CREDIT RISK ASSESSMENT OF AGRICULTURAL COMPANIES IN THE REPUBLIC OF SERBIA: COMPARISON OF LOGISTIC REGRESSION AND DISCRIMINANT ANALYSIS

Dragana Tekić; Beba Mutavdžić; Dragan Milić; Nebojša Novković; Vladislav Zekić; Tihomir Novaković

doi:10.5937/ekoPolj2104881T

Authors

Dragana Tekić University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia https://orcid.org/0000-0002-1924-6196
Beba Mutavdžić University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia https://orcid.org/0000-0002-7631-0465
Dragan Milić University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia https://orcid.org/0000-0003-0377-1540
Nebojša Novković University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia https://orcid.org/0000-0003-2419-5765
Vladislav Zekić University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia https://orcid.org/0000-0002-7377-2402
Tihomir Novaković University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia https://orcid.org/0000-0002-8405-3403

DOI:

https://doi.org/10.5937/ekoPolj2104881T

Keywords:

credit risk, financial risk, agricultural companies, logistic regression, discriminat 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.

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References

Ahsan Ul Haq, M., Irum Sajjad, 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. National Institute of Bank Management, Pune, India.

Basu, A., Ghosh, A., Mandal, A., Mart´In, N. & Pardo, L. A. (2017). 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. doi: 10.1214/17-EJS1295

Brusco, J. M., Voorhees, M. C., Calantone, J. R., Brady, K. M. & 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. doi: 10.1080/03610918.2017.1419262

Dragosavac, M. (2014). Teorijski concept upravljanja kreditnim rizikom. Škola biznisa. Visoka poslovna škola strukovnih studija Novi Sad, 1,108-116. [in English: Dragosavac, M. (2014). Theoretical concept of credit risk management, School of business, 1, 108-116.] doi: 10.5937/skolbiz1-5797

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. doi: 10.18267/j.pep.446

Hair, J., Black, W., Babin, B., Anderson, R. & Tatham, R. (2006). Multivariate data analysis, Pearson Prentice Hall, New Jersey.

Heil, K. &Schmidhalter, U. (2014). Using discriminant analysis and logistic regression in mapping quaternary sediments. Math Geosci, 46 (3), 361-376. doi: 10.1007/s11004-013-9486-x

Hosmer, W. D., Lemeshow, S. &Stradivant, X. R. (2013). Applied Logistic Regression, Third Edition, John Wiley & Sons, Inc., Hoboken, New Jersey.

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, Lj. (2012). Statistical methods in credit risk management. Review of Contemporary Entrepreneurship, Business, and Economic Issues,25 (2), 319-324.

Meyers, L., G. Gamst, G. & Guarino, A. (2006). Applied multivariate research: design and interpretation. Newbury Park, CA: Sage publications, London.

Menard, S. (2002). Applied logistic regression analysis (2nd ed.). Thousand Oaks: Sage.

Milić, D., Mijić, K., Jakšić D. (2018). Opportunistic management behavior in reporting earnings of agricultural companies. Custos e @gronegocio 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. Economics of Agriculture, 66 (2),569-578. Doi

5937/ekoPolj1902569M16. 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 Scientifc Research and Review, 8 (2), 26-38.

Sbârcea, I. (2008). Management of credit riskis in agriculture. Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, 3 (3), 70-73.

Sůvová, H. (2002) The bank approach to a credit obligor – a farm business – in the context of credit risk and capital adequacy. Agric. Econ. – Czech, 48,395-398.

Spasojević, J. (2012). Credit risk and credit derivatives. Bankarstvo, 1, 104-137.

Tekić, D., Mutavdžić, B., Novaković, T. &Pokuševski, M. (2020). Analysis of development of local self-government units in Vojvodina. Economics of Agriculture, 67 (2),431-443. doi: 10.5937/ekoPolj2002431T

Tillmanns, S. & Manfred, Krafft. (2017) Handbook of Market Research. Springer International Publishing AG 2017, C. Homburg et al. (eds).

Walker, D. & Smith, T. Jmasm. (2016) 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. Prentice Hall, London, United Kingdom.

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). Faculty of Business President University.