Abstract
The objective of our work is to assess the reliability of the machine learning techniques estimating the default probabilities. In our work, we followed an alternative nonlinear separation method known as the Support Vector Machine (SVM) for the default risk analysis. We did not need any parameter restrictions or prior assumptions in our estimates. We analysed more than 42,000 Italian companies, using the AIDA dataset spanning the years 2011 through to 2016, proposing an SVM model based on the performance measures such as ratios of leverage, liquidity and activity. The results of our work point out that using nonlinear techniques for predicting bankruptcy allows to achieve better performances than traditional statistical ones and, moreover, shows the important predictors to estimate default probabilities.
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Notes
- 1.
For the training dataset the starting year is 2011 and for the validating one is the 2014.
- 2.
For an introduction to the validation framework see (Sobehart et al. 2001).
- 3.
Bibliography
Abdou, H. A., & Pointon, J. (2009). Credit scoring and decision-making in Egyptian public sector banks. International Journal of Managerial Finance, 5(4), 391–406.
Aghion, P., & Bolton, P. (1992). An “incomplete contracts” approach to financial contracting. Review of Economic Studies, 59(3), 473–494.
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–611.
Altman, E., Haldeman, R., & Narayanan, P. (1977). Zeta analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 7, 29–54.
Altman, E. I., & Kao, D. R. (1992). Rating drift in high-yield bonds. The Journal of Fixed Income Spring, 1(4), 15–20.
Ayer, M., Brunk H. D., Ewing, G. M., Reid, W. T., & Silverman, E. (1955). An empirical distribution function for sampling with incomplete information. The Annals of Mathematical Statistics, 26, 641–647.
Aziz, A., Emanuel, D. C., & Lawson, G. H. (1988). Bankruptcy prediction—An investigation of cash flow based models. Journal of Management Studies, 25(5), 419–437.
Back, B., Laitinen, T., & Sere, K. (1994). Neural networks and bankruptcy prediction. Paper presented at the 17th Annual Congress of the European Accounting Association, Venice, Italy, 1994. Abstract in Collected Abstracts of the 17th Annual Congress of the European Accounting Association 116.
Back, B., Laitinen, T., Sere, K., & Wezel, M. (1996). Choosing bankruptcy predictors using discriminant analysis, logit analysis, and genetic algorithms. Technical Report 40, TUCS Research Group.
Bailey, M. (2004). Consumer credit quality: Underwriting, scoring, fraud prevention and collections. Kingswood and Bristol: White Box Publishing.
Barlow, R. E., Bartholomew, J. M., Bremmer, J. M., & Brunk, H. D. (1972). Statistical inference under order restrictions. New York, NY: Wiley.
Beaver, W. (1966). Financial ratios as predictors of failures empirical research in accounting: Selected studies. Journal of Accounting Research, 7, 71–111. Supplement to vol. 5.
Beaver, W. (1967). Financial ratios predictors of failure, empirical research in accounting: Selected studies 1966. Journal of Accounting Research, 4, 71–111.
Berger, A. N., & Frame, W. S. (2007). Small business credit scoring and credit availability. Journal of Small Business Management, 47, 5–22.
Bierman, H., & Hausman, W. H. (1970). The credit granting decision. Management Science, 16(8), 519–532.
Bilderbeek, J. (1979, May). An empirical study of the predictive ability of financial ratios in the Netherlands. Zeitschrift fur Betriebswirtsthaft, 49, 388–407.
Black, F., Jensen, M. C., & Scholes, M. (1972). The capital asset pricing model: Some empirical tests (pp. 79–121). Studies in the Theory of Capital Markets. New York, NY: Praeger.
Blum, M. (1974). Failing company discriminant analysis. Journal of Accounting Research, 12(1), 1–25.
Breeden, D. T. (1979). An intertemporal asset pricing model with stochastic consumption and investment opportunities. Journal of Financial Economics, 7, 265–296.
Carty, L., & Fons, J. (1994, June). Measuring changes in corporate credit quality. Journal of Fixed Income, 4, 27–41.
