Determination of Financial Failure Indicators by Gray Relational Analysis and Application of Data Envelopment Analysis and Logistic Regression Analysis in BIST 100 Index

Document Type : Research Paper

Authors

Department of Business Administration, Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya, Turkey

Abstract

Financial failure prediction models have been developed by using Logistic Regression (LR) analysis from traditional statistical methods and Data Envelopment Analysis (DEA), which is a mathematically based nonparametric method over the financial reports of the companies traded in The Istanbul Stock Exchange National 100 Index (BIST 100) between the years 2014-2016. In the development of these models, the variables included in the model are as important as the method applied. For this reason, the gray relational analysis method has been considered in determining the indicators that affect the financial situation of the companies. As a result of the analysis, it was determined that the LR model, which is one of the prediction models, has a higher rate of prediction power than the data envelopment analysis in predicting the financial failure of the companies. However, DEA is also an easy and fast method for predicting financial failures, and is recommended to companies on the indicators that they need to improve in order to be successful. As a result of the study, it has been found that both methods are feasible in the prediction of financial failure, but these methods also have different advantages and disadvantages.

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References

Aktaş, R. (1997). Mali başarısızlık (işletme riski) tahmin modelleri. Ankara: Türkiye İş Bankası Kültür Yayınları.
Altan, M., & Candoğan, M. A. (2014). Bankaların finansal performanslarının değerlemesinde geleneksel ve gri ilişki analizi: Katılım bankalarında karşılaştırılmalı bir uygulama. Selçuk Üniversitesi İktisadi ve İdari Bilimler Fakültesi Sosyal ve Ekonomik Araştırmalar Dergisi, 14(27), 374-396.
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
Avkıran, K. N. (2011). Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks. Omega, 39(3), 323-334.
Beaver, H. W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-102.
Berk, N. (1990). Finansal yönetim. İstanbul: Türkmen Kitabevi.
Chan, J., & Tong, T. (2007). Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach. Materials and Design, 28(5), 1539–1546.
Chen, M. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261-11272.
Cielen, A., Peeters, L., & Vanhoof, K. (2004). Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research, 154(2), 526-532.
Feng, C., & Wang, R. (2000) Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142.
Huang, C., Dai, C., & Guo, M. (2015). A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection. Applied Mathematics and Computation, 251(C), 431-441.
Jardin, D. P. (2016). A two-stage classification technique for bankruptcy prediction. European Journal of Operational Research, 254(1), 236-252
Determination of Financial Failure Indicators by Gray … 185
Jawabreh, O. A., Rawashdeh, A. F., & Senjelawi, O. (2017). Using Altman's z-score model to predict the financial failure of hospitality companies-case of jordan. International Journal of Information, Business and Management, 9(2), 144-160.
Kuo, Y., Yang, T., & Huang G. W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & Industrial Engineering, 55(1), 80-93.
Küçükönder, H., Efe, E., Akyol, E., Şahin, M., & Üçkardeş, F. (2004). Çok değişkenli istatistiksel analizlerin hayvancılıkta kullanımı. In 4th National Animal Science Congress, (pp.550-555). Isparta, Turkey: (Oral proceedings book).
Li, Z., Crook, J., & Andreeva, G. (2014). Chinese companies distress prediction: An application of data envelopment analysis. Journal of the Operational Research Society, 65, 466-479.
Li, Z., Crook, J., & Andreeva, G. (2017). Dynamic prediction of financial distress using malmquist DEA, Expert Systems with Applications, 80, 94-105.
Manaseer, R. S., & Oshaibat, D. S. (2018). Validity of Altman z score model to predict financial failure: Evidence from Jordan. International Journal of Economics and Finance, 10(8), 181-189.
Mellahi, K., & Wilkinson, A. (2004). Organizational failure: A critique of recent research and a proposed integrative framework. International Journal of Management Reviews, 5(6), 21-41.
Min, J. H., & Lee, Y. C. (2008). A practical approach to credit scoring. Expert Systems with Applications, 35, 1762-177.
Monelos, L. P., Sanchez, P. C., & Lopez, R. M. (2011). DEA as a business failure prediction tool application to the case of Galician SMEs. Contaduría y Administración, 59(2), 65-96.
Ohlson, A. J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
Okka, O. (2009). Analitik finansal yönetim. Ankara: Nobel Yayınları.
Özbek, A. (2017). Çok kriterli karar verme yöntemleri ve excel ile problem çözümü. Ankara: Seçkin Yayıncılık.
Özdemir, S. F. (2011). Finansal başarısızlık ve finansal tablolara dayalı tahmin yöntemleri. Ankara: Siyasal Kitabevi.
186 (IJMS) Vol. 14, No. 1, Winter 2021
Paradi, J. C., Wilson, D., & Yang, X. (2014). Data envelopment analysis of corporate failure for non-manufacturing firms using a slacks-based measure. Journal of Service Science and Management, 7(4), 277-290.
Premachandra, I. M., Bhabra, S. G., & Sueyosh, T. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research, 193(2), 412-424.
Premachandra, I., Chen, Y., & Watson, J. (2011) DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment. Omega, 39, 620-626.
Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010). Evaluation of credit risk based on firm performance. European Journal of Operational Research, 201(3), 873–881.
Ramanathan, R. (2003). An introduction to data envelopment analysis (1st ed.). Sage Publications.
Sallehuddin, R., Shamsuddin, M., & Hashim, M. (2008). Application of grey relational analysis for multivariate time series., In (Eds. Jeng-Shyang Pan, Ajith Abraham, Chin-Chen Chang) 8th International Conference on Intelligent Systems Design and Applications(pp. 437-432). Kaohsiung, Taiwan: IEEE.
Shepherd, A. D. (2003). Learning from business failure: Propositions of grief recovery for the self-employed. The Academy of Management Review, 28(2), 318-328.
Shetty, U., Pakkala, T. P. M., & Mallikarjunappa, T. (2012). A modified directional distance formulation of DEA to assess bankruptcy: An application to IT/ITES companies in India. Expert Systems with Applications, 39, 1988-1997.
Shiri, M. M., & Salehi, M. (2012). Prediction of financial distress in Tehran Stock Exchange using DEA approach. Indian Journal of Science and Technology, 5(10), 3461- 3473.
Sun, J., & Li, H. (2009). Financial distress early warning based on group decision making. Science Direct Computers & Operations Research, 36(3), 885-906.
Wijekoon, N., & Azeez, A. A. (2015). An integrated model to predict corporate failure of listed companies in Sri Lanka. International Journal of Business and Social Research, 5(7), 1-14.
Determination of Financial Failure Indicators by Gray … 187
Yıldırım, İ. (2006). İşletmelerde mali başarısızlıkların tahmininde erken uyarı sistemleri ve Türkiye için bir model önerisi (Unpublished master’s thesis). Gazi University.
Zavgren, C. (1985). Assessing the vulnerability to failure of American industrial firms: A logistic analysis. Journal of Business Finance and Accounting, 12(1), 19-45.
Xu, X., & Wang, Y. (2009) Financial failure prediction using efficiency as a predictor. Expert Systems with Applications,36, 366-373.
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
Volume 14, Issue 1
January 2021
Pages 163-187

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