This paper will show the effect of cryptocurrencies, specifically Bitcoin and Litecoin, on a diversified portfolio of traditional and alternative assets. By using weekly closing price of these data, I use a single-index model to find betas, Sharpe ratios, and asset correlations. Then using the Markowitz Portfolio Optimization model to find optimal weights both with and without percentage restrictions. To date there is little academic research into cryptocurrency portfolio management. This paper expands upon a similar study done in the summer of 20131 through the Université Libre de Bruxelles. However, their data was from before a major spike in Bitcoin demand in November that same year, and did not include Litecoin. This paper fills the gap.

This paper uses a back-propagating neural network (BPN) to predict the price movements of major crypto currencies, leveraging technical factors as well as measurements of collective sentiment derived from the micro-blogging network Twitter. Our dataset consists of daily, hourly and minutely price levels for Bitcoin, Ether and Litecoin along with 8 popular technical indicators, as well as all tweets with the currencies' cash tags during respective time periods. Insprired by previous research which suggest that artificial neural networks are superior forecasting models in this setting, we were able to create a system generating automated investment decisions on a daily, hourly and minutely time basis. The study concluded that price trends are indeed predictable, with a correct prediction rate above 50% for all models, and corrensponding profitable trading strategies for all currencies on an hourly basis when neglecting trading fees, buy-sell spreads and order delays. The overall highest predictability is obtained on the hourly trading interval for Bitcoin, yielding an accuracy of 55.74% and a cumulative return of 175.1% between October 16, 2021 and December 31, 2021. / I denna studie används ett bakåtpropagerande neoronnät (BPN) för att förutsäga prisrörelser i större kryptovalutor med hjälp av tekniska faktorer och kvantifiering av kollektivt sentimentet från mikrobloggnätverket Twitter. Vårt dataset består av dagliga, timvisa och minutvisa prisnivåer för Bitcoin, Ether och Litecoin tillsammans med 8 populära tekniska indikatorer, samt alla tweets med valutornas "cash tags" under respektive tidsperiod. Med inspiration från tidigare forskning som hävdar att artificiella nauronnät är överlägsna prognosmodeller i denna typ av analys kunde vi skapa ett system som genererar automatiska investeringsbeslut på daglig, timvis och minutvis basis. Vi hävdar med denna studie att pristrender är förutsägbara för dessa kryptovalutor, med en korrekt förutsägelsefrekvens på över 50% för alla modeller, och med lönsamma handelsstrategier för alla valutor på timbasis när man bortser från handelsavgifter, köp- och säljspreadar och orderfördröjningar. Den högsta förutsägbarheten erhålls på timhandelsintervallet för Bitcoin, vilket ger en nogrannhet på 55,74% och en ackumulerad avkastning på 175,1% mellan den 16 oktober 2021 och den 31 december 2021.

The cryptocurrencies are growing more and more popular, both due to their independency on institutions and the feeling of anonymity they provide. This is, however, also accompanied by an increasing number of their abuse for criminal activities. This thesis explores the principles of current cryptocurrencies as well as techniques used for increasing anonymity of their usage. Based on the findings, it proposes a solution attemping to deanonymise activity in select cryptocurrencies.

This student paper discusses principals of data collecting and subsequent analysis of data on digital currency exchanges followed by proposition and full implementation of research oriented system capable of solving all relevant tasks and presenting a way for implementing solutions for broad spectrum of related problems.

This thesis describes important existing cryptocurrencies and their basis principles. Especially it describes differences between this cryptocurrencies and theid basis principles. Also describes posibilites for analysis of Bitcoin blockchain. Next part describes improvments of tool for blockchain analysis. Futher it describes cryptocurrency analyzing tool, and it's implemented extensions.

Cryptocurrencies are becoming popular and the demand for monitoring transactions inside them increases alongside with it. In this thesis, I describe few of the most widespread cryptocurrencies built on top of a blockchain and how to obtain information of their transactions in order to raise alarms. I discuss existing solutions and describe application Cryptoalarm designed for monitoring transactions involving specific addresses in order to raise alarms. Cryptoalarm scans blockchains of cryptocurrencies such as Bitcoin, Bitcoin Cash, Litecoin, Zcash, Dash, Ethereum and raises alarms about address activities in real-time.

The purpose of this paper is to select the best GARCH-type model for modelling the volatility of Bitcoin, Bitcoin Cash, Litecoin, Dogecoin and Ethereum. GARCH (1,1), IGARCH(1,1), EGARCH(1,1), TGARCH(1,1) and CGARCH(1,1) are used on the cryptocurrencies closing day return. We select the model with the highest Maximum Likelihood and run an OLS regression on the conditional volatility to measure the day-of-the-week effect. The findings show that EGARCH(1,1) model best suits Bitcoin, Litecoin, Dogecoin and Ethereum data and that the GARCH(1,1) model suits best Bitcoin data. The results show a significant presence of day-of-the-week effects on the conditional volatility of some days for Bitcoin, Bitcoin Cash and Ethereum. Wednesday has a significant negative effect on Bitcoin conditional volatility. Friday, Saturday and Sunday are found to be significant and positive on Bitcoin Cash conditional volatility. Finally, Saturday is found to be significant and positive on Ethereum conditional volatility.

