Purpose The purpose of this paper is to analyse the interactive and individual influences of socio-demographic and behavioural-cognitive factors on the frequency and quality of wine consumption, as well as importance of the brand and advertising on selection. Design/methodology/approach The survey was prepared on the basis of the selected factors. The research was carried out on a sample of 207 randomly selected respondents. The analysis was done using the classification decision tree. Findings The results show the dominant influence of socio-demographic factors, such as region, place of living (urban-rural areas), family size, age, income and education of consumers as well as behavioural-cognitive factors, such as the price importance, place of purchase and product characteristics, in all analysed target variables. Apart from the similarities with traditional wine markets, the specificities related to an emerging market have also been determined. Research limitations/implications The limitations of this research concern sample size as well as the research conducted over the period of one year. Practical implications The practical objective of this paper is to help wine marketers to develop more effective positioning strategies for a specific emerging market. Originality/value This research combines critical factors based on related studies, including the antecedents and outcome variables, to develop more comprehensive models for better understanding of the wine consumers’ behaviour. In major and traditional wine-making countries, the consumption of wine is fairly predictable. In emerging markets, the commercial strategies are, for the most part, based on certain specificities and are quite interesting for the surveys.

For prediction of risk in car insurance we used the nonparametric data mining techniques such as clustering, support vector regression (SVR) and kernel logistic regression (KLR). The goal of these techniques is to classify risk and predict claim size based on data, thus helping the insurer to assess the risk and calculate actual premiums. We proved that used data mining techniques can predict claim sizes and their occurrence, based on the case study data, with better accuracy than the standard methods. This represents the basis for calculation of net risk premium. Also, the article discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such as Montenegrin.

Abstract:Environmental degradation by greenhouse gas (GHG) emissions has been an important challenge of sustainable economic development and climate changes control. Industry is the major source of CO 2 emissions, whereas 84% of global anthropogenic methane and nitrous-oxide emissions emerge from agriculture. The impact of agro-economic factors on GHG emissions in European developing economies (Southeastern Europe in focus) as compared with European advanced economies has been examined in this paper.The results have confirmed the existence of significant differences in impact of these factors depending on the level of economic development. For both groups of economies, we have confirmed the Environmental Kuznets Curve (EKC) hypothesis (inverted U-shaped relationship between GDP per capita and carbon dioxide emissions), but different sectoral outputs, too. We have also established different impacts of agro emission sources. In developing economies, we have recognized livestock breeding as a predominant factor and recommended measures for reducing the emissions in this sector, following developed economies. The findings may be useful to European developing economies as a support to implementation of binding commitments emerging from the UN Framework Convention on Climate Change (UNFCCC). In the panel analysis, we have taken into consideration the non-stationarity of the series, heterogeneity of the sample, and also examined a dynamic specification.

This article analyses the preferences of different types of investors to stock characteristics in the Montenegrin stock market. The majority of papers deal with stock portfolio analysis of the institutional investors. Since the number of individual investors in the Montenegrin market is much higher, the analysis of their trading behaviour is also very significant. In this article, using data mining techniques, we tested trading behaviour with stocks for both types of investors. We prove that data mining techniques, such as logistic regression, clustering and decision trees, provide good results in this type of analysis. The analysis may be useful to the future investors, brokers and stock exchange.

In this study we developed a support vector machine (SVM) rule extraction method for discovering the effects of the features of investors and stock and corporate performance on stock trading preferences. We used this system to combine strengths of two approaches: SVM as an accurate classifier and a decision tree (DT) as a generator of interpretable models. The method is applied to Montenegro data in order to generate interpretable rules for stock market decision-makers. The results showed that this method, in terms of accuracy and interdependency of factors, outperformed the methods for detecting stock trading preferences from previous studies.