Individual Cryptocurrencies Research Articles

The role of promotion versus prevention-orientation to predict individual cryptocurrency participation

We investigate cryptocurrency participation within the context of Regulatory Focus Theory (RFT) among 1,519 individuals in Denmark, Finland and Sweden. Analysis of survey responses identifies the tendency for promotion-focused investors to gravitate towards the high-risk, high-reward potential of cryptocurrencies, while prevention-focused investors adopt a more cautious approach that prioritizes safer assets. Our results which demonstrate that RFT can be used to predict current and future cryptocurrency participation contribute to understanding the profile of participants in the rapidly growing cryptocurrency market.

Legal Perspective of Cryptocurrency and Benefit to Economy and Society

Cryptocurrency is a unique digital signature which is allotted to every individual that is a part of this whole network. This digital signature is used to identify which users have participated in the transaction. The capacity of each block is almost 1 Mb so each block can store in its thousands of transactions. At present time used some crptocurrencies Bitcoin (CRYPTO:BTC); Ethereum (CRYPTO:ETH);Tether (CRYPTO:USDT);BinanceCoin(CRYPTO:BNB);USDCoin (CRYPTO:USDC);XRP(CRYPTO:XRP);Terra (CRYPTO:LUNA);Solana (CRYPTO:SOL); Cardano(CRYPTO:ADA);Avalanche (CRYPTO:AVAX) Bitcoin is considered the first cryptocurrency created, and other individual cryptocurrencies are known as “altcoins” (a combo word derived from "alternative coin").The concept of Crypto-currency and bitcoin were first proposed in 2008 by someone using the pseudonym Santoshi Nakamoto,who described how cryptology and an open distributed ledger can be combined into digital currency application(Nakamoto 2008).The main aim of this research paper to conclude that how much extent benefit to the economy and society from the crypto-currency. And secondory aim is to judge legal acceptance of the cryptocurrencyin the various countries.

Sentiment matters: the effect of news-media on spillovers among cryptocurrency returns

This paper explores the relationship between news media sentiment and spillover effects in the cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of spillover specific to individual cryptocurrencies. Subsequently, we employ unique data on cryptocurrency-specific sentiment to assess its impact on these spillover measures using panel fixed effects regression analysis. Our findings indicate that news media sentiment plays a significant role in explaining the spillover dynamics within the cryptocurrency market. Unlike traditional assets, it appears that only positive sentiment affects the spillovers among cryptocurrencies, suggesting an asymmetric effect. Taking into account various characteristics of cryptocurrencies, we find that sentiment's impact on spillover is more pronounced in community-based coins than in those driven by firms. An examination of news content suggests that sentiment pertaining to emotional and risk aspects of cryptocurrencies predominantly influences these spillovers. Additionally, a comparative analysis of sentiment derived from social media and traditional news sources reveals a stronger influence of the former on spillover effects. Through extensive robustness checks, our research consistently affirms the pivotal role of sentiment in driving spillovers among cryptocurrency returns, underlining the importance of sentiment analysis in understanding the dynamics of the cryptocurrency market.

Zephyr: A Cost-Effective, Zero-Knowledge Light Client for Enhanced Blockchain Interoperability

Blockchains are siloed by nature. A longtime limitation of blockchain technology is that individual cryptocurrencies are bound to their own chains. Users and applications need to transfer arbitrary data and funds across blockchains. At its peak in 2021, cross-chain bridges custody over 20B in various cryptocurrencies, serving users' needs to engage in DeFi, gaming, etc. Plagued by a lack of ability to do so in a decentralized way, applications force its users to rely on centralized exchanges to achieve cross-blockchain interoperability. User flow thus becomes significantly more complicated, and developers are prevented from defining and building applications that are uniquely enabled by on-chain bridges. Furthermore due to hacks of over 2B, a loss of faith in natively on-chain solutions force users to look to off-chain solutions like Coinbase.

Emotional spillovers in the cryptocurrency market

Using 10 significant cryptocurrencies, we construct emotional spillovers and provide inferences about the transmission of fear and greed shocks. The results show intense emotional bonding signifying a high level of cryptocurrency interdependence through the sentiment channel of crypto traders and investors. The amplitude of idiosyncratic (own) connectedness increases significantly for most cryptocurrencies in their greed phase, implying that positive investor sentiment is more substantial than negative sentiment in driving the trading of individual cryptocurrencies. Though market conditions and the pandemic alter the role of most cryptocurrencies, some cryptocurrencies display persistence. Ethereum cash and, to some extent, Ripple remain considerably isolated, whereas Ethereum and Dash remain highly interlocked with the rest of the cryptocurrencies. Dash is the leading net transmitter, whereas Ripple is the leading net receiver of fear and greed shocks.

