Cryptocurrency Market Research Articles

Role of Blockchain Technology in Smart Era: A Review on Possible Smart Applications

By the end of 2021, the worldwide cryptocurrency market valuation had hit a whole high of $3 trillion. Blockchain technology underpins cryptocurrencies such as Bitcoin and Ethereum. The adoption of blockchain, as well as the technology and products it enables, will continue to have a significant influence on company operations. However, blockchain technology is much more than a secure cryptocurrency transfer method. It may be utilised in areas other than finance, including as healthcare, insurance, voting, welfare benefits, gaming and artist royalties. The global economy is prepared for the blockchain revolution, with the technology already having an influence on business and society on many levels. If the term “revolution” seems extreme, consider that eight of the world’s ten largest corporations are developing a variety of blockchain-based solutions. Any enterprise or organisation that is engaged in the recording and oversight of any type of transaction stands to profit from shifting its operations to a blockchain-based platform. This paper discusses about the various roles blockchain has taken up over the years in several different industries along with the future opportunities and scope of expansion to numerous other professional sectors.

Decoding the cryptocurrency user: An analysis of demographics and sentiments

In recent years, new payment methods have emerged, aimed at improving convenience for users. Cryptocurrencies, in principle, are no different. In this study, we seek to analyze the general population's attitudes towards the adoption of cryptocurrencies as a payment method. To achieve this, we have developed a descriptive survey that targets both current cryptocurrency users and non-users, recognizing that differences in perception may exist. Additionally, we have conducted a sentiment analysis of open-ended questions to understand respondents' views on the future of the cryptocurrency market and its potential as a payment tool, utilizing different lexicons in the English language. Our findings indicate that most cryptocurrency users prefer to invest in these digital assets, often choosing coins based on their popularity rather than other intrinsic features. E-commerce payments are the most attractive activity, followed by international transactions when using cryptocurrencies as a payment method. However, high volatility and a lack of ease of use are the most common difficulties reported by users. Our study also highlights the importance of regulation in a time when users are increasingly demanding higher levels of oversight, in contrast to the past. While users are concerned about the instability and volatility of cryptocurrencies, they also value the anonymity these transactions offer. Our analysis showcases an innovative approach to analyzing interviews and qualitative questionnaires that can be applied in other research fields.

Native Market Factors for Pricing Cryptocurrencies

The cryptocurrency market has been growing frantically in number of cryptocurrencies, online exchanges, and market capitalization, which has amplified the need for comprehensive and robust pricing models. Using a database of all eligible cryptocurrencies listed on the CoinMarketCap website, we study the relationship between returns and several potential pricing factors, such as size (market capitalization), momentum, liquidity, and maturity. The analysis was conducted from December 27, 2013, to December 29, 2020, using weekly data for 3'667 cryptocurrencies. Results point out that portfolios of cryptocurrencies with smaller market capitalization, higher reversal, lower liquidity, and lower maturity tend to offer higher returns. The 5-factor model that additionally includes illiquidity and maturity performs better than the 3-factor model previously proposed in the literature, meaning that illiquidity and maturity significantly help capture the cross-sectional cryptocurrency risk premia. The 5-factor model presented seems robust to different procedures to construct portfolios and factors.

Cryptocurrency co-investment network: token returns reflect investment patterns

Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behaviour. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.

Twitter sentiment analysis and bitcoin price forecasting: implications for financial risk management

PurposeThe present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.Design/methodology/approachWe utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.FindingsOur findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.Practical implicationsOur study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.Originality/valueWe present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.

Cryptocurrency Turmoil: Unraveling the Collapse of a Unified Stablecoin (USTC) through Twitter as a Passive Sensor.

