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Undersampling bankruptcy prediction: Taiwan bankruptcy data

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  • Haoming Wang
  • Xiangdong Liu

Abstract

Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losses. Many studies have proposed the insolvency imbalance problem, but little attention has been paid to the effect of the undersampling technology. Therefore, a framework is used to spot-check algorithms quickly and combine which undersampling method and classification model performs best. The results show that Naive Bayes (NB) after Edited Nearest Neighbors (ENN) has the best performance, with an F2-measure of 0.423. In addition, by changing the undersampling rate of the cluster centroid-based method, we find that the performance of the Linear Discriminant Analysis (LDA) and Naive Bayes (NB) are affected by the undersampling rate. Neither of them is uniformly declining, and LDA has higher performance when the undersampling rate is 30%. This study accordingly provides another perspective and a guide for future design.

Suggested Citation

  • Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    • Handle: RePEc:plo:pone00:0254030
    DOI: 10.1371/journal.pone.0254030
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    References listed on IDEAS

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    Cited by:

    1. Asyrofa Rahmi & Chia‐chi Lu & Deron Liang & Ayu Nur Fadilah, 2024. "Splitting long‐term and short‐term financial ratios for improved financial distress prediction: Evidence from Taiwanese public companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2886-2903, November.
    2. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    3. Stanislav Letkovský & Sylvia Jenčová & Petra Vašaničová, 2024. "Is Artificial Intelligence Really More Accurate in Predicting Bankruptcy?," IJFS, MDPI, vol. 12(1), pages 1-19, January.

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