Predicting bank insolvencies using machine learning techniques |
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Affiliation: | 1. Professor of Finance, School of Finance, Shanxi University of Finance and Economics, Taiyuan, China;2. Associate Professor of Finance, La Rochelle Business School – Excelia Group, La Rochelle, France;3. Adjunct Faculty of Finance, La Rochelle Business School – Excelia Group, La Rochelle, France;4. Professor of Finance, School of Finance, Yunnan University of Finance and Economics, Kunming, Yunnan, China;1. ETH Zürich, COSS group, Europe;2. University of Bari, Italy;3. University of Molise, Italy |
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Abstract: | Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks. |
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Keywords: | Bank’s insolvencies Forecasting Random Forests Support Vector Machines Neural Networks Conditional inference trees |
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