Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, we compare its performance with those of multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

[1]  V. Srinivasan,et al.  CGX: An expert support system for credit granting , 1990 .

[2]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[3]  Stephanie M. Bryant,et al.  A case-based reasoning approach to bankruptcy prediction modeling , 1997, Intell. Syst. Account. Finance Manag..

[4]  Patrick Massin,et al.  CREDIT RISK MODELLING: CURRENT PRACTICES AND APPLICATIONS , 1999 .

[5]  Carol E. Brown,et al.  Artificial neural networks in accounting and finance: modeling issues , 2000, Intell. Syst. Account. Finance Manag..

[6]  Alan K. Reichert,et al.  An Examination of the Conceptual Issues Involved in Developing Credit-Scoring Models , 1983 .

[7]  Peter J. Elmer,et al.  An Expert System Approach to Financial Analysis: The Case of S&L Bankruptcy , 1988 .

[8]  B. Roy.  THE OUTRANKING APPROACH AND THE FOUNDATIONS OF ELECTRE METHODS , 1991 .

[9]  Ahmet Burak Emel,et al.  A credit scoring approach for the commercial banking sector , 2003 .

[10]  Ingoo Han,et al.  Hybrid neural network models for bankruptcy predictions , 1996, Decis. Support Syst..

[11]  Anurag Agarwal,et al.  Predicting the outcome following bankruptcy filing: a three-state classification using neural networks , 1997, Intell. Syst. Account. Finance Manag..

[12]  Ingoo Han,et al.  Integration of Case-based Forecasting, Neural Network, and Discriminant Analysis for Bankruptcy Prediction , 1996 .

[13]  Jose A. Lopez,et al.  Evaluating Credit Risk Models , 1999 .

[14]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[15]  Guido Dedene,et al.  A Comparison of State-of-The-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection , 2002 .

[16]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[17]  Jonathan Crook,et al.  Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms , 1997 .

[18]  Namsik Chang,et al.  Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction , 1999, J. Manag. Inf. Syst..

[19]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[20]  Rashmi Malhotra,et al.  Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems , 2001, Eur. J. Oper. Res..

[21]  George Mavrotas,et al.  A multicriteria approach for evaluating the performance of industrial firms , 1992 .

[22]  Ingoo Han,et al.  A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction , 2002, Expert Syst. Appl..

[23]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[24]  Harris Drucker,et al.  Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.

[25]  Cliff T. Ragsdale,et al.  Combining Neural Networks and Statistical Predictions to Solve the Classification Problem in Discriminant Analysis , 1995 .

[26]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

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

[28]  Pamela K. Coats,et al.  Recognizing Financial Distress Patterns Using a Neural Network Tool , 1993 .

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

[30]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[31]  Rolph E. Anderson,et al.  Nederlandse samenvatting en bewerking van 'Multivariate data analysis, 4th Edition, 1995' , 1998 .

[32]  John Mingers,et al.  Neural Networks, Decision Tree Induction and Discriminant Analysis: an Empirical Comparison , 1994 .

[33]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[34]  M. Peel,et al.  Predicting corporate failure— Some results for the UK corporate sector , 1986 .

[35]  Chih-Chou Chiu,et al.  Credit scoring using the hybrid neural discriminant technique , 2002, Expert Syst. Appl..

[36]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[37]  Michael Y. Hu,et al.  Two-Group Classification Using Neural Networks* , 1993 .

[38]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[39]  Kurt Fanning,et al.  A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction , 1994 .

[40]  Warren S. Sarle,et al.  Stopped Training and Other Remedies for Overfitting , 1995 .

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

[42]  Andreas Weigend,et al.  On overfitting and the effective number of hidden units , 1993 .

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

[44]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

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

[46]  V. Srinivasan,et al.  Designing Expert Financial Systems: A Case Study of Corporate Credit Management , 1988 .

[47]  Alexander Gammerman,et al.  Machine-learning algorithms for credit-card applications , 1992 .

[48]  Vijay S. Desai,et al.  A comparison of neural networks and linear scoring models in the credit union environment , 1996 .

[49]  Koen Vanhoof,et al.  Bankruptcy prediction using a data envelopment analysis , 2004, Eur. J. Oper. Res..

[50]  Robert A. Eisenbeis,et al.  Problems in applying discriminant analysis in credit scoring models , 1978 .

[51]  H. Frydman,et al.  Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .

[52]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[53]  Marimuthu Palaniswami,et al.  Selecting bankruptcy predictors using a support vector machine approach , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[54]  John E. Moody,et al.  The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems , 1991, NIPS.

[55]  Ah Chung Tsoi,et al.  Lessons in Neural Network Training: Overfitting May be Harder than Expected , 1997, AAAI/IAAI.

[56]  Ingoo Han,et al.  Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis , 1997 .

[57]  Mu-Chen Chen,et al.  Credit scoring and rejected instances reassigning through evolutionary computation techniques , 2003, Expert Syst. Appl..

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

[59]  P. Falbo,et al.  Credit-scoring by enlarged discriminant models , 1991 .

[60]  Constantin Zopounidis,et al.  A survey of business failures with an emphasis on prediction methods and industrial applications , 1996 .

[61]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[62]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .