally, this work contributes to the role of the business model in explaining income smoothing in two main ways. First, in line with recent studies (Di Fabio, 2019), it addresses the role of the business model as a whole in explaining smoothing propensity, not limiting the observation to its partial features. Second, it highlights that two, not yet explored, institutional features—i.e. supervisory strictness and restrictions to banking activities—moderate the propensity of risk-averse business models to smooth income. The findings of the analyses provided in this volume are of interest to standard setters and European regulators. First, they suggest that regulators and supervisors should adequately assess the actual effects of their decisions on bank accounting behaviours, considering that the characteristics of national authorities and the extent to which regulation assigns monitoring powers to other subjects do affect transparency of bank annual reports. The role of transparency of information diffused by banks is crucial and complementary to bank supervision in ensuring market discipline (Rochet, 1992; Cordella & Yeyati, 1998; Bushman, 2016), through which market participants monitor banks’ excessive risk-taking. From this perspective, if the features of the supervisory system favour manipulative behaviours, this lowers

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transparency and undermines market participants’ ability to monitor bank behaviours. In the European context, the implications of monitoring structures on discretion in estimating loan loss provisions for opportunistic purposes should be particularly considered in the light of the recent application of IFRS 9 mandating an expectedloss approach, which allows prompter recognition of loan losses but also implies more discretion in estimating provisions (Hashim et al., 2016). Supervisors, who have long stressed the appropriateness of this new approach, should consider that it could provide managers greater income smoothing opportunities (Novotny-Farkas, 2016) that could be exploited, particularly by banks under strict supervisory regimes. From this perspective, such a change could represent a double-edged sword (Bushman, 2016). A useful strategy to preserve information transparency could employ regulatory tools to balance the supervisory power with adequate levels of other monitoring levers. In addition, evidence on the broad use of provisions for smoothing purposes indicates that prudential and accounting regulators should devote attention to requirements on provisions-related disclosure. As remarked by Peterson and Arun (2018) and Di Fabio et al. (2021), improving this type of disclosure could favour reliability of data provided, eventually assisting bondholders and stockholders in assessing loan portfolio quality. From a regulatory perspective, evidence on the relevance of the business model to explain accounting behaviour remarks the key part played by the business model analysis in understanding the entities’ reporting incentives and the reasons that explain manipulative practices in annual reports. In particular, the positive association between supervisory strictness and retail-funded banks’ income smoothing deserves attention; regulation strengthening monitoring subjects could produce the unintended effect to induce accounting manipulations in banks connected by riskaverse business models. Empirical results should be considered in the light of certain limitations. For instance, the analyses do not consider internal governance and ownership-related variables, while these firm-level characteristics could affect monitoring’s influence on banks’ income smoothing. In this respect, there is room for future research exploring the effects of corporate governance mechanisms and identifying other institutional variables affecting smoothing behaviours. More generally, future empirical research could benefit from the methodology for identifying bank business models explained in this volume.

References Bushman, R. M. (2016). Transparency, accounting discretion, and bank stability. Economic Policy Review, 22(1), 129, vi. Cordella, T., & Yeyati, E. L. (1998). Public disclosure and bank failures. Staff Papers, 45(1), 110–131.

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Di Fabio, C. (2019). Does the business model influence income smoothing? Evidence from European banks. Journal of Applied Accounting Research, 20(3), 311–330. Di Fabio, C., Ramassa, P., & Quagli, A. (2021). Income smoothing in European banks: The contrasting effects of monitoring mechanisms. Journal of International Accounting, Auditing and Taxation, 43, 100385. Fonseca, A. R., & González, F. (2008). Cross-country determinants of bank income smoothing by managing loan-loss provisions. Journal of Banking and Finance, 32(2), 217–228. García-Osma, B., Mora, A., & Porcuna, L. (2019). Prudential supervisors’ independence and income smoothing in European banks. Journal of Banking and Finance, 102, 156–176. Gebhardt, G. U., & Novotny-Farkas, Z. (2011). Mandatory IFRS adoption and accounting quality of European banks. Journal of Business Finance and Accounting, 38(3–4), 289–333. Hashim, N., Li, W., & O’Hanlon, J. (2016). Expected-loss-based accounting for impairment of financial instruments: The FASB and IASB proposals 2009–2016. Accounting in Europe, 13(2), 229–267. Novotny-Farkas, Z. (2016). The interaction of the IFRS 9 expected loss approach with supervisory rules and implications for financial stability. Accounting in Europe, 13(2), 197–227. Peterson, O. K., & Arun, T. G. (2018). Income smoothing among European systemic and non-systemic banks. The British Accounting Review, 50(5), 539–558. Rochet, J. C. (1992). Capital requirements and the behavior of commercial banks. European Economic Review, 36, 1137–1178.

