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Predicting private company failure: A multi-class analysis

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  • Jones, Stewart
  • Wang, Tim

Abstract

This study utilizes an advanced machine learning method known as TreeNet® (Salford Systems, 2017) to predict a variety of private company failure states, ranging from binary settings (i.e. failed vs non-failed) to more complex multi-class settings with up to five states of failure. Based on a large global sample, TreeNet® proved to be a significantly better predictor of private company failure than conventional models such as logistic regression. While the out-of-sample predictive performance of TreeNet® is best in binary settings, the model also produces strong area under the ROC curve (AUC) results for the multi-class models. We also find that the predictive performance of financial variables is significantly enhanced when combined with external risk factors such as macro-economic variables and other non-financial measures. The results of this study have several implications for the private company failure literature and the usefulness of machine learning methods in accounting and finance more generally.

Suggested Citation

  • Jones, Stewart & Wang, Tim, 2019. "Predicting private company failure: A multi-class analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 161-188.
    • Handle: RePEc:eee:intfin:v:61:y:2019:i:c:p:161-188
    DOI: 10.1016/j.intfin.2019.03.004
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    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. Everett, Jim & Watson, John, 1998. "Small Business Failure and External Risk Factors," Small Business Economics, Springer, vol. 11(4), pages 371-390, December.
    3. Stewart Jones, 2018. "Special Issue on Earnings Management," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 133-135, June.
    4. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 42-54, January.
    5. Julia Krause & Thorsten Sellhorn & Kamran Ahmed, 2017. "Extreme Uncertainty and Forward-looking Disclosure Properties," Abacus, Accounting Foundation, University of Sydney, vol. 53(2), pages 240-272, June.
    6. Maureen F. McNichols & Stephen R. Stubben, 2018. "Research Design Issues in Studies Using Discretionary Accruals," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 227-246, June.
    7. Jones,Stewart & Hensher,David A. (ed.), 2008. "Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction," Cambridge Books, Cambridge University Press, number 9780521689540.
    8. David Johnstone, 2016. "Advances in Equity Valuation: Research on Accounting Valuation," Abacus, Accounting Foundation, University of Sydney, vol. 52(1), pages 1-4, March.
    9. James Ohlson & Erik Johannesson, 2016. "Equity Value as a Function of (eps1, eps2, dps1, bvps, beta): Concepts and Realities," Abacus, Accounting Foundation, University of Sydney, vol. 52(1), pages 70-99, March.
    10. Thomas R. Dyckman & Stephen A. Zeff, 2015. "Accounting Research: Past, Present, and Future," Abacus, Accounting Foundation, University of Sydney, vol. 51(4), pages 511-524, December.
    11. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    12. Stewart Jones, 2016. "Current issues in CEO compensation," Abacus, Accounting Foundation, University of Sydney, vol. 52(4), pages 611-618, December.
    13. 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.
    14. 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.
    15. James A. Ohlson, 2015. "Accounting Research and Common Sense," Abacus, Accounting Foundation, University of Sydney, vol. 51(4), pages 525-535, December.
    16. Mramor, Dusan & Valentincic, Aljosa, 2003. "Forecasting the liquidity of very small private companies," Journal of Business Venturing, Elsevier, vol. 18(6), pages 745-771, November.
    17. Campbell R. Harvey, 2017. "Presidential Address: The Scientific Outlook in Financial Economics," Journal of Finance, American Finance Association, vol. 72(4), pages 1399-1440, August.
    18. 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.
    19. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    20. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    21. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    22. Yaowen Shan & Terry Walter, 2016. "Towards a Set of Design Principles for Executive Compensation Contracts," Abacus, Accounting Foundation, University of Sydney, vol. 52(4), pages 619-684, December.
    23. Julio Pindado & Luis F. Rodrigues, 2004. "Parsimonious Models of Financial Insolvency in Small Companies," Small Business Economics, Springer, vol. 22(1), pages 51-66, February.
    24. Jae H. Kim & Kamran Ahmed & Philip Inyeob Ji, 2018. "Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 524-546, December.
    25. Graham Hall, 1994. "Factors Distinguishing Survivors From Failures Amongst Small Firms In The Uk Construction Sector," Journal of Management Studies, Wiley Blackwell, vol. 31(5), pages 737-760, September.
    26. Martina K. Linnenluecke & Jacqueline Birt & Xiaoyan Chen & Xin Ling & Tom Smith, 2017. "Accounting Research in Abacus, A&F, AAR, and AJM from 2008–2015: A Review and Research Agenda," Abacus, Accounting Foundation, University of Sydney, vol. 53(2), pages 159-179, June.
    27. Filipe, Sara Ferreira & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Forecasting distress in European SME portfolios," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 112-135.
    28. David A. Hensher & Stewart Jones & William H. Greene, 2007. "An Error Component Logit Analysis of Corporate Bankruptcy and Insolvency Risk in Australia," The Economic Record, The Economic Society of Australia, vol. 83(260), pages 86-103, March.
    29. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    30. Thomas R. Dyckman, 2016. "Significance Testing: We Can Do Better," Abacus, Accounting Foundation, University of Sydney, vol. 52(2), pages 319-342, June.
    31. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    32. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    33. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    34. Bhimani, Alnoor & Gulamhussen, Mohamed Azzim & Lopes, Samuel Da-Rocha, 2010. "Accounting and non-accounting determinants of default: An analysis of privately-held firms," Journal of Accounting and Public Policy, Elsevier, vol. 29(6), pages 517-532, November.
    35. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    36. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, vol. 162(2), pages 312-325, June.
    37. Jones,Stewart & Hensher,David A. (ed.), 2008. "Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction," Cambridge Books, Cambridge University Press, number 9780521869287.
    38. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    39. Ronita Ram & Susan Newberry, 2017. "Agenda Entrance Complexity in International Accounting Standard Setting: The Case of IFRS for SMEs," Abacus, Accounting Foundation, University of Sydney, vol. 53(4), pages 485-512, December.
    40. Gerald J. Lobo & Luc Paugam & Hervé Stolowy & Pierre Astolfi, 2017. "The Effect of Business and Financial Market Cycles on Credit Ratings: Evidence from the Last Two Decades," Abacus, Accounting Foundation, University of Sydney, vol. 53(1), pages 59-93, March.
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    3. Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan, 2022. "Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1386-1415, November.
    4. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
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    6. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    Private company failures; Multi-class; Machine learning; Gradient boosting; Logit; Macroeconomic variables; Accounting-based indicators;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting

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