This study sought to explore whether Twitter, as a passive sensor, could have foreseen the collapse of the Unified Stablecoin (USTC). In May 2022, in just a few days, the cryptocurrency went to near-zero valuation. Analyzing 244,312 tweets from 89,449 distinct accounts between April and June 2022, this study delved into the correlation between personal sentiments in tweets and the USTC market value, revealing a moderate correlation with polarity. While sentiment analysis has often been used to predict market prices, the results suggest the challenge of foreseeing sudden catastrophic events like the USTC collapse solely through sentiment analysis. The analysis uncovered unexpected global interest and noted positive sentiments during the collapse. Additionally, it identified events such as the launch of the new Terra blockchain (referred to as "Terra 2.0") that triggered positive surges. Leveraging machine learning clustering techniques, this study also identified distinct user behaviors, providing valuable insights into influential figures in the cryptocurrency space. This comprehensive analysis marks an initial step toward understanding sudden and catastrophic phenomena in the cryptocurrency market.

The benefits of using IPA in relation to RPA for the cryptocurrency sector, in making decisions on their sale and purchase in the stock market

Since 2009 when the first cryptocurrency Bitcoin began to be inserted into the market of electronic currencies, today in 2023 there are more than 19,850 electronic cryptocurrencies [1]. According to information from the coinecko website, the cryptocurrency market has expanded dramatically from a market capitalization of $1 million in 2013 to $3 trillion in November 2021 [2]. Referring to the latest statistical data, 3 are the cryptocurrencies that rule the e-commerce market in November 2023, Bitcoin,Ethereum AND Tether USDt [3]. Robotic Process Automation (RPA) is a growing trend in the restructuring of business processes, combined with digital transformation. This technology can be applied in different areas of business processes and by organizations from any activity sector [4].With continuous advances in automated processes through RPA, mechanisms involving Artificial Intelligence (AI) were incorporated to influence real-life decision-making [5]. Artificial Intelligence (AI) allows improving the accuracy and execution of RPA processes in extracting information and recognizing, classifying, predicting and optimizing processes [6].Nowadays, artificial intelligence is affecting the way people process computer data, televisions have started to create avatars that they use for news reporting. In this paper we will study the impact that the use of automatic sale and purchase of electronic cryptocurrencies can have using IPA and RPA and the possibility of this process being realized through this process.

Can a self-exciting jump structure better capture the jump behavior of cryptocurrencies? A comparative analysis with the S&P 500

This paper introduces a stochastic volatility model with an independent self-exciting jump structure model (SE-SVIJ) to capture the jump dynamics of cryptocurrency daily returns. The empirical results show that the SE-SVIJ model can provide a less volatile and less persistent volatility process. We find clear evidence of self-exciting jump clustering in the cryptocurrency market. The SE-SVIJ model identifies more jumps than the stochastic volatility model with independent jumps (SVIJ). We also conduct a comparison between the cryptocurrency and S&P 500 markets in terms of jump behavior: S&P 500 returns have more frequent negative jumps and larger negative jumps on average. In comparison, the cryptocurrency markets suffer from positive jumps continuously, and also suffer from an interaction effect between positive and negative jumps during the high-volatility regime. We also demonstrate the effectiveness of the SE-SVIJ model in modeling cryptocurrency return and volatility, as reflected by volatility comparisons and tail fitting.

COVID-19 vaccine confidence index and economic uncertainty indices: empirical evidence from the payment-based system cryptocurrency market

PurposeWe investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns.Design/methodology/approachWe used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrency’s countries and the data spans from 22 March 2021 to 6 May 2022.FindingsThis research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figures’ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns.Originality/valueThis study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returns’ new determinant.