Appendix

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 C. Di Fabio, National Supervision and Income Smoothing in Banks’ Annual Reports, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-74011-5

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Country AT BE BG CY CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV MT NL PL PT

Average official supervisory power index 11.08 11.00 11.00 11.46 10.00 9.62 10.54 12.46 10.62 6.85 9.31 8.00 8.92 11.62 13.69 8.77 10.23 12.62 11.62 11.08 12.92 10.62 10.08 12.92

Average independence— legal index 3.00 2.00 3.00 3.00 2.00 1.00 2.00 1.54 2.08 2.54 2.54 – 1.54 3.00 3.00 2.54 1.46 1.46 2.00 2.46 2.00 2.00 2.00 3.00 Average private monitoring index 7.08 7.54 7.54 9.00 7.00 7.92 8.92 – 9.00 7.46 9.08 10.00 8.46 7.54 8.46 10.54 8.00 8.38 7.54 7.92 8.46 9.38 8.54 6.46

Average external audit index 6.46 7.00 6.46 6.08 5.00 6.00 6.46 7.00 6.46 5.54 5.46 6.00 5.92 7.00 7.00 6.46 6.08 7.00 7.00 5.00 6.54 7.00 6.00 5.92 Average restrictiveness of capital regulation index 4.00 8.00 9.00 9.00 – 8.00 5.00 8.00 8.00 6.00 8.00 3.00 7.00 8.00 4.00 8.00 6.00 7.00 7.00 0.00 7.00 8.00 8.00 4.00

Average restrictiveness of regulation of banking activities index 5.46 4.54 5.92 8.08 6.00 6.00 6.54 8.00 6.00 5.38 5.46 5.00 6.54 5.46 5.46 6.00 7.46 8.00 8.54 5.46 6.54 5.00 6.08 7.00

Table A.1 Average values of indexes used to derive variables for supervisory and regulatory features presented by country

92 Appendix

RO SE SI SK

11.85 8.00 14.00 11.89

3.00 2.00 1.54 2.08

6.54 7.00 7.46 7.08

6.54 5.00 7.00 5.54

8.00 – 7.00 6.00

5.92 6.00 6.00 7.54

Appendix 93

94

Appendix

Table A.2 IFRS 9 and the business model assessment Business model assessment The objective of the business model assessment is to classify and measure financial instruments consistent with how the entity manages them to generate cash flows and create value. Therefore, managers should start the evaluation considering the activities performed and then reconduct them to one of the business model types foreseen by IFRS 9. Compared with the variety of business models at the entity level (i.e. the bank business model), IFRS 9 foresees only three basic business models at the portfolio level. The rationale behind this classification of business models concerns the effects of managerial strategy on the entity way to exploit instruments for cash flow generation purposes. Reducing the business model to its essential elements should help to identify the measurement criteria. It also serves to avoid recognizing unsubstantiated profit and loss, so financial statements can better reflect how the entity uses financial assets to create value and realize cash flows. This approach could help intuitively understand how far the instrument destination is from the market perspective under a going concern assumption. Fair value ex IFRS 13 is an exit price; thus, what should be relevant to provide useful information is the link between cash flow generation and the instrument sale on the market. This link is fragile for instruments managed to realize cash flows collecting contractual payments over the instrument life but stronger in market-oriented business models. According to this logic, cash flows derive principally from: • The collection of contractual cash flows • Both the collection of cash flows and sales • Revenues from sales Distinguishing between these fundamental features is a matter of fact; judgement should be exercised using, as inputs, all relevant evidence (historical) available at the assessment date. To better define the business model, managers should refer to: (i) How the performance and the financial assets held within that business model are evaluated and reported (ii) The risks threatening the entity performance and how these risks are managed (iii) The management compensation (if it is based on either the asset fair value or the contractual cash flows collected) From the portfolio and its strategic connotation (e.g. banking book for a bank), managers should extrapolate its function (e.g. objective of collecting cash flows through repayments) and then reconduct the strategic connotation to the basic business models ex IFRS 9, based on the way in which value is created and realized (e.g. hold to collect business model). IFRS 9 explicitly distinguishes between two basic models indicating the process of value realization, namely ‘hold to collect’ and ‘hold to collect and sell’ ones. Business models not belonging to one of these two categories are not explicitly defined. ‘Hold to collect’ business models In the scope of ‘hold to collect’ business models, financial instruments are managed to realize cash flows collecting contractual payments over the instrument life. Particularly, assets’ portfolio is managed to collect these contractual cash flows instead of managing the overall return by both holding and selling assets. To verify whether cash flows are generated through the maintenance of the assets, managers should rely on information concerning frequency, value and timing of past sales, the reasons for those sales and expectations about future sales activity. However, information about the sales is not meaningful per se, as it should be evaluated in conjunction with motivations underlying sales and contingencies existing at that time. (continued)

