| Abstract [eng] |
This master's thesis aims to evaluate herding behavior in the black and green cryptocurrency market during the Covid-19 pandemic and compare the obtained results to the pre-pandemic period. To achieve the goal, 4 main tasks are formulated. The work consists of 3 main parts, including analysis of theoretical assumptions of herding behavior in the cryptocurrency market, methodology and main hypotheses and description of the main results obtained during the research. Lastly, the conclusion and proposals of the work are being presented. After analysis author suggests the conclusion that only a small number of authors tend to divide cryptocurrencies into green and black, although it is theoretically possible to do so. This work makes this distinction, which brings new view to the existing literature. The author uses the most appropriate and accurate methods for determining herding behavior in financial markets: Cross-Sectional Standard Deviation (CSSD) and Cross-Sectional Absolute Deviation (CSAD). After evaluation of the results, the author reaches conclusion that herding behavior was detected for all black cryptocurrencies during both pre-pandemic and pandemic periods, which assumes that this market is ineffective. Author also notes that the existence of herding behavior is not affected by the fluctuations of the market rise or fall. Evaluation of green cryptocurrencies shows conflicting results - herding behavior only occurred for certain green cryptocurrencies in a certain period of time. As a result of that, the author puts forward a new assumption that the herding behavior depends on the time of existence of the cryptocurrency - the older the cryptocurrency was created, the more likely it is to be affected by the herding behavior. Author suggests investigating this assumption in further researches. After evaluating the research results, the author recommends conducting further and in-depth research on the mentioned topic, especially distinguishing between black and green cryptocurrencies, since only a small number of researchers use this classification. Finally, it is recommended that both investors and decision makers evaluate the information presented in this thesis on market biases to make appropriate decisions and ensure market efficiency. |
