This project uses software development to investigate the link between software and finance. The focus of the work is developing and implementing a trading algorithm which seeks to make profit by making trades based on arbitrage opportunities between currencies. Specifically, the sets of currencies examined are two fiat currencies and one cryptocurrency. Trades are made by combining a blockchain system, which maintains the cryptocurrency, and the live foreign exchange market, which enables fiat currency exchange. The main methodologies for carrying out the research are test-driven development and the use of a simulation to facilitate trades. By passing all of the unit tests, the software is verified. In addition, data gathered during runs of the simulation show that the algorithm successfully identifies arbitrage opportunities and turns a profit on average over many runs. This project proposes an interesting topic for further research in the field of blockchain technology used for financial trading. / Detta projekt bygger på mjukvaruutveckling för att undersöka kopplingen mellan programvara och finans. Arbetet fokuserar på att utveckla och implementera en algoritm för valutahandel som försöker skapa vinst genom att genomföra handel baserade på arbitragemöjligheter mellan valutor. Specifikt är de uppsättningar valutakurserna som undersöks två ordinarie valutor och en kryptovaluta. Handel utförs genom att kombinera ett s.k. blockchain-system, som upprätthåller kryptovalutan, och den ordinarie valutamarknaden för vanlig valutaväxling. De viktigaste metoderna för att genomföra undersökningen är testdriven utveckling och användande av simulering för att genomföra valutahandel. Mjukvaran verifieras med hjälp av en uppsättning enhetstester. Dessutom visar datan som samlats under simuleringar att algoritmen framgångsrikt identifierar arbitragemöjligheter och i genomsnitt ger en vinst över många körningar. Detta projekt utgör på så sätt ett intressant ämne för vidare forskning inom blockchain- teknik för finansiell handel.

This paper is an attempt to analyze the role of transaction fees in a proof-of-stake cryptocurrency currently in development. The authors have employed a microeconomic, static equilibrium approach to model a market in which the cryptocurrency is exchanged for a physical good. Furthermore, the relationship between transaction capacity and the size of the network has been investigated. It has been shown that the total amount of validator capital and the number of validators can be controlled by setting a fixed fee on transactions as well as a minimum capital requirement on individual validators. The total surplus in the economy has then been optimized by setting a fee and the authors have discussed how a minimal capital requirement could be used to also optimize transaction capacity. / Denna kandidatuppsats ’r ett försök att analysera transaktionsavgiftens roll i en "proof-of-stake"-baserad kryptovaluta under utveckling. Författarna har använt sig av en statisk jämviktsmodell från mikroekonomin för att modellera en marknad på vilken kryptovalutan i fråga används för att handla en fysisk vara. Fortsättningsvis har förhållandet mellan systemets transaktionskapacitet och dess storlek undersökts. Det har visats att den totala mängden kapital från validerare samt antalet validerare kan kontrolleras genom att sätta en fast avgift för transaktioner samt ett minimikrav på kapital för enskilda validerare. Sedan har det totala överskottet optimerats genom att bestämma en avgift och författarna har diskuterat hur ett minimikrav på kapital hade kunnat användas för att också optimera systemets transaktionskapacitet.

