USING GENETIC ALGORITHM TO GOING CONCERN ASSESSMENT IN TERMS OF AUDITING: EVIDENCE FROM BORSA ISTANBUL INDUSTRIAL INDEX

Yıl 2016, ICAFR 16 Özel Sayısı, 685 - 693, 01.10.2016

Ramazan Terzi Metin Atmaca Serkan Terzi

Öz

The purpose of this paper is to develop a reliable model to going concern assessment in terms of auditing. For this purpose, the variables derived from the financial statements of the companies constantly listed in Borsa Istanbul BIST Industrial Index between the years 2009-2014 are used in this research. In the paper genetic algorithm, one of the machine learning algorithms, is used. The research concluded that current ratio and ratio of inventories to total assets variables are significant in to going concern assessment in auditing. Also the success rate of the models developed using genetic algorithm has been determined as an average of 73%. In addition, Type I errors of the generated rules have been determined as the average of 20%. Therefore the models generated for BIST Industrial Index can be said to be reliable

Anahtar Kelimeler

Going Concern, Auditing, Genetic Algorithm, Borsa Istanbul

DENETİM AÇISINDAN İŞLETMENİN SÜREKLİLİĞİNİN DEĞERLENDİRİLMESİNDE GENETİK ALGORİTMANIN KULLANIMI: BORSA İSTANBUL SINAÎ ENDEKSİ ÖRNEĞİ

Öz

Bu çalışmanın amacı, denetim açısından işletmenin sürekliliğinin değerlendirilmesinde güvenilir bir model oluşturmaktır. Bu amaçla araştırmada Borsa İstanbul BİST Sınai Endeksinde 2009-2014 yılları arasında sürekli olarak kote olan işletmelerin finansal tablolarından elde edilen değişkenler kullanılmıştır. Araştırmada makine öğrenme algoritmalarından biri olan genetik algoritma kullanılmıştır. Yapılan araştırma sonucunda cari oran ve stokların toplam aktife oranı değişkenlerinin denetim aşamasında işletmenin sürekliliğinin değerlendirilmesinde önemli olduğu tespit edilmiştir. Ayrıca genetik algoritma kullanılarak oluşturulan modellerin başarı oranları ortalama %73 olarak tespit edilmiştir. Ayrıca oluşturulan kuralların Tip I hataları ise ortalama %20 olarak belirlenmiştir. Bu nedenle BİST Sınaî Endeksi için oluşturulan modellerin güvenilir olduğu söylenebilir.

