IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v88y2024ics0927538x24003019.html

Corporate governance and financial distress in China a multi-dimensional nonlinear study based on machine learning

Author

Listed:
  • Meng, Qingbin
  • Zheng, Xinxing
  • Wang, Solomon

Abstract

Promoting governance efficiency is crucial for preventing corporate financial distress. However, previous research has been constrained by limited dimensions of governance predictors and insufficient linear estimations to predict financial distress. To address this issue, this study gathers 37 corporate governance indicators of Chinese publicly listed firms from 2009 to 2022 in the dimensions of ownership structure, board features and executive traits. The LightGBM machine learning approach is then used to compare the predicting power of these individual indicators, as well as the predicting power of the dimensions. The SHAP (SHapley Additive exPlanations) method is further adopted to conduct an in-depth interpretability analysis upon the established prediction. Our approach identifies the nonlinear effects of important corporate governance indicators on financial distress and prioritizes those indicators based on their impact levels. Our results show that the most influential indicator is institutional ownership, followed by managerial ownership and executive compensation disparity. In addition, the dimension of ownership structure has the highest predicting power among the three. Overall, our study provides new insights into how firms can optimize their corporate governance mechanisms to prevent financial distress.

Suggested Citation

  • Meng, Qingbin & Zheng, Xinxing & Wang, Solomon, 2024. "Corporate governance and financial distress in China a multi-dimensional nonlinear study based on machine learning," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:pacfin:v:88:y:2024:i:c:s0927538x24003019
    DOI: 10.1016/j.pacfin.2024.102549
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X24003019
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2024.102549?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 61(1), pages 329-376, March.
    2. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 199-235, March.
    3. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "The Aftermath of Financial Crises," American Economic Review, American Economic Association, vol. 99(2), pages 466-472, May.
    4. Nakano, Makoto & Nguyen, Pascal, 2012. "Board size and corporate risk-taking: Further evidence from Japan," MPRA Paper 38990, University Library of Munich, Germany.
    5. Andreou, Christoforos K. & Andreou, Panayiotis C. & Lambertides, Neophytos, 2021. "Financial distress risk and stock price crashes," Journal of Corporate Finance, Elsevier, vol. 67(C).
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    7. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    8. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    9. Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
    10. Anderson, Ronald C. & Fraser, Donald R., 2000. "Corporate control, bank risk taking, and the health of the banking industry," Journal of Banking & Finance, Elsevier, vol. 24(8), pages 1383-1398, August.
    11. Levi, Maurice & Li, Kai & Zhang, Feng, 2014. "Director gender and mergers and acquisitions," Journal of Corporate Finance, Elsevier, vol. 28(C), pages 185-200.
    12. Denis, David J & Denis, Diane K & Sarin, Atulya, 1997. "Agency Problems, Equity Ownership, and Corporate Diversification," Journal of Finance, American Finance Association, vol. 52(1), pages 135-160, March.
    13. Main, Brian G M & O'Reilly, Charles A, III & Wade, James, 1993. "Top Executive Pay: Tournament or Teamwork?," Journal of Labor Economics, University of Chicago Press, vol. 11(4), pages 606-628, October.
    14. Yim, Soojin, 2013. "The acquisitiveness of youth: CEO age and acquisition behavior," Journal of Financial Economics, Elsevier, vol. 108(1), pages 250-273.
    15. Suraj Srinivasan, 2005. "Consequences of Financial Reporting Failure for Outside Directors: Evidence from Accounting Restatements and Audit Committee Members," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 43(2), pages 291-334, May.
    16. Vincent L. Barker , III & George C. Mueller, 2002. "CEO Characteristics and Firm R&D Spending," Management Science, INFORMS, vol. 48(6), pages 782-801, June.
    17. 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.
    18. Wei Ting & Sin‐Hui Yen & Chien‐Liang Chiu, 2008. "The Influence of Qualified Foreign Institutional Investors on the Association between Default Risk and Audit Opinions: Evidence from the Chinese Stock Market," Corporate Governance: An International Review, Wiley Blackwell, vol. 16(5), pages 400-415, September.
    19. Rafael La Porta & Florencio Lopez‐De‐Silanes & Andrei Shleifer, 1999. "Corporate Ownership Around the World," Journal of Finance, American Finance Association, vol. 54(2), pages 471-517, April.
    20. Pathan, Shams, 2009. "Strong boards, CEO power and bank risk-taking," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1340-1350, July.
    21. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 10(1), pages 167-179.
    22. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    23. Serfling, Matthew A., 2014. "CEO age and the riskiness of corporate policies," Journal of Corporate Finance, Elsevier, vol. 25(C), pages 251-273.
    24. Jensen, Michael C, 1986. "Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers," American Economic Review, American Economic Association, vol. 76(2), pages 323-329, May.
    25. 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.
