Denis Dolinar, Davor Zoričić and Zrinka Lovretin Golubić
In the field of portfolio management the focus has been on the out-of-sample estimation of the covariance matrix mainly because the estimation of expected return is much more challenging. However, recent research efforts have not only tried to improve the estimation of risk parameters by expanding the analysis beyond the mean-variance setting but also by testing whether risk measures can be used as proxies for the expected return in the stock market. In this research, we test the standard deviation (measure of total volatility) and the semi-deviation (measure of downside risk) as proxies for the expected market return in the illiquid and undeveloped Croatian stock market in the period from January 2005 until November 2017. In such an environment, the application of the proposed methodology yielded poor results, which helps explain the failure of the out-of-sample estimation of the maximum Sharpe ratio portfolio in earlier research in the Croatian equity market.
This paper observes the short-run effects of stock market index composition changes on stock returns on the Zagreb Stock Exchange (ZSE). In that way, event study methodology is employed in order to estimate abnormal returns and compare them amongst three subsets of stocks: those leaving the market index, those entering it, and constantly included stocks. The research included 14 regular and extraordinary revisions of the market index in the period from January 2nd, 2015 until March 21st, 2018. The results have confirmed two research hypotheses: stock exclusions from the market index have a negative effect on stock returns on the ZSE, which is consistent with the price pressure hypothesis; and there exist asymmetric effects of index composition changes on stock returns. This is the first study of this kind on the Croatian stock market, thus more questions need to be answered in future research.
Amila Pilav-Velić, Hatidža Jahić, Jasmina Okičić and Meldina Kokorović-Jukan
Education plays a central role in today’s understanding of growth and development dynamics. However, its relationship with other factors is complex. This paper aims to investigate the effect of different forms of education on youth employability in Bosnia and Herzegovina. This is done by using the USAID MEASURE – BiH National Youth Survey. Research has shown that formal education and non-formal education through internship programmes, volunteering, paid jobs other than internships are significant predictors of youth employment status. The study also has several implications for academics and practitioners since it provides new insights into both employment patterns and practices in one transition economy but also calls for further analysis of the link between education, formal and non-formal, and youth employment.
This article provides an upgraded model for actuarial projection of the dependency ratio of the pension fund in the Republic of Srpska. The nonexistence of complete upto-date life tables presents a huge problem of the pension system and life insurance industry modelling in the Republic of Srpska. Therefore, this article tries to encompass the problem by using the life tables of the Republic of Croatia as a starting point for adjustment of age-grouped life tables available for population of the Republic of Srpska. The actuarial projection model for the Pension and Disability Insurance Fund of the Republic of Srpska is upgraded by using these adjusted life tables and the best estimate mortality trend for mortality forecasting. The results of the Republic of Srpska pension fund dependency ratio projections obtained using a forecast of adjusted life tables are compared to the previous research on this topic which used the life tables of the Republic of Serbia for 2013 for the same model. This way we can observe the effect of life expectancy growth on pension fund’s dependency ratio estimates as one of the measures of pension fund’s sustainability.
The official statistics framework is based on internationally agreed standards, taking into account the core principles of impartiality, objectivity, professional independence, cost effectiveness, statistical confidentiality, minimisation of the reporting burden and high output quality. Since the latest 2007 global economic crisis, a growing demand for more, better and timelier data under limited resources for compilers and reporting agents has been observed. The concept of experimental statistics becomes more relevant, despite the lower quality in terms of coverage, data sources and harmonised definitions. The main aim of this paper is to present the methodological development of the residential property price index in Croatia from experimental to official statistics, as well as to show corresponding changes in time, which occurred due to the changes in methodological framework, institutional responsibility for compilation, coverage and data sources. A general conclusion of the paper is that publication of non-harmonized experimental statistics results, together with explanatory metadata, is better from the point of view of users than having nothing produced by official statistics.
The view on banks as investments in Croatia is challenged by two phenomena: dual holdings (owners are intensely involved in bank balance sheet as, apart from equity, they provide a significant portion of deposits and loans) and the impediments to determining the cost of equity (as only a handful of banks are traded and with questionable liquidity in the capital market). The paper contributes to the literature by applying the panel regression on the translog cost function in order to calculate the shadow cost of equity for banks in Croatia for the period from 1994 to 2016. In the next step, the Economic Value Added was calculated by taking into account the dual holding role of bank owners. The results suggest that the shareholders economic value is significantly different from the accounting value. In addition, it seems that the standard view that domestic banks are less profitable than foreign banks is only valid from the accounting perspective.
Marijana Zekić-Sušac, Rudolf Scitovski and Adela Has
Although energy efficiency is a hot topic in the context of global climate change, in the European Union directives and in national energy policies, methodology for estimating energy efficiency still relies on standard techniques defined by experts in the field. Recent research shows a potential of machine learning methods that can produce models to assess energy efficiency based on available previous data. In this paper, we analyse a real dataset of public buildings in Croatia, extract their most important features based on the correlation analysis and chi-square tests, cluster the buildings based on three selected features, and create a prediction model of energy efficiency for each cluster of buildings using the artificial neural network (ANN) methodology. The main objective of this research was to investigate whether a clustering procedure improves the accuracy of a neural network prediction model or not. For that purpose, the symmetric mean average percentage error (SMAPE) was used to compare the accuracy of the initial prediction model obtained on the whole dataset and the separate models obtained on each cluster. The results show that the clustering procedure has not increased the prediction accuracy of the models. Those preliminary findings can be used to set goals for future research, which can be focused on estimating clusters using more features, conducted more extensive variable reduction, and testing more machine learning algorithms to obtain more accurate models which will enable reducing costs in the public sector.
The transfer market of European football can be classified as a system. In this system, the effectiveness of participant teams can depend on the activity in players’ transfers. This article assesses the utility of network analysis in analysing connections between the mentioned concepts. The hypothesis is that there is causality between a club’s activity in the transfer market and its profit from transfers. This research is based on empirical transfer data of major soccer teams, which have had a significant role in the last 12 years in Europe. It is assumed that the most active clubs in the transfer system have more financial power in the transfer market, while teams which are not active in transfers have less profit from transfers. In the network analysis, the teams can be defined as a set of nodes and connected by edges (interactions). The thickness of the edges and the size of the nodes depend on the volume of transfers among clubs. The number of interactions and the amount of the transfer price can measure this volume also. Considering the results of network indices, the relationships between the two phenomena were reviewed. In order to explore these relationships, the correlations among all of the relevant variables in the transfer market were also measured.
Based on previous research it can be stated that modelling sport economics related demand curves (e.g. demand for sport events and athletes) is different from other types of modelling. The difference lies in the fact that some parts of the demand curves are nearly horizontal in case of sport goods and nearly vertical in case of athletes, because the price of sport events is inflexible and at the same time, salaries of top athletes are extremely flexible. This study investigates parameter estimation methods appropriate for the relevant demand functions of sport economics. In this cases the generally used ordinary least squares estimator is less robust, so the weighted least squares estimators are able to handle heteroskedasticity. If the distribution of the variables is known, the Newey-West heteroscedasticity corrected estimates give even stronger results. The empirical study analyses footballer transfer fees in top European leagues and identifies a threshold at which the traditional supply-demand functions are not appropriate. According to the results, word class athletes, in a way, can be considered prestige goods for which demand may be irrational.