Multinomial Modeling Methods : Predicting Four Decades of International Banking Crises

This paper examines banking crises in a large sample of countries over a forty-year period. A multinomial modeling approach is applied to panel data in order to track and capture end-to-end cyclical crisis formations, which enhances the binary focus of previous research studies. Several macroeconomic and banking sector variables are shown to be emblematic of leading indicators across the idiosyncratic stages of a banking crisis. Gross domestic product is an early warning signal across all phases, and a concomitant deterioration in consumption spending and fixed capital formation, preceded by a credit boom, signal a banking crisis to come. Currency depreciation exemplifies ensuing financial distress, reinforced by developmental constructs and regional integration. Lower real interest rates, increasing imports, and rising deposits are frequently harbingers of a recovery. Period effects underscore the dynamic evolution of common contemporaneous precursors over time. Premised on pursuing cyclical movements through multiple outcomes, our findings on forecasting performance suggest enhanced predictive power. Several multinomial logistic models generate higher predictive accuracy in contrast to probit models. Compared to machine learning methods (which encompass artificial neural networks, gradient boost, k-nearest neighbors, and random forests methods), a multinomial logistic approach outperforms during pre-crisis periods and when crisis severity is modeled, whereas gradient boost has the highest predictive accuracy across numerous versions of the multinomial model. As investors and policy makers continue to confront banking crises, leading to high economic and social costs, enhanced multinomial modeling methods make a valuable contribution to improved forecasting performance