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Forecasting the Sales of Console Games for the Italian Market

methods: Concerns and ways forward , Alejandro Raul Hernandez Montoya, Universidad Veracruzana, Mexico Plos One 13(3). Barrow D., Kourentzes N., 2016, Distributions of forecasting errors of forecast combinations: Implications for inventory management , International Journal of Production Economics, 177, pp. 24-33. Box G.E.P., Jenkins G.M., Reinsel G.C., 1994, Time Series Analysis; Forecasting and Control , 3rd Edition, Prentice Hall, Englewood Cliff, New Jersey. Brown R.G., 1959, Statistical Forecasting for Inventory Control , McGraw-Hill, New York

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Application of semi-deviation as a proxy for the expected return estimation in the Croatian equity market

References 1. Ang, A., Chen, J., Xing, Y. (2006a). Downside risk. The Review of Financial Studies , Vol. 19, No. 4, pp. 1191-1239. 2. Ang, A., Hodrick, R., Xing, Y., Zhang, X. (2006b). The Cross Section of Volatility and Expected Returns. Journal of Finance , Vol. 51, No. 1, pp. 259-299. 3. Amenc, N., Goltz, F., Martellini, L., Retkowsky, P. (2011). Efficient Indexation: An Alternative to Cap-Weighted Indices. The Journal of Investment Management, Vol. 9, No. 4, pp. 1-23. 4. Amenc, N., Goltz, F., Martellini, L. (2013). Smart Beta 2

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Testing out the disciplinary role of debt in Croatian banks


According to the agency theory, debt brings discipline to management and therefore, one way of reducing agency costs is by increasing the level of indebtedness. Even though there are a number of specificities of banks as corporations, this agency theory postulate should still be valid. This research is motivated by the need to understand microeconomics of banking better, which could be especially rewarding in CEE countries where the issues of credit risk, bank capital and corporate governance are specific. Accordingly, by testing the disciplinary role of debt in Croatian banks, we contribute to the still scarce literature on bank governance in Croatia. We operationalise the research by first generating an efficiency measure, which we believe adequately represents management efforts and ability to maximize the value of owners’ investment. In the next step, we explore the relation between banks' efficiency and leverage. Our results do not indicate that debt generally creates a discipline mechanism for bank managers in Croatia.

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A new link function for the prediction of binary variables

. C., Vargas, D. L. (2016). Large-scale no-show patterns and distributions for clinic operational research. Healthcare, Vol. 4, No. 1, pp. 1-12. 7. Gebhart, T. (2017). No-Show Management in Primary Care: A Quality Improvement Project . Available at [10 June 2018]. 8. Gelman, A, Carlin, J. B., Stern, H. S., Rubin, D. B. (2004). Bayesian Data Analysis. CRC/Chapman & Hall, Boca Raton. 9. Gelman, A., Jakulin, A., Pittau, M. G., Su, Y. S. (2008). A weakly informative default prior distribution for logistic and other regression

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Estimation of the firm‘s balance sheet channel effects during the economic crisis: Case of Croatia

-421. 7. Calomiris, C. W., Hubbard, R. G. (1995). Internal Finance and Investment: Evidence from the Undistributed Profits Tax of 1936-37. Journal of Business, Vol. 68, No. 4, pp. 443-482. 8. Campello, M., Giambona, E., Graham, J. R., Harvey, C. R. (2011). Liquidity Management and Corporate Investment During a Financial Crisis. Review of Financial Studies, Vol. 24, No. 6, pp. 1944-1979. 9. Carlstrom, C. T., Fuerst, T. S. (1997). Agency Costs, Net Worth, and Business Fluctuations: A Computable General Equilibrium Analysis. American

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An empirical study of customer usage and satisfaction with e-banking services in the Republic of Macedonia

References 1. Alam, S. S., Musa, R., Hasan, F. (2009). Corporate Customer’ Adoption of Internet Banking: Case of Klang Valley Business Firm in Malaysia. International Journal of Business and Management, Vol. 4, No.4, pp. 13-21. 2. Al-Smadi, M. (2012). Factors Affecting the Adoption of Electronic Banking: An Analyses of the perspectives of Bank’s customers. International Journal of Business and Social Science, Vol.3, No.17, pp. 294-309. 3. Bucevska, V. (2012). An Empirical Analysis of Factors Affecting the

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Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach

. Sabo, K., Scitovski, R., Vazler, I., Zekić-Sušac, M. (2011). Mathematical models of natural gas consumption. Energy Conversion and Management , Vol. 52, pp. 1721-1727. 11. Sajter, D. (2017). Methods of evaluating long-term financial effects of energy efficiency projects. Business and Economic Horizons, Vol. 13, No. 3, pp. 295-311. 12. Scitovski, R., Scitovski, S. (2013). A fast partitioning algorithm and its application to 10 earthquake investigation. Computers & Geosciences , Vol. 59, pp. 124-131. 13. Scitovski, R., Zekić-Sušac M., Has A. (2018

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Indicators of sustainable development performance: Case study of European Union countries

References 1. Alestalo, M., Hort, S. E. O., Kuhnle, S. (2009). The Nordic Model: Conditions, Origins, Outcomes, Lessons. Available at [25 February 2016]. 2. Campisi, D., de Nicola, A., Farhadi, M., Mancuso, P. (2013). Discovering the impact of ICT, FDI and human capital on GDP: a cross-sectional analysis. International Journal of Engineering Business Management, Vol. 5, No. 46, pp. 1-10. 3. Cracolici, M. F., Cuffaro, M., Nijkamp, P

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The Impact of Decision-Making Profiles on the Consistency of Rankings Obtained by Means of Selected Multiple Criteria Decision-Aiding Methods

Bibliography Allinson C.W., Hayes J., 1996, The cognitive style index: a measure of intuition-analysis for organizational research , Journal of Management Studies, 33, pp. 119-135. Edwards W., Barron F.H., 1994, SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement , Organ Behav Hum Dec, vol. 60(3), pp. 306-325. Epstein S., Pacini R., Denes-Raj V., Heier H., 1996, Individual differences in intuitive experiential and analytical – rational thinking styles , Journal of Personality and Social Psychology, 71, pp. 390

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Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines

Operational Research Society, vol. 56, no. 9, pp. 1082-1088. Sharma D., 2011, Evidence in Favor of Weight of Evidence and Binning Transformations for Predictive Modeling , Social Science Research Network. Shawe-Taylor J., Cristianini N., 2000, Support Vector Machines and Other Kernel-Based Learning Methods , Cambridge University Press. Siarka P., 2011, Quality measures of scoring models , Journal of Risk Management in Financial Institutions, London. Sojak S., Stawicki J., 2001, Wykorzystanie metod taksonomicznych do oceny kondycji ekonomicznej

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