This thesis is one in a group of several theses that are researching different subjects in the development of a new cryptocurrency. For a few years now, the cryptocurrency market has grown dramatically, in the lead of the original cryptocurrency Bitcoin. Today, most cryptocurrencies' validation-technology, including Bitcoin's, are based on Proof-of-Work (PoW), i.e., a system where transaction validation is made by servers calculating mathematical problems. PoW results in high energy consumption and slow transaction speed. In this cryptocurrency, the validation mechanism will build on a technology called Proof-of-Stake (PoS). PoS does not yield as high energy consumption and often leads to faster transaction speed. The specific technique for validation in this system is that validators bet their coins to validate transactions and get rewards in the form of transaction fees if they end up conforming the transactions that reach consensus among the validators. In particular, the purpose of this report is to research the risk and reward for validators in the betting process and from this develop a reward policy which yields a fast and secure validation. The methods used for solving the problems are simulations based on Monte Carlo methods. From the simulations, the results are discussed and compared. Also, this report will cover economic theories behind cryptocurrencies, mainly focusing on monetary policy and the transaction markets. The findings of this report are several risk functions for different topologies and winning conditions considered during the development of the cryptocurrency. Further, a conclusion was that the expected value of profit for validators need to be constant, independent of when the bets are made with regard to previous bets. From this, a reward function which distributes rewards between winning validators was formed. Another, economical conclusion from this was that, in the long run, the expected value of profit of betting should converge to zero due to a perfect competition market. / Denna kandidatuppsats är en av era uppsatser som analyserar olika områden i utvecklingen av en ny kryptovaluta. Under de senaste åren har kryptovalutamarknaden vuxit dramatiskt, där Bitcoin har varit ledande. Idag bygger de flesta kryptovalutors valideringsteknik, inklusive Bitcoins, på Proof-of-Work (PoW), d.v.s. ett system där transaktionsvalideringen görs av servrar som beräknar komplicerade matematiska problem. Detta resulterar i en hög energiförbrukning och i många fall långsamma transaktionshastigheter. I denna kryptovaluta kommer valideringsmekanismen baseras på en teknik som kallas Proof-of-Stake (PoS) vilket leder till en substantiellt lägre energiförbrukning och högre transaktionshastigheter. Tekniken för valideringen i det här systemet är baserat på att validerare i systemet satsar sina egna mynt för att validera transaktioner och får belöningar i form av transaktionsavgifter om de validerar den transaktion som når konsensus i blockkedjan. I synnerhet är syftet med rapporten att formulera riskerna och de möjliga belöningarna för validerare i bettingprocessen och därmed hitta ett belöningssystem som leder till en snabb och säker kryptovaluta när det gäller validering. De metoder som används för att lösa problemen är simuleringar baserade på Monte Carlo metoder. Slutresultatet av denna rapport är era riskfunktioner för olika topologier och villkor för de vinnande validerarna, beräkningar för väntevärde och belöningsfunktioner för validering. Vidare har slutsatsen dragits att väntevärdet av att göra ett bet måste vara konstant med avseende på när bettet görs. Från detta togs en belöningsfunktion fram som fördelar belöningar mellan vinnande validerare. En annan, ekonomisk slutsats, var att i det långa loppet kommer väntevärdet av vinsten för att betta konvergera till noll på grund av att en perfekt konkurrensmarknad antas.

Programmatically deriving sentiment has been the topic of many a thesis: it’s application in analyzing 140 character sentences, to that of 400-word Hemingway sentences; the methods ranging from naive rule based checks, to deeply layered neural networks. Unsurprisingly, sentiment analysis has been used to gain useful insight across industries, most notably in digital marketing and financial analysis. An advancement seemingly more excitable to the mainstream, Bitcoin, has risen in number of Google searches by three-folds since the beginning of this year alone, not unlike it’s exchange rate. The decentralized cryptocurrency, arguably, by design, a pure free market commodity – and as such, public perception bears the weight in Bitcoins monetary valuation. This thesis looks toward these public perceptions, by analyzing 2.27 million Bitcoin-related tweets for sentiment fluctuations that could indicate a price change in the near future. This is done by a naive method of solely