Bayesian Kernel-Based Classification for Financial Distress Detection
AbstractCorporate credit granting is a key commercial activity of financial institutions nowadays. A critical first step in the credit granting process usually involves a careful financial analysis of the creditworthiness of the potential client. Wrong decisions result either in foregoing valuable clients or, more severely, in substantial capital losses if the client subsequently defaults. It is thus of crucial importance to develop models that estimate the probability of corporate bankruptcy with a high degree of accuracy. Many studies focused on the use of financial ratios in linear statistical models, such as linear discriminant analysis and logistic regression. However, the obtained error rates are often high. In this paper, Least Squares Support Vector Machine (LS-SVM) classifiers, also known as kernel Fisher discriminant analysis, are applied within the Bayesian evidence framework in order to automatically infer and analyze the creditworthiness of potential corporate clients. The inferred posterior class probabilities of bankruptcy are then used to analyze the sensitivity of the classifier output with respect to the given inputs and to assist in the credit assignment decision making process. The suggested nonlinear kernel based classifiers yield better performances than linear discriminant analysis and logistic regression when applied to a real-life data set concerning commercial credit granting to mid-cap Belgian and Dutch firms.
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Bibliographic InfoPaper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 04/247.
Length: 40 pages
Date of creation: May 2004
Date of revision:
Credit Scoring; Kernel Fisher Discriminant Analysis; Least Squares Support Vector Machine Classifiers; Bayesian Inference;
Other versions of this item:
- Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
- NEP-ALL-2004-08-31 (All new papers)
- NEP-CMP-2004-08-31 (Computational Economics)
- NEP-ECM-2004-08-31 (Econometrics)
- NEP-ETS-2004-08-31 (Econometric Time Series)
- NEP-FIN-2004-08-31 (Finance)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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