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Corporate Governance of Banks in Poland and Slovenia

Abstract

This paper focuses on the analysis of the characteristics of corporate governance in banks in Poland and Slovenia between 2005 and 2013. It studies the impact of corporate governance in these banks on their performance. The results of our research show that Slovenia achieved lower average scores for the variables and indicators related to the transparency of corporate governance than Poland. The density of banks with the highest corporate governance index scores was higher in Poland than in Slovenia. When examining the impact of corporate governance on bank performance as measured with net interest income, the regression analysis showed that its impact is positive in both countries and that it is statistically significant in Slovenia.

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Profitability as a business goal: the multicriteria approach to the ranking of the five largest Croatian banks

Profitability as a business goal: the multicriteria approach to the ranking of the five largest Croatian banks

Background: The ranking of commercial banks is usually based on using a single criterion, the size of assets or income. A multicriteria approach allows a more complex analysis of their business efficiency. Objectives: This paper proposes the ranking of banks based on six financial criteria using a multicriteria approach implementing a goal programming model. The criteria are classified into three basic groups: profitability, credit risk and solvency. Methods/Approach: Business performance is evaluated using a score for each bank, calculated as the weighted sum of relative values of individual indicators. Results: In the process of solving the corresponding goal programming problem, the weights are calculated. It is assumed that the goal of each bank is the highest profitability. Because of the market competition among banks, the weights of indicators depend on the performance of each bank. This method is applied to the five biggest Croatian banks (ZABA, PBZ, ERSTE, RBA and HYPO). Conclusion: For the observed period (2010), the highest priority is given to profitability and then to credit risk. The ranking is achieved by using a multicriteria model.

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The Benchmarking Practices of the Economically Freest Countries in Europe and the World

Abstract

In our increasingly globalised economy, global competitiveness of countries and the means to measure it gain increasing significance. One of the ways to measure global competitiveness is by comparing an extent of the economic freedom that a country has, which also can, as surveys show, largely explain differences in living standards across the world. Seeing as how European economy is similar to most of the Western world capitalist economies in the sense that it has, for a number of reasons, very different economic policy traditions than many countries in other parts of the world, we may approach this topic from a European perspective; consequently, we can see that the main hypothesis of the work can be confirmed, and it is possible, for reasons based in economic or national image nature, to discern which is the freest of world economies by adopting the benchmarking practices of the continent. Nevertheless, the other hypothesis of the work does not fulfill itself, meaning that by adopting taxing policies of some of the wealthiest European Union economies it is not possible anymore to reach the result of the freest economy, both in the world and particularly Europe. Looking at the components and scores of the Index of Economic Freedom, it becomes apparent that the inclusion of government share components in its methodology is very controversial, similarly as the labour freedom component and even monetary freedom, albeit in lesser extent.

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Performance Analysis and Benchmarking of Commercial Banks Operating in Bosnia and Herzegovina: a DEA Approach

Abstract

Background: During the last four years, the banking sector in Bosnia and Herzegovina has been facing crisis which has caused the stagnation within the sector. Still, the results within the sector vary to a great extent from bank to bank. Objectives: The efficiency score is assessed for each bank and serves as a basis for further comparisons between banks in the period between 2008 and 2010. Methods: A modified model of Data Envelopment Analysis (DEA) has been used in order to combine several financial indicators simultaneously in a unique efficiency measure. The model provides a rounded judgement on a bank's relative efficiency. Results: Efficiency of individual banks varied throughout the observed period and not all of the banks were a part of the negative banking sector trend induced by the crisis. There is no significant difference between performance of banks in different entities of Bosnia and Herzegovina, and between smaller and larger banks. Conclusions: The results of the study can be used by bank managers to assess the performance of their banks, as observing financial ratios separately can result in a misleading conclusion. The most valuable practical implications of the findings are the provided feasible targets for the three observed years.

