IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v51y2023ics1544612322005888.html
   My bibliography  Save this article

The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress

Author

Listed:
  • Citterio, Alberto
  • King, Timothy

Abstract

We analyze the predictive power of Environmental, Social, and Governance (ESG) indicators to forecast bank financial distress using a sample of 362 commercial banks headquartered in the US and EU-28 members states from 2012 to 2019. Our results demonstrate that ESG improves the predictive capability of our model to correctly identify distress. Notably, ESG strongly reduces the likelihood of misclassifying distressed/defaulted banks as healthy. Our model, which we estimate using six alternative approaches, including traditional statistical techniques, machine learning approaches, and ensemble methods, has implications for both practical implications by banking sector supervisors, as well as literature on default prediction.

Suggested Citation

  • Citterio, Alberto & King, Timothy, 2023. "The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress," Finance Research Letters, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322005888
    DOI: 10.1016/j.frl.2022.103411
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612322005888
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2022.103411?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. El Diri, Malek & King, Timothy & Spokeviciute, Laima & Williams, Jonathan, 2021. "Hands in the cookie jar: Exploiting loan loss provisions under bank financial distress," Economics Letters, Elsevier, vol. 209(C).
    2. Wu, Meng-Wen & Shen, Chung-Hua, 2013. "Corporate social responsibility in the banking industry: Motives and financial performance," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3529-3547.
    3. George Marian Aevoae & Alin Marius Andrieș & Steven Ongena & Nicu Sprincean, 2023. "ESG and systemic risk," Applied Economics, Taylor & Francis Journals, vol. 55(27), pages 3085-3109, June.
    4. Rebel Cole & Lawrence White, 2012. "Déjà Vu All Over Again: The Causes of U.S. Commercial Bank Failures This Time Around," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 5-29, October.
    5. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    6. Laeven, Luc & Levine, Ross, 2009. "Bank governance, regulation and risk taking," Journal of Financial Economics, Elsevier, vol. 93(2), pages 259-275, August.
    7. Mare, Davide Salvatore, 2015. "Contribution of macroeconomic factors to the prediction of small bank failures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 25-39.
    8. Flannery, Mark J, 1998. "Using Market Information in Prudential Bank Supervision: A Review of the U.S. Empirical Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 30(3), pages 273-305, August.
    9. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    10. Allen N. Berger & Björn Imbierowicz & Christian Rauch, 2016. "The Roles of Corporate Governance in Bank Failures during the Recent Financial Crisis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 729-770, June.
    11. Tigran Poghosyan & Martin Čihak, 2011. "Determinants of Bank Distress in Europe: Evidence from a New Data Set," Journal of Financial Services Research, Springer;Western Finance Association, vol. 40(3), pages 163-184, December.
    12. Manthos D. Delis & Panagiotis K. Staikouras, 2011. "Supervisory Effectiveness and Bank Risk," Review of Finance, European Finance Association, vol. 15(3), pages 511-543.
    13. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    14. Azmi, Wajahat & Hassan, M. Kabir & Houston, Reza & Karim, Mohammad Sydul, 2021. "ESG activities and banking performance: International evidence from emerging economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    15. Carmona, Pedro & Climent, Francisco & Momparler, Alexandre, 2019. "Predicting failure in the U.S. banking sector: An extreme gradient boosting approach," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 304-323.
    16. Petropoulos, Anastasios & Siakoulis, Vasilis & Stavroulakis, Evangelos & Vlachogiannakis, Nikolaos E., 2020. "Predicting bank insolvencies using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1092-1113.
    17. Chiaramonte, Laura & Casu, Barbara, 2017. "Capital and liquidity ratios and financial distress. Evidence from the European banking industry," The British Accounting Review, Elsevier, vol. 49(2), pages 138-161.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Tianjiao & Zhu, Weidong & Wu, Yong & Wu, Zihao & Zhang, Chao & Hu, Xue, 2023. "An explainable financial risk early warning model based on the DS-XGBoost model," Finance Research Letters, Elsevier, vol. 56(C).
