All human interaction can be depicted as exchanges. We exchange trivial information, feelings, assets and more. Valuable exchanges have one thing in common; they all require some degree of trust. In today’s society we rely on institutionalized trust when commencing an exchange of value. Typically, this role is filled by a vast ecosystem consisting of commercial banks, clearinghouses and other third parties. The recent rise of Bitcoin, Ethereum and consequent attention on the underlying technology, blockchain, questions the future of current ecosystem. This report aims at uncovering what blockchain is, what different implementations are currently available and how it would affect today’s ecosystem. It does so through semistructured interviews with actors within the current ecosystem as well as weighing in the views of blockchain evangelists. It highlights five key aspects that are crucial when implementing blockchain technology within the existing banking paradigm. Today’s organized societies require law and order which, to a large extent, is limited within the realm of public blockchain technology. With the insight of society’s infrastructural limitations, this paper argue that the current transaction system of our society favors a permissioned implementation with trusted nodes. Such a system would result in more efficient financial markets and lower costs of transacting. However, this paper acknowledge the virtues and reasons behind the rise of public blockchains. Given recent developments within the field and interesting concepts, the report does not dare to exclude a future of banking relying on public blockchain technology as the underlying database.

Initial Coin Offerings (ICOs) came into worldwide attention in 2018, when over $11.6 billion flowed through them. The CME Group launched Bitcoin futures contracts in December 2017, giving large funds their first regulated exposure to digital assets. As digital assets move towards the mainstream of finance, institutional investors have followed. This study comparatively analyzes Hedge Fund investment in digital assets against that of other institutional investment firm types (Private Equity and Venture Capital) by analyzing their crypto holdings and rebuilding an equally weighted portfolio for each fund. Under these conditions, the study succeeds in finding significant differences between hedge fund results in the sample and those of private equity/venture capital firms. Specifically, this study shows through the composite portfolios built that digital asset investments made by hedge funds generate a much higher return than that of private equity and venture capital firms. Average hedge fund investments have much higher trading volumes and market capitalizations than those made by private equity and venture capital firms, suggesting that PE and VC firms are taking higher risks by investing in new and little-known crypto projects. The results of this study signal that the hedge fund business model is much better suited for the high-risk, high-volatility cryptocurrency market than strategies employed by venture capital and private equity firms.

No / The surprise advent of the peer-to-peer payment system Bitcoin in 2009 has raised various concerns regarding its relationship to established economic market ideologies. Unlike fiat currencies, Bitcoin is based on open-source software; it is a secure cryptocurrency, traded as an investment between two individuals over the internet, with no bank involvement. Computationally, this is a very innovative solution, but Bitcoin’s popularity has raised a number of security and trust concerns among mainstream economists. With cities and countries, including San Francisco and Germany, using Bitcoin as a unit of account in their financial systems, there is still a lack of understanding and a paucity of models for studying its use, and the role Bitcoin might play in real physical economies. This project tackles these issues by analysing the ramifications of Bitcoin within economic models, by building a computational model of the currency to test its performance in financial market models. The project uses established agent-based modelling techniques to build a decentralised Bitcoin model, which can be ‘plugged into’ existing agent-based models of key economic and financial markets. This allows various metrics to be subjected to critical analysis, gauging the progress of digital economies equipped with Bitcoin usage. This project contributes to the themes of privacy, consent, security and trust in the digital economy and digital technologies, enabling new business models of direct relevance to NEMODE. As computer scientists, we consider Bitcoin from a technical perspective; this contrasts with and complements other current Bitcoin research, and helps document the realizable risks Bitcoin and similar currencies bring to our current economic world. This report outlines a comprehensive collection of risks raised by Bitcoin. Risk management is a discipline that can be used to address the possibility of future threats which may cause harm to the existing systems. Although there has been considerable work on analysing Bitcoin in terms of the potential issues it brings to the economic landscape, this report performs a first ever attempt of identifying the threats and risks posed by the use of Bitcoin from the perspective of computational modeling and engineering. In this project we consider risk at all levels of interaction when Bitcoin is introduced and transferred across the systems. We look at the infrastructure and the computational working of the digital currency to identify the potential risks it brings. Additional information can be seen in our forthcoming companion report on the detailed modeling of Bitcoin.

No / Employing a TVP-VAR dynamic connectedness analysis, we identify avenues through which the collapse of the FTX exchange manifested contagion effects throughout a number of financial markets. Results indicate that interaction effects become significantly pronounced, coinciding with key milestones during the collapse of FTX and related companies. Specifically, sources of contagion stem from two tokens created by the exchange and related companies, namely FTT Token and Serum. Such results further develop the expanding literature based on the inherent contagion effects of such unregulated products. / Conlon acknowledges the support of Science Foundation Ireland under Grant Number 16/SPP/33 and 13/RC/2106 and 17/SP/5447.

