Prediction of Independent Audit Firm Switching By Using Machine Learning Methods: The Case of Türkiye

Yıl 2025, Cilt: 18 Sayı: 2, 239 - 259, 11.08.2025

Ahmet Çankal , Erdem Kürklü

https://doi.org/10.29067/muvu.1539635

Öz

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.

Anahtar Kelimeler

Audit Firm Switch , Financial Ratios , Machine Learning , Mutual Information

Etik Beyan

-

Destekleyen Kurum

-

Teşekkür

-

Makine Öğrenmesi Yöntemlerini Kullanarak Bağımsız Denetim Firma Değişikliğinin Tahmini: Türkiye Örneği

Öz

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.

Anahtar Kelimeler

Denetim Firma Değişikliği , Finansal Oranlar , Makine Öğrenmesi , Karşılıklı Bilgi

Etik Beyan

-

Destekleyen Kurum

-

Teşekkür

-

Kaynakça

• Abu Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., and Prasath, V. S. (2019). Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big data, 7(4), 221-248. doi: 10.1089/big.2018.0175
• Adjirackor, T., Asare, D. D., Asare, F. D., and Gagakuma, W. (2017). Financial ratios as a tool for profitability in Aryton drugs. Research Journal of Finance and Accounting, 8(14).
• Aisyah, L. and Faridah, R. (2023). Voluntary Auditor Switching in Listed Companies: What Influences It?. Asian Journal of Islamic Economics and Business, 1(1), 42-63.
• Al-Garadi, M. A., Mohamed, A. K., Al-Ali, X. Du, I. Ali and M. Guizani, (2020). “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security,” in IEEE Communications Surveys and Tutorials, vol. 22, no. 3, pp. 1646-1685, doi: 10.1109/COMST.2020.2988293
• Altass, S. (2023). Auditor Switching, Tenure, and Corporate Performance: The Saudi Evidence. Quality-Access to Success, 24(192).
• Barros, R. C., Basgalupp, M. P., Carvalho, A. C., and Freitas, A. A. (2012). A hyper-heuristic evolutionary algorithm for automatically designing decision tree algorithms. Gecco’12, 1237-1244. doi: https://doi.org/10.1145/2330163.2330335
• Black, E. L., Burton, F. G., and Maggina, A. G. (2013). Auditor switching in the economic crisis: The case in Greece’’, International Journal of Accounting and Economic Studies, 1(2), 39-46.
• Boulesteix, A. L., Janitza, S., Kruppa, J., and König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6), 493-507. doi: https://doi.org/10.1002/widm.1072
• Briliani, A., Irawan, B., and Setianingsih, C. (2019). Hate speech detection in Indonesian language on Instagram comment section using K-nearest neighbor classification method’’, 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS). doi: 10.1109/IoTaIS47347.2019.8980398
• Calderon, T. G., and Ofobike, E. (2008). Determinants of client-initiated and auditor-initiated auditor changes. Managerial Auditing Journal, 23(1), 4-25.
• Cao, L. J. and Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial times series forecasting. IEEE Transactions on Neural Networks, 14(6).
• Chen, C. L., Chang F. H. and Yen G. (2008). The information contents of auditor change infinancial distress prediction-empirical findings from the TALEX-listed firms. Draft of paper retrieved from www.google.com, at January 12.
• Chen, J., Wang, X., and Zhai, J. (2009). Pruning decision tree using genetic algorithms. IEEE Computer Society, doi: 10.1109/AICI.2009.351
• Chen, T., and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acmsigkdd international conference on knowledge discovery and data mining, San Francisco, California, USA, p.785-794.
• Cheng, C. B., Chen, C. L., and Fu, C. J. (2005). Financial distress prediction by a radial basis function network with logit analysis learning. An International Journal Computers and Mathematics with Applications, doi: 10.1016/j.camwa.2005.07.016
• Chung, H., Kim, Y. and Sunwoo, H. Y. (2021). Korean evidence on auditor switching for opinion shopping and capital market perceptions of audit quality. Asia-Pacific Journal of Accounting & Economics, 28(1), 71-93.
• Chunhong, Z., and Licheng, J. (2004). Automatic parameters selection for SVM based on GA. Proceedings of the 5th World Congress on Intelligent Control and Automation, June 15-19.
• Ding, F., Qiao, Z., Hu, M. and Lu, M. (2022). Industry specialization and audit quality: Evidence from audit firm switches in China. Asia‐Pacific Journal of Financial Studies, 51(5), 657-681.
• Eldridge, S., Kwak, W., Venkatesh, R., Shi, Y. and Kou, G. (2012). Predicting auditor changes with financial distress variables: discriminant analysis and problems with data mining approaches. The Journal of Applied Business Research, 28(6), 1357-1372.
• Ettredge, M. L., Li, C., and Scholz, S. (2007). Audit fees and auditor dismissals in the Sarbanes-Oxley era. Accounting Horizons, 21(4), 371-386.
• Ghosh, S., Dasgupta, A. and Swetapadma, A. (2019). A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification. 2019 International Conference on Intelligent Sustainable Systems (ICISS), doi:10.1109/ISS1.2019.8908018.
• Guo, Q., Koch, C. and Zhu, A. (2023). Switching Costs and Market Power in Auditing: Evidence from a Structural Approach. Available at SSRN 4166004.
• Han, J., Kamber, M., and Pei, J. (2011). Data mining concepts and techniques. Third Edition, Morgan Kaufmann, Massachusetts.
• Hu, L.Y., Huang, M.W., Ke, S.W., and Tsai, C.F. (2016). The distance function effect on k-nearest neighbor classification for medical datasets. Springer Plus, 5(1), 1-9.
• Huang, Y. and Scholz, S. (2012). Evidence of the association between financial restatement and auditor resignations. Accounting Horizon, 26(3), 439-464.
