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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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    1. Marek Durica & Jaroslav Frnda & Lucia Svabova, 2019. "Decision tree based model of business failure prediction for Polish companies," Oeconomia Copernicana, Institute of Economic Research, vol. 10(3), pages 453-469, September.
    2. Bruynseels, Liesbeth & Willekens, Marleen, 2012. "The effect of strategic and operating turnaround initiatives on audit reporting for distressed companies," Accounting, Organizations and Society, Elsevier, vol. 37(4), pages 223-241.
    3. Constantin Zopounidis & Michael Doumpos, 1999. "Business failure prediction using the UTADIS multicriteria analysis method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1138-1148, November.
    4. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    5. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    8. Salim Lahmiri & Stelios Bekiros, 2019. "Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1569-1577, September.
    9. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    10. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    11. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    12. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    13. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
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    1. 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.
    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.

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