Predicting Financial Distress as Repeated Events? Evidence from China

Whilst there is increasing research attention on predicting financial distress, the existing literature is subject to two specific limitations. The first is that a firm can experience a financial distress event (e.g., loan default, bankruptcy) more than once, yet most studies that model corporate financial distress prediction treat financial distress as occurring only once. This approach leads to an inefficient use of data with all subsequent events being ignored and subsequently a decrease in statistical power. Second, to account for the lack of independence between observations, the extant research utilising hazard analysis relies on robust standard errors, which, while innocuous to one-time event data, ignore the possible downward bias in coefficient estimates for repeated event data. To address these two limitations, we treat financial distress as repeated events and apply more advanced methods (generalised estimating equations, random effects, fixed effects, and a hybrid approach incorporating both random effects and fixed effects) to account for the lack of independence between observations in discrete time hazard analysis. These different approaches are applied to a sample of listed companies in China over the 2007‒2021 period. We find that a hybrid approach achieves better in-sample prediction than other approaches, and that, despite the problem of overfitting, the hybrid approach achieves slightly better out-of-sample prediction over one- to two-year time horizons and substantially better out-of-sample prediction over three- to five-year time horizons than other approaches. In our robustness test, the hybrid approach outperforms other approaches even more significantly, particularly over a longer time horizon, in Cox proportional hazard analysis