Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objectives regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of smooth support vector machines (SSVM), and investigate how important factors such as the selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Moreover, we show that oversampling can be employed to control the trade-off between error types, and we compare SSVM with both logistic and discriminant analysis. Finally, we illustrate graphically how different models can be used jointly to support the decision-making process of loan officers. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[2]  Chunsheng Zhou,et al.  The term structure of credit spreads with jump risk , 2001 .

[3]  Robert Tibshirani,et al.  1-norm Support Vector Machines , 2003, NIPS.

[4]  W. Beaver.  Financial Ratios As Predictors Of Failure , 1966 .

[5]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[6]  David R. Musicant,et al.  Robust Linear and Support Vector Regression , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[9]  Wolfgang Karl Härdle,et al.  Estimation of Default Probabilities with Support Vector Machines , 2006 .

[10]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[11]  R. C. Merton,et al.  On the Pricing of Corporate Debt: The Risk Structure of Interest Rates , 1974, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[12]  Eduardo S. Schwartz,et al.  A Simple Approach to Valuing Risky Fixed and Floating Rate Debt , 1995 .

[13]  Martin Weber,et al.  Generally accepted rating principles: A primer , 2001 .

[14]  Yuh-Jye Lee,et al.  SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[15]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[16]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[17]  Glenn Fung,et al.  A Feature Selection Newton Method for Support Vector Machine Classification , 2004, Comput. Optim. Appl..

[18]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[19]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[20]  Pierre Mella-Barral,et al.  Strategic Debt Service , 1997, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[21]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[22]  Jack C. Lee,et al.  A semiparametric method for predicting bankruptcy , 2007 .

[23]  Marialuisa Restaino,et al.  VARIABLE SELECTION IN FORECASTING MODELS FOR CORPORATE BANKRUPTCY , 2010 .

[24]  H. Leland.,et al.  Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads , 1996, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[25]  Su-Yun Huang,et al.  Model selection for support vector machines via uniform design , 2007, Comput. Stat. Data Anal..

[26]  Yuh-Jye Lee,et al.  Incremental Forward Feature Selection with Application to Microarray Gene Expression Data , 2008, Journal of biopharmaceutical statistics.

[27]  Wolfgang Härdle,et al.  Graphical Data Representation in Bankruptcy Analysis , 2006 .

[28]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[29]  Matthias W. Seeger,et al.  Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.

[30]  Wolfgang Härdle,et al.  Estimating Probabilities of Default with Support Vector Machines , 2007, SSRN Electronic Journal.

[31]  Daniel Martin,et al.  Early warning of bank failure: A logit regression approach , 1977 .

[32]  Su-Yun Huang,et al.  Reduced Support Vector Machines: A Statistical Theory , 2007, IEEE Transactions on Neural Networks.

[33]  Bernhard Schölkopf,et al.  Sparse Greedy Matrix Approximation for Machine Learning , 2000, International Conference on Machine Learning.