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Corporate failure prediction using threshold-based models
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AI
DOI：10.1002/for.2842.png)
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Abstract
En 中文
Corporate failure prediction literature indicates that models' performance depends on more than the complexity of the prediction method. Recent advances cite the relevance of sampling approaches to model performance. Therefore, this study proposes a novel approach that implements threshold models for corporate failure prediction efforts. Threshold models estimate asymptotically conservative confidence regions, in which the samples are split by size. Then, single-based classifiers and a combination of multiple classifiers are employed in each region to estimate the prediction accuracy. This article offers a comparison of the proposed threshold-based corporate failure model with two benchmark models, previously used in studies in the same context. The empirical results show that the proposed threshold-based model clearly outperforms conventional models, in particular with the combination of multiple classifiers. The superiority of the threshold-based model stems from its ability to discern failed firms, which represent the most important class in financial terms. This study thus provides initial evidence of the utility of threshold models in corporate failure prediction efforts.
Keywords:
classification
failure prediction
threshold model
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