    2. Dhasmana, Samriddhi & Ghosh, Sajal & Kanjilal, Kakali, 2023. "Does investor sentiment influence ESG stock performance? Evidence from India," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    3. Yin Shi & Xiaoni Li & Maher Asal, 2023. "Impact of sustainability on financial distress in the air transport industry: the moderating effect of Asia–Pacific," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    4. Jiajia Liu & Zhenzhen Ge & Yahan Wang, 2024. "Role of environmental, social, and governance rating data in predicting financial risk and risk management," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(1), pages 260-273, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    2. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    3. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
    4. Jorge E. Galán, 2021. "CREWS: a CAMELS-based early warning system of systemic risk in the banking sector," Occasional Papers 2132, Banco de España.
    5. Papanikolaou, Nikolaos I., 2018. "To be bailed out or to be left to fail? A dynamic competing risks hazard analysis," Journal of Financial Stability, Elsevier, vol. 34(C), pages 61-85.
    6. Kočenda, Evžen & Iwasaki, Ichiro, 2020. "Bank survival in Central and Eastern Europe," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 860-878.
    7. Betz, Frank & Oprică, Silviu & Peltonen, Tuomas A. & Sarlin, Peter, 2014. "Predicting distress in European banks," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 225-241.
    8. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
    9. Basim Alzugaiby & Jairaj Gupta & Andrew Mullineux & Rizwan Ahmed, 2021. "Relevance of size in predicting bank failures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3504-3543, July.
    10. Avino, Davide E. & Conlon, Thomas & Cotter, John, 2019. "Credit default swaps as indicators of bank financial distress," Journal of International Money and Finance, Elsevier, vol. 94(C), pages 132-139.
    11. Buckmann, Marcus & Gallego Marquez, Paula & Gimpelewicz, Mariana & Kapadia, Sujit & Rismanchi, Katie, 2021. "The more the merrier? Evidence from the global financial crisis on the value of multiple requirements in bank regulation," Bank of England working papers 905, Bank of England.
    12. Avignone, Giuseppe & Altunbas, Yener & Polizzi, Salvatore & Reghezza, Alessio, 2021. "Centralised or decentralised banking supervision? Evidence from European banks," Journal of International Money and Finance, Elsevier, vol. 110(C).
    13. Parrado-Martínez, Purificación & Gómez-Fernández-Aguado, Pilar & Partal-Ureña, Antonio, 2019. "Factors influencing the European bank’s probability of default: An application of SYMBOL methodology," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 223-240.
    14. Buckmann, Marcus & Gallego Marquez, Paula & Gimpelewicz, Mariana & Kapadia, Sujit & Rismanchi, Katie, 2023. "The more the merrier? Evidence on the value of multiple requirements in bank regulation," Journal of Banking & Finance, Elsevier, vol. 149(C).
    15. Evžen Kočenda & Ichiro Iwasaki, 2022. "Bank survival around the world: A meta‐analytic review," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 108-156, February.
    16. Mikko Makinen & Laura Solanko, 2018. "Determinants of Bank Closures: Do Levels or Changes of CAMEL Variables Matter?," Russian Journal of Money and Finance, Bank of Russia, vol. 77(2), pages 3-21, June.
    17. Sanders, Austen & Willison, Matthew, 2021. "Measure for measure: evidence on the relative performance of regulatory requirements for small and large banks," Bank of England working papers 922, Bank of England.
    18. Viral V Acharya & Lea Borchert & Maximilian Jager & Sascha Steffen, 2021. "Kicking the Can Down the Road: Government Interventions in the European Banking Sector," Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4090-4131.
    19. Andrieș, Alin Marius & Sprincean, Nicu, 2023. "ESG performance and banks’ funding costs," Finance Research Letters, Elsevier, vol. 54(C).
    20. Viral V. Acharya & Lea Borchert & Maximilian Jager & Sascha Steffen, 2023. "Kicking the Can Down the Road: Government Interventions in the European Banking Sector," CRC TR 224 Discussion Paper Series crctr224_2023_446, University of Bonn and University of Mannheim, Germany.

    More about this item

    Keywords

    Financial distress; Bank default; Prediction models; ESG;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • M14 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Corporate Culture; Diversity; Social Responsibility
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322005888. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.