DETECTING FINANCIAL INFORMATION MANIPULATION BY USING SUPERVISED MACHINE LEARNING TECHNICS: SVM, PNN, KNN, DT

Year 2020, Issue: 29, 165 - 174, 10.10.2020

Osman Musa Aydın , Ramazan Aktaş

https://doi.org/10.18092/ulikidince.748742

Abstract

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.

Keywords

Financial Information Manipulation, Supervised Machine Learning, SVM, KNN, Beneish

DENETİMLİ MAKİNE ÖĞRENMESİ TEKNİKLERİNİ KULLANARAK FİNANSAL BİLGİ MANİPÜLASYONUNUN TESPİTİ: SVM, PNN, KNN, DT

Abstract

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.

Keywords

Finansal Bilgi Manipülasyonu, Denetimli Makine Öğrenmesi, SVM, KNN, Beneish

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