Trends in crypto-currencies and blockchain technologies: A monetary theory and regulation perspective

The internet era has generated a requirement for low cost, anonymous and rapidly verifiable transactions to be used for online barter, and fast settling money have emerged as a consequence. For the most part, e-money has fulfilled this role, but the last few years have seen two new types of money emerge. Centralised virtual currencies, usually for the purpose of transacting in social and gaming economies, and crypto-currencies, which aim to eliminate the need for financial intermediaries by offering direct peer-to-peer online payments. We describe the historical context which led to the development of these currencies and some modern and recent trends in their uptake, in terms of both usage in the real economy and as investment products. As these currencies are purely digital constructs, with no government or local authority backing, we then discuss them in the context of monetary theory, in order to determine how they may be have value under each. Finally, we provide an overview of the state of regulatory readiness in terms of dealing with transactions in these currencies in various regions of the world

Cryptocurrency: History, Advantages, Disadvantages, and the Future

Cryptocurrency is a digital asset that has seen a large amount of attention within the past five years. Its origin is intriguing to some based upon its newness, yet it has invoked mysticism and skepticism in others. Bitcoin is the most recognizable currency, receiving heavy media attention. There are several other cryptocurrencies as well, less in the spotlight. Most appealing to cryptocurrency could include lack of government oversight, and increased privacy available to the consumer(s) (Bunjaku, Gjorgieva-Trajkovska, and Miteva-Kacarski, 2017, p. 37). Additional advantages include the simplicity in the start-up process, the ease of transferability, and the opportunity to have a seamless process in investing and/or exchanging monies. Cryptocurrency creates the ability to invest for some people groups that could never invest before and diversify investment portfolios (Theron and van Vuure, 2018, p. 2). While the newness of cryptocurrency certainly has been appealing for some, it also has been perceived oppositional by others. There has been concerns identified with regard to the level of trust required, an obvious and significant drawback if valid. Another identified disadvantage to cryptocurrency is its low amount of oversight and liquidity hurt for investing future. The ability for cryptocurrency to be used for illegal and/or evil activity is an ethical drawback (Nian and Chuen, 2015, p. 15). Lastly, the uncertainty of the future is a significant drawback. The future of cryptocurrency requires much economic forecasting. The new changes that cryptocurrency will bring to traditional economic institutes is an area which cryptocurrency needs to explored more. Lastly, is cryptocurrency a fad or an economic bubble

Do the rich get richer? An empirical analysis of the BitCoin transaction network

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze BitCoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions, and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling relation between the degree and wealth associated to individual nodes.Comment: Project website: http://www.vo.elte.hu/bitcoin/; updated after publicatio

Leveraging Twitter data to understand the dynamics of social media interactions on cryptocurrencies

Rapid technological change in the last decades has led to the emergence of new platforms and fields such as cryptocurrencies and social media data. Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide. The social media platform has millions of users where tweets are continuously shared every second. Therefore, tweets are useful when a large amount of data is generated to conduct a social media analysis. In addition, Twitter is broadly utilized by investors and financial analysts to gather valuable information. Several studies have shown that the content posted on Twitter can predict the movement of cryptocurrency prices. However, limited research has been conducted on the dynamics of Twitter interactions on cryptocurrencies among users. By leveraging 1724328 tweets, this research aims to understand the dynamics of social media users’ interactions on cryptocurrencies. Essentially by shedding light on larger cryptocurrencies contrary to smaller. The findings reveal that Twitter users are more positive than negative about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The thesis contributes to demonstrating the effectiveness of the Social set analysis framework to analyze and visualize a massive amount of social media data and user-generated data created on social media platforms such as Twitter