A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction

Ilyes Abid (), Rim Ayadi, Khaled Guesmi and Farid Mkaouar
Additional contact information
Ilyes Abid: ISC Paris Business School
Rim Ayadi: Univ Lyon, Université Lumière Lyon 2
Khaled Guesmi: Paris School of Business
Farid Mkaouar: LIRSA, CNAM

Annals of Operations Research, 2022, vol. 313, issue 2, No 2, 605-623

Abstract: Abstract The purpose of the paper is to propose two new procedures that deal with overfitting problem using neural techniques for variable selection and business failure prediction. The first procedure, called HVS-AUC, is based simultaneously on (i) the backward search, (ii) the HVS measure (Heuristic for Variable Selection), and (iii) the AUC criterion (Area Under Curve). The second procedure, called forward-AUC, is based on (i) the forward search and (ii) the AUC criterion. Using a sample of bankrupt and non-bankrupt firms in France, the implementation of the procedures using neural networks shows that the profitability, the repayment capacity, the taxation, and the importance of investment have a strong explanatory power in bankruptcy prediction. These procedures also provide more parsimonious and more efficient models compared to Linear Discriminant Analysis.

Keywords: Bankruptcy prediction; Neural networks; Variable selection; Classification (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-021-04236-4

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