Predicting cryptocurrency returns for real-world investments: A daily updated and accessible predictor

This paper uses a daily updated and accessible Crypto Fear and Greed Index (FG) to predict cryptocurrency returns. Investors can easily use return predictions from FG for real-world cryptocurrency investments. Empirical results show that FG has significant in-sample and out-of-sample predictive power at forecast horizons ranging from one day to one week. The predictive power of FG exists for individual cryptocurrencies and different market indices. From an investment perspective, we use different evaluation indicators and demonstrate that FG can bring substantial economic benefits to investors with different degrees of risk aversion.

LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Predicting price trends of cryptocurrencies is a challenging task due to the highly speculative cryptocurrency market. Prior studies mainly investigate predictors such as historical trading data, macroeconomic development, and public interests in cryptocurrencies for price trend prediction while ignoring the predictive role of the relations between cryptocurrencies. In fact, the price movement of a cryptocurrency may be affected by those of other cryptocurrencies, thus, incorporating cryptocurrency interrelations can further improve the prediction performance. In this paper, we propose a novel end-to-end model for price trend prediction using long short-term memory (LSTM) and relationwise graph attention network (ReGAT), in which both individual cryptocurrency features and cryptocurrency relations are considered. Specifically, a cryptocurrency network is built by using shared features between cryptocurrencies. Further, LSTM is used to profile sequential patterns of individual cryptocurrency features. To fully extract network features, ReGAT is designed to aggregate information conveyed by different types of relations while automatically differentiating their importance. Finally, the conventional and network features are concatenated to predict the price trend. The effectiveness of LSTM-ReGAT is validated using real-world cryptocurrency market data. The trading simulations for Bitcoin and portfolios reveal that our model obtains the highest profits. Our study provides insightful implications for investment decision support in cryptocurrency market.

Türkiye’deki Bireysel Kripto Para Yatırımcılarının Kripto Paralara Yaklaşımları

Son dönemlerde hızla gelişen blockchain teknolojisinin bir ürünü olarak bilinen kripto paralar, resmi bir şekilde bir varlık veya değişim aracı olarak merkezi otoriteler tarafından kabul edilmiş olmamasına rağmen yatırımcılar tarafından yoğun talep görmektedir. Kripto para ekonomisi her geçen gün hızla büyümektedir. Herhangi bir fiziksel karşılığı olmayan, tamamen sanal ortamda kullanılan, aracılara ihtiyaç duymadan kişiler arası işlemlere imkan veren ve şifreli bir sisteme sahip olan kripto paraların bir değişim veya yatırım aracı olduğuna dair farklı görüşler bulunmaktadır. Ama son dönemlerde popülaritesinin giderek artmasıyla tüm dünyada olduğu gibi Türkiye’de de yatırımcıların yöneldiği araçlardan biri haline gelmiş bulunmaktadır. Bu sebeple bu araştırmada, Türkiye’deki bireysel kripto para yatırımcılarının kripto paralara yaklaşımları açıklanmaya çalışılmaktadır. Çalışmaya esas oluşturacak veriler sosyal medya platformları (Facebook, Twitter, Telegram, WhatsApp v.b) üzerinden Türkiye’deki 440 bireysel kripto para yatırımcılarına anket uygulanarak toplanmıştır. Uygun görülen 396 anket paket program yardımıyla yorumlanmıştır. Uygulanan çoklu regresyon analizi sonucunda ekonomiyle ilgili risk faktörüyle birlikte fayda faktörünün yatırımcıların kripto paralara yatırım yapma eğilimini önemli düzeyde etkilediği, kripto para yatırımcılarına güven faktörüyle birlikte ekonomi ve kripto paralarla ilgili endişe faktörünün kripto paralara yatırım yapma eğilimini önemli düzeyde etkilemediği sonucuna ulaşılmıştır

Факторы доходности Ethereum как платформы для создания децентрализованных приложений

By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.

