Machine Learning for Financial Crisis Prediction

Abstract

We investigate the potential applications of using machine-learning models in financial crisis prediction. We aim to identify crises one or two years ahead of their start dates by recognizing trends in a variety of economic variables. We look at two different datasets of banking crises, as well as currency and inflation crises. For consistency in analysis, we manually construct the crisis variables for the years 2017-2020. By analyzing the models in both cross-validation and forecasting experiments, we show that machine-learning models can outperform logistic regression in financial crisis prediction. We employ a Shapley value framework in an attempt to mitigate the black box nature of the machine-learning models. We show that the global economic climate is of vital importance in identifying banking and currency crises. Wages are shown to be the most important predictor of inflation crises. We then investigate the nonlinear relationships between the predictors and their Shapley values to further understand the driving forces behind the model predictions.