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.
Publisher
Centre for Evaluation in Education and Science (CEON/CEES)
Reference24 articles.
1. 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;
2. Bandyopadhyay, A. (2017). Credit Risk Models for Managing Bank's Agricultural Loan Portfolio. National Institute of Bank Management, Pune, India;
3. 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;
4. 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;
5. 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;
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