Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques

We are interested in forecasting bankruptcies in a probabilistic way. Speciflcally, we compare the classiflcation performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman’s Z-score), logistic regression, least-squares support vector machines and difierent instances of Gaussian processes (GP’s) -that is GP’s classiflers, Bayesian Fisher discriminant and Warped GP’s. Our contribution to the fleld of computational flnance is to introduce GP’s as a potentially competitive probabilistic framework for bankruptcy prediction. Data from the repository of information of the US Federal Deposit

[1]  Arturo Estrella,et al.  Capital Ratios as Predictors of Bank Failure , 2000 .

[2]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[3]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

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

[5]  Grace Wahba,et al.  Spline Models for Observational Data , 1990 .

[6]  Arnulfo Rodriguez,et al.  Understanding and predicting sovereign debt rescheduling: a comparison of the areas under receiver operating characteristic curves , 2006 .

[7]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[8]  Pedro Isasi Viñuela,et al.  Early bankruptcy prediction using ENPC , 2008, Applied Intelligence.

[9]  Ke Wang,et al.  Multi-Period Corporate Default Prediction with Stochastic Covariates , 2005 .

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

[11]  Andrew W. Lo,et al.  Computational finance , 1999, Comput. Sci. Eng..

[12]  Amir F. Atiya,et al.  Bankruptcy prediction for credit risk using neural networks: A survey and new results , 2001, IEEE Trans. Neural Networks.

[13]  E. Altman,et al.  Revisiting Credit Scoring Models in a Basel 2 Environment , 2002 .

[14]  Theofanis Sapatinas,et al.  Discriminant Analysis and Statistical Pattern Recognition , 2005 .

[15]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[16]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[17]  Kyung-shik Shin,et al.  A genetic algorithm application in bankruptcy prediction modeling , 2002, Expert Syst. Appl..

[18]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[19]  Angela Y. N. Yip,et al.  A Hybrid Case-Based Reasoning Approach to Business Failure Prediction , 2003, HIS.

[20]  D. Mackay,et al.  Introduction to Gaussian processes , 1998 .

[21]  D. Bamber.  The area above the ordinal dominance graph and the area below the receiver operating characteristic graph , 1975 .

[22]  Johan A. K. Suykens,et al.  Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis , 2002, Neural Computation.

[23]  G. Wahba.  Spline models for observational data , 1990 .

[24]  A. O'Hagan,et al.  Curve Fitting and Optimal Design for Prediction , 1978 .

[25]  David Barber,et al.  Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[27]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[28]  Carl E. Rasmussen,et al.  Warped Gaussian Processes , 2003, NIPS.

[29]  T. N. Thiele,et al.  Theory Of Observations , 1903 .

[30]  Christopher K. I. Williams.  Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[31]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[32]  Franco Varetto.  Genetic algorithms applications in the analysis of insolvency risk , 1998 .

[33]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[34]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[35]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[36]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[37]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

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

[39]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[40]  Tom Minka,et al.  A family of algorithms for approximate Bayesian inference , 2001 .

[41]  L. Gucht,et al.  High-Yield Bond Default and Call Risks , 1999, Review of Economics and Statistics.

[42]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[43]  Kaisa Sere,et al.  Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms , 1996 .

[44]  G. Wahba,et al.  A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines , 1970 .

[45]  Neil D. Lawrence,et al.  Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis , 2006, J. Mach. Learn. Res..

[46]  Shu-Heng Chen,et al.  Genetic Algorithms and Genetic Programming in Computational Finance , 2002 .

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

[48]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[49]  Koen Vanhoof,et al.  Credit classification: A comparison of logit models and decision trees , 1998 .

[50]  G. Grimmett,et al.  Probability and random processes , 2002 .

[51]  Antanas Verikas,et al.  Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey , 2010, Soft Comput..

[52]  Gene H. Golub,et al.  Matrix computations , 1983 .

[53]  David Mackay,et al.  Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .