Browsing by Subject "banking crisis"

Banking crises have been found to cause significant fiscal and real costs for the economy. For this reason, macroprudential policymakers have developed various analytical models for predicting new banking crises ahead of time. With this information policymakers can undertake targeted countermeasures to reduce the negative impacts. In the prediction exercise binary regression models (especially logit) have been the main analytical tool for long. However, due to the complex dynamics and rare occurrences, accurate crisis prediction remains a difficult task for these models. In line with the recent developments in technology and artificial intelligence, scholars have started investigating the possibilities of using machine learning methods in banking crisis prediction. Despite the promise of more flexible distributional assumptions and enhanced modeling of non-linear relationships, the early results on predictive performance have been mixed. One explanation for this could be the large variety of models and empirical setups that different authors have used. As a result, it remains unclear whether the results are driven by changes in the underlying empirical setups, or the superiority of the machine learning models themselves. To investigate this problem, this thesis collects out-of-sample prediction results from eleven banking crisis papers published between 2017 and 2023. After implementing a normalization procedure to enhance comparability between the papers, the results are pooled for analysis to gain insights into which machine learning models perform the best. Additional robustness checks are also carried out to investigate the stability of the results. This thesis makes two main contributions to the literature. The first one is finding systematic differences in predictive performance between machine learning models. Neural network, random forest and boosted/bagged tree models have on average delivered the best predictive performance in comparison to logit models. In contrast, k-nearest-neighbors, decision tree and support vector machine models consistently underperform the logit benchmarks. The second contribution is creating novel connections between the banking crisis and machine learning literatures. The empirical results obtained in this thesis are contrasted and found to be aligned with the machine learning literature. In addition, a critical review of the practical implications resulting from the use of machine learning is conducted. Issues with interpretability, modeling and class-imbalances are highlighted.