Bağımsız Denetim Görüşlerinin Tahmin Edilmesinde Lojistik Regresyon ve Yapay Sinir Ağı Yöntemlerinin Karşılaştırılması: BİST Kimya İlaç Petrol Lastik ve Plastik Ürünler Sektöründe Bir Uygulama
Öz
Bu çalışma yapay sinir ağı ve lojistik regresyon yöntemlerini kullanarak bağımsız denetim görüşlerinin tahmin edilmesi amacıyla yapılmıştır. Bu kapsamda Borsa İstanbul Kimya İlaç Petrol Lastik ve Plastik Ürünler sektöründe işlem gören şirketlerin 2010-2020 dönemlerindeki finansal tabloları ve denetim raporları ele alınmıştır. Bağımsız denetim görüşlerinin Yapay Sinir Ağı yöntemiyle yapılan sınıflandırma tahmininde % 96,5 oranında, Lojistik Regresyon yöntemiyle yapılan sınıflandırma tahmininde ise % 94,3 oranında doğru sınıflandırma performansı göstermişlerdir. Araştırma sonuçlarına göre yapay sinir ağı modelinin daha yüksek sınıflandırma tahmini ortaya koyduğu tespit edilmiştir. Çalışma kapsamında ele alınan modeller, denetim planlama, risk değerlendirme ve kalite kontrol çalışmalarında bağımsız denetçiler, iç denetçiler, yöneticiler, ortaklar, dış kaynak sağlayıcılar, ticari ilişkilerde bulunanlar, yatırımcılar, çalışanlar, danışmanlık kuruluşları, kamu düzenleyicileri ve finansal analistler gibi çok geniş bir karar verici çevrenin kararlarını destekleyici bir araç olarak kullanılabileceği öngörülmektedir.
Anahtar Kelimeler
Bağımsız Denetim Görüş Tahmini Lojistik Regresyon Yapay Sinir Ağı WEKA
Kaynakça
- Adiloğlu, B., ve Vuran, B. (2011). A Multicriterion Decision Support Methodology For Audit Opinions: The Case Of Audit Reports Of Distressed Firms In Turkey. International Business & Economics Research Journal (IBER), 10(12), 37-48.
- Adiloğlu, B., ve Vuran, B. (2017). Identification of Key Performance Indicators of Auditor’s Reports: Evidence from Borsa Istanbul (BIST). PressAcademia Procedia, 3(1), 854-859.
- BDS 700 Finansal Tablolara İlişkin Görüş Oluşturma ve Raporlama
- BDS 705 Bağımsız Denetçi Raporunda Olumlu Görüş Dışında Bir Görüş Verilmesi
- Büyüktanır, T., ve Toraman, T. (2020). Auditor's Opinions Prediction with Machine Learning Algorithms. In 2020 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
- Caramanis, C., ve Spathis, C. (2006). Auditee And Audit Firm Characteristics As Determinants Of Audit Qualifications: Evidence From The Athens Stock Exchange. Managerial Auditing Journal, 21 (9), 905-920.
- Dopuch, N., Holthausen, R. W., ve Leftwich, R. W. (1987). Predicting Audit Qualifications With Financial And Market Variables. Accounting Review, 62(3), 431-454.
- Doumpos, M., Gaganis, C., & Pasiouras, F. (2005). Explaining Qualifications İn Audit Reports Using A Support Vector Machine Methodology. Intelligent Systems in Accounting, Finance & Management: International Journal, 13(4), 197-215.
- Gaganis C, Pasiouras F, Doumpos M. (2007). Probabilistic Neural Networks For The İdentification Of Qualified Audit Opinions. Expert Syst. Appl. 32, 114-124.
- https://towardsdatascience.com/5-smote-techniques-for-oversampling-your-imbalance-data- b8155bdbe2b5 (12.10.2021)
- https://www.stockeys.com/ (12.11.2021)
- https://www.tmud.org.tr/tr/tmud-yayinlar (10.12.2021)
- Laitinen, E. K., ve Laitinen, T. (1998). Qualified Audit Reports İn Finland: Evidence From Large Companies. European Accounting Review, 7(4), 639-653.
- Moalla, H. (2017). Audit Report Qualification/Modification: Impact Of Financial Variables in Tunisia. Journal of Accounting in Emerging Economies.
- Nawaiseh, A. K., ve Abbod, M. F. (2020). Financial Statement Audit Utilising Naive Bayes Networks, Decision Trees, Linear Discriminant Analysis and Logistic Regression. In International Conference on Business and Technology (pp. 1305-1320). Springer, Cham.
- Pourheydari, O., Nezamabadi-pour, H., ve Aazami, Z. (2012). Identifying Qualified Audit Opinions By Artificial Neural Networks. African Journal of Business Management, 6(44), 11077-11087.
- Spathis, C., Doumpos, M., ve Zopounidis, C. (2003). Using Client Performance Measures To Identify Pre- Engagement Factors Associated With Qualified Audit Reports in Greece. The International Journal of Accounting, 38(3), 267-284.
- Stanisic, N., Radojevic, T., ve Stanic, N. (2019). Predicting The Type Of Auditor Opinion: Statistics, Machine Learning, Or A Combination Of The Two?. The European Journal of Applied Economics, 16(2), 1-58.
- Yaşar, A. (2016). Olumlu Görüş Dışındaki Denetim Görüşlerinin Veri Madenciliği Yöntemleriyle Tahminine İlişkin Karar ve Birliktelik Kuralları. Mali Çözüm Dergisi/Financial Analysis, 26(133), 81-109.
