ANT COLONY OPTIMIZATION APPROACH TO PREDICTING FINANCIAL DISTRESS: A RESEARCH IN BORSA ISTANBUL

Abstract

Purpose: The purpose of this paper is to develop a reliable model to determine predicting financial distress of manufacturing companies in Borsa Istanbul. In order to achieve this purpose, 18 variables calculated from financial statements of 100 manufacturing companies quoted in Borsa Istanbul between 2005-2019 are used in the analysis. Approach: The ant colony algorithm-based approach (AntMiner+) is used in the optimization algorithm for predicting the financial distress. Findings: As a result of the paper, rules for financial distress with the highest success rate are determined. Some of the variables that are directly related to the risk of financial distress are determined. In addition, It has been determined that the success rate for the generated rules between 82%-93%, and the average success is 88%. Thus, the algorithm used during the analyses is found valid according to the literature. Originality: There are few studies in the literature that use the ant colony algorithm (AntMiner+) to detect financial failure. In Turkey, there are no studies in determining financial distress with this algorithm.

Keywords

• Financial Distress
• Ant Colony
• Borsa Istanbul

FİNANSAL BAŞARISIZLIK TAHMİNİNDE KARINCA KOLONİSİ OPTİMİZASYON YAKLAŞIMI: BORSA İSTANBUL’DA BİR ARAŞTIRMA

Abstract

Amaç: Bu çalışmanın amacı, Borsa İstanbul İmalat Sanayiinde işlem gören şirketlerin finansal başarısızlık tahmininde güvenilir bir model geliştirmektir. Bunun için Borsa İstanbul’da kayıtlı 100 üretim şirketinin 2005-2019 yılları arasında finansal tablolarından elde edilen 18 değişken analizde kullanılmıştır. Yaklaşım: Finansal başarısızlık tahmininde optimizasyon algoritmaları içinde yer alan karınca kolonisi algoritması tabanlı bir yaklaşım (antminer +) kullanılmıştır. Bulgular: Yapılan çalışma sonucunda finansal başarısızlık tahmininde en yüksek başarım oranına sahip kurallar tespit edilmiştir. Bazı değişkenlerin finansal başarısızlık riski ile doğrudan ilişkili olduğu belirlenmiştir. Ayrıca oluşturulan kuralların başarı oranlarının %82-%93 arasında olduğu belirlenmiş olup, ortalama başarı oranı ise %88 olarak tespit edilmiştir. Bu nedenle kullanılan algoritmanın literatüre göre geçerli olduğu tespit edilmiştir. Özgünlük: Literatürde finansal başarısızlığın tespiti için karınca koloni algoritmasının (AntMiner+) kullanıldığı az sayıda çalışma mevcuttur. Türkiye’de ise finansal başarısızlık tespitinde bu algoritmayla yapılan çalışma bulunmamaktadır.

Keywords

• Finansal Başarısızlık
• Karınca Kolonisi
• Borsa İstanbul

References

1. Altaş, D., Giray, S. (2005). Mali Başarısızlığın Çok Değişkenli İstatistiksel Yöntemlerle Belirlenmesi: Tekstil Sektörü Örneği. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 5(2), 13-28.
2. Altman, E.I. (1968). Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609.
3. Arora, V., Ravi, V. (2013). Data Mining Using Advanced Ant Colony Optimization Algorithm and Application to Bankruptcy Prediction. International Journal of Information Systems and Social Change, 4(3), 33-56.
4. Atiya, A.F. (2001). Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results. IEEE Transactions on Neural Networks, 12(4), 929-935.
5. Beaver, W.H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, (4), 71-102.
6. Casey, C., Bartczak, N. (1985). Using Operating Cash Flow Data to Predict Financial Distress: Some Extensions. Journal of Accounting Research, 23(1), 384-401.
7. Chen, W.S., Du, Y.K. (2009). Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model. Expert Systems with Applications, (36), 4075-4086.
8. Chung, K.C., Tan, S.S., Holdsworth, D.K. (2008). Insolvency Prediction Model Using Multivariate Discriminant Analysis and Artificial Neural Network for the Finance Industry in New Zealand. International Journal of Business and Management, 3(1), 19-29.

