The relationship between liquidity and return predictability may be an im- portant aspect to consider when investing in cryptoassets. We examine this relation using both cross-sectional as well as panel data. First, we calculate a set of predictability measures and aggregate the results into four variables. We then regress the predictability variables on a set of controls and two measures of liquidity, specifically the Amihud illiquidity ratio and the Corwin-Schultz spread estimate. The other independent variables include the logarithm of volume, turnover ratio and Garman-Klass volatility. Results from the cross- sectional analysis indicate that liquidity negatively impacts the degree of return predictability. Moreover, findings from a subset of panel data, including only 50 cryptoassets with the largest market capitalization, provide some evidence in favor of this relationship. Results from full panel data, however, present contradictory evidence. For these regressions, liquidity is found to be either in- significant or to possess a positive impact on the degree of return predictability. Altogether, we obtain mixed evidence about the effect of cryptoasset liquidity on return predictability. JEL Classification C53, C58, G14 Keywords Cryptoassets, Predictability, Liquidity, Panel data Title...

No / This book provides an interdisciplinary critical perspective regarding risk, uncertainty, and governance challenges of cryptocurrencies. It considers the perspectives of several disciplines including accounting, cybersecurity, cyberlaw, economics, ethics, finance, financial regulation, shariah (Islamic) law and technology. Distinguished from other books on similar topic, our in-depth analysis and critical discourse on cryptocurrency risk categories are supplemented by research evidence gathered from surveys and interviews with stakeholders. The inclusion of an Islamic insight matters given mixed views at present regarding the permissibility of cryptocurrencies albeit some countries have imposed somewhat restricted function of cryptocurrencies for non-religious reasons. It is envisaged that this book will help enlighten stakeholders on this aspect of uncertainty and inspire fit and proper governance strategies for the public interest.

This diploma thesis deals with the legal aspects of income tax for natural and legal persons in contexts which may arise when dealing with cryptoassets of all kinds. The modern phenomenon of cryptoassets has been the subject of much discussion, especially in recent times, but legislation and comprehensive methodologies governing procedure for taxation of income are lacking. Therefore, the main objective of this thesis is to analyse income tax on cryptoassets for natural and legal persons. If we reformulate this objective into a question, it would be: How do we correctly tax income from cryptoassets for natural and legal persons? Within the framework of my thesis, I also had to deal with the technological and legal aspects of cryptoassets and taxes. The concept of a cryptoasset, including its definition and content, is still a matter of debate even now, more than 10 years after its creation. Similarly, it was necessary to analyse the Czech legislation and determine what a cryptoasset is from the point of view of Czech law within the framework of the thesis. By analysing the legislation and professional publications and using the deductive method, I came to the conclusion that the current legal framework in the Czech Republic is sufficient to answer the main question. The most frequent operations with...

The work attempts to forecast the sign of the price change for cryptoasset time series through classification. The main purpose is to find evidence concerning market efficiency of the cryptoasset markets, potential trading strategies, and differences between the modelled assets. Supporting vector machines, random forests, and multilayer perceptron models are used. An additional model aggre- gates the results of the previous three. Bitcoin, Ether, XRP, and Binance Coin are the modelled cryptoassets. The input variables include transformed daily closing prices up to five lags, trading volumes, volatility, and moving averages. Random forest models perform the best, followed by supporting vector ma- chines, and multilayer perceptrons. Aggregation does not produce improved forecasting performance. The two older assets, Bitcoin and Ethereum, are found to be less forecastable than the newer, Binance Coin and XRP. Dif- ferences between the assets exist as exhibited through forecastability. Higher classification accuracies are not found to imply better trading performance. JEL Classification C15, C69, G13, G14, G17 Keywords cryptoassets, machine learning, forecasting, cryptocurrencies Title Predictive Power of Machine Learning in Cryp- toassets

[pt] A principal contribuição deste trabalho é: Discutir os desafios e oportunidades no surgimento das Finanças Descentralizadas. Este trabalho fornece uma visão abrangente dos princípios fundamentais que sustentam as tecnologias Blockchain, como arquiteturas de sistema e algoritmos de consenso distribuído. Em seguida, nos concentramos em possíveis soluções de Blockchain para o setor financeiro, discutindo seus desafios e oportunidades. / [en] The main contribution of this work is: Discuss the challenges and opportunities in the emergence of Decentralized Finance. This work provides a comprehensive overview of the fundamental principles that underpin Blockchain technologies, such as system architectures and distributed consensus algorithms. Next, we focus on possible Blockchain solutions for the financial sector by discussing their challenges and opportunities.