IDEAS home Printed from https://ideas.repec.org/a/kap/jmgtgv/v27y2023i2d10.1007_s10997-022-09643-8.html
   My bibliography  Save this article

Corporate governance and financial distress: lessons learned from an unconventional approach

Author

Listed:
  • Alberto Tron

    (Università Commerciale L. Bocconi)

  • Maurizio Dallocchio

    (Università Commerciale L. Bocconi)

  • Salvatore Ferri

    (Università degli Studi Parthenope)

  • Federico Colantoni

    (University of St. Gallen)

Abstract

Using a and a unique set of Italian non-listed Unlikely to Pay (UTP) positions, that consist in the phase that precedes the insolvency but where it is still possible for the company to succeed in restructuring, this paper aims to analyze the relationships between corporate governance characteristics and financial distress status. We compare the performance of corporate governance variables in predicting corporate defaults, using both the Logit and Random Forest models, which previous researchers have deemed to be the most efficient machine learning techniques. Our results show that the use of corporate governance variables – especially with regards to CEO renewal and stability in the composition of the board of directors – increases the accuracy of the Random Forest technique and influences the success of the turnaround process. This paper also confirms the Random Forest technique’s ability to significantly outperform the Logit model in terms of accuracy.

Suggested Citation

  • Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    • Handle: RePEc:kap:jmgtgv:v:27:y:2023:i:2:d:10.1007_s10997-022-09643-8
    DOI: 10.1007/s10997-022-09643-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10997-022-09643-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10997-022-09643-8?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    2. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    3. La Porta, Rafael & Lopez-de-Silanes, Florencio & Shleifer, Andrei & Vishny, Robert, 2000. "Investor protection and corporate governance," Journal of Financial Economics, Elsevier, vol. 58(1-2), pages 3-27.
    4. Michael C. Jensen, 2010. "The Modern Industrial Revolution, Exit, and the Failure of Internal Control Systems," Journal of Applied Corporate Finance, Morgan Stanley, vol. 22(1), pages 43-58, January.
    5. Gales, Lawrence M. & Kesner, Idalene F., 1994. "An analysis of board of director size and composition in bankrupt organizations," Journal of Business Research, Elsevier, vol. 30(3), pages 271-282, July.
    6. Piesse, J. & Wood, D., 1992. "Issues in assessing MDA models of corporate failure: A research note," The British Accounting Review, Elsevier, vol. 24(1), pages 33-42.
    7. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    8. Fahlenbrach, Rüdiger & Stulz, René M., 2009. "Managerial ownership dynamics and firm value," Journal of Financial Economics, Elsevier, vol. 92(3), pages 342-361, June.
    9. Warner, Jerold B. & Watts, Ross L. & Wruck, Karen H., 1988. "Stock prices and top management changes," Journal of Financial Economics, Elsevier, vol. 20(1-2), pages 461-492, January.
    10. Kahya, Emel & Theodossiou, Panayiotis, 1999. "Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology," Review of Quantitative Finance and Accounting, Springer, vol. 13(4), pages 323-345, December.
    11. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    12. Luigi Zingales, 2000. "In Search of New Foundations," Journal of Finance, American Finance Association, vol. 55(4), pages 1623-1653, August.
    13. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    14. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    15. Manzaneque, Montserrat & Priego, Alba María & Merino, Elena, 2016. "Corporate governance effect on financial distress likelihood: Evidence from Spain," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 19(1), pages 111-121.
    16. 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.
    17. Yermack, David, 1996. "Higher market valuation of companies with a small board of directors," Journal of Financial Economics, Elsevier, vol. 40(2), pages 185-211, February.
    18. Daniel Bjorkegren & Darrell Grissen, 2017. "Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment," Papers 1712.05840, arXiv.org, revised Dec 2019.
    19. 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.
    20. Andrea Caputo & Alberto Tron, 2016. "The attestation of corporate turnaround plans in Italy: operating problems and possible solutions," International Journal of Critical Accounting, Inderscience Enterprises Ltd, vol. 8(1), pages 30-44.
    21. Orlando Llanos-Contreras & Jose Arias & Carlos Maquieira, 2021. "Risk taking behavior in Chilean listed family firms: a socioemotional wealth approach," International Entrepreneurship and Management Journal, Springer, vol. 17(1), pages 165-184, March.
    22. Mauro Paoloni & Massimiliano Celli, 2018. "Crisi delle PMI e strumenti di warning. Un test di verifica nel settore manifatturiero," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2), pages 85-106.
    23. Foreman, R. Dean, 2003. "A logistic analysis of bankruptcy within the US local telecommunications industry," Journal of Economics and Business, Elsevier, vol. 55(2), pages 135-166.
    24. 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.
    25. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    26. L. Lin & J. Piesse, 2004. "Identification of corporate distress in UK industrials: a conditional probability analysis approach," Applied Financial Economics, Taylor & Francis Journals, vol. 14(2), pages 73-82.
    27. Huson, Mark R. & Malatesta, Paul H. & Parrino, Robert, 2004. "Managerial succession and firm performance," Journal of Financial Economics, Elsevier, vol. 74(2), pages 237-275, November.
