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Discriminating the default risk of small enterprises: : Stacking model with different optimal feature combinations

Published: 01 November 2023 Publication History

Abstract

Small enterprise default discrimination establishes a risk discriminant model based on financial data, non-financial data, and external macro conditions of small enterprises to obtain their default discriminant. This study constructs the final default discriminant model, taking the predicted default probability vectors from the first modelling as independent variables. From the 2 e feature combinations composed of e features, it maximises the default discriminant precision of the same training sample. Accordingly, the study inversely infers three optimal feature combinations corresponding to three models, including logistic regression, support vector machine, and linear discriminant analysis, ensuring the discrimination accuracy of the first modelling by the stacking method. Moreover, five features—industry prosperity index, EBITDA margin, current assets turnover ratio, net profit, and per capita disposable income of urban residents—account for 11% of the features in the optimal feature combination, but their importance accounts for 63%; thus, they are critical to the default risk of small enterprises. Further, the macro features significantly influence the default risk of small enterprises. For example, the importance of four macro features—industry prosperity index, consumer price index, per capita disposable income of urban residents, and Engel coefficient—accounts for 26.26% of the features in the optimal feature combination. Notably, the importance of the ‘industry prosperity index’, which is the greatest influencing factor in the feature combination, accounts for 17.63%. Ultimately, the discrimination accuracy of the proposed model is better than that of the seven classical default discriminant models.

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Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal  Volume 229, Issue PA
Nov 2023
1358 pages

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 November 2023

Author Tags

  1. AUC
  2. CDF-g
  3. EBITDA
  4. FDA
  5. GBDT
  6. LDA
  7. LR
  8. M−DFIFS
  9. MLP
  10. RF
  11. ROC
  12. SVM
  13. XGBoost

Author Tags

  1. Default of small enterprises
  2. Default discriminant
  3. Imbalanced sample
  4. Stacking model
  5. Optimal feature combination
  6. Big data

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