Pena Centeno, Tonatiuh and Martinez Jaramillo, Serafin and Abudu, Bolanle (2009): Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora.
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We are interested in forecasting bankruptcies in a probabilistic way. Specifcally, we com- pare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and different instances of Gaussian processes (GP's) -that is GP's classifiers, Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computa- tional finance 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 Insurance Corporation is used to test the predictions.
|Item Type:||MPRA Paper|
|Original Title:||Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora|
|English Title:||Bankruptcy prediction: a comparison of some statistical and machine learning techniques|
|Keywords:||Bankruptcy prediction, Artificial intelligence, Supervised learning, Gaussian processes, Z-score.|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Tonatiuh Pena Centeno|
|Date Deposited:||25. Jan 2010 13:51|
|Last Modified:||17. Feb 2014 17:49|
E. I. Altman. Revisiting credit scoring models in a Basel 2 environment. Technical report, Stern School of Business, New York University, May 2002.
E. I. Altman. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4):589–609, September 1968.
A. F. Atiya. Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions On Neural Networks, 12:929–935, July 2001.
B. Back, T. Laitinen, K. Sere, and M. van Wezel. Choosing bankruptcy predictors using discriminant analysis, logit analysis, and genetic algorithms. Technical Report 40, Turku Centre for Computer Science, September 1996.
D. Bamber. The area above the ordinal dominance graph and the area below the receiver operating graph. Journal of Mathematical Psychology, 12:387–415, 1975.
Basel Committee on Banking Supervision. International Convergence of Capital Measurement and Capital Standards. A Revised Framework. Bank for International Settlements, June 2004.
W. H. Beaver. Financial ratios as predictors of failures. Journal of Accounting Research, 4: 71–111, 1966.
C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, UK, 1995.
C. M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag, New York, USA, 2006.
G. E. Box and G. C. Tiao. Bayesian Inference in Statistical Analysis. Wiley Classics Library, Published 1992. John Wiley & Sons Ltd., New York, USA, 1973.
S.-H. Chen, editor. Genetic Algorithms and Genetic Programming in Computational Finance. Kluwer Academic, 2002.
C. Cortes and V. V. Vapnik. Support vector networks. Machine Learning, 20:273–297, 1995.
D. Duffie, L. Saita, and K. Wang. Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3):635–665, 2007.
B. Efron. Bootstrap methods: Another look at the Jackknife. The Annals of Statistics, 7: 1–26, 1979.
J. Egan. Signal Detection Theory and ROC Analysis. Series in Cognition and Perception. Academic Press, New York, 1975.
B. Engelmann, E. Hayden, and D. Tasche. Testing rating accuracy. Risk, 16:82–86, 2003.
A. Estrella, S. Park, and S. Peristiani. Capital ratios as predictors of bank failure. Federal Reserve Bank of New York Economic Policy Review, pages 33–52, July 2000.
T. E. Fawcett. ROC graphs: Notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Labs, 2003.
T. E. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27:861–874, 2006.
R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179, 1936.
G. H. Golub and C. F. Van Loan. Matrix computations. Johns Hopkins Studies in Mathematical Sciences. Johns Hopkins University Press, Baltimore, MD, USA, 3rd edition, 1996.
C. Goodhard. Monetary relationships: A view from Threadneedle Street. Papers in Monetary Economics, 1975. Reserve Bank of Australia.
G. Grimmett and D. Stirzaker. Probability and Random Processes. Oxford University Press, Oxford, UK, 3rd edition, 2004.
P. Joos, K. Vanhoof, H. Ooghe, and N. Sierens. Credit classification: A comparison of logit models and decision trees. In 10th European Conference on Machine Learning. Proceedings Notes of the Workshop on Application of Machine Learning and Data Mining in Finance, pages 59–72, Chemnitz, Germany, April 24 1998.
Credit Metrics - Technical Document. JP Morgan, New York, April 1997.
G. S. Kimeldorf and G. Wahba. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Annals of Mathematical Statistics, 41(2):495–502, 1970.
D. G. Krige. Two-dimensional weighting moving average trend surfaces for ore evaluation. Journal of the South African Institute of Mining and Metallurgy, 1966.
D. J. C. Mackay. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems, 6(3): 469–505, 1995.
D. J. C. Mackay. Introduction to Gaussian processes. In C. M. Bishop, editor, Neural Networks and Machine Learning, volume 168 of NATO ASI Series, pages 133–165. Springer-Verlag, Berlin, Germany, 1998.