• Ismail, S. H., Aliahmed H., Nassir, A., and Hamid, M. A. (2008). Why Malaysian second board companies switch auditors: Evidence of Bursa Malaysia. International Research Journal of Finance and Economics, 13, 123-130.
• Jain, S. and Agarwalla, S. K. (2023). Big-4 auditors and audit quality: A novel firm life-cycle approach. Meditari Accountancy Research, 31(5), 1436-1452. https://doi.org/10.1108/MEDAR-06-2021-1344
• Kamarudin, K. A., Islam, A., Habib, A. and Wan Ismail, W. A. (2022). Auditor switching, lowballing and conditional conservatism: evidence from selected Asian countries. Managerial Auditing Journal, 37(2), 224-254.
• Kirkos, E. (2012). Predicting auditor switches by applying data mining, Journal of Applied Economic Sciences, 7(3), 344-347.
• Kwak, W., Eldridge, S., Shi, Y., and Kou, G. (2011). Predicting auditor changes using financial distress variables and the multiple criteria linear programming (MCLP) and other data mining approaches. The Journal of Applied Business Research, 27(5), 73-84.
• Lei, R., and Liu, H. (2021). Financial distress prediction using GA-BP neural network model. International Journal of Economics and Finance, 13(3).
• Liaw, A., and Wiener, M. (2022). Classification and regression by random forest. R News, 2(3).
• Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72(16), 3507-3516.
• Liu, Y. (2021, March). Analysis on Bilibili Video Categorical Segmentation Using XGBoost. In 2021 2nd International Conference on E-Commerce and Internet Technology (ECIT) (pp. 259-263). IEEE
• Nasser, A. T., Wahid, E. A., Nazri, S. N., and Hudaib, M. (2006). Auditor client relationship: The case of audit tenure and auditor switching in Malaysia. Managerial Auditing Journal, 21(7), 724-737.
• Nassif, A. B., Azzeh, M., Capretz, L. F., and Ho, D. (2013). A Comparison between Decision Trees and Decision Tree Forest Models for Software Development Effort Estimation. Computational Intelligence Applications in Software Engineering (CIASE), Beirut.
• Nazri, S. N., Smith, M., and Ismail, Z. (2012). Factors influencing auditor change: Evidence from Malaysia. Asian Review of Accounting, 20 (3), 22-44.
• Okun, O. (2011). Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations. Information Science Reference - Imprint of: IGI Publishing, Hershey, PA.
• Patel, H. H., and Prajapati, P. (2018). Study and analysis of decision tree-based classification algorithms. International Journal of Computer Sciences and Engineering, 6(10).
• Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8).
• Pradhan, A. (2012). Support vector machine a survey. International Journal of Emerging Technology and Advanced Engineering, 2(8).
• Saalem, Q. and Rehman, R. U. (2011). Impacts of Liquidity Ratios on Profitability. Interdisciplinary Journal of Research in Business, 1(7), 95-98.
• Saluy, A. B., Kemalsari, N., Handiman, U. T., Arwiya, P., Faridi, A., Caya, B. A. and Machmud, H. (2024). Human Resources Perspective: Audit Fee, Internal Control, and Audit Materiality Affect Auditor Switching. WSEAS Transactions on Business and Economics, 21, 21-34.
• Seethapathy, S. K. and Babu, C. N. (2021). Enhanced approach for soil classification using boosted c5.0 decision tree algorithm. BSSS Journal of Computer: XII(I),11-21.
• Shuai, Y., Zheng, Y., and Huang, H. (2018). Hybrid software obsolescence evaluation model based on PCA-SVM-GridSearchCV,in:2018 IEEE 9th international conference on software engineering and service science (ICSESS), IEEE (2018) 449–53. Doi: 10.1109/ICSESS.2018.8663753
• Siva, S. S., Geetha, S., and Kannan, A. (2012). Decision tree based light weight intrusion detection using a wrapper approach. Expert Systems with Applications, 39, 129-141.
• Song, Y. Y., and Lu, Y. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2).
• Srinivasa, R., Yashashwini, S., Venkatesh, K. andYaswanth, S. P. (2020). Prediction of Diabetes Using Machine Learning, International Journal of Advanced Science and Technology, 29(06), p.7593-7601.
• Susanto, Y. K. (2018). Auditor switching: management turnover, qualified opinion, audit delay, financial distress. International Journal of Business, Economics and Law, 15(5), 125-132.
• Suyono, E., Feng, Y., and Riswan, M. (2013). Determinant factors affecting the auditor switching: An Indonesian case. Global Review of Accounting and Finance, 4(2), 103-116.
• Ünal, M. and Altay, A. (2015). Kurumsal yönetimin bağımsız dış denetime etkisi ve denetim firması seçimindeki rolü: BIST imalat sektöründe bir uygulama. Journal of Accounting and Taxation Studies, 8(2), 91-106.
• Wan Mohamed, W. A., Hussain. W. S., and Mohd Rodzi, N. K. (2007). Characteristics’ of companies that change and do not change and do not change auditor- an empirical investigation of Malaysian public listed companies. Unpublished manuscript, University of Teknology MARA, Shah Alam, Malaysia.
• Witten H., Frank E., Hall M., and Pal C. (2016). Data mining: practical machine learning tools and techniques, (4th ed.). ‎ Morgan Kaufmann.
• Yang, F. J. (2018). An Implementation of Naive Bayes Classifier. 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Doi 10.1109/CSCI46756.2018.00065.
• Yim, J., and Mitcbell, H. (2005). A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis. Nova Economia, 15(1), 73-93.
• Zhou, J., Qiu, Y., Khandelwal, M., Zhu, S., and Zhang, X. (2021). Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences, 145, 21-34.