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Multiperiod Bankruptcy Prediction Models with Interpretable Single Models

Author

Listed:
  • Ángel Beade

    (University of A Coruña)

  • Manuel Rodríguez

    (University of A Coruña
    Cátedra AECA-Abanca, UIE University)

  • José Santos

    (University of A Coruña)

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.

Suggested Citation

  • Ángel Beade & Manuel Rodríguez & José Santos, 2024. "Multiperiod Bankruptcy Prediction Models with Interpretable Single Models," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1357-1390, September.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:3:d:10.1007_s10614-023-10479-z
    DOI: 10.1007/s10614-023-10479-z
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