jats Journal of Accounting and Taxation Studies 1308-3740 2564-6591 Ankara Serbest Muhasebeci Mali Müşavirler Odası 10.29067/muvu.1539635 Accounting, Auditing and Accountability (Other) Muhasebe, Denetim ve Mali Sorumluluk (Diğer) Prediction of Independent Audit Firm Switching By Using Machine Learning Methods: The Case of Türkiye Makine Öğrenmesi Yöntemlerini Kullanarak Bağımsız Denetim Firma Değişikliğinin Tahmini: Türkiye Örneği https://orcid.org/0000-0002-3639-8861 Çankal Ahmet OSMANİYE KORKUT ATA ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ, YÖNETİM BİLİŞİM SİSTEMLERİ PR. https://orcid.org/0000-0001-7075-6995 Kürklü Erdem OSMANIYE KORKUT ATA UNIVERSITY, OSMANİYE VOCATIONAL SCHOOL, DEPARTMENT OF TRANSPORTATION SERVICES 08 11 2025 18 2 239 259 08 27 2024 02 25 2025 Copyright © 2008, Journal of Accounting and Taxation Studies 2008 Journal of Accounting and Taxation Studies

This study aimed to predict independent audit firm switching of the companies traded in Borsa Istanbul Star Market (BIST STARS) in Türkiye by using financial ratios and machine learning algorithms. In this context, 13 financial datasets of 158 companies traded in BIST STARS in the 2019-2021 period were used as input variables. First, the significance values of the input variables were found by using the Mutual Information (MI) method. Then, input variables were grouped sequentially in order of importance to select the most accurate subset representing the data. Among the machine learning algorithms, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes,K-Nearest Neighbors, and XGBoost algorithm methods were used for group selection. GridSearchCV technique was applied to optimize the initial parameters of the methods. As a result of the experiments, the XGBoost algorithm was found to be the most successful method in predicting the change of independent audit firm with an accuracy value of 88.4%. It was sufficient for the method to use 8 attributes selected from 13 financial datasets. On the other hand, the Return on Assets (ROA) was determined as the most important attribute.

Bu çalışmanın amacı, Türkiye'de Borsa İstanbul Yıldız Pazar’da (BIST Yıldız Pazar) işlem gören işletmelerin bağımsız denetim firması değişikliğini, finansal oranlar ve makine öğrenmesi algoritmaları kullanarak tahmin etmektir. Bu kapsamda, 2019-2021 döneminde Borsa İstanbul Yıldız Pazar’da işlem gören 158 işletmeye ait 13 finansal veri kümesi girdi değişkenleri olarak kullanılmıştır. Öncelikle Mutual Information (MI) yöntemi kullanılarak girdi değişkenlerinin önem değerleri bulunmuştur. Daha sonra girdi değişkenleri önem sırasına göre gruplandırılarak veriyi en doğru şekilde temsil eden alt küme seçilmiştir. Makine öğrenmesi algoritmaları arasında Destek Vektör Makinesi, Karar Ağacı, Rastgele Orman, Naive Bayes, K-En Yakın Komşu ve XGBoost yöntemleri grup seçiminde kullanılmıştır. Yöntemlerin başlangıç parametrelerini optimize etmek için GridSearchCV tekniği uygulanmıştır. Yapılan deneyler sonucunda XGBoost algoritmasının %88,4 doğruluk değeri ile bağımsız denetim şirketi değişikliğini tahminlemede en başarılı yöntem olduğu bulunmuştur. Yöntem için 13 finansal veri setinden seçilen 8 niteliğin kullanılması yeterli olmuştur. Öte yandan Aktif Kârlılık Oranı (ROA) en önemli nitelik olarak belirlenmiştir.

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