Abstract: Regional banks as savings and cooperative banks are widespread in continental Europe. In the aftermath of the financial crisis, however, they had problems keeping their profitability which is an important quantitative indicator for the health of a bank and the banking sector overall. We use a large data set of bank-level balance sheet items and regional economic variables to forecast profitability for about 2,000 regional banks. Machine learning algorithms are able to beat traditional estimators as ordinary least squares as well as autoregressive models in forecasting performance.
Conclusion: In the aftermath of the financial crisis regional banks had problems keeping up their profitability. Banks’ profitability is an important indicator for the stability of the banking sector. We use a data set of bank-level balance sheet items and regional economic variables to forecast profitability. For the 2,000 savings and cooperative banks from eight European countries and the 2000-2015 time period, we found that machine learning algorithms are able to beat traditional estimators as ordinary least squares as well as autoregressive models in forecasting performance. Therefore, our paper is in line with the literature on machine learning models and their superior forecasting performance (Khandani et al., 2010; Butaru et al., 2016; Fitzpatrick & Mues, 2016). The performance of the machine learning algorithms was particularly well during the European debt crisis which points out the importance of our forecasting exercise as during this time policy makers’ interest in banks’ profitability was enhanced as further potential rescue packages for banks could deteriorate fiscal stability. Policy makers and, especially, regulators should therefore use these algorithms instead of traditional estimators in combination with their even larger regulatory data sets in regard to size and frequency to forecast banks’ profitability or other balance sheet items of interest.