Anahtar Kelimeler

İşletmenin Sürekliliği, Denetim, Genetik Algoritma, Borsa İstanbul

Kaynakça

• AICPA (2015). Statements on auditing standards. Erişim Tarihi: 05.10.2015, http://www.aicpa.org/research/standards/auditattest/pages/sas.aspx
• Altman, E. I. & McGough, T. (1974). Evaluation of a company as a going concern. Journal of Accounting”, Auditing and Finance, 6(4), 4-19.
• Asare, S. K. (1992). The auditor's going-concern decision: interaction of task variables and the sequential processing of evidence. The Accounting Review, 67(2), 379- 393.
• Bellovary, J. L., Giacomino, D. E. & Akers, M. D. (2007). A review of going concern prediction studies: 1976 to present. Journal of Business & Economics Research, 5, 9-28.
• Carey, P. T., Geiger, M. A. & O’Connell, B. T. (2008). Costs associated with going- concern-modified audit opinions: an analysis of the Australian audit market. Abacus, 44(1), 61-81.
• Carson, E., Fargher, N. L., Geiger, M. A., Lennox, C. S., Raghunandan, K. & Willekens, M. (2013). Audit reporting for going-concern uncertainty: a research synthesis. Auditing: A Journal of Practice & Theory, 32(1), 353-384.
• Chen, K. C. W. & Church, B. K. (1992). Default on debt obligations and the issuance of going-concern opinions. Auditing: A Journal of Practice and Theory, 11(2), 30– 50.
• Geiger, M. A., Raghunandan, K. & Rama, D. V. (1998). Costs associated with going- concern modified audit opinions: an analysis of auditor changes, subsequent opinions, and client failures. Advances in Accounting, 16, 117-139.
• Geiger, M. A. & Raghunandan, K. (2002). Going-concern opinions in the ‘new’ legal environment. Accounting Horizons, 16(1), 17-26.
• Goodman, B., Braunstein, D. N., Reinstein, A. & Gregory, G. W. (1995). Explaining auditors going concern decisions: assessing managements capability. Journal of Applied Business Research, 11(3), 82-93.
• Haron, H., Hartadi, B., Ansari, M. & Ismail, I. (2009). Factors influencing auditors' going concern opinion. Asian Academy of Management Journal, 14(1), 1-19.
• Harris, C. R. & Harris, W. T. (1990, April). An expert decision support system for auditor `going concern' evaluations. Poster session presented at the Symposium on Applied Computing, Arkansas. KAP. (2015). http://kap.gov.tr/
• KGK. (2015). TMS 1 Finansal tabloların sunuluşu. Erişim Tarihi: 05.09.2015, http://www.kgk.gov.tr/contents/files/TFRS_2015/TMS/TMS1.pdf
• KGK. (2015). Bağımsız denetim standardı 570: işletmenin sürekliliği. Erişim Tarihi: 05.09.2015, http://www.kgk.gov.tr/contents/files/BDS/BDS_570.pdf
• Koh, H. C. & Tan, S. S. (1999). A neural network approach to the prediction of going concern status. Accounting and Business Research, 29(3), 211-216.
• Kuruppu, N., Laswad, F. & Oyelere, P. (2003). The efficacy of liquidation and bankruptcy prediction models for assessing going concern. Managerial Auditing Journal, 18(6-7), 577-590.
• Lenard, M. J., Alam, P., Booth, D. & Madey, G. (2001). Decision-making capabilities of a hybrid system applied to the auditor’s going-concern assessment. International Journal of Intelligent Systems in Accounting, Finance & Management, 10, 1-24.
• Louwers, T. J. (1998). The relation between going-concern opinions and the auditor's loss function. Journal of Accounting Research, 36(1), 143-156.
• Moradi, M., Salehi, M., Yazdi, H. S. & Gorgani, M. E. (2012). Going concern prediction of Iranian companies by using fuzzy c-means. Open Journal of Accounting, 1, 38- 46.
• Mutchler, J. F. (1984). Auditors’ perceptions of the going-concern option decision. Auditing: A Journal of Practice and Theory, 3(2), 17-30.
• Mutchler, J .F. (1985). A multivariate analysis of the auditor's going-concern opinion decision. Journal of Accounting Research, 23(2), 668-682.
• Martens, D., Bruynseels, L., Baesens, B., Willekens, M. & Vanthienen, J. (2008). Predicting Going Concern Opinion with Data Mining. Decision Support Systems, 45, 765-777.
• Raghunandan, K. & Rama, D. V. (1995). Audit reports for companies in financial distress: before and after SAS no. 59. Auditing: A Journal of Practice & Theory, 14(1), 50- 63.
• Salehi, M. & Fard, F. Z. (2013). Data mining approach to prediction of going concern using classification and regression tree (CART). Global Journal of Management and Business Research, 13(3), 25-29.
• Terzi, S. (2012). Hileli Finansal Raporlama: Önleme ve Tespit: İMKB İmalat Sanayiinde Bir Araştırma. 1. Baskı, İstanbul: Beta Yayınları.
• Uzay, Ş. & Güngör Tanç, Ş. (2010). İMKB’de işlem gören şirketlerin bağımsız denetim raporlarında işletmenin sürekliliği kavramının analizi. Muhasebe Bilim Dünyası Dergisi, 2, 143-179.
• Vanstraelen, A. (1999). The auditor’s going concern opinion decision: a pilot study. International Journal of Auditing, 3, 41-57.