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Impact of European Central Bank and Federal Reserve System statements on cryptocurrency markets via sentiment analysis
Krejcar, Vilém ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
This study explores the impact of public statements from major central banks, specifically the FED and the ECB, on Bitcoin volatility from 2018 to 2021. Utilizing high-frequency data, we computed Bitcoin's volatility and extracted sentiment scores from the central banks' communications using two methods: the FinBERT language model and the state-of-the-art Generative AI GPT-4 model with tailored prompt. The GPT-4 model, capturing more nuanced senti- ment from text, was deemed superior. Our analysis involved comparing various models, with the HAR model emerging as the most e ective for this study. The research findings are particularly significant: negative sentiment from the ECB during the pandemic was associated with immediate and significant increases in Bitcoin volatility, indicating a market reaction of caution when faced with negative emission. These findings highlight the significant impact of central bank sentiment on Bitcoin volatility, confirming the initial hypothesis of this research. Additionally, the results provide a motivation to incorporate Genera- tive Artificial Intelligence into academic research as a tool for uncovering novel insights. JEL Classification C32, C55, C58, E58, G15 Keywords central banks, sentiment analysis, volatility, Bit- coin, GenAI, HAR, FED, ECB Title Impact of European...
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Analysis of consumer behaviour among DotA 2 players and its effect on individual performance
Krejcar, Vilém ; Krištoufek, Ladislav (advisor) ; Hronec, Martin (referee)
This thesis examine the possible e ect of consumer behaviour, specifically gold sources distribution and e ectiveness in spending, on rank between DotA 2 players. Relevant literature from the past years was summarised and dataset containing more than 91 thousand data points from OpenDota API used to determine the relationship was constructed. Ordered Logistic Regression and K-Means Clustering algorithms were applied on four datasets of 12 variables divided based on the player's position and the outcome of the game, conclud- ing that only a fraction of the variation (McFadden's R2 of 6.2% and Cox & Snell's R2 11.7% on average) has been explained and cluster analysis resulted in a mean accuracy of 38.25%. Those results were supported by the correlation analysis indicating low correlation coe cient values of dependent variable rank. Despite such output, patterns were discovered across individual rank clusters from metrics acclaiming higher e ectiveness of more experienced players re- garding consumer behaviour with strong statistical significance. It has been concluded that the e ect is present, yet its magnitude by itself is not su cient to predict the regressand. The outcome implies possible presence of other vari- ables that have an e ect on the explained variable which are not included in the...
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