- Yaşar, A., Yakut, E., ve Gutnu, M. M. (2015). Predicting Qualified Audit Opinions Using Financial Ratios: Evidence From The Istanbul Stock Exchange. International Journal of Business and Social Science, 6(8), 57-67.
Comparison of the Logistic Regression and the Artificial Neural Network Methods in Audit Opinion Prediction: An Application in BIST Chemistry Pharmaceutical Oil Rubber and Plastic Products Sector
Öz
This study was conducted to predict independent audit opinions using the artificial neural network and the logistic regression methods. In this context, the financial statements and audit reports of the companies in Borsa Istanbul Chemicals, Petroleum, Rubber, and Plastic Products sector for the period of 2010-2020 were discussed. They showed a correct classification performance of 96.5% in the classification estimation made by the artificial neural network method and 94.3% in the classification estimation made by the logistic regression method. According to the results of the research, it was determined that the artificial neural network model revealed higher classification prediction. It is envisaged that the models discussed within the scope of the study can be used as an auxiliary tool to support decisions of independent auditors, internal auditors, managements, partners, investors, foreign resource providers, employees, commercial relations, regulatory public institutions, consultancy institutions, financial analysts and the public in audit planning, risk assessment, and quality control studies.
Anahtar Kelimeler
Audit opinion prediction Logistic regression Artificial neural network WEKA
Kaynakça
- Adiloğlu, B., ve Vuran, B. (2011). A Multicriterion Decision Support Methodology For Audit Opinions: The Case Of Audit Reports Of Distressed Firms In Turkey. International Business & Economics Research Journal (IBER), 10(12), 37-48.
- Adiloğlu, B., ve Vuran, B. (2017). Identification of Key Performance Indicators of Auditor’s Reports: Evidence from Borsa Istanbul (BIST). PressAcademia Procedia, 3(1), 854-859.
- BDS 700 Finansal Tablolara İlişkin Görüş Oluşturma ve Raporlama
- BDS 705 Bağımsız Denetçi Raporunda Olumlu Görüş Dışında Bir Görüş Verilmesi
- Büyüktanır, T., ve Toraman, T. (2020). Auditor's Opinions Prediction with Machine Learning Algorithms. In 2020 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
- Caramanis, C., ve Spathis, C. (2006). Auditee And Audit Firm Characteristics As Determinants Of Audit Qualifications: Evidence From The Athens Stock Exchange. Managerial Auditing Journal, 21 (9), 905-920.
- Dopuch, N., Holthausen, R. W., ve Leftwich, R. W. (1987). Predicting Audit Qualifications With Financial And Market Variables. Accounting Review, 62(3), 431-454.
- Doumpos, M., Gaganis, C., & Pasiouras, F. (2005). Explaining Qualifications İn Audit Reports Using A Support Vector Machine Methodology. Intelligent Systems in Accounting, Finance & Management: International Journal, 13(4), 197-215.
- Gaganis C, Pasiouras F, Doumpos M. (2007). Probabilistic Neural Networks For The İdentification Of Qualified Audit Opinions. Expert Syst. Appl. 32, 114-124.
- https://towardsdatascience.com/5-smote-techniques-for-oversampling-your-imbalance-data- b8155bdbe2b5 (12.10.2021)
- https://www.stockeys.com/ (12.11.2021)
- https://www.tmud.org.tr/tr/tmud-yayinlar (10.12.2021)
- Laitinen, E. K., ve Laitinen, T. (1998). Qualified Audit Reports İn Finland: Evidence From Large Companies. European Accounting Review, 7(4), 639-653.
- Moalla, H. (2017). Audit Report Qualification/Modification: Impact Of Financial Variables in Tunisia. Journal of Accounting in Emerging Economies.
- Nawaiseh, A. K., ve Abbod, M. F. (2020). Financial Statement Audit Utilising Naive Bayes Networks, Decision Trees, Linear Discriminant Analysis and Logistic Regression. In International Conference on Business and Technology (pp. 1305-1320). Springer, Cham.
- Pourheydari, O., Nezamabadi-pour, H., ve Aazami, Z. (2012). Identifying Qualified Audit Opinions By Artificial Neural Networks. African Journal of Business Management, 6(44), 11077-11087.
- Spathis, C., Doumpos, M., ve Zopounidis, C. (2003). Using Client Performance Measures To Identify Pre- Engagement Factors Associated With Qualified Audit Reports in Greece. The International Journal of Accounting, 38(3), 267-284.
- Stanisic, N., Radojevic, T., ve Stanic, N. (2019). Predicting The Type Of Auditor Opinion: Statistics, Machine Learning, Or A Combination Of The Two?. The European Journal of Applied Economics, 16(2), 1-58.
- Yaşar, A. (2016). Olumlu Görüş Dışındaki Denetim Görüşlerinin Veri Madenciliği Yöntemleriyle Tahminine İlişkin Karar ve Birliktelik Kuralları. Mali Çözüm Dergisi/Financial Analysis, 26(133), 81-109.
- Yaşar, A., Yakut, E., ve Gutnu, M. M. (2015). Predicting Qualified Audit Opinions Using Financial Ratios: Evidence From The Istanbul Stock Exchange. International Journal of Business and Social Science, 6(8), 57-67.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Ekonometrik ve İstatistiksel Yöntemler |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Erken Görünüm Tarihi | 23 Haziran 2023 |
| Yayımlanma Tarihi | 30 Haziran 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 25 Sayı: 44 |
Kaynak Göster
EBSCO SOBİAD ProQuest Türk Eğitim İndeksi