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Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach

Author

Listed:
  • 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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    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. 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.
    5. 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.
    6. 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.

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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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