Multiperiod Bankruptcy Prediction Models with Interpretable Single Models
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http://hdl.handle.net/2183/36891
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Multiperiod Bankruptcy Prediction Models with Interpretable Single ModelsData
2023Cita bibliográfica
Beade, A., Rodríguez López, M. & Santos Reyes, J. (2023). Multiperiod Bankruptcy Prediction Models with Interpretable Single Models. Computational Economics. https://doi.org/10.1007/s10614-023-10479-z
Resumo
[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 specifc point in the future. Two possibilities for the implementation of multiperiod prediction models are considered:
Multi-Model multiperiod Bankruptcy Prediction Models (MMBPM) and SingleModel multiperiod Bankruptcy Prediction Models (SMBPM). The former considers
the conditional probabilities obtained by individual models predicting bankruptcy at
specifc times in the future, while the latter is a single model predicting bankruptcy
at a specifc time interval in the future. The results show that there are no signifcant
diferences 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.
Palabras chave
Business failure
Multiperiod
Explainable Artificial Intelligence
Interpretability
Genetic programming
Multiperiod
Explainable Artificial Intelligence
Interpretability
Genetic programming
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Dereitos
Atribución 4.0 Internacional
ISSN
0927-7099
https://doi.org/10.1007/s10614-023-10479-z
https://doi.org/10.1007/s10614-023-10479-z