The behavior of market participants often does not rely on market signals, but replicates the investment decisions of other parties. The convergence of their investment behavior leads to the emergence of herd behavior with negative implications for financial stability. Moreover, this phenomenon may be even more pronounced in times of crisis. Although herding is an interesting topic which invites the interest of academic researchers, it still has not been sufficiently studied in terms of comparing the herd effect between differently developed stock markets. The first objective of this research was to determine the herd behavior during the COVID-19 pandemic using static and rolling regression analysis. The second objective was to investigate whether the herd behavior was triggered by the pandemic, while the third objective was to compare the differences in herd behavior between differently developed European stock markets. The results show that this phenomenon is most pronounced in emerging markets, followed by frontier markets and developed markets. Therefore, the results of this study are of particular importance for individual and institutional investors to achieve efficient risk diversification and for financial authorities to establish rules and avoid an increase in herd behavior.

This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects.

Seasonality, as one of the key features of tourist activity, represents an unavoidable topic in scientific and professional analyses, whose aim it is to determine its causal effects. The economic consequences of seasonality are the kind of consequences that are most often emphasized and considered in discussions. They include inefficiencies in resource use, income variability, changes in the employment level, etc. However, only a few analyses have provided a more in-depth discussion of the common problems of seasonality among the Mediterranean countries. The most commonly used method of calculating the Gini coefficient will be used in order to estimate seasonality. This coefficient is characterized by certain advantages, such as taking into account the distribution asymmetry and the relative insensitivity to extreme values, as well as the stability indication of overnight stays distribution in a single year. Based on individual calculations of the seasonality index, this paper will provide a unique view of the similarities and differences between countries with regards to seasonal tourism oscillations. The analysis will cover the total number of tourists’ overnight stays in hotels and similar accommodation facilities, RevPAR revenue, and the average room price – ADR per month between 2007 and 2017. This paper will focus on determining the seasonal characteristics of the selected Mediterranean countries. The quantitative seasonality calculation will be a key indicator for seasonality measurements across different research areas. The contribution of this paper is reflected in the application of financial and non-financial indicators in hotel business that has not yet been implemented. Keywords: Tourism seasonality, RevPAR, ADR, gini index

Pension funds have a pivotal role in financial systems, managing significant assets and contributing to economic growth through capital market investments. Identifying the key factors that influence their performance is essential for ensuring sustainable and adequate pension systems. The paper aims to identify the determinants of pension fund performance that directly contribute to competitiveness in financial markets by enabling efficient capital allocation and improved governance. The research employed two methods: a systematic literature review (SLR) and a bibliometric analysis (BA). The results indicate that five main factors influence pension fund performance: capital market developments, fund size and investment characteristics, corporate governance, behavioural and political influences, and regulatory and legislative environment. A bibliometric analysis reveals current trends in pension fund performance research and examines the relationship between the quality of a country’s pension system and the origin of relevant research. The results provide valuable insights for fund managers, policymakers, and other stakeholders, thereby enhancing the decisionmaking process and deepening the understanding of the implications for pension fund performance, pension systems, and pension beneficiaries