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Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology

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  • Kahya, Emel
  • Theodossiou, Panayiotis

Abstract

The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit. Copyright 1999 by Kluwer Academic Publishers

Suggested Citation

  • 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.
    • Handle: RePEc:kap:rqfnac:v:13:y:1999:i:4:p:323-45
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    Cited by:

    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. Balcaen S. & Ooghe H., 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?," Vlerick Leuven Gent Management School Working Paper Series 2004-16, Vlerick Leuven Gent Management School.
    3. G. Yi & S. Coleman & Q. Ren, 2006. "CUSUM method in predicting regime shifts and its performance in different stock markets allowing for transaction fees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(7), pages 647-661.
    4. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 196(1), pages 289-311, July.
    5. Amit Sareen & Sudhi Sharma, 2022. "Assessing Financial Distress and Predicting Stock Prices of Automotive Sector: Robustness of Altman Z-score," Vision, , vol. 26(1), pages 11-24, March.
    6. Aaro Hazak & Kadri Männasoo, 2007. "Indicators of corporate default : an EU based empirical study," Bank of Estonia Working Papers 2007-10, Bank of Estonia, revised 04 Sep 2007.
    7. Ch. Spathis & M. Doumpos & C. Zopounidis, 2002. "Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques," European Accounting Review, Taylor & Francis Journals, vol. 11(3), pages 509-535.
    8. 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.
    9. 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.
    10. Brindescu-Olariu Daniel & Golet Ionut, 2013. "Prediction Of Corporate Bankruptcy In Romania Through The Use Of Logistic Regression," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 976-986, July.
    11. Elisa Ughetto & Andrea Vezzulli, 2011. "What role can mutual guarantee consortia play for financing innovation? A firm-level study for Italy," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 3(4), pages 294-319.
    12. 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.
    13. Eka Bertuah & Erlane K. Ghani*, 2018. "Bulls and Bears and Bankruptcy- An Early Warning of Distress," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 962-969:5.
    14. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.
    15. Kosmidou K. & Doumpos M. & Zopounidis C., 2002. "A Multicriteria Hierarchical Discrimination Approach for Credit Risk Problems," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 53-68, January -.
    16. Rida Prihatni SE. Ak. MSi. Author_Email: hatney_yes@yahoo.com & Adam Zakaria SE. Ak. MSi., 2011. "The Financial Performance Analysis Using Altman Z-Score And Its Effect To Stock Price Banking Sector In Indonesian Stock Exchange," 2nd International Conference on Business and Economic Research (2nd ICBER 2011) Proceeding 2011-187, Conference Master Resources.
    17. Ekaterina Tzvetanova, 2019. "Adaptation of the Altman’s Corporate Insolvency Prediction Model – The Bulgarian Case," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 125-142.
    18. 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..
    19. Elisa Ughetto & Andrea Vezzulli, 2008. "Guarantee-backed loans and R&D investments. Do mutual guarantee consortiums value R&D?," KITeS Working Papers 227, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Dec 2008.
    20. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.
    21. repec:arp:tjssrr:2019:p:95-102 is not listed on IDEAS
    22. Huaiwen ZHANG & Adnan Khurshid & Xinyu WANG & Alina Mirela BĂLTĂŢEANU, 2021. "Corporate Financial Risk Assessment and Role of Big Data; New Perspective Using Fuzzy Analytic Hierarchy Process," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 181-199, June.

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