Prediction of financial information manipulation by using support vector machine and probabilistic neural network

Authors:
Hulisi Öğüt;Ramazan Aktaş;Ali Alp;M. Mete Doğanay
Affiliations:
Department of Business Administration, TOBB University of Economics and Technology, Söğütözü Caddesi No. 43, Ankara 06560, Turkey;Department of Business Administration, TOBB University of Economics and Technology, Söğütözü Caddesi No. 43, Ankara 06560, Turkey;Department of Business Administration, TOBB University of Economics and Technology, Söğütözü Caddesi No. 43, Ankara 06560, Turkey;Department of Business Administration, Çankaya University, Öğretmenler Caddesi No. 14, Balgat, Ankara 06530, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Different methods have been used to predict financial information manipulation that can be defined as the distortion of the information in the financial statements. The purpose of this paper is to predict financial information manipulation by using support vector machine (SVM) and probabilistic neural network (PNN). A number of financial ratios are used as explanatory variables. Test performance of classification accuracy, sensitivity and specificity statistics for PNN and SVM are compared with the results of discriminant analysis, logistics regression (logit), and probit classifiers, which have been used in other studies. We have found that the performance of SVM and PNN are higher than that of the other classifiers analyzed before. Thus, both classifiers can be used as automated decision support system for the detection of financial information manipulation.