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Macroeconomic Factors in Modelling the SMEs Bankruptcy Risk. The Case of the Polish Market

Bibliography Agrawal K., Maheshwari Y., 2014, Default risk modelling using macroeconomic variables , Journal of Indian Business Research, vol. 6. Altman E.I., 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy , The Journal of Finance, vol. 23, no. 4, pp. 589-609. Bellotti A., Crook J., 2009, Credit scoring with macroeconomic variables using survival analysis , Journal of the Operational Research Society, no. 60(12), pp. 1699-1707. Bellovary J.L., Giacomino D.E., Akers M.D., 2007, A review of

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The Effects of Expenditures for Labour Market Policy on Unemployment Rate

sensitivity to unmeasured biases in observational studies”, Journal of the American Statistical Association, Vol. 109, No. 505, pp. 133-144. 13. Heckman, J. J., Pinto, R. (2015), “Causal analysis after Haavelmo”, Econometric Theory, Vol. 31, No. 1, pp. 115-151. 14. Huber, M., Lechner, M., Wunsch, C. (2013), “The performance of estimators based on the propensity score”, Journal of Econometrics, Vol. 175, No. 1, pp. 1-21. 15. Južnik Rotar, L. (2012), “How effective is the Slovenian institutional training program in improving youth's chances of reemployment

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Factors of a Successfully Implemented Compulsory Settlement

chapter 11: an overview of the law and economics of financially distressed firms. Coase-Sandor Working Paper Series in Law and Economics (Working Paper No. 43). Retrieved from http://www.law.uchicago.edu/files/files/43.Baird_.Chapter11.pdf . Berk, A., Peterlin, J., & Ribarič, P. (2005). Obvladovanje tveganja: skrivnosti celovitega pristopa . Ljubljana: GV Založba. Branch, B., & Xu, M. (2008). The power of Z-score to predict the post-bankruptcy performance. Retrieved from http

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Dealing with Interpretability Issues in Predicting Firm Growth: Factor Analysis Approach

can do to make a difference”, OECD Publishing 17. Sampagnaro, G. (2013), “Predicting rapid-growth SMEs through a reversal of credit-scoring principles”, International Journal of Entrepreneurship and Small Business, Vol.18 No.3, pp. 313-331. 18. Schielzeth, H. (2010), “Simple means to improve the interpretability of regression coefficients”, Methods in Ecology and Evolution, Vol.1 No. 2, pp. 103-113. 19. Segarra, A., Teruel, M. (2009), “Small firms, growth and financial constraints”, Xarxa de Referència en Economia Aplicada (XREAP) 20. Sheng

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Inability of Gearing-Ratio as Predictor for Early Warning Systems

York: John Wiley & Sons. 70. Thomas, L. C., Edelman, D. B., Crook, J. N. (2002). “Credit scoring and its applications”, Philadelphia: Society for Industrial and Applied Mathematics. 71. Tsai, B. H. (2013), “An early warning system of financial distress using multinomial logit models and a bootstrapping approach”, Emerging Markets Finance & Trade, Vol. 49, No. 2, pp. 43-69. 72. Yim, J., Mitchell, H. E. (2007), “Predicting financial distress in the Australian financial service industry”, Australian Economic Papers, Vol. 46, No. 4, pp. 375-388. 73. Zenzerovic, R

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A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

References 1. Apte, C., Weiss, S.(1997), “Data Mining with Decision Trees and Decision Rules”, Future Generation Computer Systems , Vol. 13, No.2, pp. 197-210. 2. Behzad, M., Asghar, K., Eazi, M., Palhang, M. (2009), „Generalization performance of support vector machines and neural networks in runoff modeling“, Expert Systems with Applications, Vol. 36, No.4, pp. 7624-7629. 3. Bensic, M., Sarlija, N., Zekic-Susac, M. (2005), „Modeling Small Business Credit Scoring Using Logistic Regression, Neural Networks, and

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