    28. Han Donker & Bernard Santen & Saif Zahir, 2009. "Ownership structure and the likelihood of financial distress in the Netherlands," Applied Financial Economics, Taylor & Francis Journals, vol. 19(21), pages 1687-1696.
    29. Altman, Edward I. & Saunders, Anthony, 1997. "Credit risk measurement: Developments over the last 20 years," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1721-1742, December.
    30. 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.
    31. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    32. 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.
    33. Davis, E. Philip & Karim, Dilruba, 2008. "Comparing early warning systems for banking crises," Journal of Financial Stability, Elsevier, vol. 4(2), pages 89-120, June.
    34. Kim, Yungsan, 1996. "Long-Term Firm Performance and Chief Executive Turnover: An Empirical Study of the Dynamics," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 12(2), pages 480-496, October.
    35. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    36. Velia Gabriella Cenciarelli & Giulio Greco & Marco Allegrini, 2018. "External audit and bankruptcy prediction," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 22(4), pages 863-890, December.
    37. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    38. 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.
    39. Switzer, Lorne N. & Tu, Qiao & Wang, Jun, 2018. "Corporate governance and default risk in financial firms over the post-financial crisis period: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 196-210.
    40. Jerry Goodstein & Kanak Gautam & Warren Boeker, 1994. "The effects of board size and diversity on strategic change," Strategic Management Journal, Wiley Blackwell, vol. 15(3), pages 241-250, March.
    41. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    42. Giovanni Schiuma & Antonio Lerro & Damiano Sanitate, 2008. "The Intellectual Capital Dimensions Of Ducati'S Turnaround: Exploring Knowledge Assets Grounding A Change Management Program," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 12(02), pages 161-193.
    43. Bhimani, Alnoor & Gulamhussen, Mohamed Azzim & Lopes, Samuel, 2009. "The effectiveness of the auditor's going-concern evaluation as an external governance mechanism: Evidence from loan defaults," The International Journal of Accounting, Elsevier, vol. 44(3), pages 239-255, September.
    44. 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.
    45. McGurr, Paul T. & DeVaney, Sharon A., 1998. "Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models," Journal of Business Research, Elsevier, vol. 43(3), pages 169-176, November.
    46. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    47. Seema Miglani & Kamran Ahmed & Darren Henry, 2020. "Corporate governance and turnaround: Evidence from Australia," Australian Journal of Management, Australian School of Business, vol. 45(4), pages 549-578, November.
    48. Ligang Zhou & Kin Lai & Jerome Yen, 2014. "Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 241-253.
    49. Brockman, Paul & Turtle, H. J., 2003. "A barrier option framework for corporate security valuation," Journal of Financial Economics, Elsevier, vol. 67(3), pages 511-529, March.
    50. 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.
    51. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    52. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    53. Tesi Aliaj & Aris Anagnostopoulos & Stefano Piersanti, 2020. "Firms Default Prediction with Machine Learning," Papers 2002.11705, arXiv.org.
    54. Jingsi Leng & Aydin Ozkan & Neslihan Ozkan & Agnieszka Trzeciakiewicz, 2021. "CEO overconfidence and the probability of corporate failure: evidence from the United Kingdom," The European Journal of Finance, Taylor & Francis Journals, vol. 27(12), pages 1210-1234, August.
    55. Westgaard, Sjur & van der Wijst, Nico, 2001. "Default probabilities in a corporate bank portfolio: A logistic model approach," European Journal of Operational Research, Elsevier, vol. 135(2), pages 338-349, December.
    Full references (including those not matched with items on IDEAS)

    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. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    2. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    3. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    4. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
    5. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2022. "Does board committee independence affect financial distress likelihood? A comparison of China with the UK," Asia Pacific Journal of Management, Springer, vol. 39(2), pages 723-761, June.
    6. 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.
    7. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    8. Manzaneque, Montserrat & Priego, Alba María & Merino, Elena, 2016. "Corporate governance effect on financial distress likelihood: Evidence from Spain," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 19(1), pages 111-121.
    9. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    10. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    17. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    18. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    19. Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
    20. Ashraf, Sumaira & Félix, Elisabete G.S. & Serrasqueiro, Zélia, 2020. "Development and testing of an augmented distress prediction model: A comparative study on a developed and an emerging market," Journal of Multinational Financial Management, Elsevier, vol. 57.

    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:kap:jmgtgv:v:27:y:2023:i:2:d:10.1007_s10997-022-09643-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.