COMPARISON OF VARIOUS METHODS FOR ESTIMATING THE FINANCIAL FAILURES OF FIRMS: CASE OF BIST

Year 2023, Issue: 40, 149 - 169, 08.08.2023

Melike Kurtaran Çelik , Ahmet Kurtaran

https://doi.org/10.18092/ulikidince.1167940

Abstract

The aim of this study is to create the best forecasting model by comparing the forecasting power of Discriminant Analysis and Artificial Neural Networks models, which are widely used in the literature, among various forecasting models developed to date, in order to predict the financial failures of companies. In the study, the probability of financial failure of the companies was tried to be estimated 1, 2 and 3 years ago by using the financial ratios of 355 companies traded in BIST. For this purpose, separate forecasting models were created for each year before the financial failure, according to discriminant analysis and artificial neural networks. As a result of the study, the artificial neural network model, which was created with the discriminant analysis rates of the models that best predicted the probability of financial failure of the companies, compared to one and three years ago; Two years ago, it was determined that there was an artificial neural network model created with all financial ratios.

Keywords

Financial Failure, Financial Forecasting, Bankruptcy, Discriminant Analysis, Artificial Neural Networks

FİRMALARIN FİNANSAL BAŞARISIZLIKLARININ TAHMİNİNDE ÇEŞİTLİ YÖNTEMLERİN KARŞILAŞTIRILMASI: BIST ÖRNEĞİ

Abstract

Bu çalışmanın amacı firmaların finansal başarısızlıklarını önceden tahmin edebilmek için, bugüne kadar geliştirilen çeşitli tahmin modellerinden literatürde oldukça yaygın kullanılan Diskriminant Analizi ve Yapay Sinir Ağları modellerinin tahmin güçlerini karşılaştırarak en iyi tahmin modelinin oluşturulmasıdır. Çalışmada BIST’te işlem gören 355 firmaya ait finansal oranlar kullanılarak firmaların finansal başarısızlığa düşme olasılıkları 1, 2 ve 3 yıl öncesinden tahmin edilmeye çalışılmıştır. Bu amaçla finansal başarısızlıktan önceki her bir yıl için diskriminant analizine ve yapay sinir ağlarına göre ayrı ayrı tahmin modelleri oluşturulmuştur. Çalışma sonucunda firmaların finansal başarısızlığa düşme olasılıklarını en iyi tahmin eden modellerin bir ve üç yıl öncesine göre diskriminant analizi oranlarıyla oluşturulan yapay sinir ağı modeli; İki yıl öncesine bakıldığında tüm finansal oranlarla oluşturulan yapay sinir ağı modeli olduğu tespit edilmiştir.

Keywords

finansal başarısızlık, finansal tahmin, iflas, diskriminant analizi, yapay sinir ağları

References

• Aktaş, R. (1993). Endüstri İşletmeleri İçin Mali Başarısızlık Tahmini (Çok Boyutlu Model Uygulaması), Türkiye iş Bankası Kültür Yayınları, Ankara, No: 32.
• Altman, E., I. (1968). Financial Ratios, Diskriminant Analysis and the Prediction of Corporate Bankruptcy, The Journal of Finance, 23(4), 589-609.
• Altman, E., I. ve Lorris, B. (1976). A Financial Early Warning System for Over-The-Counter Broker-Dealers, The Journal of Finance, 41(4), 1201-1217.
• 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.
• Beaver, W.H. (1996). Financial Ratios as Predictors of Failure, Selected Studies, 70-112.
• Beaver, W. H. (1968). Market Prices, Financial Ratios and Predictors of Failure, Journal of Accounting Research, 179-195.
• Blum, M. (1974). Failing Company Discriminant Analysis, Journal of Accounting Research, 1-26. Charalambous, C., Charitou, A. ve Kaourou, F. (2000). Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction, Annals of Operations Research, Vol.99, 403-425.
• Deakin, E.B. (1972). A Diskriminant Analysis of Predictors of Business Failure, Journal of Accounting Ressearch, 10 (Spiring), 167-179.
• Deakin, E.B. (1976). Distributions of Financial Accounting Ratios: Some Empirical Evidence, The Accounting Review, 90-96.
• Edmister, R. O. (1972). An Ampiricial Test of Financial Ratio Analysis For Small Business Failure Prediction, Journal of Financial and Quantitative Analysis, 17 (March), 1477-1493.
• Elam, R. (1975). The Effect of Lease Data on the Predictive Ability of Financial Ratios, The Accounting Review, 25-43.
• Jo, H., Han, I. ve LEE, H. (1997). Bankruptcy Prediction Using Case-Based Reasoning, Neural Networks, and Discriminant Analysis, Expert Systems With Applications, Vol. 13, 97-108.
• Meyer, P.A. ve Pifer, H.W. (1970). Prediction of Bank Failures, The Journal of Finance, 25 (4), 853-868.
• Odom, M. D. ve Sharda, R. (1990). A Neural Network Model For Bankruptcy Prediction, IEEE International Conference on Neural Network, Vol. 2, 163-168.
• Perez, M. (2006). Artificial Neural Networks And Bankruptcy Forecasting: A State Of The Art, Neural Comput & Applic, 15, 154–163.
• Sınkey, J. F. (1975). A Multivariate Statistical Analysis of The Characteristics of Problem Banks, The Journal of Finance, 30 (1), 21-36.
• Taffler, R. J. (1984). Empiricial Models For The Monitoring of UK Corporations, Journal of Banking and Finance, Vol.8, 199-227.
• Tamari, M. (1996). Financial Ratios as a Means of Forecasting Bankruptcy, Management International Review, 15-21.
• Tamari, M. (1970). The Nature of Trade”, Oxford Economic Papers, New Series, 22 (3), 406-419.
• Wilcox, J. W. (1976). The Glamber’s Ruin Approach to Business Risk, Sloan Management Rewiev, 33-46.
• Yıldız, B. (1999). Finansal Başarısızlığın Öngörülmesinde Yapay Sinir Ağı Kullanımı ve Amprik Bir Çalışma (Yayımlanmamış Doktora Tezi), Dumlupınar Üniversitesi, Kütahya.
• Zhang, G. vd. (1999). Artificial Neural Networks in Bankruptcy Prediction: General Framework and Crossvalidation Analysis, European Journal of Operational Research, 16-32.
• www.jcravrasyarating.com