GERÇEĞE AYKIRI FİNANSAL TABLOLARIN TESPİTİNDE VERİ MADENCİLİĞİNİN KULLANIMI: TÜRKİYE’DE FİNANS SEKTÖRÜ ÜZERİNE AMPRİK BİR ARAŞTIRMA

Yıl 2012, Cilt: 1 Sayı: 96, 76 - 94, 01.07.2012

İlker Kıymetli Şen Serkan Terzi

Öz

Anahtar Kelimeler

-

GERÇEĞE AYKIRI FİNANSAL TABLOLARIN TESPİTİNDE VERİ MADENCİLİĞİNİN KULLANIMI: TÜRKİYE’DE FİNANS SEKTÖRÜ ÜZERİNE AMPİRİK BİR ARAŞTIRMA

Öz

Bu çalışmanın amacı, İstanbul Menkul Kıymetler Borsası (İMKB) finans sektöründe işlem gören şirketler için gerçeğe aykırı finansal tabloları belirlemek amacıyla güvenilir bir model geliştirmektir. Çalışmada kullanılan veriler, İMKB finans sektörüne kote olan şirketlerin denetlenmiş finansal tablolarından elde dilmiştir. Yapılan çalışmada elde edilen bulgular, önceki çalışmalarla uyumlu olup, yayınlanan finansal tabloların tahrif edilme riski taşıdığını göstermektedir. Ayrıca seçilen değişkenlerden bazılarının gerçeğe aykırı finansal tablolarının belirlenmesinde iyi bir gösterge olduğu sonucuna ulaşılmıştır

Anahtar Kelimeler

Tahrif Etme , Finansal Tablolar , Veri Madenciliği , Finans Sektörü

Kaynakça

• Albayrak, A.S. and S. Koltan Yilmaz. 2009. Data mining: decision tree algo- rithms and an application on ISE data. Suleyman Demirel University the Journal of Faculty of Economics and Administrative Sciences, 14 (1): 31-52.
• Ata, H.A. and I.H.Seyrek. 2009. The use of data mining techniques in detect- ing fraudulent financial statements: an application on manufacturing firms. Suleyman Demirel University the Journal of Faculty of Economics and Administrative Sciences, 14 (2): 157-170.
• Atiya, A.F. 2001. Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neural Networks, 12 (4):
• Cerullo, M.J. and M.V. Cerullo. 2006. Using neural network software as a fo- rensic accounting tool. ISACA Journal, 2: 1-5. Retrieved February 14, 2012, from http://www.isaca.org/Journal/Past-Issues/2006/Volume- 2/Pages/ Using-Neural-Network-Software-as-a-Forensic-Accounting- Tool1.aspx
• Erol, M. 2008. The expectations from auditing against corruptions (errors and tricks) in the enterprises. Suleyman Demirel University the Journal of Faculty of Economics and Administrative Sciences, 13 (1): 229-237.
• Fanning, K.M. and K.O. Cogger. 1998. Neural network detection of manage- ment fraud using published financial data. International Journal of In- telligent Systems in Accounting, Finance & Management, 7 (1): 21–41.
• Gaganis, C. 2009. Classification techniques for the identification of falsified financial statements: a comparative analysis. Intelligent Systems In Ac- counting, Finance and Management, 16 (3): 207-229.
• Kaynar, O. and S. Tastan. 2009. Comparison of MLP artificial neural network and ARIMA method in time series analysis. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33: 161-172.
• Kirkos, E., C. Spathis and Y. Manolopoulos. 2007. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32 (4): 995-1003.
• Kurtaran Celik, M. 2007. Prediction of financial failure of banks with traditional and new methods. Journal Management and Economics, 17 (2): 129-143.
• Kucukkocaoglu, G., Y. Keskin Benli and C. Kucuksozen. 2007. Detecting the manipulation of financial information by using artificial neural network models. ISE Review, 9 (36): 1-30.
• Kotsiantis, S., E. Koumanakos, D. Tzelepis and V. Tampakas. 2007. Forecasting fraudulent financial statements using data mining. International Jour- nal of Information and Mathematical Sciences, 3 (2): 104-110.
• Koyuncugil, A.S. and N. Ozgulbas. 2008. Strengths and weaknesses of SMEs listed in ISE: a CHAID decision tree application. Dokuz Eylul University the Journal of Faculty of Economics and Administrative Sciences, 23 (1): 1-21.
• Liou, F.M. 2008. Fraudulent financial reporting detection and business failure prediction models: a comparison. Managerial Auditing Journal, 23 (7):
• Persons, O. S. 1995. Using financial statement data to identify factors associ- ated with fraudulent financial reporting. Journal of Applied Business Research, 11 (3): 38-46.
• Ravisankar, P., V. Ravi, G.R. Rao and I. Bose. 2011. Detection of financial state- ment fraud and feature selection using data mining techniques. Deci- sion Support Systems, 50 (2): 491-500.
• Spathis, C., M. Doumpos and C. Zopounidis. 2002. Detecting falsified financial statements: a comparative study using multicriteria analysis and multi- variate statistical techniques. The European Accounting Review, 11 (3): 509-535.
• Terzi, S. 2012. Detection of fraud and financial impropriety with data mining. Journal of Accounting and Finance, 54: 51-63.