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

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

Ramazan Terzi Metin Atmaca Serkan Terzi

Abstract

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

Keywords

Going Concern, Auditing, Genetic Algorithm, Borsa Istanbul

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

Abstract

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.

Keywords

İşletmenin Sürekliliği, Denetim, Genetik Algoritma, Borsa İstanbul

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