Abstract
Banking regulation faces multiple challenges that call for rethinking the way it is designed. In this paper, we argue that regulators should focus more on simple equity requirements instead of elaborating complex rules. Such a constraint in equity is however opposed by the banking industry because of its presumed adverse impact on banks’ performance. Using various techniques (Lasso, Random Forest, Support Vector Regression, Artificial Neural Network) on a large dataset of banks’ balance sheet variables, we show that the equity ratio (equity over total assets) has a clear positive effect on banks’ performance when measured by the return on assets, while the impact of this ratio on the return on equity is most of the time negative. Strong equity requirements do not therefore impede banks’ performance, but do reduce the shareholder value. This may be the reason why the banking industry so fiercely opposes strong equity requirements. In addition, from a methodological perspective, we provide evidence that Random Forest performs better than other techniques at dealing with banks’ balance sheet data. Doing so, we provide avenues for future research dealing with these kind of data.
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