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This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.

This paper explores mood anomalies, specifically the seasonal affective disorder (SAD) effect on the Zagreb Stock Exchange (ZSE). SAD is defined as a syndrome of depressive episodes in human behavior due to the changing of the season. Thus, the motive of this research is to gain better insights into the investors’ sentiment regarding SAD effects. The purpose of the research is to observe how investors’ sentiment affects the return and risk series on ZSE and if this could be exploitable. Using daily data on stock market return CROBEX for the period January 2010—February 2021, SAD effects are tested to explore if seasonal changes affect the stock returns and risk. Besides the SAD variable in the model, some control variables are included as well: Monday, tax, and COVID-19 effect. The results indicate that SAD effects exist on ZSE, even with controlling for mentioned effects; and asymmetries around winter solstice exist. Implications of such findings can be found in simulating trading strategies, which could incorporate such information to gain profits. Limitations of the research focus on one market, observing static parameters of the estimated models, and observing simple trading strategies. Thus, future research should focus on international diversification possibilities, time-varying models, and fully exploring the exploitation possibilities of such findings.

Abstract. Performing business according to contemporary requirements influences companies for continuous usage of modern managerial tools, such as a human resource information system (HRIS) and electronic recruitment (ER). Human resources have been recognised as curtail resources and the main source of a competitive advantage in creation of successful business performance. In order to attract and select the top employees, companies use quality information software for attracting internal ones, and electronic recruitment for attracting the best possible external candidates.The main aim of this paper is to research the level of the usage of HRIS and ER within medium-size and large Croatian companies. Moreover, the additional aim of this paper is to evaluate the relationship among the usage of these modern managerial tools and the overall success of human resource management within these companies.For the purpose of this paper, primary and secondary research has been conducted in order to reveal the level of the usage of HRIS and ER as well as the overall success of human resource management in Croatian companies. The companies' classification (HRIS and ER) is done by using the non-hierarchical k-means cluster method as well as the nonparametric Kruskal Wallis test. Further, the companies are ranked by the multicriteria PROMETHEE method. Relevant nonparametric tests are used for testing the overall companies' HRM. Finally, binary logistic regression is estimated, relating binary variable HRM and HRIS development. After detailed research, it can be concluded that large Croatian companies apply HRIS in majority (with a positive relation to HRM performance), but still require certain degrees of its development.

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Background: In making investment decisions, asset risk and return are two crucial criteria on which investors base their decision. Objectives: This paper provides risk and return analysis and compares different traditional and alternative investments with special emphasis on the COVID-19 crisis. Assets included in the analysis are stocks, bonds, commodities, real estate, foreign exchange, cryptocurrencies, renewable energy sources, gold, and oil. Methods/Approach: The risk measures of standard deviation, Value at Risk (VaR), Conditional Value at Risk (CVaR), and Sharpe ratio are used to compare the representatives of each asset class. Results: The crisis had the highest impact on the risk of crude oil, renewable energy sources, real estate, and stocks, a slightly lower impact on the risk of commodities and gold, and a very low impact on the risk of bonds, foreign exchange, and cryptocurrencies. The order of assets regarding earning potential during the crisis, compared to the period before the crisis, changed significantly for commodities in a positive way and for gold and bonds in a negative way. Conclusions: This research shows that stocks won against all other assets, including gold and cryptocurrencies, during the COVID-19 crisis. The good features of a new alternative investment – renewable energy sources – with excellent earning potential are shown.

This paper presents a novel multi-objective De Novo programming approach developed to address production planning problems. The proposed model incorporates increasing costs or quantity discounts for certain raw materials which were not considered in previous approaches presented in scientific literature. The efficiency of the proposed methodology was tested using a bakery production planning example. The multi-objective De Novo programming model was solved using various multi-objective programming approaches: the original De Novo programming approach, several goal programming approaches, and the global criterion method. The results indicate the successful application of the proposed methodology in solving production planning problems, with no significant difference in the efficiency of the applied multi-objective programming methods.