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Firm Failure Prediction Models: A Critique and a Review of Recent Developments

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Advances in Entrepreneurial Finance

Abstract

This chapter first argues that the literature on financial distress and failure prediction has totally ignored the cause of failure – managers and owner-managers as decision makers – and instead has almost exclusively focused on the effect of failure, the financial data. The chapter then provides a review of the current state of the failure prediction literature. Recent studies that focus on small and medium-sized enterprises (SMEs) are covered next. We arrive at the same conclusion that after 35 years of academic inquiry into bankruptcy prediction, and despite all the sophisticated models and methodologies used in studies of the effects of firm failure, there is “no academic consensus as to the most useful method for ­predicting corporate bankruptcy.” At the end, the chapter discusses how psychological ­phenomena and principles, also known as heuristics or mental shortcuts, might be utilized in building more powerful success/failure prediction models.

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Notes

  1. 1.

    Studies conducted by scholars like Kahneman and Lovallo (1993), Camerer and Lovallo (1999), and Wu and Knott (2005) are among the few exceptions in this respect; as will be further discussed in this chapter.

  2. 2.

    Theoretically, the significance of a board of directors lies in the fact that such a body can “debias” many of the top management’s cognitive biases that could lead to very expensive errors in judgment, including errors leading to failure and bankruptcy. This is also an example regarding the forgotten human/managerial side in failure studies that we intend to emphasize in this chapter.

  3. 3.

    We are not denying the possible roles that macro elements such as economic conditions and ­regulatory factors can play in a given company’s failure. However, we believe our “two element-model” contains the factors that are among the most relevant for explaining why firms develop financial distress or fail.

  4. 4.

    It should be noted that despite all the sophisticated models and methodologies used in studies of the effects of firm failure, it is not surprising that a comprehensive review of the related literature concludes that after 35 years of academic research into bankruptcy prediction, there is “no academic consensus as to the most useful method for predicting corporate bankruptcy.” See Aziz and Dar (2006), p. 26.

  5. 5.

    Beaver cites Fitzpatrick (1932), Winakor and Smith (1935), and Merwin (1942). These studies were unavailable for review for this article but Beaver describes the Fitzpatrick and Merwin studies as comparisons of mean ratios from failed and non-failed firms and the Winakor and Smith study as an analysis of ratio trends over 10 years prior to failure.

  6. 6.

    Beaver does not explain this calculation in any detail but the “no-credit interval” is defined as “immediate assets (current assets excluding inventory and prepaid expenses) minus current liabilities, divided by total operating expenses excluding depreciation” on p. 419 of Palepu et al. (2007)

  7. 7.

    Altman discusses the history and evolution of his work with classical statistical models in Altman (2000), a working paper, and provides discussions of the links between these ratios and the risk of bankruptcy.

  8. 8.

    See Altman (2000) for a summary of these developments and the predictive accuracy of the model using different samples.

  9. 9.

    Altman (2000) notes his lack of a large private firm database prevented him from performing out of sample evaluations of this private firm model.

  10. 10.

    See Altman (1993) for an extensive survey of this work.

  11. 11.

    Ramaswami (1987), Dugan and Forsyth (1995), and Kahya and Theodossiou (1999).

  12. 12.

    Aziz and Dar (2006) provide a table that summarizes the main characteristics of many different statistical approaches that have been used by various authors.

  13. 13.

    See Balcaen and Ooghe (2006) for an extensive review of these shortcomings. See Grice and Dugan (2003) for a discussion of the problems related to LOGIT and PROBIT. See Platt and Platt (2002) for a discussion of the problems associated with the sample selection methodologies used in most studies.

  14. 14.

    See Aziz and Dar (2006) for an excellent overview of these various AI/ES approaches as they are applied to bankruptcy prediction.

  15. 15.

    See, for example, Salchenberger et al. (1992), Zhang et al. (1999), and Serrano-Cinca (1996).

  16. 16.

    There have actually been a number of studies that apply AI/ES approaches to SME data. These studies are discussed in greater detail in Sect. 10.3.

  17. 17.

    See Altman, Sabato and Wilson (2009) for a discussion of these complaints about AI/ES approaches.

  18. 18.

    The remaining studies were based on theoretical models not discussed in this paper.

  19. 19.

    See Aziz and Dar (2006), p. 26.

  20. 20.