    26. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    27. Eriksson, Tor, 1999. "Executive Compensation and Tournament Theory: Empirical Tests on Danish Data," Journal of Labor Economics, University of Chicago Press, vol. 17(2), pages 262-280, April.
    28. Claessens, Stijn & Djankov, Simeon & Lang, Larry H. P., 2000. "The separation of ownership and control in East Asian Corporations," Journal of Financial Economics, Elsevier, vol. 58(1-2), pages 81-112.
    29. Antoinette Schoar & Luo Zuo, 2017. "Shaped by Booms and Busts: How the Economy Impacts CEO Careers and Management Styles," The Review of Financial Studies, Society for Financial Studies, vol. 30(5), pages 1425-1456.
    30. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    31. 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.
    32. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    33. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 22, pages 59-82.
    34. Pound, John, 1988. "Proxy contests and the efficiency of shareholder oversight," Journal of Financial Economics, Elsevier, vol. 20(1-2), pages 237-265, January.
    35. Weisbach, Michael S., 1988. "Outside directors and CEO turnover," Journal of Financial Economics, Elsevier, vol. 20(1-2), pages 431-460, January.
    36. Gennaro Bernile & Vineet Bhagwat & P. Raghavendra Rau, 2017. "What Doesn't Kill You Will Only Make You More Risk-Loving: Early-Life Disasters and CEO Behavior," Journal of Finance, American Finance Association, vol. 72(1), pages 167-206, February.
    37. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    38. 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.
    39. Huang, Jiekun & Kisgen, Darren J., 2013. "Gender and corporate finance: Are male executives overconfident relative to female executives?," Journal of Financial Economics, Elsevier, vol. 108(3), pages 822-839.
    40. Ulrike Malmendier & Geoffrey Tate & Jon Yan, 2011. "Overconfidence and Early‐Life Experiences: The Effect of Managerial Traits on Corporate Financial Policies," Journal of Finance, American Finance Association, vol. 66(5), pages 1687-1733, October.
    41. Faccio, Mara & Marchica, Maria-Teresa & Mura, Roberto, 2016. "CEO gender, corporate risk-taking, and the efficiency of capital allocation," Journal of Corporate Finance, Elsevier, vol. 39(C), pages 193-209.
    42. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    43. Tsun‐Siou Lee & Yin‐Hua Yeh, 2004. "Corporate Governance and Financial Distress: evidence from Taiwan," Corporate Governance: An International Review, Wiley Blackwell, vol. 12(3), pages 378-388, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Jie & Niu, Yanfang & Shi, Wenlei & Zhu, Kanghuan, 2025. "Predicting ESG disclosure quality through board secretaries' characteristics: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 76(C).
    2. Yasmine Souissi & Anis Jarboui, 2025. "Does Corporate Governance Shape the Relationship between Corporate Social Responsibility Performance and Financial Distress in Large European Companies?," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 15(1), pages 57-82.
    3. Deng, Shangkun & Li, Yongqi & Zhu, Yingke & Wang, Bingsen & Ning, Hong & Yi, Siyu & Shimada, Tatsuro, 2025. "Financial distress warning and risk path analysis for Chinese listed companies: An interpretable machine learning approach," Economic Modelling, Elsevier, vol. 152(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hui Li & Ting Sun & Jinquan Zhang, 2024. "Prediction of corporate financial distress based on corporate social responsibility: New evidence from DANP, VWP and MEOWA weights methodologies," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(5), pages 4537-4565, December.
    2. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    3. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    4. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    5. 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.
    6. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    7. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    8. Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    9. Nandita Mishra & Shruti Ashok & Deepak Tandon, 2024. "Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models," Global Business Review, International Management Institute, vol. 25(6), pages 1540-1558, December.
    10. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    11. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    12. 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.
    13. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    14. Lu, Yang-Cheng & Shen, Chung-Hua & Wei, Yu-Chen, 2013. "Revisiting early warning signals of corporate credit default using linguistic analysis," Pacific-Basin Finance Journal, Elsevier, vol. 24(C), pages 1-21.
    15. Çolak, Gönül & Korkeamäki, Timo, 2021. "CEO mobility and corporate policy risk," Journal of Corporate Finance, Elsevier, vol. 69(C).
    16. Almaskati, Nawaf & Bird, Ron & Yeung, Danny & Lu, Yue, 2021. "A horse race of models and estimation methods for predicting bankruptcy," Advances in accounting, Elsevier, vol. 52(C).
    17. 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).
    18. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    19. Thao Tran & Ngoc Hong Nguyen & Binh Thien Le & Nam Thanh Vu & Duc Hong Vo, 2023. "Examining financial distress of the Vietnamese listed firms using accounting-based models," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-17, May.
    20. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:pacfin:v:88:y:2024:i:c:s0927538x24003019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/pacfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.