Predicting the cryptocurrency market using social media metrics and search trends during COVID-19

Predicting the cryptocurrency market using social media metrics and search trends during COVID-19

Internet publishing as a forecasting tool in the crypto market

Subject. This article examines the relationship between the sentiment caused by the news on the CoinTelegragh professional forum and the changes in Bitcoin, Litecoin and Ethereum cryptocurrencies. Objectives. The article aims to assess the impact of the sentiment of various Internet publications on the volatility of cryptocurrencies, as well as the predictive power of Google Trends and the VIX Index for cryptocurrencies. Methods. For the study, we used the cross-quantilogram method and the VADER sentiment analysis model. Results. The article finds that the Google Trends Index in a short period of one to three days can be used to predict the closing prices of Bitcoin, Litecoin, and Ethereum, while the VIX Index (Stock Market Uncertainty) has no relationship with the cryptocurrency market. This means that cryptocurrencies can be used as a safe-haven asset when the background market is highly volatile. Conclusions. The crypto market has a complex sentiment component, with its prices and trading activity determined by popularity, emotion, and sentiment. The findings confirm previous studies, which claim that during the period of prevalence of negative news and publications, the crypto market gets narrowed, the trading volume drops off, and the interest of Internet users gets low to a minimum. The euphoria in the market, on the contrary, attracts new unqualified investors, and this is confirmed by the number of views of basic information about cryptocurrencies on Wikipedia.

Someone like you: Lottery-like preference and the cross-section of expected returns in the cryptocurrency market

This study sketches how crypto speculators place their bets and investigates the impact of speculative behavior on cryptocurrency pricing. We conjecture that investors favor comparable alternatives to well-known, successful cryptocurrencies as compensation for missed get-rich-quick opportunities. Our verification begins with developing a composite lottery identification indicator that encapsulates the unique characteristics of cryptocurrencies. Intriguing findings involve the following: A low price effect exists in the cryptocurrency market; small and illiquid cryptocurrencies no longer exhibit superior performance within the most speculative portfolios; investors’ lottery-like preferences are time-varying and differ across cryptocurrencies with different features. Collectively, these findings corroborate our conjecture from various angles.

The implication of cryptocurrency volatility on five largest African financial system stability

This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.

Asymmetric dynamics between cryptocurrency uncertainty and the oil and gold markets: evidence from Granger causality in quantiles

ABSTRACT This research examines the causal relationships between cryptocurrency uncertainty, the price of crude oil, and the price of gold using weekly data from 30 December 2013, to 21 February 2021, applying Granger-causality analysis on each quantile. Under this approach, we are able to distinguish between median and tail relationships for conditional quantiles. We find a bidirectional causal relationship between cryptocurrency uncertainty and crude oil prices, implying that crude oil price volatility is one source of cryptocurrency uncertainty, whereas a causal relationship from cryptocurrency uncertainty or crude oil prices to gold suggests that gold hedges cryptocurrency uncertainty and crude oil price shocks. Our research calls on governments to maintain cryptocurrency market stability to reduce market volatility in crude oil prices. Investors and fund managers can consider adding gold assets to portfolios that contain cryptocurrencies or crude oil in order to hedge against the risks of cryptocurrency uncertainty and crude oil price volatility.

Do people gamble or invest in the cryptocurrency market? Transactional-level evidence from Thailand

Should cryptocurrencies be viewed as a gambling tool or a risky investment instrument? While their unpredictable returns resemble gambling, recent studies show their prices having a time-varying correlation with traditional risky assets like the S&P 500. Many institutional investors have, thus, included cryptocurrencies in their portfolios for diversification. This paper examines the behavior of cryptocurrency market participants and investigates whether they behave like gamblers or investors. Literature shows that gamblers often engage in loss-chasing behavior, escalating risk-taking after losses that may go on indefinitely. In contrast, investors typically exhibit risk aversion after losses, though some might increase risk-taking after “paper losses,” a phenomenon called the “realization effect.” Our study found high-risk individuals exhibit gambling-like behavior, chasing both realized and paper losses. In contrast, we found limited evidence that low-risk individuals engage in risk aversion and realization effect, similar to investors. These insights are crucial for policymakers, as loss-chasing can lead to the situation where people taking on more risk than they can afford, potentially resulting in bankruptcy.