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Qualquer dúvida, não hesite em nos contatar. (11) 3799-7732. Atenciosamente, Isabele Garcia on 2018-07-11T20:16:39Z (GMT) / Submitted by Brendan Anson Pritchard (bpanson@gmail.com) on 2018-07-11T20:55:03Z No. of bitstreams: 1 Dissertation-BrendanAnsonV9-1.pdf: 1286517 bytes, checksum: bfafb586060f6c84a656ffa94abe7cae (MD5) / Approved for entry into archive by Simone de Andrade Lopes Pires (simone.lopes@fgv.br) on 2018-07-11T21:06:20Z (GMT) No. of bitstreams: 1 Dissertation-BrendanAnsonV9-1.pdf: 1286517 bytes, checksum: bfafb586060f6c84a656ffa94abe7cae (MD5) / Approved for entry into archive by Isabele Garcia (isabele.garcia@fgv.br) on 2018-07-11T21:20:32Z (GMT) No. of bitstreams: 1 Dissertation-BrendanAnsonV9-1.pdf: 1286517 bytes, checksum: bfafb586060f6c84a656ffa94abe7cae (MD5) / Made available in DSpace on 2018-07-11T21:20:32Z (GMT). No. of bitstreams: 1 Dissertation-BrendanAnsonV9-1.pdf: 1286517 bytes, checksum: bfafb586060f6c84a656ffa94abe7cae (MD5) Previous issue date: 2018-05-29 / The study examines the risk and reward potential of arbitrage in the digital asset market. Specifically, it looks at exchange to exchange and statistical arbitrage, or pairs trading, for the cryptocurrencies, Bitcoin (BTC) and Litecoin (LTC). In this instance they are traded on the LTC/BTC pair. The LTC/BTC is examined with pairs trading by performing statistical tests and implementing automated trading strategy to determine potential profit levels. Subsequently, additional trading strategies are examined based on the concepts of the statistical results in this study and other technical analysis indicators. The study outlines the profit potential of exchange to exchange arbitrage but also shows how this type of arbitrage is in fact quite risky and not as simple as the large spreads would suggest. Pairs trading strategies are instead put forward as a method of profiting on the price movement disparities in the digital asset market without running the same risks as exchange to exchange arbitrage. The strategies proposed are based on statistical tests as well as technical analysis indicators that both aim at predicting price trend and direction and try to profit off abnormal price movements and subsequent normalization. It turns out that a range of profit levels can be achieved. All though the strategies proposed are too rudimentary to consider for live trading, they do prove the basic proof of concept that there are ways to profit from pairs trading in the digital asset market. Trading strategies can be formed that provide considerable returns while reducing risk that would otherwise be encountered with long term investment positions and/or exchange to exchange arbitrage in the digital asset market. / O seguinte estudo examina o potencial de risco e recompensa de arbitragem no mercado de ativos digitais. Especificamente, analisa a arbitragem entre bolsas de cryptomoeda e arbitragem estatística, ou pairs trading, para as cryptomoedas, Bitcoin (BTC) e Litecoin (LTC). Neste caso, elas são negociadas no par LTC/BTC. O LTC/BTC é examinado em pares e negociadas por meio da realização de testes estatísticos e implementando a estratégia de negociação automatizada para determinar os níveis potenciais de lucro. Subsequentemente, estratégias adicionais de negociação são examinadas com base nos conceitos dos resultados estatísticos deste estudo e outros indicadores de análise técnica. O estudo delineia o potencial de lucro de arbitragem entre bolsas, mas também mostra como esse tipo de arbitragem é, na verdade, bastante arriscado e não tão simples quanto as grandes margens sugeririam. Estratégias de negociação em pares são apresentadas como um método de lucrar com as disparidades de movimento de preços no mercado de ativos digitais, sem correr os mesmos riscos que a troca por arbitragem de câmbio. As estratégias propostas baseiam-se em testes estatísticos, assim como em indicadores de análise técnica que visam prever a direção e a tendência do preço e tentar lucrar com movimentos ou tempos anormais de preços e normalização subsequente. Ficou comprovado que diferentes de níveis de lucro podem ser alcançados. Embora as estratégias propostas sejam rudimentares demais para serem consideradas para negociação com dinheiro vivo, elas provam o conceito básico de que existem maneiras de lucrar com a negociação de pares no mercado de ativos digitais. Estratégias de negociação podem ser formadas, proporcionando retornos consideráveis e, ao mesmo tempo, reduzindo o risco de que outra forma seja encontrada em posições de investimento de longo prazo e / ou em troca de arbitragem de câmbio no mercado de ativos digitais.