Multiperiod Bankruptcy Prediction Models with Interpretable Single Models

Ángel Beade, Manuel Rodríguez and José Santos ()
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Ángel Beade: University of A Coruña
Manuel Rodríguez: University of A Coruña
José Santos: University of A Coruña

Computational Economics, 2024, vol. 64, issue 3, No 2, 1357-1390

Abstract: Abstract This study considers multiperiod bankruptcy prediction models, an aspect scarcely considered in research despite its importance, since creditors must assess the risk of loans over the entire life of the debt and not at a specific point in the future. Two possibilities for the implementation of multiperiod prediction models are considered: Multi-Model multiperiod Bankruptcy Prediction Models (MMBPM) and Single-Model multiperiod Bankruptcy Prediction Models (SMBPM). The former considers the conditional probabilities obtained by individual models predicting bankruptcy at specific times in the future, while the latter is a single model predicting bankruptcy at a specific time interval in the future. The results show that there are no significant differences between the two approaches when compared using data after the learning period. However, SMBPMs have the important advantage of interpretability for decision-making, which is discussed with examples. Moreover, a comparison of SMBPM performance with external references is performed.

Keywords: Business failure; Multiperiod; Explainable Artificial Intelligence; Interpretability; Genetic programming (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10479-z

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