Comparing the Performance of the Expected Returns of Cryptocurrencies Using CAPM and D-CAPM Approaches

The present study compares the expected returns of cryptocurrencies using the capital asset pricing and the downside capital asset pricing models. For this purpose, fifty cryptocurrencies were studied as representative of risky assets during the five-year from 2018 to 2022 with daily frequency. Using the conditional variance test, eighteen cryptocurrencies were accepted and the rest were homogeneously rejected in the variance heterogeneity test. Among the eighteen cryptocurrencies, nine were randomly selected as the portfolio, including high-volatility, low-volatility, and medium-volatility cryptocurrencies. First, using the t-Student statistic, the total return and downside return were compared. Results showed that the capital asset pricing model and the downside risk asset pricing model can be fitted in cryptocurrencies. Using the Wald-Fisher model, we investigated the justification for selecting a more appropriate model based on downside returns. The results of the research showed that D-CAPM and CAPM show the relationship between risk and return in cryptocurrencies appropriately, and the portfolios obtained from the mentioned models show the efficiency of the two models in the cryptocurrency market.

The Exchange Inclusion Effect in the Cryptocurrency Market

The Exchange Inclusion Effect in the Cryptocurrency Market

Behavioral biases of cryptocurrency investors: a prospect theory model to explain cryptocurrency returns

PurposeThe study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.Design/methodology/approachThe study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.FindingsThis study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.Originality/valueThe study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

LEGALIZATION OF CRYPTOCURRENCY IN UKRAINE

The rapid development of information technologies, the globalization of the world economy, and the formation of a digital economy in Ukraine lead to the transformation of socio-economic relations. The growth of digitalization of the economy, the large-scale introduction of information technologies into all spheres of human life contribute to the emergence of new industries, one of which is the crypto industry, with the appearance of which in 2008, the money market totally changed forever. More and more markets are collapsing, while more and more regulators from different countries are busy implementing legislation regarding the legalization, use and taxation of cryptocurrencies. The article is devoted to the study of the peculiarities of cryptocurrency legalization in Ukraine. The peculiarities of the law “ Pro virtualʹni aktyvy” and the stages of its implementation are considered. The draft law on amendments to the Tax Code of Ukraine regarding cryptocurrency taxation has been analyzed. The number of cryptocurrency users in Ukraine and other countries of the world, such as the USA, Venezuela, Kenya, North Africa, etc., was studied. The paper analyzes how countries such as Great Britain, the Netherlands, the USA, China, Japan and Canada regulate the cryptocurrency market and whether transactions with them are legalized at the legislative level. Conclusions were also made regarding the feasibility of legalizing cryptocurrency in Ukraine.So far, we have the Law, but for the final settlement of these issues, many different by-laws, instructions and documents still need to be developed. But already today it can be said that the State is dealing with the issue of cryptocurrency relations and is on the way to its settlement.

Applicability Analysis of Cryptocurrency Market Based on Capital Asset Pricing Model

At present, there are approximately 9300 types of cryptocurrencies in the cryptocurrency market, with Bitcoin, Ethereum, and other representative cryptocurrencies receiving widespread attention. Currently, scholars have conducted a lot of research on stock pricing using the Capital Asset Pricing Model (CAPM), but there is little research on the application of CAPM in the emerging cryptocurrency market. This article uses the CAPM to conduct an applicability analysis of CAPM of the top 10 cryptocurrencies in the cryptocurrency market from August 1 2021 to July 31 2023. The analysis process involves first conducting a mathematical analysis of the returns and coefficient of cryptocurrencies, and then using the OLS regression model to fit the cryptocurrency data and CAPM formula with analyses of the OLS regression results. The research conclusion of this article is that in most cases, CAPM is not applicable in the cryptocurrency market. This article demonstrates an applicability analysis of CAPM in the cryptocurrency field, providing reference value for scholars conducting research in this field and investors in cryptocurrencies.