ui̇i̇i̇d Uluslararası İktisadi ve İdari İncelemeler Dergisi 1307-9832 1307-9859 Kenan ÇELİK 10.18092/ulikidince.748742 DETECTING FINANCIAL INFORMATION MANIPULATION BY USING SUPERVISED MACHINE LEARNING TECHNICS: SVM, PNN, KNN, DT DENETİMLİ MAKİNE ÖĞRENMESİ TEKNİKLERİNİ KULLANARAK FİNANSAL BİLGİ MANİPÜLASYONUNUN TESPİTİ: SVM, PNN, KNN, DT Aydın Osman Musa TOBB EKONOMİ VE TEKNOLOJİ ÜNİVERSİTESİ Aktaş Ramazan TOBB EKONOMİ VE TEKNOLOJİ ÜNİVERSİTESİ 10 10 2020 29 165 174 06 06 2020 08 14 2020 Copyright © 2008, Uluslararası İktisadi ve İdari İncelemeler Dergisi 2008 Uluslararası İktisadi ve İdari İncelemeler Dergisi

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 algorithms. Comparative analysis is made to decide better algorithm for classification by applying all algorithms separately to the data that is collected by skimming weekly bulletins of Capital Market Board of Turkey 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.

Bu çalışma kapsamında, finansal bilgi manipülasyonunu tahmin etmek için geleneksel tahmin algoritmaları ve denetimli makine öğrenmesi yöntemleri kullanılmaktadır. Geleneksel tahmin algoritması olarak logit kullanılırken, denetimli makine öğrenmesi yöntemlerinden destek vektör makinesi (SVM), olasılıksal sinir ağı (PNN), k-en yakın komşu (KNN) ve karar ağacı (DT) algoritmaları kullanılmıştır. Önceki çalışmalara göre, destek vektör makinesi ve olasılıksal sinir ağı algoritmaları geleneksel tahmin algoritmalarından daha yüksek performans göstermektedir. 2009-2018 yılları arasında Sermaye Piyasası Kurulu'nun haftalık bültenlerini gözden geçirerek toplanan verilere tüm algoritmalar ayrı ayrı uygulanmıştır. Hangi algoritmanın finansal bilgi manipülasyonunu tespitinde daha başarılı olduğuna karar vermek amacıyla karşılaştırmalı analiz yapılmıştır. Karşılaştırmalı analizde, algoritmaların duyarlılık ve özgünlük istatistiklerinin performansına bakılmıştır. Elde edilen sonuçlar, KNN ve SVM’nin diğer algoritmalardan daha iyi performansa sahip olduğunu ve kullanılan tüm algoritmaların önceki literatürün sonuçlarına kıyasla yüksek performansa sahip olduğunu göstermektedir.

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