Resource title

Support Vector Machines (SVM) as a technique for solvency analysis

Resource image

image for OpenScout resource :: Support Vector Machines (SVM) as a technique for solvency analysis

Resource description

This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and insolvent. The advantages and disadvantages of the method are discussed. The comparison of the SVM with more traditional approaches such as logistic regression (Logit) and discriminant analysis (DA) is made on the Deutsche Bundesbank data of annual income statements and balance sheets of German companies. The out-of-sample accuracy tests confirm that the SVM outperforms both DA and Logit on bootstrapped samples.

Resource author

Laura Auria, Rouslan A. Moro

Resource publisher

Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/27334

Resource license

Adapt according to the presented license agreement and reference the original author.