    See Aziz and Dar (2006), p. 26.

  21. 21.

    See Keasey and Watson ( 1986 ), Keasey and Watson ( 1987 ), Keasey and Watson ( 1988 ) and Keasey and Watson ( 1991 ).

  22. 22.

    This section is built upon the discussion on cognitive biases in Yazdipour (2009).

  23. 23.

    For a good coverage of the latest literature on the issue see Slovic ( 1972 ), Slovic ( 1987 ), Slovic and Peters ( 2006 ), Olsen ( 2008 ) and Sheffrin ( 2007)

  24. 24.

    Normally, in investment situations investors in high risk assets require high returns. However, from what we have learned from psychology, if an individual develops a “good feeling” (positive affect) for a high-risk investment, she/he may require low return from such an investment.

  25. 25.

    Finucane et al. ( 2000 ), p.2 note that it was Zajonc ( 1980 ) who first made this argument.

  26. 26.

    See Tversky and Kahneman (1973, 1974).

References

  • Abouzeedan, A., and M. Busler. 2004. Typology analysis of performance models of small and medium-size enterprises (SMEs). Journal of International Entrepreneurship 2: 155–177.

    Article  Google Scholar 

  • Altman, E. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23(4): 589–609.

    Article  Google Scholar 

  • Altman, E. 1993. Corporate financial distress and bankruptcy: A complete guide to predicting and avoiding distress and profiting from bankruptcy. New York: Wiley.

    Google Scholar 

  • Altman, E. 2000. Predicting financial distress of companies: Revisiting the Z-score and ZETA® models. New York: New York University.

    Google Scholar 

  • Altman, E.I., and E. Hotchkiss. 2005. Corporate financial distress and bankruptcy, 3rd ed. New York: Wiley.

    Google Scholar 

  • Altman, E.I., G. Marco, and F. Varetto. 1994. Corporate distress diagnosis: comparisons using linear discriminant analysis and the neural networks (the Italian experience) Journal of Banking & Finance 18: 505–529.

    Google Scholar 

  • Altman, E.I., and G. Sabato. 2007. Modeling credit risk for SMEs: Evidence from the US market. ABACUS 43(3): 332–357.

    Article  Google Scholar 

  • Altman, E., R. Haldeman, and P. Narayanan. 1977. ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance 1(1): 29–54.

    Article  Google Scholar 

  • Altman E., N. Wilson, and G. Sabato. 2010. The Value of Non-Financial Information in SME Risk Management. Journal of Credit Risk 6(2): 1–33.

    Article  Google Scholar 

  • Angelini, E., G. di Tollo, and A. Roli. 2008. A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance 48: 733–755.

    Article  Google Scholar 

  • Atiya, A.E. 2001. Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks 12(4): 929–935.

    Article  Google Scholar 

  • Aziz, M.A., and H.A. Dar. 2006. Predicting corporate bankruptcy: Where we stand? Corporate Governance 6(1): 18–33.

    Article  Google Scholar 

  • Balcaen, S., and H. Ooghe. 2006. 35 years of studies on business failure: An overview of the ­classic statistical methodologies and their related problems. The British Accounting Review 38: 63–93.

    Article  Google Scholar 

  • Beaver, W.H. 1966. Financial ratios as predictors of failures. Empirical research in accounting: Selected studies. Journal of Accounting Research 3: 71–111.

    Article  Google Scholar 

  • Camerer, C., and D. Lovallo. 1999. Overconfidence and excess entry: An experimental approach. The American Economic Review 89(1): 306–318.

    Article  Google Scholar 

  • Chen, H., S.Y. Huang, and C.S. Lin. 2009. Alternative diagnosis of corporate bankruptcy: A neuro fuzzy approach. Expert Systems with Applications 36: 7710–7720.

    Article  Google Scholar 

  • Coats, P.K., and L.F. Fant. 1993. Recognizing financial distress patterns using a neural network tool. Financial Management 22(3): 142–155.

    Article  Google Scholar 

  • Cooper, A., C. Woo, and W. Dunkelberg. 1988. Entrepreneurs’ perceived chances for success. Journal of Business Venturing 3: 97–108.

    Article  Google Scholar 

  • Dimitras, A.I., R. Slowinski, R. Susmaga, and C. Zoupounidis. 1999. Business failure prediction using rough sets. European Journal of Operation Research 114(2): 263–280.

