Predicting Financial Statement Frauds Using Machine Learning Methods and Logistic Regression: The Case of Borsa Istanbul

Barış Aksoy
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Barış Aksoy: Sivas Cumhuriyet University

Journal of Finance Letters (Maliye ve Finans Yazıları), 2021, vol. 36, issue 115, 27-58

Abstract: This study aims to create an effective model to predict one year before whether 88 firms, continuously traded at Borsa Istanbul between 2000- 2019, commit fraud in their financial statements. For this purpose, financial statement fraud was predicted by using Artificial Neural Networks (ANN), Classification and Regression Trees (CART) and Support Vector Machine (SVM) and Logistic Regression (LR) methods among machine learning methods. As a result, the overall prediction accuracy of ANN (96.15%), CART (96.15%), SVM (80.77%) and LR (80.77%) test samples were obtained. ANN and CART classified correctly in test samples all 13 firms that fraudulent in their financial statements. This shows that all methods used in this study, can be used in studies to predict financial statement fraud.

Keywords: Financial Statement Fraud; Machine Learning Methods; Logistic Regression; Borsa Istanbul (search for similar items in EconPapers)
JEL-codes: C45 C63 G17 G32 M42 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:acc:malfin:v:36:y:2021:i:115:p:27-58

DOI: 10.33203/mfy.733855

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