attributing rise or fall based on the severity of aggregated Twitter sentiment change over periods ranging between 5 minutes and 4 hours, and then shifting these predictions forward in time 1, 2, 3 or 4 time periods to indicate the corresponding BTC interval time. The prediction model evaluation showed that aggregating tweet sentiments over a 30 min period with 4 shifts forward, and a sentiment change threshold of 2.2%, yielded a 79% accuracy. / Ämnet sentiment analysis, att programmatiskt härleda underliggande känslor i text, ligger som grund för många avhandlingar: hur det tillämpas bäst på 140 teckens meningar såväl som på 400-ords meningar a’la Hemingway, metoderna sträcker sig ifrån naiva, regelbaserade, till neurala nätverk. Givetvis sträcker sig intresset för sentiment analys utanför forskningsvärlden för att ta fram insikter i en rad branscher, men framförallt i digital marknadsföring och financiell analys. Sedan början på året har den digitala valutan Bitcoin stigit trefaldigt i sökningar på Google, likt priset på valutan. Då Bitcoins decentraliserade design är helt transparant och oreglerad, verkar den under ideala marknadsekonomiska förutsättningar. På så vis regleras Bitcoins monetära värde av marknadens uppfattning av värdet. Denna avhandling tittar på hur offentliga uppfattningar påverkar Bitcoins pris. Genom att analysera 2,27 miljoner Bitcoin-relaterade tweets för sentiment ändringar, föutspåddes ändringar i Bitcoins pris under begränsade förhållningar. Priset förespåddes att gå upp eller ner beroende på graden av sentiment ändring under en tidsperiod, de testade tidsperioderna låg emellan 5 minuter till 4 timmar. Om en förutspånning görs för en tidsperiod, prövas den emot 1, 2, 3 och 4 skiftningar framåt i tiden för att ange förutspådd Bitcoin pris interval. Utvärderingen av förutspåningar visade att aggregerade tweet-sentiment över en 30-minutersperiod med 4 skift framåt och ett tröskelvärde för förändring av sentimentet på 2,2 % gav ett resultat med 79 % noggrannhet.

Background: In the last few years, there has been rapid growth in the use of cryptocurrency, as it is a form of digital currency and was developed using blockchain technology, so it is almost impossible to counterfeit cryptocurrency. Due to these features, it has attracted a lot of popularity and attention in the market. There has been a research gap in predicting accurate cryptocurrency prices by using sentiment analysis. This study will use Artificial Intelligence-based methods and sentiment analysis to develop a model for predicting cryptocurrency prices. By using the mentioned methods in this thesis, the developed model will provide precise results. Objectives: The objective of the thesis is to compare artificial intelligence models for cryptocurrency price prediction and analyze the importance of sentiment analysis by understanding the public pulse in cryptocurrencies and how it affects price fluctuations, analyzing the correlation within news articles, social media posts, and price fluctuations, as well as evaluating the model performance by employing metrics like RSME, MSE, MAE, and R2 error. Methods: The thesis follows the use of a systematic literature review along with an experimental model for comparing artificial intelligence models. Sentiment analysis played a crucial role in understanding market dynamics. By using linear regression, random forest, and gradient boosting algorithms artificial intelligence models are built to predict cryptocurrency prices using sentiment analysis. The developed models are then compared using performance metrics. This research has analyzed and evaluated each model's performance in predicting cryptocurrency prices. Results: The results of the systematic literature review indicated that market sentiment affects cryptocurrency prices. Prices have increased when market sentiment has been positive, whereas prices dropped when sentiment has been negative. The correlation between cryptocurrency values and market mood, however, is complicated as it depends on a variety of factors. Based on the evaluation measures, the random forest artificial intelligence model is the most accurate in predicting cryptocurrency prices after evaluating the three artificial intelligence models. Conclusions: This study utilized sentiment analysis and artificial intelligence to forecast cryptocurrency prices. It highlighted the significance of sentiment analysis as a tool for predicting the short-term price of cryptocurrencies by demonstrating how negative sentiment is correlated with decreases in price compared to positive sentiment with price increases. However, it recognized that it was necessary to take into consideration the complexity and broad range of effects on cryptocurrency markets. Research in the future will examine comprehensive sentiment analysis methods and broadening data sources.

