New forecasting methods for an old problem : predicting 147 years of systemic financial crises

A reflection on the lackluster growth over the decade since the Global Financial Crisis has renewed interest in preventative measures for a long-standing problem. Advances in machine learning algorithms during this period present promising forecasting solutions. In this context, the paper develops new forecasting methods for an old problem by employing 13 machine learning algorithms to study 147 year of systemic financial crises across 17 countries. It entails 12 leading indicators comprising real, banking and external sectors. Four modelling dimensions encompassing a contemporaneous pooled format through an expanding window, transformations with a lag structure and 20-year rolling window as well as individual format are implemented to assess performance through recursive out-of-sample forecasts. Findings suggest fixed capital formation is the most important variable. GDP per capita and consumer inflation have increased in prominence whereas debt-to-GDP, stock market and consumption were dominant at the turn of the 20th century. Through a lag structure, banking sector predictors on average describe 28 percent of the variation in crisis prevalence, real sector 64 percent and external sector 8 percent. A lag structure and rolling window both improve on optimised contemporaneous and individual country formats. Nearly half of all algorithms reach peak performance through a lag structure. As measured through AUC, F1 and Brier scores, top performing machine learning methods consistently produce high accuracy rates, with both random forests and gradient boosting in front with 77 percent correct forecasts. Top models contribute added value above 20 percentage points in most instances and deals with a high degree of complexity across several countries.