Default Prediction Framework With Optimal Feature Set and Matching Ratio

Guotai Chi, Fengshan Bai, Hongping Tan and Ying Zhou

Journal of Forecasting, 2025, vol. 44, issue 7, 2067-2088

Abstract: We propose a default prediction framework that incorporates imbalance handling and feature selection. For imbalance handling, we determine the optimal ratio of non‐default to default firms by minimizing the Type‐II error of the majority voting deep fully connected network (MV‐DFCN) model. For feature selection, we design a two‐stage process that first eliminates highly correlated and redundant features, and then refines the feature set using backward selection. Experimental results show that the DFCN model within the proposed framework outperforms baseline models in terms of G‐Mean and AUC and achieves the lowest Type‐II error rate. Furthermore, the framework outperforms eight baseline combinations of imbalance handling and feature selection strategies. Additionally, SHAP values are used to assess feature contributions, and nine features with statistically significant impacts are identified.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/for.3284

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:44:y:2025:i:7:p:2067-2088

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().