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This article investigates the connections between the prices of biofuels and many traded commodities and other relevant assets in Europe, USA and Brazil. The analysis uses a comprehensive dataset covering price data for 38 traded titles over the period 2003-2020. We utilize the minimum spanning tree approach to identify price connections in a complex trading system. Our analysis of mutual price connections discovers the major defining features of world leading biofuels markets during the period since the ground-breaking policy initiatives of the 2003 EU Transport Fuel Directive and Energy Taxation Directive. We provide characteristics of main bioethanol and biodiesel markets with respect to government policies and technical and local features of the production and consumption of particular biofuels. Despite a relatively long and dynamically evolving history of biofuels, we find that the biofuels systems in USA, Brazil and Europe do not converge towards the same pattern of relations among fossil fuels, biofuels, agricultural commodities and financial assets.

This thesis empirically examines the alternatives to traditional asset allocation strategies based on clustering mechanisms. Portfolio selection strategies utilizing hierarchical clustering are compared to the market benchmark and traditional methods: minimum-variance and equally weighted allocation, focusing on market stress periods. The allocation strategies are tested on daily stock prices of the S&P 100 index constituents from 2005 to 2021. The performance of Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk Contribution (HERC) portfolios is evaluated across several market stress periods, including the financial crisis of 2007-2008 and the global coronavirus (COVID-19) pandemic in 2020. Empirical results do not prove the superiority of hierarchical clustering allocation strategies over traditional strategies in risk-adjusted performance. JEL Classification G01, G10, G11 Keywords portfolio selection, hierarchical clustering, HRP, HERC, market stress Title Cluster-based asset allocation strategies during market stress periods

Since the 1970s, economists have started studying the concept of inflation hedging as a way to protect investments. With the recent high inflation rates, investors might be interested if newly created assets such as cryptocurrencies can be effective against inflation. This thesis paper aims to find out whether thelargest crypto asset Bitcoin can be used as an inflation hedge. To answer this question, Fisher coefficient estimation and hedging demand for the US, Euro Area, and the Czech Republic for the period between November 2014 and October 2022 will be analyzed. In addition, the vector autoregressive model (VAR)will be used for the US market in the same time frame. The results showed overall positive Bitcoin returns but all three methods indicated no or negative correlation between inflation rates in three regions and Bitcoin returns. The thesis paper concludes that Bitcoin cannot be used as an inflation hedge as notall requirements are met. Keywords Cryptocurrency, Bitcoin, gold, inflation, inflation hedge, Fisher coefficient,VAR model Title Can Bitcoin serve as an inflation hedge asset in the US, Euro Area, andCzech markets?

Left-sided athletes are often perceived as better performing as they can leverage their minority status within the sports world. While various specific left-sided athletes, such as Lionel Messi and Rafael Nadal, perform at the very top of their disciplines, these might be simply non-representative outliers. The current thesis puts the hy- pothesis of left-sided over-performance to test via a battery of tests and regressions. My thesis thoroughly analyses the prevalence and the performance of left-handed/left-footed athletes across 5 differ- ent sports. As majority of the current studies are focusing only on a few performance metrics in the given sport, my work broad- ens the knowledge on the topic since it compares the performance of left-sided and right-sided athletes in many categories in order to cover a great portion of the in-game action. Furthermore, this thesis also expands the current understanding of the (potential) left-sided advantage in direct encounters between both teams and individu- als, achieving so by implementing predictive Bradley-Terry models that are based on past matches. The overall results are rather sur- prising: in the majority of the performance comparisons between left-handers/left-footers and right-handers/right-footers, no signifi- cant difference between the two...

The thesis deals with the issue of the Video Assistant Referee in football. It evaluates the consequences of its implementation in Czech Fortuna Liga on the sample of 678 matches held during two and half seasons. The results from the models designed to treat count data were compared with relevant literature. In the form of both simple and multiple regression with additional control variables was investigated the relationship between VAR and the set of match- changing incidents, including yellow cards, red cards and penalty kicks, and the relationship between VAR and errors of on-pitch referees. The terms presence of VAR, VAR interventions and VAR as the whole were di erentiated. Whereas a significant statistical association of VAR as the whole was not revealed for yellow and red cards, a 56% increase in the number of penalties associated with VAR as the whole significantly performed. Furthermore, the negative and highly significant 118% association of the presence of VAR was reckoned in the case of errors of on-pitch referees. Subsequently, the percentage decreased due to VAR interventions, however, not su ciently to reveal a negative and significant association in errors of on-pitch referees for VAR as the whole. The exception created errors based on factual decisions. JEL Classification Z21, F21, Z29,...

In this thesis, we study the impact of individual retail investors on the financial markets. We follow the GameStop retail trading frenzy from the beginning of 2021 and the retail investors aggregated on Reddit's r/wallstreetbets. The tools employed include natural language processing, wavelet analysis and vector error correction models. The results propose that the retail investor sentiment is highly susceptible to high volatility, extreme returns and frequent news coverage. Social media is shown to exacerbate these behavioural tendencies. We find evidence that retail investor sentiment is able to predict short-term returns for stocks specifically targeted by retail investors. The findings are, however, dependent on the investment horizon. Over long horizons, we find evidence for the reversal of the relationship. Lastly, while the effect of news and social media is similar in the long run, we show that Reddit sentiment, as opposed to news sentiment, is a significant predictor of retail targeted stocks in the near term. JEL Classification C55 C58, G12, G14, G41 Keywords Sentiment, Social media, GameStop, Reddit, Natural language processing, Wavelet analysis Title Gamified Stock Markets, Sentiment and Volatility: Evid- ence from the GameStop frenzy 1

Bitcoin is often compared to gold for its gold-like features such as a store of value, a limited supply, and a safe haven. However, due to Bitcoin's extreme price movements, investors might rather look for a safe haven against Bitcoin. In this thesis, we study such properties among traditional assets. Specifically, we analyze gold, oil, and stocks as safe havens for Bitcoin on a sample period from 2014 until March 2022. We find that gold acts as a strong safe haven suggesting gold's traditional role as a shelter during uncertainty holds also for this crypto asset. 1

This thesis aims to scrutinise price gaps in the stock market. The key objective is to analyse candlestick charts surrounding price gaps and determine whether any patterns accompany their presence. Firstly, the thesis briefly describes candlestick patterns, literature relevant to price gaps and Convolutional Neural Network (CNN) as the method of choice. Price gaps are studied in a 5-minute time frame in the data of all S&P 500 constituents in the years from 2015 to 2021. By feeding images of the candlestick chart into the CNN, the proposed model reaches an Accuracy of 74.2% in predicting whether a future price will be higher or lower than the price at the gap. This result can be translated into a statement that the CNN detects hidden patterns around the price gaps. Furthermore, the thesis finds that these patterns di er across individual stocks. The thesis also shows that including news sentiment in the analysis does not improve the ability to discover patterns. JEL Classification C45, C55, C88, G14, G15, G41 Keywords price gap, convolutional neural network, pattern detection, news sentiment Title Price gaps in the stock market