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Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios

Published online by Cambridge University Press:  23 January 2015

Paolo Giordani ,
Tor Jacobson ,
Erik von Schedvin  and
Mattias Villani
Paolo Giordani
Affiliation:
[email protected], Research Division, Sveriges Riksbank, Stockholm, SE-103 37, Sweden
Tor Jacobson
Affiliation:
[email protected], Research Division, Sveriges Riksbank, Stockholm, SE-103 37, Sweden
Erik von Schedvin
Affiliation:
[email protected], Research Division, Sveriges Riksbank, Stockholm, SE-103 37, Sweden
Mattias Villani
Affiliation:
[email protected], Division of Statistics, Linköping University, Linköping, 581 83, Sweden.
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Abstract

We demonstrate improvements in predictive power when introducing spline functions to take account of highly nonlinear relationships between firm failure and leverage, earnings, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive nonlinearities yields substantially improved bankruptcy predictions, on the order of 70%–90%, compared with a standard logistic model. The spline model provides several important and surprising insights into nonmonotonic bankruptcy relationships. We find that low-leveraged as well as highly profitable firms are riskier than those given by a standard model, possibly a manifestation of credit rationing and excess cash-flow volatility.

Type
Research Articles
Information
Journal of Financial and Quantitative Analysis , Volume 49 , Issue 4 , August 2014 , pp. 1071 - 1099
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2015 

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References

Agarwal, R., and Gort, M.. “The Evolution of Markets and Entry, Exit and Survival of Firms.” Review of Economics and Statistics, 78 (1996), 489–498.Google Scholar
Almeida, H.; Campello, M.; and Weisbach, M.. “The Cash Flow Sensitivity of Cash.” Journal of Finance, 59 (2004), 1777–1804.CrossRefGoogle Scholar
Altman, E. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” Journal of Finance, 23 (1968), 589–611.Google Scholar
Altman, E. “Railroad Bankruptcy Propensity.” Journal of Finance, 26 (1971), 333–345.Google Scholar
Altman, E. “The Success of Business Failure Prediction Models.” Journal of Banking and Finance, 8 (1984), 171–198.Google Scholar
Altman, E. “Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta Models.” Working Paper, New York University (2000).Google Scholar
Altman, E., and Narayanan, P.. “An International Survey of Business Failure Classification Models.” Financial Markets, Institutions & Instruments, 6 (1997), 1–57.Google Scholar
Altman, E., and Saunders, A.. “Credit Risk Measurement: Developments over the Last Twenty Years.” Journal of Banking and Finance, 21 (1997), 1721–1742.Google Scholar
Beaver, W. “Financial Ratios as Predictors of Failure.” Journal of Accounting Research, 4 (1966), 71–111.CrossRefGoogle Scholar
Berg, D. “Bankruptcy Prediction by Generalized Additive Models.” Applied Stochastic Models in Business and Industry, 23 (2007), 129–143.Google Scholar
Bharath, S., and Shumway, T.. “Forecasting Default with the Merton Distance to Default Model.” Review of Financial Studies, 21 (2008), 1339–1369.Google Scholar
Campbell, J.; Hilscher, J.; and Szilagyi, J.. “In Search of Distress Risk.” Journal of Finance, 63 (2008), 2899–2939.Google Scholar
Chava, S., and Jarrow, R.. “Bankruptcy Prediction with Industry Effects.” Review of Finance, 8 (2004), 537–569.Google Scholar
Dakovic, R.; Czado, C.; and Berg, D.. “Bankruptcy Prediction in Norway: A Comparison Study.” Applied Economics Letters, 17 (2010), 1739–1746.Google Scholar
Denison, D.; Mallick, B.; and Smith, A.. “Bayesian Methods for Nonlinear Classification and Regression.” Chicester, UK: John Wiley & Sons (2003).Google Scholar
Hastie, T., and Tibshirani, R.. Generalized Additive Models. Boca Raton, FL: Chapman & Hall (1990).Google Scholar
Hastie, T.; Tibshirani, R.; and Friedman, J.. The Elements of Statistical Learning, 2nd ed., Springer Series in Statistics. New York: Springer (2009).Google Scholar
Hosmer, D., and Lemeshow, S.. Applied Logistic Regression, 2nd ed. New York: Wiley (2000).Google Scholar
Jacobson, T.; Lindé, J.; and Roszbach, K.. “Firm Default and Aggregate Fluctuations.” Journal of the European Economic Association, 11 (2013), 945–972.Google Scholar
Jensen, M. “Agency Costs of Free Cash Flow, Corporate Finance and Takeovers.” American Economic Review, 76 (1986), 323–329.Google Scholar
Jovanovic, B. “Selection and the Evolution of Industry.” Econometrica, 50 (1982), 649–670.Google Scholar
Lang, L.; Poulsen, A.; and Stulz, R.. “Asset Sales, Firm Performance, and the Agency Costs of Managerial Discretion.” Journal of Financial Economics, 37 (1995), 3–37.Google Scholar
Li, Q., and Racine, J.. Nonparametric Econometrics: Theory and Practice. Princeton, NJ: Princeton University Press (2007).Google Scholar
McFadden, D. “The Measurement of Urban Travel Demand.” Journal of Public Economics, 3 (1974), 303–328.Google Scholar
Merton, R. “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates.” Journal of Finance, 29 (1974), 449–470.Google Scholar
Minton, B., and Schrand, C.. “The Impact of Cash Flow Volatility on Discretionary Investment and the Costs of Debt and Equity Financing.” Journal of Financial Economics, 54 (1999), 423–460.Google Scholar
Nance, D.; Smith, C. Jr.; and Smithson, C.. “On the Determinants of Corporate Hedging.” Journal of Finance, 48 (1993), 267–284.Google Scholar
Ohlson, J. “Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research, 18 (1980), 109–131.Google Scholar
Opler, T.; Pinkowitz, L.; Stulz, R.; and Williamson, R.. “The Determinants and Implications of Corporate Cash Holdings.” Journal of Financial Economics, 52 (1999), 3–46.CrossRefGoogle Scholar
Ruppert, D.; Wand, M.; and Carroll, R.. Semiparametric Regression. Cambridge, UK: Cambridge University Press (2003).Google Scholar
Schwarz, G. “Estimating the Dimension of a Model.” Annals of Statistics, 6 (1978), 461–464.CrossRefGoogle Scholar
Shumway, T. “Forecasting Bankruptcy More Accurately: A Simple Hazard Model.” Journal of Business, 74 (2001), 101–124.Google Scholar
Smith, M., and Kohn, R.. “Nonparametric Regression Using Bayesian Variable Selection.” Journal of Econometrics, 75 (1996), 317–343.CrossRefGoogle Scholar
Stiglitz, J., and Weiss, A.. “Credit Rationing in Markets with Imperfect Information.” American Economic Review, 71 (1981), 393–410.Google Scholar
Zmijewski, M. “Methodological Issues Related to the Estimation of Financial Distress Prediction Models.” Journal of Accounting Research, 22 (1984), 59–82.CrossRefGoogle Scholar