Cryptocurrencies are one of the most prominent applications of blockchain systems. While cryptocurrencies promise many features and advantages, such as decentralization, anonymity, and ease of access, those very features can be abused. For instance, as documented in various recent works, cryptocurrencies have been frequently abused in many different forms of cybercrime. Despite the plethora of works on measuring and understanding the abuse of cryptocurrencies in the digital space, there has been no work on systemizing this knowledge by comprehensively understanding those contributions, contrasting them based on their merit, and understanding the gap in this research space. This thesis initiates the systematic review and understanding of the literature on cryptocurrencies and their utilization in cybercrime. Starting with a rich set of research efforts published exclusively at some of the most highly selective avenues in the cyber security research community, we built a taxonomy of cryptocurrencies, enumerating the most prominent ones based on their use. We then categorize the literature in this space based on the objectives of the tools built and the blockchain systems they target. We then extend our effort by categorizing the reviewed work on cybercrime based on the type of attacks (coordinated vs. individual fraud) and targeted entity (marketplace vs. exchange). Each of those elements in our taxonomy enumerates various aspects of abuse, including their use in underground forums, the trade of drugs, weapons, and stolen credentials, money laundering, malware distribution, ransomware, etc. Accompanying our review, we discuss the gaps in research that call for further investigation.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24740号 / 情博第828号 / 新制||情||139(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 梅野 健, 教授 山下 信雄, 准教授 加嶋 健司 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The emergence of digital currency is becoming prevalent in the age of globalization – specifically, cryptocurrencies. Cryptocurrencies and blockchain are two recently discovered concepts currently being explored by researchers and developers. Cryptocurrency is a subset of digital currency that encompasses revolutionary technology, shifting political and economic spheres in nation-states. Certain governments are more prone to the adoption of cryptocurrencies and three comparative case study countries, Iran, Russia, and Venezuela, have shared attributes that result in adoption. Observed factors that result in the adoption of cryptocurrencies include corruption, GDP level, economic volatility, and Western sanctions. These factors will be applied in the case study countries to analyze the adoption of native government-backed cryptocurrency.

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While distributed databases offer great promise their decentralized nature poses a number of security and privacy issues. In what ways can parties misbehave? If a database is truly distributed can a malicious actor hide their misdeeds by presenting conflicting views of the database? Can we overcome such deceit and either prevent it by eliminating trust assumptions or detect such perfidy and hold the malicious party to account? We study these questions across two distributed databases: RPKI (Resource Public Key Infrastructure), which is used to authenticate the allocation and announcement of IP prefixes; and Bitcoin, a cryptocurrency that utilizes a permissionless database called a blockchain to track the transfer and ownership of bitcoins. The first part of this dissertation focuses on RPKI and the potential of RPKI authorities to misbehave. We consider the methods, motivations, and impact of this misbehavior and how an RPKI authority can present inconsistent views to hide this misbehavior. After studying the problem we propose solutions to detect and identify such misbehavior. Now we turn our attention to Bitcoin. We look at ways an attacker can manipulate Bitcoin's Peer-to-Peer network to cause members of the network to have inconsistent views of Bitcoin's blockchain and subvert Bitcoin's core security guarantees. We then propose countermeasures to harden Bitcoin against such attacks. The final part of this dissertation discusses the problem of privacy in Bitcoin. Many of the protocols developed to address Bitcoin's privacy limitations introduce trusted parties. We instead design privacy enhancing protocols that use an untrusted intermediary to mix \aka anonymize, bitcoin transactions via blind signatures. To do this we must invent a novel blind signature fair-exchange protocol that runs on Bitcoin's blockchain. This dissertation favors a dirty slate design process. We work to layer protections on existing protocols and when we must make changes to the underlying protocol we carefully weigh compatibility and deployment considerations. This philosophy has resulted in some of the research described in this dissertation influencing the design of deployed protocols. In the case of Bitcoin our research is currently used to harden a network controlling approximately a trillion dollars.