DSpace Repository

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

Show simple item record

dc.contributor.author Öğüt, Hulusi
dc.contributor.author Aktaş, Ramazan
dc.contributor.author Alp, Ali
dc.contributor.author Doğanay, M. Mete
dc.date.accessioned 2016-04-29T07:55:57Z
dc.date.available 2016-04-29T07:55:57Z
dc.date.issued 2009-04
dc.identifier.citation Öğüt, H., Aktaş, R., Alp, A., Doğanay, M.M. (2009). Prediction of financial information manipulation by using support vector machine and probabilistic neural network. Expert Systems with Applications, 36(3), 5419-5423. http://dx.doi.org/10.1016/j.eswa.2008.06.055 tr_TR
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/20.500.12416/949
dc.description.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. tr_TR
dc.language.iso eng tr_TR
dc.publisher Pergamon-Elsevier Science LTD tr_TR
dc.relation.isversionof 10.1016/j.eswa.2008.06.055 tr_TR
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Financial Information Manipulation tr_TR
dc.subject Support Vector Machine tr_TR
dc.subject Probabilistic Neural Network tr_TR
dc.title Prediction of financial information manipulation by using support vector machine and probabilistic neural network tr_TR
dc.type article tr_TR
dc.relation.journal Expert Systems with Applications tr_TR
dc.contributor.authorID 1109 tr_TR
dc.contributor.authorID 6974 tr_TR
dc.contributor.authorID 112010 tr_TR
dc.identifier.volume 36 tr_TR
dc.identifier.issue 3 tr_TR
dc.identifier.startpage 5419 tr_TR
dc.identifier.endpage 5423 tr_TR
dc.contributor.department Çankaya Üniversitesi, İktisadi İdari Bilimler Fakültesi, İşletme Bölümü tr_TR


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record