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Table A.2 (continued) Although the overall objective of this business model is to hold financial assets to collect contractual cash flows, the entity does not need to hold all the instruments until maturity; incidental sales occur or are expected to occur in the future. Additionally, the business model assessment should not consider stress tests scenarios that are unlikely at present. However, it is to note that, to be consistent with a ‘hold to collect’ business model, the sales should be infrequent (even if significant in value) or insignificant in value both individually and in aggregate (even if frequent). On the contrary, sales contrast the objective of collecting contractual cash flow. Additionally, the entity could sell financial instruments consistent with the ‘hold to collect’ business model close to the maturity of the instruments, and the proceeds from them approximate the value of the contractual cash flows to be still collected. ‘Hold to collect and sell’ business models In the scope of ‘hold to collect and sell’ business models, the entity manages financial instruments to realize cash flows by collecting contractual payments and selling assets. These activities are both considered integral to achieving the objective of the business model, indeed. IFRS 9 highlights that various objectives can be consistent with this business model, such as managing everyday liquidity needs, maintaining a specific interest yield profile or matching the financial asset duration to the duration of the funding liabilities. Compared to a ‘hold to collect’ business model, this kind of business model typically exhibits higher sales’ frequency or amount. In this case, selling is no more only incidental to the business model (and eventually part of risk management activities). Selling is crucial to achieving the business model’s primary objective. It is not possible to define a priori a threshold for the frequency or value of sales to distinguish between the two kinds of business model. However, the underlying reasons, timing, frequency and amount of sales can represent critical elements to differentiate between the business models. Under IFRS 9, the appropriate measurement for instruments managed to collect contractual cash flows and sell the instruments is fair value through other comprehensive income; therefore, this category would not be residual. In practice, an entity might individuate instruments held to collect contractual cash flows, instruments held for trading, instruments managed on a fair value basis and those designated at fair value through the fair value option. The remaining instruments could then be measured at fair value through other comprehensive income, applying de facto a residual logic for simplicity. Other business models If, as a result of the business model assessment, the business model cannot be classified as ‘hold to collect’ or as ‘hold to collect and sell’, then financial assets are measured at fair value through profit or loss. Without stating it explicitly, IFRS 9 conceives the residual category as encompassing models based on trading activities. These models result in active buying and selling; thus, the entity makes decisions based on the assets’ fair values and manages the assets to realize those fair values. In such a scenario, the collection of contractual cash flows is not integral to the business model’s primary objective. If the assets generate contractual cash flows, their collection would be only incidental to the business model’s goal. These assets are held neither to collect contractual cash flows nor to collect contractual cash flows and sell. Additionally, residual types of business models emerge if the entity manages a portfolio of financial assets by evaluating its performance based on fair value. These assets should be measured at fair value through profit or loss, based on the view that fair value through profit or loss provides more relevant information about the actual management criteria. Examples Case 1 Consider an entity reacting to an increase in the asset credit risk by selling its financial assets. In this case, the credit quality of financial assets is relevant for the entity to collect cash flows and sales are performed within credit risk management activities to minimize potential credit losses (continued)

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Table A.2 (continued) due to credit deterioration. Credit risk management activities are planned consistent with the business model. In this case, sales are not inconsistent with the ‘hold to collect’ business model because selling financial assets not meeting the credit criteria approved within the investment policy is compatible with a ‘hold to collect’ business model. Case 2 Consider an entity whose business model is to purchase loan portfolios. Payments are not received on a timely basis; the entity attempts to realize the contractual cash flows through various means (which, however, do not include selling the loans), and it enters interest rate swaps to change the rate on particular loans from floating to fixed. In this case, the objective of the business model is to hold the assets to collect the contractual cash flows, regardless of negative expectations on some loans and the use of derivatives to modify the cash flows. Case 3 Consider an entity that anticipates capital expenditure in a few years and invests its excess cash in a portfolio of short- and long-term financial assets whose return is the basis for managerial retribution. Several assets have contractual lives exceeding the entity anticipated period. However, the entity holds them not only to collect the contractual cash flows but also to sell them to reinvest in financial instruments characterized by a higher return. In this case, the aim of the business model is both collecting contractual cash flows and selling financial assets. The entity decides whether to collect contractual cash flows or selling assets depending on the possibility to improve the portfolio’s return. Case 4 Consider a financial institution that holds financial assets to meet daily liquidity needs. To minimize the costs of liquidity need, the entity actively manages the portfolio’s return by collecting contractual payments and gains and losses from the assets’ selling. Then, it reinvests in higher performance financial assets or can better match the duration of its liabilities. Additionally, in the past, the entity’s strategy consisted of frequent sales significant in value, which will continue in the future. The aim of this business model is the maximization of the portfolio’s return to meet liquidity needs. This aim is fulfilled by both collecting contractual cash flows and selling financial assets, which are complementary.