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Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach

Author

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  • Qunfeng LIAO

    (School of Management, The University of Michigan-Flint, 303 E. Kearsley Street, Flint, MI 48502, (810) 762-3266, qunliao@umflint.edu)

  • Seyed MEHDIAN

    (School of Management, The University of Michigan-Flint, 303 E. Kearsley Street, Flint, MI 48502, (810) 762-3266, qunliao@umflint.edu)

Abstract

In this paper, we follow Anderson et al. (2009) and suggest a simple approach to employ a set of financial ratios as inputs to estimate an aggregate bankruptcy index (ABI). This index is a within sample measure, ranges between 0 and 1, and ranks the firms on the basis of their relative financial distress. ABI can be used to predict the propensity of financial failure and corporate bankruptcy. For the purpose of comparison and assessment of the robustness of this index, we estimate Z-score by multivariate discriminant analysis, using the same set of financial ratios to compare the predictive accuracy of two approaches. We find that, to some extent, ABI can predict the bankruptcy of the firms more accurately than Z-score. The empirical results of the paper suggest that ABI has relatively robust predictive power and, therefore, can be applied together with other, based on parametric and non-parametric models to predict corporate bankruptcy.

Suggested Citation

  • Qunfeng LIAO & Seyed MEHDIAN, 2016. "Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 33-51, June.
    • Handle: RePEc:aic:revebs:y:2016:j:17:liaoq
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    References listed on IDEAS

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    1. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    2. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    3. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    4. Anderson, Ronald C. & Duru, Augustine & Reeb, David M., 2009. "Founders, heirs, and corporate opacity in the United States," Journal of Financial Economics, Elsevier, vol. 92(2), pages 205-222, May.
    5. Ruey-Ching Hwang & Huimin Chung & Jiun-Yi Ku, 2013. "Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market," Journal of Financial Services Research, Springer;Western Finance Association, vol. 43(3), pages 321-341, June.
    6. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    7. Edward I. Altman & Ben Branch, 2015. "The Bankruptcy System's Chapter 22 Recidivism Problem: How Serious is It?," The Financial Review, Eastern Finance Association, vol. 50(1), pages 1-26, January.
    8. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    9. Scott, James, 1981. "The probability of bankruptcy: A comparison of empirical predictions and theoretical models," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 317-344, September.
    10. Wu, Y. & Gaunt, C. & Gray, S., 2010. "A comparison of alternative bankruptcy prediction models," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(1), pages 34-45.
    11. Edward I. Altman, 1973. "Predicting Railroad Bankruptcies in America," Bell Journal of Economics, The RAND Corporation, vol. 4(1), pages 184-211, Spring.
    12. 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.
    13. Li, Ming-Yuan Leon & Miu, Peter, 2010. "A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 818-833, September.
    14. 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.
    15. 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.
    16. 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.
    17. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
    18. Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
    19. James P. Bedingfield & Philip M. J. Reckers & A. J. Stagliano, 1985. "Distributions Of Financial Ratios In The Commercial Banking Industry," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 8(1), pages 77-81, March.
    20. Mensah, Ym, 1984. "An Examination Of The Stationarity Of Multivariate Bankruptcy Prediction Models - A Methodological Study," Journal of Accounting Research, Wiley Blackwell, vol. 22(1), pages 380-395.
    21. 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.
    22. 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.
    23. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    24. 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.
    25. Johnsen, Thomajean & Melicher, Ronald W., 1994. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models," Journal of Economics and Business, Elsevier, vol. 46(4), pages 269-286, October.
    26. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
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    2. Kong, Qunxi & Li, Rongrong & Wang, Ziqi & Peng, Dan, 2022. "Economic policy uncertainty and firm investment decisions: Dilemma or opportunity?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    3. Munene Halldess Nguta & Ken Mugambi, 2021. "Analysis of the Related Party Transactions interms of managerial and financial issues for the Kenyan Savings and Credit Cooperatives," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 10(1), pages 48-61, January.
    4. Chuanzhen Li & Liang Zhao & Yiwen Zhang, 2024. "Economic policy uncertainty and cash dividend policy: evidence from China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    5. Lucie Kureková & Pavlína Hejduková, 2016. "Construction Industry and Payment Discipline in the Czech Republic," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2016(3), pages 53-68.
    6. Halldess Nguta Munene & James Ndegwa & Thomas Senaji & Kenneth M. Mugambi, 2020. "Influence of Board Characteristics on Financial Distress of Deposit Taking SACCOs in Nairobi County, Kenya," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 9(4), pages 97-110, October.
    7. Javed Iqbal & Furrukh Bashir & Rashid Ahmad & Hina Arshad, 2022. "Predicting Bankruptcy through Neural Network:Case of PSX Listed Companies," iRASD Journal of Management, International Research Alliance for Sustainable Development (iRASD), vol. 4(2), pages 299-315, june.

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    More about this item

    Keywords

    corporate bankruptcy prediction; financial distress; aggregate bankruptcy index;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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