Yes / This paper explores the relationship between news media sentiment and spillover effects in the cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of spillover specific to individual cryptocurrencies. Subsequently, we employ unique data on cryptocurrency-specific sentiment to assess its impact on these spillover measures using panel fixed effects regression analysis. Our findings indicate that news media sentiment plays a significant role in explaining the spillover dynamics within the cryptocurrency market. Unlike traditional assets, it appears that only positive sentiment affects the spillovers among cryptocurrencies, suggesting an asymmetric effect. Taking into account various characteristics of cryptocurrencies, we find that sentiment’s impact on spillover is more pronounced in community-based coins than in those driven by firms. An examination of news content suggests that sentiment pertaining to emotional and risk aspects of cryptocurrencies predominantly influences these spillovers. Additionally, a comparative analysis of sentiment derived from social media and traditional news sources reveals a stronger influence of the former on spillover effects. Through extensive robustness checks, our research consistently affirms the pivotal role of sentiment in driving spillovers among cryptocurrency returns, underlining the importance of sentiment analysis in understanding the dynamics of the cryptocurrency market. / The full-text of this article will be released for public view at the end of the publisher embargo on 11 Sep 2025.

Yes / Smart contracts have recently attracted interest from diverse fields including law and finance. Ethereum in particular has grown rapidly to accommodate an entire ecosystem of contracts which run using its own crypto-currency. Smart contract developers can opt to verify their contracts so that any user can inspect and audit the code before executing the contract. However, the huge numbers of deployed smart contracts and the lack of supporting tools for the analysis of smart contracts makes it very challenging to get insights into this eco-environment, where code gets executed through transactions performing value transfer of a crypto-currency. We address this problem and report on the use of unsupervised clustering techniques and a seed set of verified contracts, in this work we propose a framework to group together similar contracts within the Ethereum network using only the contracts publicly available compiled code. We report qualitative and quantitative results on a dataset and provide the dataset and project code to the research community. / Link to conference webpage: http://icccn.org/icccn17/workshop/

This research paper studies the Day-of-the-week effect in the cryptocurrency market. Using multiple regression, we analyze the effect using 12 counterfactual optimized portfolios of the cryptocurrencies, as well as the 10 cryptocurrencies alone. Our findings show that well-optimized cryptocurrency portfolios are not subject to Day-of-the-week effects. A positive Monday and a negative Thursday effect were confirmed in Bitcoin, Ethereum, and Ripple, as well as a negative Sunday effect for Ripple.

This thesis focuses on the mining of cryptocurrency with emphasis on analysis of communication between miner and server. It describes basic principles of cryptocurrencies, mining and employed communication protocols. The next part of thesis is about design and implement modification of existing system (catalogue). This modification will add temporality to the catalog, what increase reliability of stored metadata. Description, functionality and purpose of existing system is included in the next text.

In this paper, I analyze Bitcoin price formation and adoption rates at a global and national level. In determining Bitcoin prices, I consider contemporaneous and lagged values of traditional determinants of currencies, such as inflation and industrial production, and digital currency specific factors, primarily public interest. Using monthly time-series data across five years (2011 – 2016), I find that global public interest in Bitcoin, measured by Google searches for the keyword ‘Bitcoin,’ has a positive and significant impact on Bitcoin prices. I extend the analysis to a country level by employing a proxy for adoption rates, represented by the number of local Bitcoin client downloads, which is a useful predictor of prices. I examine pooled data across 12 countries to show that searches for ‘Bitcoin’ can be used to predict adoption rates and, consequently, prices. To the best of my knowledge, this is the first academic article to study Bitcoin usage at a national level. I find that contemporaneous values of traditionally used macroeconomic determinants of currency prices, except inflation, do not have a significant impact on Bitcoin prices.

This thesis analyzes the performance and process of constructing portfolios of cryptocurrency pairs based on cointegrated relationships indicated by the Augmented Dickey-Fuller test, Johansen’s test and Phillips Peron’s test. Pairs are tested for cointegration over a 3-month and a 6-month window and then traded over a trading window of the same length. The cryptocurrencies included in the study are 14 cryptocurrencies with the highest market capitalization on April 24th 2019. One trading strategy has been applied on every portfolio following the 3-month and the 6-month methodology with thresholds at 1.75 and stop-losses at 4 standard deviations. The performance of each portfolio is compared with their corresponding buy and hold benchmark. All portfolios outperformed their buy and hold benchmark, with and without transaction costs set to 2%. Following the 3-month methodology was superior to the 6- month method and the portfolios formed through Phillips Peron’s test had the highest return for both window methods.