Chakrabarti, B., & Varadachari, R. (2004). Quantitative methods for default probability estimation—A first step towards Basel II. i-flex solutions.
Chandler, G. G., & Coffman, J. Y. (1979). A comparative analysis of empirical vs. judgemental credit valuation. The Journal of Retail Banking, 1(2), 15–26.
Charitou, A., & Trigeorgis, L. (2002, July 4–6). Option-based bankruptcy prediction. Paper presented at 6th Annual Real Options Conference, Paphos, Cyprus, pp. 1–25.
Collins, R., & Green, R. (1982). Statistical methods for bankruptcy prediction. Journal of Economics and Business, 34(4), 349–354.
Crook, J. N. (1996). Credit scoring: An overview (Working Paper Series No. 96/13). British Association, Festival of Science. University of Birmingham, The University of Edinburgh.
Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(March), 167–179.
Deng, N. Y., & Tian, Y. J. (2004). New methods in data mining: Support vector machine. Beijing: Science Press.
Edmister, R. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative Analysis, 7, 1477–1493.
Engelmann, B., Hayden, E., & Tasche, D. (2003). Testing rating accuracy. Risk, 16, 82–86.
Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. New York, NY: Chapman & Hall.
Falkenstein, E., Boral, A., & Carty, L. (2000). Riskcalc for private companies: Moody’s default model. As published in Global Credit Research.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.
Fama, E. F., & French, K. R. (1996). The capm is wanted, dead or alive. Journal of Finance, 51(5), 1947–1958.
Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607–636.
Fitzpatrick, P. (1932). A comparison of the ratios of successful industrial enterprises with those of failed companies. Certified Public Accountant, 6, 727–731.
Franke, G., Stapleton, R. C., & Subrahmanyam, M. G. (1999). When are options overpriced? The black-scholes model and alternative characterisations of the pricing kernel. European Finance Review, 3, 79–102.
Gentry, J. A., Newbold, P., & Whitford, D. T. (1985). Classifying bankrupt firms with funds flow components. Journal of Accounting Research, 23(1), 146–160.
Gombola, M., Haskins, M., Ketz, J., & Williams, D. (1987). Cash flow in bankruptcy prediction. Financial Management, 16(Winter), 55–65.
Gupton, G. M., Finger, C. C., & Bhatia, M. (1997). CreditMetrics – Technical Document. Morgan Guaranty Trust Company.
Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society, 160(3), 523–541.
Härdle, W. Moro, R.A. and Schäfer, D. (2005). Predicting bankruptcy with support vector machines. In Statistical tools for finance and insurance. Berlin: Springer Verlag.
Härdle, W., Moro, R. A., & Schäfer, D. (2007). Graphical data representation in bankruptcy analysis. In Handbook for data visualization. Berlin: Springer Verlag.
Härdle, W., Müller, M., Sperlich, S., & Werwatz, A. (2004). Nonparametric and semiparametric models. Heidelberg: Springer.
Härdle, W., & Simar, L. (2003). Applied multivariate statistical analysis. Berlin: Springer.
Haykin, S. (1999). Neural networks: A comprehensive foundations. Upper Saddle River, NJ: Prentice Hall.
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21, 1263–1284.
Herrity, J. V., Keenan, S. C., Sobehart, J. R., Carty, L. V., & Falkenstein, E. G. (1999). Measuring private firm default risk. Special Comment, Moody’s Investors Service.
Horowitz, J. L. (2001). The bootstrap (Vol. 5). Amsterdam: Elsevier Science B.V.
Keasey, K., & Watson, R. (1991). Financial distress models: A review of their usefulness. British Journal of Management, 2(2), 89–102.
Keenan, S. C., & Sobehart, J. R. (1999). Performance measures for credit risk models (Research Report #1-10-10-99). Moody’s Risk Management Services.
Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with gaussian kernel. In Neural Computation, 15, 1667–1689.