    Article  Google Scholar 

  • Dugan, M., and T. Forsyth. 1995. The relationship between bankruptcy model predictions and stock market perceptions of bankruptcy. The Financial Review 30: 507–528.

    Article  Google Scholar 

  • Edmister, R. 1972. An empirical test to financial ratio analysis for small business failure prediction. The Journal of Financial and Quantitative Analysis 7: 1477–1493.

    Article  Google Scholar 

  • Etemadi, H., A.A. Rostamy, and H.F. Dehkordi. 1990. A genetic programming model for bankruptcy prediction: Empirical evidence from Iran. Expert Systems with Applications 36: 3199–3207.

    Article  Google Scholar 

  • Finucane, M.L., A. Alhakami, P. Slovic, and S.M. Johnson. 2000. The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making 13: 1–17.

    Article  Google Scholar 

  • Fitzparick, P.J. 1932. A comparison of ratios of successful industrial enterprises with those of failed firms. Certified Public Accountant, pp. 598–605, 656–662, 727–731.

    Google Scholar 

  • Grice, J.S., and M.T. Dugan. 2003. Re-estimation of the Zmijewski and Ohlson bankruptcy prediction models. Advances in Accounting 20: 77–93.

    Article  Google Scholar 

  • Grunert, J., L. Norden, and M. Weber. 2004. The role of non-financial factors in internal credit ratings. Journal of Banking and Finance 29(2): 509–531.

    Article  Google Scholar 

  • Kahneman, D., and D. Lovallo. 1993. Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science 39(1): 17–31.

    Article  Google Scholar 

  • Kahya E., and P.T. Theodossiou. 1999. Predicting corporate financial distress: A time-series CUSUM methodology. Review of Quantitative Finance and Accounting, 13(4): 323–345.

    Article  Google Scholar 

  • Kallberg, J.G., and G.F. Udell. 2003. The value of private sector business credit information sharing: The US case. Journal of Banking and Finance 27: 449–469.

    Article  Google Scholar 

  • Keasey, K., and R. Watson. 1986. The prediction of small company failure: Some behavioural evidence for the UK. Accounting and Business Research 17(65): 49–57.

    Google Scholar 

  • Keasey, K., and R. Watson. 1987. Non-financial symptoms and the prediction of small business failure: A test of the Argenti Hypothesis. Journal of Business, Finance & Accounting 14(3): 335–354.

    Article  Google Scholar 

  • Keasey, K., and R. Watson. 1988. The non-submission of accounts and small company financial failure prediction. Accounting and Business Research 19: 47–54.

    Google Scholar 

  • Keasey, K., and R. Watson. 1991. The state of the art of small firm failure prediction: Achievements and prognosis. International Small Business Journal 9(4): 11–29.

    Article  Google Scholar 

  • Kim, H.S., and S.Y. Sohn. 2010. Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research 201: 838–846.

    Article  Google Scholar 

  • Lee, K.C., I. Han, and Y. Kwon. 1996. Hybrid neural network models for bankruptcy predictions. Decision Support Systems 18(1): 63–72.

    Article  Google Scholar 

  • Merwin, C.L. 1942. Financing small corporations in five manufacturing industries, 1926-1936. New York: National Bureau of Economic Research.

    Google Scholar 

  • Meyer, P.A., and H. Pifer. 1970. Prediction of bank failures. The Journal of Finance 25: 853–868.

    Article  Google Scholar 

  • Minniti, M., and M. Levesque. 2008. Recent developments in the economics of entrepreneurship. Journal of Business Venturing 23: 603–612.

    Article  Google Scholar 

  • Mramor, D., and A. Valentincic. 2002. Forecasting the liquidity of very small private companies. Journal of Business Venturing 18: 745–771.

    Article  Google Scholar 

  • Ohlson, J.A. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18(1): 109–131.

    Article  Google Scholar 

  • Olsen, R.A. 2008. Perceptions of financial risk: Axioms and affect. The Icfai University Journal of Behavioral Finance 5(4): 58–80.

    Google Scholar 

  • Palepu, K.G., E. Peek, V.L. Bernard, and E. Peck. 2007. Business analysis and valuation: Text and cases. London: Thompson Learning.

    Google Scholar 

  • Platt, H.D., and M.B. Platt. 2002. Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance 26: 184–199.

