Predicting financial distress as repeated events

Timothy Wang, Wei Cui and Stewart Jones

Accounting Research Journal, 2025, vol. 39, issue 1, 40-59

Abstract: PurposeThe purpose of this paper is to model financial distress as repeated events using discrete-time hazard analysis and evaluate which method that accounts for dependence on prior event history provides superior predictive performance.Design/methodology/approachUsing quarterly data from China’s listed firms (1995–2021), the authors estimate financial distress prediction models in discrete-time hazard models using, in turn, cluster-robust standard errors only, generalized estimating equations, random effects, fixed effects and a hybrid approach. Models are validated using both cross-sectional and longitudinal test samples and assessed via areas under the curve and recall.FindingsRandom effects and the hybrid method consistently outperform other methods in predictive accuracy and better capture within-firm correlation and event history.Research limitations/implicationsThe methods to control for dependence upon event history are generalizable to other recurrent events such as credit rating changes or mergers, supporting broader adoption of repeated-event modelling.Originality/valueTo the best of the authors’ knowledge, this is the first study to model financial distress as repeated events using time-dependent methods with time-varying predictors and prior event history.

Keywords: Repeated events; Generalized estimating equations; Random effects; Fixed effects; Hybrid approach; Cluster-robust standard errors (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:arjpps:arj-08-2025-0289

DOI: 10.1108/ARJ-08-2025-0289

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