356 research outputs found

    Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis

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    This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market

    Government Regulation Effect On The Volatility Of Top Trading Cryptocurrencies

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    I study whether news and the sentiment of the news regarding cryptocurrency regulation affects the volatility of Bitcoin, Binance, and Ethereum, measured as the standard deviation of the 1st difference of the log of the price with a right sided overlapping window of 7 days. I utilise a modified dynamic causal model with Newey-West heteroskedastic autocorrelation standard errors to estimate both the impact and cumulative effects that regulation news has on the three cryptocurrencies included in the study. My results show the volatility of all three cryptocurrencies react most strongly to negative regulatory news, with Binance being affected the most with an increase of 16.329% after 9 periods following an event, followed by Ethereum with an increase of 8.240% and Bitcoin with an increase of 8.180%. Positive news is also found to affect the volatilities; however, it is a much smaller effect and is only significant for Bitcoin, which experienced an increase of 4.597% in volatility 9 periods following an event. The results are robust to controlling potential omitted variable bias including the volatility of the S&P500 index, consumer confidence, inflation, and federal funds rates

    Cryptocurrency Research in the Field of Information Systems: A Literature Review and its Implications for Sharing Economy Research

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    Cryptocurrency has been widely adopted as an asset for investment with the rise of numerous well-known cryptocurrency exchanges. Practitioners and enthusiasts have begun to promote cryptocurrency as a means of payment in the sharing economy. This new trend has also received attention from academia, especially among information systems (IS) scholars. Thus, the purpose of this paper is to consolidate knowledge about cryptocurrency in the field of IS through a systematic literature review and provide insights for researchers to seek opportunities for cryptocurrency research in the context of the sharing economy

    The Predictive Power of Social Media within Cryptocurrency Markets

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    Blockchain technology has generated a great deal of interest in recent years, as has the associated area of cryptocurrency trading, not only on the part of individuals but also from traditional financial institutions and hedge funds. However, there is currently limited knowledge as to how to predict future cryptocurrency price movements. This thesis investigates whether online indicators, especially from social media, can be harnessed to predict cryptocurrency price movements – to achieve this, three experiments are conducted. The first experiment analyses time-evolving relationships between chosen online indicators and associated cryptocurrency prices; relationships are considered over short, medium and long-term durations. The work introduces and evaluates several influential factors from the social media platform Reddit, a platform previously unexplored within cryptocurrency prediction literature. It is found that medium and longer-term relationships strengthen in bubble market regimes (compared to non-bubble regimes). The second experiment utilises these promising new factors as inputs to a predictive model. The model used was originally designed to detect influenza epidemic outbreaks, and is repurposed here to model epidemic-like cryptocurrency price bubbles, demonstrating how social media can be used to track the epidemic spread of an investment idea. The predictive power of the model is validated through the generation of a profitable trading strategy. Having considered quantitative count-based metrics in the previous chapters (e.g. posts per day, submissions per day, new authors per day etc.), the next experiment considers the content of social media submissions. More specifically, the third experiment analyses social media submission content to investigate whether certain topics of discussion precede upcoming shorter term (positive or negative) price movements. Information evidencing time-varying interest in various topics is retrieved from social media submissions, upon which hidden interactions with the associated cryptocurrency price are deciphered. It is found that certain topics precede major positive or negative price movements, and also additional analysis shows that certain discussion topics exhibit longer-term relationships with cryptocurrency market prices

    A behavioural finance explanation of speculative bubbles: evidence from the bitcoin price development

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    In 2008 a group of programmers, alias Satoshi Nakamoto, introduced bitcoin. Bitcoin is a cryptocurrency or virtual money derived from mathematical cryptography and is conceived as an alternative to government authorised currency. The founder anticipated, through bitcoin’s construction and his digital mining processes, that bitcoin prices would be relatively stable. However, the recent bitcoin price decline proves that bitcoin is extraordinarily volatile and is not that stable as hoped. Although some scientists have already shown that the fundamental value of bitcoin is zero, the price of bitcoin has reached over 19.000$ in December 2018. Since then, bitcoin prices dropped nearly 70% from their peak value and showed in addition to that the typical trends of a speculative bubble. Hyman Minsky and Charles Kindleberger discussed three different patterns of speculative bubbles. One is when price rises in an accelerating way and then crashes very sharply after reaching its peak. Another is when the price rises and is followed by a more similar decline after reaching its peak. The third is when the price rises to a peak, which is then followed by a period of gradual decline known as the period of financial distress, to be followed by a much sharper crash at some later time. One of the key findings of this study is that all these three patterns occurred during 2017-18 for the bitcoin price. Therefore, the purpose of this paper is to analyse the historical bitcoin prices in context with the typical five-step characteristics of a speculative bubble. Furthermore, each phase of a speculative bubble is explained by a behavioural finance approach and answer the price development of this cryptocurrency. The result is frightening, bitcoin can be seen as a perfect textbook example of a speculative bubble

    Portfolio management:will hodling cryptos maximize investor returns?

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    Abstract. Background and Objectives: This thesis aims to investigate whether adding cryptocurrencies to a portfolio of traditional assets enhances the portfolio performance. Moreover, the previous research conducted in this area focused only on Bitcoin. This study devotes to examine the benefits of 18 cryptocurrencies together, which have been selected based on their market capitalization. Out of these 18 cryptocurrencies under study, we also aim to analyze the effects of the more recent additions in the crypto market, which is a stable coin and a utility coin, thereby making our study relevant to the present-day innovations in the crypto space. In addition to this, since the Blockchain and Cryptocurrency technology are newly founded ideations in the financial ecosystem, we scheme to provide a brief overview of this technology by taking into consideration aspects such as their advantages, disadvantages, types of blockchain and cryptocurrencies along with their working and the market players involved for their functioning. Data and Methodology: The times series data was obtained from Coinmarketcap and Yahoo Finance. The time period of research was from 01st January 2014 to 28th February 2020. We employ the Mean-Variance analysis of Markowitz (1952) and Sharpe-ratio of Sharpe (1964) and calculate the mean returns, standard deviation, and Sharpe-ratio and optimize three sets of portfolios: Maximization of Sharpe-ratio (with no short sale), Maximization of Sharpe-ratio (with short sale) and a Minimum Variance portfolio. Results: Results showcase that including cryptocurrencies in a portfolio of traditional assets, provides an improved Sharpe-ratio in comparison to the standard portfolio, which consists of traditional assets only. Moreover, on the construction of the correlation matrix, overall there is no significant correlation among the cryptocurrencies and traditional assets. Results also state that despite Bitcoin being the leader in the crypto space with a market dominance of 62%, it shows lower benefits in the portfolios constructed. However, on the other hand, we found exceptional results by the inclusion of other altcoins and Utility coin (Binance Coin). Furthermore, it should also be noted that cryptocurrencies are risky assets where, although they provide high returns than traditional assets, but they also exhibit extreme volatility. Moreover, the results are based on the limited availability of historical data for most of the cryptoassets, due to which conclusion from the results must be drawn with caution. However, cryptocurrencies have the potential to be analyzed and included for diversification benefits
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