Machine learning for predicting bank stability : the role of income diversification in European banking

There is an ongoing debate about the role of income diversification in enhancing bank stability within the financial services industry in Europe. Some advocate for diversification, while others argue that its importance should not be overstated. Some financial institutions are encouraged to focus on their traditional investments instead of income diversification, while others suggest that income diversification can stabilize or destabilize, depending on the regulatory environment. These conflicting results indicate a lack of clear evidence regarding the effectiveness of income diversification. Therefore, this paper aims to study the impact of income diversification on bank stability and enhance the predictive performance of bank stability by analyzing the period from 2000 to 2021 using a sample from 26 European countries, based on aggregate bank data. It employs a hybrid method that combines econometric techniques, specifically the generalized method of moments and a fixed-effects model, with machine-learning algorithms such as Random Forest and Support Vector Machine. These methods are applied to enhance the reliability and predictive power of the analysis by addressing the problem of endogeneity (via generalized method of moments) and capturing non-linearities, interactions, and high-dimensional patterns (via machine learning). The econometric findings reveal that income diversification can reduce non-performing loans, improve bank solvency, and enhance the Z-score, indicating the significant role of income diversification in improving bank stability. Conversely, the results also show that the machine-learning algorithms used play a crucial role in enhancing the predictive performance of bank stability.