9. Çelik, M.K. (2010). Bankaların Finansal Başarısızlıklarının Geleneksel ve Yeni Yöntemlerle Öngörüsü. Celal Bayar Üniversitesi Yönetim ve Ekonomi Dergisi, 17(2), 129-143.
10. Deakin, E.B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), 167-179.
11. Dorigo, M., Birattari, M., Stutzle, T. (2006). Ant Colony Optimization. Computational Intelligence Magazine, IEEE, 1(4), 28-39.
12. Ekşi, İ.H. (2011). Classification of Firm Failure with Classification and Regression Trees. International Research Journal of Finance and Economics, (76), 113-120.
13. Etemadi, H., Rostamy, A.A.A., Dehkordi, H.F. (2009). A Genetic Programming Model for Bankruptcy Prediction: Empirical Evidence from Iran. Expert Systems with Applications, (36), 3199-3207.
14. Gepp, A., Kumar, K. (2008). The Role of Survival Analysis in Financial Distress Prediction, International Research Journal of Finance and Economics, (16), 13-34.
15. Halim, M.S.A., Jaafar, M., Osman, O., Akbar, S. (2010). The Contracting Firm’s Failure and Financial Related Factors: A Case Study of Malaysian Contracting Firms, International Research Journal of Finance and Economics, (52), 28-39.
16. Khodadadi, V., Zandinia, A., Nouri, M. (2010). Application of Ant Colony System for Bankruptcy Prediction of Companies Listed in Tehran Stock Exchange. Business Intelligence Journal, 3(2), 89-100.
17. Lee, M.C. (2014). Business Bankruptcy Prediction Based on Survival Analysis Approach. International Journal of Computer Science & Information Technology, 6(2), 103-119.
18. Lee, T.E. (2012). A New Artificial Bee Colony Based Clustering Method and Its Application to the Business Failure Prediction. The Computer, Consumer and Control (IS3C) Symposium, Taichung, 72-75.
19. Liou, F.M. (2008). Fraudulent Financial Reporting Detection and Business Failure Prediction Models: A Comparison. Managerial Auditing Journal, 23(7), 650-662.
20. Martens, D., Backer, M.D., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B. (2007). Classification with Ant Colony Optimization. IEEE Transactions on Evolutionary Computation, 11(5), 651-665.
21. Martin, A., Aswathy, V., Venkatesan, V.P. (2012). Framing Qualitative Bankruptcy Prediction Rules Using Ant Colony Algorithm. International Journal of Computer Applications, 41(21), 27-31.
22. Meyer, P.A., Pifer, H.W. (1970). Prediction of Bank Failures. The Journal of Finance, 25(4), 853-868.
23. Moyer, R.C. (1977). Forecasting Financial Failure: A Re-Examination. Financial Management, 6(1), 11-17.
24. Parpinelli, R.S., Lopes, H.S., Freitas, A.A. (2001). An Ant Colony Based System for Data Mining: Applications to Medical Data. Proc. 2001 Genetic and Evolutionary Computation Conference (GECCO-2001), USA, 791–797.
25. Rezaei, F., Toolami, B.N. (2012). Comparison of Ant Colony Algorithm with Methods of Multiple Discriminant Analysis and Logit in Financial Distress Prediction. Journal of Basic and Applied Scientific Research, 2(9), 8924-8931.
26. Salehi, M., Abedini, B. (2009). Financial Distress Prediction in Emerging Market: Empirical Evidences from Iran. Business Intelligence Journal, 2(2), 398-409.
27. Shin, K.S., Lee, Y.J. (2002). A Genetic Algorithm Application in Bankruptcy Prediction Modeling, Expert Systems with Applications, 23(3), 321-328.
28. Sori, Z.M., Jalil, H.A. (2009). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Distress. Journal of Money, Investment and Banking, (11), 5-15.
29. Sun, J., Li, H. (2012). Financial Distress Prediction Using Support Vector Machines: Ensemble vs. Individual. Journal Applied Soft Computing, 12(8), 2254-2265.
30. Terzi, S. (2011). Finansal Rasyolar Yardımıyla Finansal Başarısızlık Tahmini: Gıda Sektöründe Ampirik Bir Araştırma. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(1), 1-18.
31. Terzi, S., Kıymetli Şen, İ., Üçoğlu, D. (2012). Comparison of Financial Distress Prediction Models: Evidence from Turkey. European Journal of Social Sciences, 32(4), 607-618.
32. Terzi, R., Atmaca, M., Terzi, S. (2016). Denetim Açısından İşletmenin Sürekliliğinin Değerlendirilmesinde Genetik Algoritmanın Kullanımı: Borsa İstanbul Sinaî Endeksi Örneği. Uluslararası Yönetim İktisat ve İşletme Dergisi, 12(12), 685-693.
33. Wang, S., Wu, L., Zhang, Y., Zhou, Z. (2009). Ant Colony Algorithm Used for Bankruptcy Prediction. The Second International Symposium on Information Science and Engineering, China, 137-139.
34. Xi, Y., Han, Q., Brabazon, A. (2004). An Ant-Clustering Model for Solvency Prediction. The International Conference on Artificial Intelligence, IC-AI'04, USA, 687-690.
35. Yap, B.C.F., Yong, D.G.F., Poon, W.C. (2010). How Well Do Financial Ratios and Multiple Discriminant Analysis Predict Company Failures in Malaysia. International Research Journal of Finance and Economics, (54), 166-175.
36. Yap, B.C.F., Munaswamy, S., Mohamad, Z. (2012). Evaluating Company Failure in Malaysia Using Financial Ratios and Logistic Regression. Asian Journal of Finance & Accounting, 4(1), 330-344.
37. Yim, J., Mitchell, H. (2005). A Comparison of Corporate Distress Prediction Models in Brazil: Hybrid Neural Networks, Logit Models and Discriminant Analysis. Journal Nova Economia, 15(1), 73-93.
38. Yüzbaşıoğlu, N,. Yörük, N., Demir, M.Ö., Bezirci, M., Arslan, M. (2011). Comparison of Financial Failure Estimation Models for Turkey: An Empirical Study Directed Towards Automotive and Spare Parts Sector. Middle Eastern Finance and Economics, (11), 95-106.
39. Zhang, Y.D., Wu, L. (2011). Bankruptcy Prediction by Genetic Ant Colony Algorithm. Advanced Materials Research, (186), 459-463.