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Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach

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  • Jiaming Liu
  • Chengzhang Li
  • Peng Ouyang
  • Jiajia Liu
  • Chong Wu

Abstract

Financial distress prediction is a major issue in the burgeoning fintech field. Given the importance of the reliability of the prediction results, there is an urgent need for the explanatory ability of the financial distress prediction model. From the modeling and explanation point of view, this study employs four prevailing tree‐based gradient boosting models, namely, gradient boosting decision tree, extreme gradient boosting, light gradient boosting machine, and categorical boosting, to build financial distress prediction models by using financial data of listed companies in China from 1998 to 2014 and five different prediction time spans. We observe that tree‐based gradient boosting models have better prediction performance than other prediction models. To explore the reasons for the prediction results, we deploy TreeSHAP. Then, we use Shapley regression to examine the statistically significant relationships between financial indicators and financial distress. We discover that financial indicators, such as net asset value per share and ratio of operating profits to current liabilities, are significantly related to financial distress. There is usually a nonlinear relationship between the financial predictors and prediction target. Thus, this study provides an effective method for financial distress prediction and an explanation of the results for listed companies in China.

Suggested Citation

  • Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:5:p:1112-1137
    DOI: 10.1002/for.2931
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    as
    1. Andreou, Christoforos K. & Andreou, Panayiotis C. & Lambertides, Neophytos, 2021. "Financial distress risk and stock price crashes," Journal of Corporate Finance, Elsevier, vol. 67(C).
    2. Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
    3. Hernandez Tinoco, Mario & Holmes, Phil & Wilson, Nick, 2018. "Polytomous response financial distress models: The role of accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 276-289.
    4. Liu, Bai & Ju, Tao & Bai, Min & Yu, Chia-Feng (Jeffrey), 2021. "Imitative innovation and financial distress risk: The moderating role of executive foreign experience," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 526-548.
    5. ElBannan, Mona A., 2021. "On the prediction of financial distress in emerging markets: What matters more? Empirical evidence from Arab spring countries," Emerging Markets Review, Elsevier, vol. 47(C).
    6. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    7. Tsai, Chih-Fong & Sue, Kuen-Liang & Hu, Ya-Han & Chiu, Andy, 2021. "Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction," Journal of Business Research, Elsevier, vol. 130(C), pages 200-209.
    8. Li, Yuanhui & Li, Xiao & Xiang, Erwei & Geri Djajadikerta, Hadrian, 2020. "Financial distress, internal control, and earnings management: Evidence from China," Journal of Contemporary Accounting and Economics, Elsevier, vol. 16(3).
    9. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    10. 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.
    11. García, C. José & Herrero, Begoña, 2021. "Female directors, capital structure, and financial distress," Journal of Business Research, Elsevier, vol. 136(C), pages 592-601.
    12. 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.
    13. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    15. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    16. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    17. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    18. Mangena, Musa & Priego, Alba Maria & Manzaneque, Montserrat, 2020. "Bank power, block ownership, boards and financial distress likelihood: An investigation of Spanish listed firms," Journal of Corporate Finance, Elsevier, vol. 64(C).
    19. Sami Ben Jabeur & Youssef Fahmi, 2018. "Forecasting financial distress for French firms: a comparative study," Empirical Economics, Springer, vol. 54(3), pages 1173-1186, May.
    20. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    21. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    22. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    23. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
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