Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7304
Title: Prediction of financial information manipulation by using support vector machine and probabilistic neural network
Authors: Ögüt, Hulisi
Aktaş, Ramazan
Alp, Ali
Doğanay, M. Mete
Keywords: Financial information manipulation
Support vector machine
Probabilistic neural network
Issue Date: 2009
Publisher: Pergamon-Elsevier Science Ltd
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. (C) 2008 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2008.06.055
https://hdl.handle.net/20.500.11851/7304
ISSN: 0957-4174
Appears in Collections:İşletme Bölümü / Department of Management
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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