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Comparing the Performance of Corporate Bankruptcy Prediction Models Based on Imbalanced Financial Data

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  • Seol-Hyun Noh

    (Department of Statistical Data Science, ICT Convergence Engineering, Anyang University, Anyang 14028, Republic of Korea)

Abstract

Forecasts of corporate defaults are used in various fields across the economy. Several recent studies attempt to forecast corporate bankruptcy using various machine learning techniques. We collected financial information on 13 variables of 1020 companies listed on the KOSPI and KOSDAQ to capture the possibility of corporate bankruptcy. We propose a data processing method for small-sample domestic corporate financial data. We investigate the case of random sampling of non-bankrupt companies versus sampling non-bankrupt companies based on approximate entropy and optimized threshold based on AUC to address the imbalance between the number of bankrupt companies and the number of non-bankrupt companies. We compare the performance measures of corporate bankruptcy prediction models for the small sample data structured in two ways and the full dataset. The experimental results of this study contribute to the selection of an appropriate corporate bankruptcy prediction model.

Suggested Citation

  • Seol-Hyun Noh, 2023. "Comparing the Performance of Corporate Bankruptcy Prediction Models Based on Imbalanced Financial Data," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4794-:d:1091114
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    References listed on IDEAS

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    1. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
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    Cited by:

    1. Amélia Ferreira da Silva & José Henrique Brito & Mariline Lourenço & José Manuel Pereira, 2023. "Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence," Sustainability, MDPI, vol. 15(23), pages 1-13, December.

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