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Analysts’ bankruptcy prediction: revisiting the information value added by financial experts

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  • Gavious, Ilanit
  • Milo, Orit
  • Weihs, Hagit

Abstract

This study reexamines the question of the efficacy of information conveyed in analysts’ earnings forecasts (AEFs) in predicting firm bankruptcy. Utilizing an extended dataset and an enhanced econometric framework, we revisit and extend the seminal work of Moses (1990) to address limitations and provide comprehensive insights. We investigate the predictive power of analysts in anticipating bankruptcy, considering their forecasts and the level of coverage alongside accounting and market data. Our multivariate analyses reveal that analysts’ information significantly enhances bankruptcy prediction models, even after accounting for endogeneity effects and the ex-ante probability of failure. These findings underscore the complementary role analysts play as a source of predictive information for identifying impending bankruptcy, offering valuable insights for regulators, investors, and stakeholders to mitigate financial distress. Our study adds to the literature by demonstrating the incremental contribution of analysts’ information in bankruptcy prediction, beyond traditional accounting and market metrics. Future studies could reexamine the added value of additional informed players in the capital markets.

Suggested Citation

  • Gavious, Ilanit & Milo, Orit & Weihs, Hagit, 2025. "Analysts’ bankruptcy prediction: revisiting the information value added by financial experts," Finance Research Letters, Elsevier, vol. 85(PD).
    • Handle: RePEc:eee:finlet:v:85:y:2025:i:pd:s1544612325013935
    DOI: 10.1016/j.frl.2025.108138
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    References listed on IDEAS

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    1. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 45(3), pages 809-829, August.
    2. Afik, Zvika & Galil, Koresh, 2025. "Have ratings become more accurate?," Journal of Banking & Finance, Elsevier, vol. 170(C).
    3. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Viewpoint: Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics, Canadian Economics Association, vol. 45(3), pages 809-829, August.
    4. Alex Frino & Stewart Jones & Jin Boon Wong, 2007. "Market behaviour around bankruptcy announcements: evidence from the Australian Stock Exchange," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 47(4), pages 713-730, December.
    5. Santhosh Ramalingegowda, 2014. "Evidence from impending bankrupt firms that long horizon institutional investors are informed about future firm value," Review of Accounting Studies, Springer, vol. 19(2), pages 1009-1045, June.
    6. repec:bla:jfinan:v:59:y:2004:i:3:p:1083-1124 is not listed on IDEAS
    7. Ederington, Louis H. & Goh, Jeremy C., 1998. "Bond Rating Agencies and Stock Analysts: Who Knows What When?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(4), pages 569-585, December.
    8. 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.
    9. McNichols, M & O'Brien, PC, 1997. "Self-selection and analyst coverage," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 35, pages 167-199.
    10. Brad Barber & Reuven Lehavy & Maureen McNichols & Brett Trueman, 2001. "Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns," Journal of Finance, American Finance Association, vol. 56(2), pages 531-563, April.
    11. 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.
    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, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    14. Jeffrey M. Wooldridge, 2015. "Control Function Methods in Applied Econometrics," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 420-445.
    15. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    16. Afik, Zvika & Arad, Ohad & Galil, Koresh, 2016. "Using Merton model for default prediction: An empirical assessment of selected alternatives," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 43-67.
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