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2021, vol. 68, iss. 4, pp. 881-894
Credit risk assessment of agricultural enterprises in the Republic of Serbia: Logistic regression vs discriminant analysis
Abstract
Credit risk assessment of agricultural enterprises in the Republic of Serbia was analyzed in this research by applying discriminant analysis and logistic regressions. The aim of the research is to determine the financial indicators which financial analysts consider when analyzing a loan application that have the most influence on the decision to approve or reject a loan application. The internal determinants of credit risk of agricultural enterprises are analyzed, i.e., indicators of financial leverage, profitability, liquidity, solvency, financial stability and effectiveness. The analyzed models gave different results in significance of the observed indicators. The indicators that stood out as significant in both models are only indicators of profitability and solvency. The model of discriminant analysis has successfully classified rate 81.0%, while the logistic regression model has successfully classifies rate 89.8%. In modeling the credit risk of agricultural enterprises in the Republic of Serbia, the logistic regression model gives better results.
References
Ahsan, U.H.M., Irum, S.D., Qura-Tul-ain (2015) Performance comparison of classification techniques, artifical neural network, discriminant analysis & logistic regression. Science International, 27(3), 1803-1807
Bandyopadhyay, A. (2017) Credit Risk Models for Managing Bank's Agricultural Loan Portfolio. Pune, India: National Institute of Bank Management
Basu, A., Ghosh, A., Mandal, A., Martín, N., Pardo, L.A. (2017) A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator. Electronic Journal of Statistics, 11(2): 2741-2772
Brusco, M.J., Voorhees, C.M., Calantone, R.J., Brady, M.K., Steinley, D. (2018) Integrating linear discriminant analysis, polynomial basis expansion, and genetic search for two-group classification. Communications in Statistics: Simulation and Computation, 48(6): 1623-1636
Dragosavac, M. (2014) Teorijski koncept upravljanja kreditnim rizikom / Theoretical concept of credit risk management. Škola biznisa, br. 1, str. 108-116
Gurný, P., Gurný, M. (2013) Comparison of Credit Scoring Models on Probability of Default Estimation for US Banks. Prague Economic Papers, 22(2): 163-181
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L. (2006) Multivariate Data Analysis. Upper Saddle River, NJ: Pearson Prentice Hall, 6th Edition
Heil, K., Schmidhalter, U. (2014) Using Discriminant Analysis and Logistic Regression in Mapping Quaternary Sediments. Mathematical Geosciences, 46(3): 361-376
Hosmer, D., Lemeshow, S., Sturdivant, R.X. (2013) Applied logistic regression. Hoboken, NJ: John Wiley & Sons Inc, 3rd ed
Khanam, A.F., Hasan, K. (2013) Evaluation of Management of Agricultural Credit: A Case Study on Bangladesh Krishi Bank. Journal of Education and Practice, 4(13): 31-36
Kvesić, L. (2012) Statistical methods in credit risk management. Review of Contemporary Entrepreneurship, Business, and Economic Issues, 25(2): 319-324
Menard, S. (2002) Applied logistic regression analysis. Thousand Oaks, CA: Sage, 2nd ed
Meyers, L.S., Gamst, G., Guarino, A.J. (2006) Applied multivariate research: Design and interpretation. Newbury Park, CA-London: Sage publications
Milić, D., Mijić, K., Jakšić, D. (2018) Opportunistic management behavior in reporting earnings of agricultural companies. Custos e @gronegovion on line, 14(1): 125-142
Muhović, A., Radivojević, N., Ćurčić, N. (2019) Research of factors of non performing agricultural loans by primary data panels. Ekonomika poljoprivrede, vol. 66, br. 2, str. 569-578
Sbârcea, I. (2008) Management of credit risks in agriculture. Studies in Business and Economics, 3 (3), 70-73
Shalini, H.S. (2013) A study on causes and remedies for non-performing assets in Indian public sector banks with special reference to agricultural development branch, state bank. International Journal of Scientific Research and Review, 8(2): 26-38
Spasojević, J. (2013) Credit risk and credit derivatives. Bankarstvo, vol. 42, br. 1, str. 104-137
Sůvová, H. (2012) The bank approach to a credit obligor: A farm business: In the context of credit risk and capital adequacy. Agricultural Economics / Zemědělská ekonomika, 48(No. 9): 395-398
Tekić, D., Mutavdžić, B., Novaković, T., Pokuševski, M. (2020) Analysis of development of local self-government units in Vojvodina. Ekonomika poljoprivrede, vol. 67, br. 2, str. 431-443
Tillmanns, S., Manfred, K. (2017) Handbook of Market Research. Springer International Publishing AG, C. Homburg et al. (eds)
Walker, D., Smith, T. (2016) JMASM Algorithms and code nine pseudo R indices for binary logistic regression models. Journal of Modern Applied Statistical Methods, 15(1), 848-854
Walsh, C. (2003) Key Management Ratios. London, United Kingdom: Prentice Hall
Wen, Z.Y. (2015) The analysis of the influence of gdp, lir and m m2 towards nonperforming loans ratios (case study in agricultural bank of China in 2009 -2013). President University-Faculty of Business
 

About

article language: English
document type: Original Paper
DOI: 10.5937/ekoPolj2104881T
received: 17/01/2021
accepted: 25/11/2021
published in SCIndeks: 28/12/2021
peer review method: double-blind
Creative Commons License 4.0

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