IDEAS home Printed from https://ideas.repec.org/p/bog/wpaper/188.html
   My bibliography  Save this paper

On corporate financial distress prediction: what can we learn from private firms in a small open economy?

Author

Listed:
  • Evangelos C. Charalambakis

    (Bank of Greece)

Abstract

We use a large panel dataset that includes nearly 31,000 Greek private firms to investigate which variables impact on the prediction of corporate financial distress. Based on a multi-period logit model that accounts for industry effects, we identify six firm-specific variables that best describe the probability of financial distress for Greek private firms. In particular, the results show that profitability, leverage, the ratio of retained earnings to total assets, the ability of a firm to export, liquidity and the ability of a firm to pay out dividends are strong predictors of financial distress. We also find that GDP growth and a dummy variable that considers the effect of the Greek debt crisis affect the probability of financial distress. In-sample and out-of-sample forecast tests show that the model that includes the six firm-specific variables , GDP growth and industry dummies exhibits the highest predictive ability. Finally, the predictive ability of the model remains high when we increase the forecast horizon.

Suggested Citation

  • Evangelos C. Charalambakis, 2014. "On corporate financial distress prediction: what can we learn from private firms in a small open economy?," Working Papers 188, Bank of Greece.
    • Handle: RePEc:bog:wpaper:188
    as

    Download full text from publisher

    File URL: http://www.bankofgreece.gr/BogEkdoseis/Paper2014188.pdf
    File Function: Full Text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. 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.
    4. 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.
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    7. Omer Brav, 2009. "Access to Capital, Capital Structure, and the Funding of the Firm," Journal of Finance, American Finance Association, vol. 64(1), pages 263-308, February.
    8. 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.
    9. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    10. 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.
    11. Roni Michaely & Michael R. Roberts, 2012. "Corporate Dividend Policies: Lessons from Private Firms," Review of Financial Studies, Society for Financial Studies, vol. 25(3), pages 711-746.
    12. Dierkes, Maik & Erner, Carsten & Langer, Thomas & Norden, Lars, 2013. "Business credit information sharing and default risk of private firms," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2867-2878.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    14. Maria Vassalou & Yuhang Xing, 2004. "Default Risk in Equity Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 831-868, April.
    15. Gao, Huasheng & Harford, Jarrad & Li, Kai, 2012. "CEO pay cuts and forced turnover: Their causes and consequences," Journal of Corporate Finance, Elsevier, vol. 18(2), pages 291-310.
    16. 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.
    17. John Asker & Joan Farre-Mensa & Alexander Ljungqvist, 2011. "Comparing the Investment Behavior of Public and Private Firms," NBER Working Papers 17394, National Bureau of Economic Research, Inc.
    18. 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.
    19. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    20. Petersen, Mitchell A & Rajan, Raghuram G, 1994. "The Benefits of Lending Relationships: Evidence from Small Business Data," Journal of Finance, American Finance Association, vol. 49(1), pages 3-37, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Josef Baumgartner & Jürgen Bierbaumer-Polly & Marian Fink & Klaus S. Friesenbichler & Serguei Kaniovski & Michael Klien & Simon Loretz & Hans Pitlik & Silvia Rocha-Akis & Franz Sinabell & Alexander Sc, 2020. "Ökonomische Bewertung der in der Regierungsklausur am 16. Juni 2020 vorgestellten Maßnahmen," WIFO Studies, WIFO, number 66415, February.
    2. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research," Management Review Quarterly, Springer, vol. 72(4), pages 927-966, December.

    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. Evangelos C. Charalambakis & Ian Garrett, 2019. "On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 467-491, February.
    2. Evangelos C. Charalambakis, 2015. "On the Prediction of Corporate Financial Distress in the Light of the Financial Crisis: Empirical Evidence from Greek Listed Firms," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 22(3), pages 407-428, November.
    3. Evangelos C. Charalambakis & Ian Garrett, 2016. "On the prediction of financial distress in developed and emerging markets: Does the choice of accounting and market information matter? A comparison of UK and Indian Firms," Review of Quantitative Finance and Accounting, Springer, vol. 47(1), pages 1-28, July.
    4. Cathcart, Lara & Dufour, Alfonso & Rossi, Ludovico & Varotto, Simone, 2020. "The differential impact of leverage on the default risk of small and large firms," Journal of Corporate Finance, Elsevier, vol. 60(C).
    5. 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.
    6. Paulo V. Carvalho & José D. Curto & Rodrigo Primor, 2022. "Macroeconomic determinants of credit risk: Evidence from the Eurozone," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2054-2072, April.
    7. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    8. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    9. Bai, Qing & Tian, Shaonan, 2020. "Innovate or die: Corporate innovation and bankruptcy forecasts," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 88-108.
    10. 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.
    11. Maurice Peat & Stewart Jones, 2012. "Using Neural Nets To Combine Information Sets In Corporate Bankruptcy Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 90-101, April.
    12. Nidhi Aggarwal & Manish K. Singh & Susan Thomas, 2022. "Informational efficiency of credit ratings," Working Papers 14, xKDR.
    13. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.
    14. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    15. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research," Management Review Quarterly, Springer, vol. 72(4), pages 927-966, December.
    16. B Korcan Ak & Patricia M Dechow & Yuan Sun & Annika Yu Wang, 2013. "The use of financial ratio models to help investors predict and interpret significant corporate events," Australian Journal of Management, Australian School of Business, vol. 38(3), pages 553-598, December.
    17. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    18. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    19. Elizabeth Gutierrez & Jake Krupa & Miguel Minutti-Meza & Maria Vulcheva, 2020. "Do going concern opinions provide incremental information to predict corporate defaults?," Review of Accounting Studies, Springer, vol. 25(4), pages 1344-1381, December.
    20. Andrikopoulos, Panagiotis & Khorasgani, Amir, 2018. "Predicting unlisted SMEs' default: Incorporating market information on accounting-based models for improved accuracy," The British Accounting Review, Elsevier, vol. 50(5), pages 559-573.

    More about this item

    Keywords

    corporate financial distress; bankruptcy prediction; hazardmodel; financial statements;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:bog:wpaper:188. 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: Anastasios Rizos (email available below). General contact details of provider: https://edirc.repec.org/data/boggvgr.html .

    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.