Osman Musa AYDIN
(TOBB Ekonomi ve Teknoloji Üniversitesi, İşletme Bölümü, Ankara, Türkiye)
Ramazan AKTAŞ
(TOBB Ekonomi ve Teknoloji Üniversitesi, İşletme Bölümü, Ankara, Türkiye)
Yıl: 2020Cilt: 0Sayı: 29ISSN: 1307-9832 / 1307-9859Sayfa Aralığı: 165 - 174İngilizce

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DETECTING FINANCIAL INFORMATION MANIPULATION BY USING SUPERVISED MACHINE LEARNING TECHNICS: SVM, PNN, KNN, DT
Within the scope of this paper, traditional estimation algorithms and supervised machine learning methodsare used to estimate the manipulation of financial information. Traditional estimation algorithms, such aslogit, and supervised machine learning methods, which are support vector machine (SVM), probabilisticneural network (PNN), k-nearest neighbor (KNN) and decision tree (DT) algorithms, are utilized. According toprevious studies, support vector machine and probabilistic neural network algorithms perform higher thantraditional estimation ones in terms of the accuracy of financial information manipulation estimation.Comparative analysis is made to decide better algorithm for classification by applying all algorithms separatelyto the financial information manipulation dataset that is collected by skimming weekly bulletins of CapitalMarkets Board of Turkey and Borsa Istanbul between 2009 and 2018. Thus, it is determined which algorithmsperform better in financial information manipulation by looking at performance of classification accuracy,sensitivity and specificity statistics. The obtained results show that KNN and SVM have better performancethan the other algorithms and all utilized algorithms have high performance compared to the previousliterature’s results
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