Bayesian kernel based classification for financial distress detection
Van Gestel, Tony, Baesens, Bart, Suykens, Johan A.K., Van den Poel, Dirk, Baestaens, Dirk-Emma and Willekens, Marleen (2006) Bayesian kernel based classification for financial distress detection. European Journal of Operational Research, 172, (3), 979-1003. (doi:10.1016/j.ejor.2004.11.009).
Full text not available from this repository.
Corporate 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.
|Additional Information:||Interfaces with Other Disciplines|
|Keywords:||credit scoring, kernel, fisher discriminant analysis, least squares support vector machine classifiers, bayesian inference|
|Subjects:||H Social Sciences > HG Finance
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
|Divisions:||University Structure - Pre August 2011 > School of Management
|Date Deposited:||11 Jul 2006|
|Last Modified:||27 Mar 2014 18:23|
|Contact Email Address:||firstname.lastname@example.org|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)