D. J. C. Mackay. Information Theory, Learning and Inference Algorithms. Cambridge University Press, Cambridge, UK, 2003.
G. J. MacLachlan. Discriminant Analysis and Pattern Recognition. John Wiley & Sons Ltd., New York, USA, 1991.
C. McDonald and L. van de Gucht. High-yield bond default and call risks. Review of Economics and Statistics, 81:409–419, 1999.
T. P. Minka. A Family of Algorithms for Approximate Bayesian Inference. PhD thesis, Massachusetts Institute of Technology, 2001.
R. M. Neal. Bayesian Learning for Neural Networks. Springer-Verlag, New York, USA, 1996.
A. O’Hagan. Curve fitting and optimal design for prediction. Journal of the Royal Statistical Society, Series B (Methodological), 40(1):1–42, 1978.
C. Park and I. Han. A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23:255–264, 2002.
T. Peña Centeno and N. D. Lawrence. Optimising kernel parameters and regularisation coefficients for non-linear discriminant analysis. Journal of Machine Learning Research, 7:455–491, 2006.
D. Quintana, Y. Saez, A. Mochon, and P. Isasi. Early bankruptcy prediction using ENPC. Journal of Applied Intelligence, 2007. ISSN 0924-669X.
C. E. Rasmussen. Gaussian processes in machine learning. In O. Bousquet, U. von Luxburg, and G. Rätsch, editors, Advanced Lectures on Machine Learning, volume 3176 of Lecture Notes in Computer Science/Artifical Intelligence. Springer-Verlag, 2004.
C. E. Rasmussen and C. K. Williams. Gaussian Processes for Machine Learning. Adaptive Computation and Machnine Learning. MIT Press, Cambridge, MA, USA, 2006.
G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for AdaBoost. Technical Report NC-TR-98-021, Royal Holloway College, University of London, London, UK, 1998.
A. Rodriguez and P. Rodriguez. Understanding and predicting sovereign debt rescheduling: A comparison of the areas under receiving operating characteristic curves. Journal of Forecasting, 7(25):459–479, 2006.
B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge, MA, USA, 2002.
M. Seeger. Gaussian processes for machine learning. International Journal of Neural Systems, 14(2):69–106, 2004.
C. Serrano-Cinca, C. B. Martin, and J. Gallizo. Artificial neural networks in financial statement analysis: Ratios versus accounting data. In 16th Annual Congress of the European Accounting Association, Turku, Finland, 28-30 Apr. 1993.
K.-S. Shin and Y.-J. Lee. A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23:321–328, October 2002.
E. Snelson, C. E. Rasmussen, and Z. Ghahramani. Warped gaussian processes. In S. Thrun, L. K. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16, Cambridge, MA, USA, 2003. MIT Press.
M. Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36:111–147, 1974.
J. A. Suykens and J. Vandewalle. Least squares support vector machines. Neural Processing Letters, 9(3):293–300, 1999.
J. A. Suykens, T. Van Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle. Least Squares Support Vector Machines. World Scientific, Singapore, 2002.
K. Tam and M. Kiang. Managerial application of neural networks: The case of bank failure predictions. Management Science, 38:926–947, 1992.
T. N. Thiele. Theory of Observations. Layton, London, UK, 1903. Reprinted in Annals of Mathematical Statistics 2:165-308, 1931.
E. P. K. Tsang and S. Martinez-Jaramillo. Computational finance. In IEEE Computational Intelligence Society Newsletter, pages 3–8. IEEE Press, 2004.
T. Van Gestel, J. A. Suykens, G. Lanckriet, A. Lambrechts, B. de Moor, and J. Vandewalle. Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel discriminant analysis. Neural Computation, 14(5):1115–1147, 2002.
F. Varetto. Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking and Finance, 22:1421–1439, 1998.
A. Verikas, Z. Kalsyte, M. Bacauskiene, and A. Gelzinis. Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: A survey. Soft Computing - A Fusion of Foundations, Methodologies and Applications (Online), September 2009.
G. Wahba. Spline Models for Observational Data. Society for Industrial and Applied Mathematics. CBMS-NSF Regional Conference in Applied Mathematics 59, 1990.
C. K. Williams. Prediction with Gaussian processes: from linear regression to linear prediction and beyond. In M. I. Jordan, editor, Learning in Graphical Models, D, Behavioural and social sciences 11. Kluwer, Dordrecht, The Netherlands, 1999.
C. K. Williams and D. Barber. Bayesian classification with Gaussian processes. IEEE Transactions, Pattern Analysis and Machine Intelligence, 20(12):1342–351, 1998.
A. Y. N. Yip. A hybrid case-based reasoning approach to business failure prediction, pages 371–378. IOS Press, Amsterdam, The Netherlands, 2003.