    Article  Google Scholar 

  • Pompe, P., and J. Bilderbeek. 2005. The prediction of bankruptcy of small and medium sized industrial firms. Journal of Business Venturing 20: 847–868.

    Article  Google Scholar 

  • Ramaswami, M. 1987. Stock market perception of industrial firm bankruptcy. The Financial Review 22(2): 267–279.

    Article  Google Scholar 

  • Roll, R. 1986. The Hubris hypothesis of corporate takeover. Journal of Business 59(2): 197–216.

    Article  Google Scholar 

  • Salchenberger, L., E. Cinar, and N. Lash. 1992. Neural networks: A new tool for predicting thrift failures. Decision Sciences 23: 899–916.

    Article  Google Scholar 

  • Serrano-Cinca, C. 1996. Self organizing neural networks for financial diagnosis. Decision Support Systems 17: 227–238.

    Article  Google Scholar 

  • Sheffrin, H. 2007. Behavioral corporate finance: Decisions that create value. Irwin: McGraw Hill.

    Google Scholar 

  • Simon, M., S.H. Houghton, and K. Aquino. 1999. Cognitive biases, risk perception, and venture formation: How individuals decide to start companies. Journal of Business Venturing 15: 113–134.

    Article  Google Scholar 

  • Slovic, P. 1972. Psychological study of human judgment: Implications for investment decision making. The Journal of Finance 27(4): 779–799.

    Article  Google Scholar 

  • Slovic, P. 1987. Perception of risk. Science 236(4799): 280–286.

    Article  Google Scholar 

  • Slovic, P., and E. Peters. 2006. Risk perception and affect. Current Directions in Psychological Science 15(6): 322–326.

    Article  Google Scholar 

  • Tversky, A., and D. Kahneman. 1973. Availability: A heuristic for judging frequency and probability. Cognitive Psychology 4: 207–232.

    Article  Google Scholar 

  • Tversky, A., and D. Kahneman. 1974. Judgment under uncertainly: Heuristics and biases. Science, New Series 185(4157): 1124–1131.

    Google Scholar 

  • Vallini, C., F. Ciampi, and N. Gordini. 2009. Using artificial neural networks analysis for small enterprise default prediction modeling: Statistical evidence from Italian firms. Oxford Business and Economics Conference Program. http://www.docstoc.com/docs/21423327/Using-Artificial-Neural-Networks-Analysis-For-Small-Enterprise/.

  • Varetto, F. 1998. Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking & Finance 22: 1421–1439.

    Article  Google Scholar 

  • Wiklund, J., T. Baker, and D. Shepherd. 2008. The age-effect of financial indicators as buffers against the liability of newness. Journal of Business Venturing (forthcoming). doi:10.1016/j.jbusvent.2008.10.011.

    Google Scholar 

  • Winakor, A., and R.F. Smith. 1935. Changes in financial structure of unsuccessful industrial companies. Bureau of Business Research, Bulletin No. 51 Urbana: University of Illinois Press.

    Google Scholar 

  • Wu, B. and A.M. Knott. 2005. Entrepreneurial risk and market entry. Working Paper, U.S. Small Business Administration.

    Google Scholar 

  • Yazdipour, R. 2009. What can venture capitalists and entrepreneurs learn from behavioral economists? Journal of Strategic Change Management 18: 241–247.

    Article  Google Scholar 

  • Zajonc, R.B. 1980. Feeling and thinking: Preferences need no inferences. The American Psychologist 35: 151–175.

    Article  Google Scholar 

  • Zhang, G., M.Y. Hu, B.E. Patuwo, and D.C. Indro. 1999. Artificial neural networks in bankruptcy prediction: General framework and cross-validation. European Journal of Operational Research 116: 16–32.

    Article  Google Scholar 

  • Zmijewski, M.E. 1984. Methodological issues related to the estimation of financial distress ­prediction models. Journal of Accounting Research (Studies on Current Econometric Issues in Accounting Research) 22: 59–82.

    Google Scholar 

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  1. California State University, Fresno, CA, USA

    Richard L. Constand

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  1. Richard L. Constand
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  2. Rassoul Yazdipour
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Correspondence to Richard L. Constand .

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Constand, R.L., Yazdipour, R. (2011). Firm Failure Prediction Models: A Critique and a Review of Recent Developments. In: Advances in Entrepreneurial Finance. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7527-0_10

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