Tomislava Pavić Kramarić, Marko Miletić and Renata Kožul Blaževski
Background: Financial stability or soundness of insurance companies has gained importance over the years, especially after the financial crisis of 2008. Various stakeholders such as policy makers, regulators, the insured, etc. are interested in keeping the insurance sector stable since it contributes to overall financial stability.
Objectives: The authors explore the determinants of insurers’ soundness in selected countries in Central and Eastern Europe. The analysis covers life, non-life and composite insurers that operated in Croatia, Hungary, and Poland in the period 2013 – 2017.
Methods/Approach: A set of insurer – specific, industry – specific and macroeconomic variables are taken into consideration for having a potential influence on soundness measured by the Z-score. The variables include the size based on total assets, the share of premium ceded to reinsurance, claims growth, gross written premium growth, the premium to surplus ratio, market shares held by the five largest insurers, the share of gross written premium in the gross domestic product (GDP) and the GDP per capita growth.
Results: The findings reveal that soundness of Croatian insurers is positively influenced by the size of an insurer. Both in Hungary and Poland reinsurance plays an important factor positively affecting soundness.
Conclusions: Each of the insurance markets covered by the analysis reveals its characteristics and offers guidelines on factors influencing financial stability.
+ D de las empresas: un análisis empírico”, Herri ekonomiaz, No. 11, pp. 47-66.
13. Cainelli, G., Evangelista, R., Savona, M. (2004), „The impact of innovation on economic performance in services”, The Service Industries Journal, Vol. 24, No. 1, pp. 116-130.
14. Caliendo, M., Kopeinig, S. (2008), “Some Practical Guidance for the Implementation of Propensity Score Matching”, Journal of Economic Surveys, Vol. 22, No. 1, pp. 31-72.
15. Carboni, O. A. (2017), “The effect of public support on investment and R&D: An empirical evaluation on European
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.
Višnja Rosenzweig, Hrvoje Volarević and Mario Varović
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.
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.
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
Marjeta Zorin Bukovšek, Borut Bratina and Polona Tominc
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
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)
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
Marijana Zekić-Sušac, Sanja Pfeifer and Nataša Šarlija
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