Finansal Başarısızlık ve İflası Etkileyen Faktörlerin Genelleştirilmiş Sıralı Logit Modeli ile Analizi

Öz

Bu çalışmada, 2017 yılı içinde BİST (Borsa İstanbul)’ te kote edilen 139 tane imalat sanayi şirketine ait finansal oranlar kullanılmış, finansal başarısızlık ve iflasa yol açan faktörler genelleştirilmiş sıralı logit modeli ile belirlenmiştir. Altman-Z skor yöntemi sayesinde, bağımlı değişkenin sıralı düzeyleri ölçeklendirilmiştir. Bu nedenle erken uyarı sistemi gibi şirketleri uyarabilen ve ortaya çıkabilecek finansal başarısızlığı tahmin edebilen bir model önerilmiştir. Bağımsız değişkenler ise, şirketlerin finansal ve mali tablolarından alınan finansal oranlardan elde edilmiştir. Tahminlenen sıralı logit modeli paralellik varsayımını ihlal ettiğinden dolayı, bağımlı değişkenin sıralı yapısı dikkate alınarak, paralellik varsayımını rahatlatan bir model olan genelleştirilmiş sıralı logit modeli ile analiz edilmiş ve modelin marjinal etkilerine göre yorumlamalar yapılmıştır. Analiz sonuçlarına göre, faaliyet kâr marjı, aktif devir hızı, net kâr marjı, asit-test oranlarında meydana gelecek bir artış şirketin güvenli bölgede olma olasılığını arttırmaktadır. Aynı zamanda, finansal kaldıraç oranında meydana gelecek bir artışta şirketin güvenli bölgede olma olasılığını azaltmaktadır.

Anahtar Kelimeler

• Altman-Z Skor Yöntemi
• Finansal Başarısızlık ve İflas
• Genelleştirilmiş Sıralı Logit Modeli

ANALYSIS OF THE FACTORS WHICH AFFECT FINANCIAL FAILURE AND BANKRUPTCY WITH GENERALIZED ORDERED LOGIT MODEL

Öz

In the present study, financial ratios that belong to 139 manufacturing companies that have been quoted on BIST (Istanbul Stock Exchange) during 2017 have been used, the factors that lead to financial failure and bankruptcy have been determined with the generalized ordered logit model. By courtesy of the Altman-Z score model, ordinal levels of the dependent variable have been scaled. Thus, a model that could warn companies like an early warning system and forecast potential financial failure have been suggested. Independent variables have been obtained from the financial ratios which had been taken from the financial statements of the companies. As the estimated ordered logit model has violated the parallel lines assumption, by taking the ordinal nature of the variable dependent into account it has been analyzed with a generalized ordered logit model which relaxes the parallel lines assumption and has been interpreted according to the marginal effects of the model. According to analysis results, an increase in ratios of operating profit margin, asset turnover, net profit margin, and acid-test increases the probability of the company being in a safe zone. Meanwhile, an increase in the financial leverage ratio decreases that probability.

Anahtar Kelimeler

• Altman-Z Score
• Financial Failure and Bankruptcy
• Generalized Ordered Logit Models

Kaynakça

1. Ahlgren, M., & Goldmann, J. (2012). The Internationalization of SwedishSmes: How Does Internationalization Affect Individual Firm’s Capital and Credit Risk Structure? Department of Real Estate and Construction Management.
2. Akay, E. Ç., & Timur, B. (2017). Kadın ve Erkeklerin Mutluluğunu Etkileyen Faktörlerin Genelleştirilmiş Sıralı Logit Modeli ile Analizi. Sosyal Bilimler Araştırma Dergisi, 6(3), 88-105.
3. Aktaş, R. (1997). Mali Başarısızlık (İşletme Riski) Tahmin Modelleri. Türkiye İş Bankası Kültür Yayınları.
4. Altaş, D., & Giray, S. (2005). Mali Başarısızlığın Çok Değişkenli İstatistiksel Yöntemlerle Belirlenmesi: Tekstil Sektörü Örneği.
5. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4): 589-609.
6. Altman, E. I., & Loris, B. (1976). A financial early warning system for over‐the‐counter broker‐dealers. The Journal of Finance, 31(4): 1201-1217.
7. Altman, E. I., & Narayanan, P. (1997). An international survey of business failure Classification models. Financial Markets, Institutions & Instruments, 6(2): 1-57.
8. Amemiya, T. (1985). Advanced econometrics. Harvard university press.

9. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research: 71-111.
10. Bernhardsen, E. (2001). A Model of Bankruptcy Prediction (Master's thesis).
11. Blum, M. (1974). Failing company discriminant analysis. Journal of accounting research, 1-25.
12. Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics: 1171-1178.
13. Boes, S., & Winkelmann, R. (2004). Income and happiness: New results from generalized threshold and sequential models.
14. Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), 465-497.
15. Das S., & Rahman R. M. (2011). Application of ordinal logistic regression analysis in determining risk factors of childmal nutrition in Bangladesh. Das and Rahman Nutrition Journal, 10: 124.
16. Deakin, E. B. (1977). Business FailurePrediction: An Empirical Analysis. Financial Crises: Institutions and Markets in a Fragile Environment:72-98.
17. Edmister, R. O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative Analysis, 7(2): 1477-1493.
18. El Khoury, R., & Al Beaïno, R. (2014). Classifying manufacturing firms in Lebanon: An application of Altman’s model. Procedia-Social and Behavioral Sciences,109. 11-18.
19. Fu, V. (1999). Estimating generalized ordered logit models. Stata Technical Bulletin, 8(44).
20. Fullerton, A. S. (2009). A conceptual framework for ordered logistic regression models. Sociological Methods and Research, 38(2):306-347.
21. Greene, W. H., & Hensher, D. A. (2010). Modeling Ordered Choices: A Primer. Cambridge University Press.
22. Güriş, S., & Çağlayan, E. (2005). Ekonometri Temel Kavramlar. 2.Basım, İstanbul. Der Yayınları.
23. Güriş, S., Akay, E. Ç., Ün, T., & Kızılarslan, Ş. (2017). Multivariate Probit Modeli ile Finansal Başarısızlığın Yeniden İncelenmesi: Borsa İstanbul Örneği. Sosyal Bilimler Araştırma Dergisi, 6(3), 199-210.
24. Hayden, E. (2002). Modeling an Accounting-Based Rating System for Austrian Firms. na. International Monetary fund (IMF). World Economic Outlook, April 2019
25. Growth Slowdown, Precarious Recoveryhttps://www.imf.org/en/publications/gfsr?page=2. Erişim tarihi: 14.06.2019. Jacobs, J. J. (2007). The application of failure prediction models on non-listed companies.
26. Johnsen, T., & Melicher, R. W. (1994). Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models. Journal of Economics and Business, 46(4):269-286.
27. Kaiser, U. (2001). Moving in and out of financial distress: evidence for newly founded service sector firms.
28. Kidane, H. W. (2004). Predicting Financial Distress in IT and Services Companies in South Africa (Doctoral dissertation). University of the Free State.
29. Kinay, B. (2010). Ordered Logit Model approach for the determination of financial distress. Numéro Spécial:119.
30. Lennox, C. (1999). Identifying failing companies: a re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51(4): 347-364.
31. Libby, R. (1975). Accounting ratios and the prediction of failure: Some behavioral evidence. Journal of Accounting Research:150-161.
32. Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Advanced quantitative techniques in the social sciences, 7.
33. Meyer, P. A., & Pifer, H. W. (1970). Prediction of bank failures. The Journal of Finance, 25(4): 853-868.
34. Pranowo, K., Achsani, N. A., Manurung, A. H., & Nuryartono, N. (2010). Determinant of corporate financial distress in an emerging market economy: empirical evidence from the indonesian Stock exchange 2004-2008. International Research Journal of Finance and Economics, 52(1), 81-90.
35. Rama, K. D. (2013). An Empirical Evaluation of the Altman (1968) FailurePrediction Model on South African Jse Listed Companies (Doctoral dissertation).
36. Siddiqui, S. A. (2012). Business bankruptcy prediction models: A significant study of the Altman’s Z-score model. Available at SSRN 2128475.
37. Sinkey Jr, J. F. (1975). A multivariate statistical analysis of the characteristics of problem banks. The Journal of Finance, 30(1):21-36.
38. Terzi, S. (2011). Finansal Rasyolar Yardımıyla Finansal Başarısızlık Tahmini: Gıda Sektöründe Ampirik Bir Araştırma. Çukurova Üniversitesi İİBF Dergisi, 15(1):1-18.
39. Theodossiou, P., Kahya, E., Saidi, R., & Philippatos, G. (1996). Financial distress and corporate acquisitions: Further empirical evidence. Journal of Business Finance & Accounting, 23(5-6):699-719.
40. Wilcox, J. W. (1971). A simple theory of financial ratios as predictors of failure. Journal of Accounting Research:389-395.
41. Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata Journal, 6(1), 58-82.
42. Williams, R. (2016). Understanding and interpreting generalized ordered logit models. The Journal of Mathematical Sociology, 40(1), 7-20.