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Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks

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Abstract

The paper aims to develop an early warning model that separates previously rated banks (337 Fitch-rated banks from OECD) into three classes, based on their financial health and using a one-year window. The early warning system is based on a classification model which estimates the Fitch ratings using Bankscope bank-specific data, regulatory and macroeconomic data as input variables. The authors propose a “hybridization technique” that combines the Extreme learning machine and the Synthetic Minority Over-sampling Technique. Due to the imbalanced nature of the problem, the authors apply an oversampling technique on the data aiming to improve the classification results on the minority groups. The methodology proposed outperforms other existing classification techniques used to predict bank solvency. It proved essential in improving average accuracy and especially the performance of the minority groups.

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Notes

  1. The concept of soundness is commonly used to denote, for example, an ability to withstand adverse events. Solvency is reflected in the positive net worth of a bank, as measured by the difference between the assets and liabilities (excluding capital and reserves) in its balance sheet. The likelihood of remaining solvent will depend, inter alia, on banks’ being profitable, well managed, and sufficiently well capitalized to withstand adverse events. In a dynamic and competitive market economy, efficiency and profitability are linked, and their interaction will indicate the prospects for future solvency. Inefficient banks will make losses and will eventually become insolvent and illiquid. Undercapitalized banks, that is, those with low net worth, will be fragile in the sense of being more prone to collapse when faced with a destabilizing shock, such as a major policy change, a sharp asset price adjustment, financial sector liberalization, or a natural disaster (Lindgren et al. 1996).

  2. Fitch evaluates the current banks’ solvency however our intention is to develop an early waning model in which we predict the solvency in a future time.

  3. In the Appendix we explain how all these indexes-variables, which are proxies of regulatory aspects, are constructed.

  4. In support of this view, Sironi (2003) finds that credit ratings outperform the balance sheet variables in predicting spreads on bank subordinated notes and debentures in Europe. Other studies have shown that changes in credit ratings cause changes in equity prices of banks in the United States (Billett et al. 1998; Schweitzer et al. 1992) and in Europe (Gropp and Richards 2001), indicating that rating agencies are believed by the market to have superior information.

  5. This is a comprehensive, global database containing information on public and private banks commercialized by the Bureau van Dijk Group. The database is updated to December 6, 2012 (version number 1349).

  6. The class which reports the minimum sensitivity value.

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Acknowledgements

The research work of F. Martínez-Estudillo and F. Fernández-Navarro was partially supported by the TIN2014-54583-C2-1-R project of the Spanish Ministry of Economy and Competitiveness (MINECO).

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Authors and Affiliations

  1. Department of Economics, Universidad Loyola Andalucia, Córdoba, Spain

    D. Fernández-Arias & M. López-Martín

  2. Deparment of Quantitative Methods, Universidad Loyola Andalucia, Córdoba, Spain

    F. Martínez-Estudillo & F. Fernández-Navarro

  3. Department of Financial Economics and Accounting, Universidad Loyola Andalucia, Córdoba, Spain

    T. Montero-Romero

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  1. D. Fernández-Arias
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  2. M. López-Martín
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  4. F. Martínez-Estudillo
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  5. F. Fernández-Navarro
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Correspondence to D. Fernández-Arias.

Appendix A: Definition of Regulatory Variables

Appendix A: Definition of Regulatory Variables

See Table 6.

Table 6 Definition of regulatory variables
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Fernández-Arias, D., López-Martín, M., Montero-Romero, T. et al. Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks. Comput Econ 52, 275–297 (2018). https://doi.org/10.1007/s10614-017-9676-6

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