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Shoaib Abdul Basit, Thomas Kuhn and Mumtaz Ahmed

+ 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

Open access

Natalia Nehrebecka

Bibliography Abildgren K., Buchholst B.V., Staghøj J., 2011, Bank-firm relationships and the performance of non-financial firms during the financial crisis 2008-09 microeconometric evidence from large-scale firm-level data , Working Paper, no. 73. Danmarks National Bank. Akbani R., Kwek S., Japkowicz N., 2004, Applying support vector machines to imbalanced datasets , In Machine Learning: ECML 2004. Springer, pp. 39-50. Anderson R., 1999, The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation

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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.

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Deni Memić and Selma Škaljić-Memić

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.

Open access

Laura Južnik Rotar

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

Open access

Ana Bilandžić, Jeger Marina and Nataša Šarlija

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

Open access

Mario Situm

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

Open access

Marijana Zekić-Sušac, Sanja Pfeifer and Nataša Šarlija

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

Open access

Dušan Munđar and Diana Šimić

References 1. Arabzad, A.C. (2014). Football Match Results Prediction Using Artificial eural Networks: The Case of Iran Pro League. International Journal of Applied Research on Industrial Engineering. Vol. 1, No. 3, pp 159-179. 2. Constantinou, A.C., Fenton, N.E., Neil, M. (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. Knowledge-Based Systems, 36, pp. 322-339. 3. Dixon, M.J., Coles, S.G. (1997). Modelling Association Football Scores and Inefficiencies in the