İşletme Bölümü / Department of Business Administration

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Detecting Financial Information Manipulation by Using Supervised Machine Learning Technics: Svm, Pnn, Knn, Dt
(2020) Aydın, Osman Musa; Aktaş, Ramazan
Within the scope of this paper, traditional estimation algorithms and supervised machine learning methods are used to estimate the manipulation of financial information. Traditional estimation algorithms, such as logit, and supervised machine learning methods, which are support vector machine (SVM), probabilistic neural network (PNN), k-nearest neighbor (KNN) and decision tree (DT) algorithms, are utilized. According to previous studies, support vector machine and probabilistic neural network algorithms perform higher than traditional 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 separately to the financial information manipulation dataset that is collected by skimming weekly bulletins of Capital Markets Board of Turkey and Borsa Istanbul between 2009 and 2018. Thus, it is determined which algorithms perform 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 performance than the other algorithms and all utilized algorithms have high performance compared to the previous literature’s results