Khandani, B., Lozano, M., & Carty, L. (2001). Moody’s riskcalc for private companies: The german model. Rating Methodology, Moody’s Investors Service.
Lennox, C. (1999). Identifying failing companies: A re-evaluation of the logit, probit and da approaches. Journal of Economics and Business, 51, 347–364.
Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1), 13–37.
Lo, A. W. (1986). Logit versus discriminant analysis: A specification test and application to corporate bankruptcies. Journal of Econometrics, 31(2), 151–178.
Lussier, R. N. (1995). A non-financial business success versus failure prediction model for young firms. Journal of Small Business Management, 33(1), 8–20.
Mammen, E. (1991). Estimating a smooth monotone regression function. Annals of Statistics, 19, 724–740.
Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments (Cowles Foundation Monograph No. 16). New York, NY: Wiley.
Mayers, D. (1972). Non-marketable assets and capital market equilibrium under uncertainty. In Studies in the theory of capital markets (pp. 223–247). New York: Praeger.
Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica, 41(5), 867–887.
Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance, 29, 449–470.
Merwin, C. (1942). Financing small corporations in five manufacturing industries, 1926–36. New York: National Bureau of Economic Research.
Micha, B. (1984). Analysis of business failures in France. Journal of Banking & Finance, 8, 281–291.
Mossman, Ch. E., Bell, G. G., Swartz, L. M., & Turtle, H. (1998). An empirical comparison of bankruptcy models. The Financial Review, 33(2), 35–54.
Myers, S. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5(2), 147–175.
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109–131.
Ooghe, H., Joos, P., & De Bourdeaudhuij, C. (1995). Financial distress models in Belgium: The results of a decade of empirical research. International Journal of Accounting, 30, 245–274.
Platt, H., Platt, M., & Pedersen, J. (1994). Bankruptcy discrimination with real variables. Journal of Business, Finance and Accounting, 21(4), 491–510.
Platt, H. D., & Platt, M. B. (1990). Development of a class of stable predictive variables: The case of bankruptcy prediction. Journal of Business Finance & Accounting, 17(1), 31–51.
Ramser, J., & Foster, L. (1931). A demonstration of ratio analysis (Bulletin No. 40). Urbana: Bureau of Business Research, University of Illinois.
Roll, R. (1997). A critique of the asset pricing theory’s tests. Part I: On past and potential testability of the theory. Journal of Financial Economics, 4(2), 129–176.
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360.
Serrano, C., Martin, B., & Gallizo, J. L. (1993, April 28–30). Artificial neural networks in financial statement analysis: Ratios versus accounting data. Technical report, paper presented at the 16th Annual Congress of the European Accounting Association, Turku, Finland.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425–442.
Sobehart, J., Keenan, S., & Stein, R. (2001). Benchmarking quantitative default risk models: A validation methodology. Algo Research Quarterly, 4, 55–69.
Taffler, R. J., & Tisshaw H. (1977). Going, going, gone—Four factors which predict. Accountancy, 88(March), 50–54.
Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172.
Tikhonov, A. N. (1963). On solving ill-posed problem and method regularization. Doklady Akademii Nauk USSR, 153, 501–504.
Tikhonov, A. N., & Arsenin, V. Y. (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston.
Vapnik, V. (1979). Estimation of dependencies based on empirical data. Moscow: Nauka.
Vapnik, V. (1995). The nature of statistical learning theory. New York, NY: Springer.
Vapnik, V. (1997). Statistical learning theory. New York, NY: Wiley.
Wilson, R. L., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11, 545–557.
Winakor, A., & Smith, R. (1935). Changes in the financial structure of unsuccessful industrial corporations (Bulletin No. 51). Urbana: Bureau of Business Research, University of Illinois.
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Merella, P., Schiesari, R. (2019). A Nonlinear Approach to Assess the Risk–Reward Ratio Using the Machine Learning Technique. In: De Vincentiis, P., Culasso, F., Cerrato, S. (eds) The Future of Risk Management, Volume II. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-16526-0_8
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