What drives the popularity of stablecoins? Measuring the frequency dynamics of connectedness between volatile and stable cryptocurrencies

Stablecoins are a relatively recent phenomenon in the cryptocurrency market, gaining prominence particularly since 2018. These digital currencies are usually pegged to nonvolatile underlying assets, offering a solution to the problem of the high price volatility of nonstable cryptocurrencies such as Bitcoin. Therefore, stablecoins seem attractive to participants in the cryptocurrency market, especially in times of turmoil. This paper aims to measure the spillover effect of shocks in volatile cryptocurrencies (Bitcoin, Ethereum, Litecoin and the Cryptocurrencies Index) on the activity of investors in the stablecoin market (Tether, USD Coin, Binance USD, DAI, Paxos, Huobi USD, and Gemini USD). Using the spectral representation of variance decomposition, the paper measures the strength of the spillover effect and investigates whether the cryptocurrency market processes information rapidly or slowly. The results suggest that shocks in the volatile cryptocurrency market moderately drive the popularity of stablecoins. The spillover effects on stablecoin popularity are short-lived, as they are observed mostly within up to 3 days of the shock. The findings indicate that investors use stablecoins as safe haven assets after bad news in the volatile cryptocurrency market; however, they react more strongly to news related to individual cryptocurrencies and not so intensively to the general sentiment in the cryptocurrency market. Investors with larger capital are more resistant to shocks.

Crypto Price Prediction as an Investment Opportunity: An Empirical Study of Three Global Cryptocurrencies

Aims: To determine the investment feasibility of evaluating cryptocurrency opportunities as an investment product under the possibility of crypto price valuation selection. The study analyzes three indicators: asset price returns in unrelated time, selection of cryptocurrency investment price weights, and crypto price forward contract opportunities on ARCH-GARCH probability forecasts in the selection of price valuations by individual cryptocurrency prices.
 Study Design: Quantitative research.
 Place and Duration of Study: The period from 10 September 2021 to 4 September 2022 using sample data downloaded from the Yahoo Finance website database with metric data retrieval bound in amount, data quantity, or distance relative to writing opportunities to examine the distribution of the amount of research data.
 Methodology: This study employed Bitcoin (BTC), Ethereum (ETH), and Tether (USDT) cryptocurrencies as the research objects with used panel and multiple regression analysis methodologies and using forecasting the appropriate ARCH and GARCH methods
 Results: The results show that the prediction of future crypto price selection in BTC and ETH tokens has a probability of 78.6% and 59.6%, respectively. The study highlights the prediction of future BTC and ETH price selection with 79.21% and 78.64% forecast results as found in the ARCH-GARCH(1, 0, 1) technique. Meanwhile, USDT token has no possibility to be forecasted in the future, leaving a 7.3% possibility of crypto price selection under probability by investors in the form of high (or different) price fluctuation inequalities.
 Conclusion: Conclusions could state that the partial (combined) selection of crypto coin price assessments and individual crypto assets can reduce the expected return from the selection of the asset price so that this form of investment in crypto assets can reduce the level of observation of return on wealth from crypto assets for investors especially in expecting the chance on that investment.

Assessing the Risk Characteristics of the Cryptocurrency Market: A GARCH-EVT-Copula Approach

The cryptocurrency market offers significant investment opportunities but also entails higher risks as compared to other asset classes. This article aims to analyse the financial risk characteristics of individual cryptocurrencies and of a broad cryptocurrency market portfolio. We construct a portfolio comprising the 20 largest cryptocurrencies, which cover 82.1% of the total cryptocurrency market. The returns are examined for extreme tail risks by the application of Extreme Value Theory. We utilise the GARCH-EVT approach in combination with a novel algorithm to automatically determine the optimal threshold to model the tail distribution. Furthermore, we aggregate the individual market risks with a t-Student Copula to investigate possible diversification effects on a portfolio level. The empirical analysis indicates that all examined cryptocurrencies show high volatility in their price movements, whereby Bitcoin acts as the most stable cryptocurrency. All return distributions are heavy-tailed and subject to extreme tail risks. We find strong, positive intra-market correlations, in particular with the two largest cryptocurrencies Bitcoin and Ethereum. No diversification effect can be achieved by aggregating market risks. On the contrary, a negligibly lower expected return and higher joint extreme returns can be observed. From this analysis, it can be concluded that investments in individual cryptocurrencies as well as in a portfolio show extreme risks of losses. From the investor’s point of view, a possible strategy of risk reduction through portfolio formation within cryptocurrencies is only promising to a limited extent and does not offer a satisfactory solution to significantly reduce the risk within this asset class.

Is liquidity risk priced in cryptocurrency markets?

ABSTRACT This paper investigates the pricing of liquidity risk in the cross-section of cryptocurrencies from January 2017 to December 2020. The cryptocurrencies with high liquidity risk (beta) earned a risk-adjusted return of 4.4% higher weekly than those with low liquidity risk after controlling for the market, size, and reversal factors. Furthermore, the positive relation between expected cryptocurrency returns and liquidity risk is robust when I use cross-sectional regression tests for individual cryptocurrencies and alternative liquidity measures. The results suggest that liquidity risk is an important determinant of expected cryptocurrency returns.

Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning

As an emerging asset, cryptocurrencies have attracted more and more attention from investors and researchers in recent years. With the gradual convergence of the investors in cryptocurrency and traditional financial markets, the research on investor trading behavior from the perspective of microstructure has become increasingly important in cryptocurrency market. In this paper, we study whether investors’ informed trading behavior can significantly predict cryptocurrency returns. We use various machine learning algorithms to verify the contribution of informed trading to the predictability of cryptocurrency returns. The results show that informed trading plays a role in the prediction of some individual cryptocurrency returns, but it cannot significantly improve the prediction accuracy in an average sense of the whole market. The lack of market supervision of cryptocurrency market may be the main factor for relatively low efficiency of this market, and policymakers need to pay attention to it.

Do news headlines matter in the cryptocurrency market?

ABSTRACT The paper examines the influence of investor sentiment based on news headlines on the Cryptocurrency Market Index and ten individual cryptocurrency returns. We capture investors’ sentiment from cryptocurrency-specific news headlines. We use a lexicon-based Natural Language Processing (NLP) technique to construct a unique sentiment indicator, and the sentiment scores are generated using two financial dictionaries: Henry(2008)(HE) and Loughran and Mcdonald(2011)(LM). The findings of the study show that news sentiment has a significant impact on cryptocurrency returns. When the investors’ sentiment is optimistic or bullish, the cryptocurrency market experiences herding behaviour, leading to an increase in prices. The diverse and heterogeneous nature of the various cryptocurrencies causes each individual cryptocurrency to respond differently to sentiment. Further, we see that sentiment has a more pronounced impact on young, small, and volatile cryptocurrencies. Our study is among the few studies that use cryptocurrency-specific news headlines rather than news bodies to build a news sentiment indicator. JEL codes: E49, G14, G15

Does volatility in cryptocurrencies drive the interconnectedness between the cryptocurrencies market? Insights from wavelets

We present a multi-scale and time-frequency analysis of the degree of integration and the lead-lag relationship between six cryptocurrencies (i.e., Bitcoin, Bitcoincash, Ethereum, Litecoin, Ripple, and Tether) and the cryptocurrency-implied volatility index (VCRIX). As a result, the wavelet techniques—bi-wavelet, partial wavelet, bivariate contemporary correlations (BCC), wavelet multiple correlations (WMC) and wavelet multiple cross-correlations (WMCC) are applied. Findings from the study provide that the interdependencies between the cryptocurrencies and VCRIX are high and mostly positive across investment horizons. Furthermore, the comovements between the cryptocurrencies designate long memory dynamics. The high comovements between cryptocurrencies are highly influenced by idiosyncratic shocks they possess rather than the VCRIX. In addition, the BCC and the WMC indicate that there is a high integration among all the cryptocurrencies. Categorically, the VCRIX could not lead or lag the interdependencies among the cryptocurrencies in the WMCC analysis. Findings from the study, therefore, divulge that investing in a single or few cryptocurrencies is highly risky due to the adverse impact of the VCRIX on individual cryptocurrencies. In general, investors should effectively hedge against volatilities in the cryptocurrency markets due to the significant predictive ability of VCRIX as an effective proxy.

Individual Cryptocurrency Investors: Evidence From A Population Survey

Cryptocurrencies, such as Bitcoin, are a highly volatile asset class where very high returns are offset by large losses. This study examines the financial success of individual investments in cryptocurrencies and analyzes whether it relates to similar explanatory factors as for investments in other asset classes. For this purpose, a nationally representative survey data set of 3,864 German citizens is used, of which 354 (9.2%) reported owning cryptocurrencies in March 2019. We analyze the subpopulation of 225 cryptocurrency owners who classify as investors. 56% of them experienced positive returns, while 29% had negative results. The remaining respondents broke even. The average investment was €1,773 in a portfolio of two cryptocurrencies. At the time of the survey, the average portfolio value had risen to €7094 — an average gain of 300%. While nearly half of the investors (44%) outperformed Bitcoin market returns, not a single one of the early investors (2009–2012) did. We find that net income, the degree of cryptocurrency knowledge and the degree of ideological motivation for owning cryptocurrency have positive effects on returns. This first scientific analysis of individual investment in cryptocurrencies provides a basis for future research and for regulatory decision-making.

Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> coin is an individual cryptocurrency, but it is derived from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> . The change in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> prices affects the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> coin through the past days’ prices of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.

DIGITAL CURRENCY OF THE CENTRAL BANK: RISKS AND WAYS OF GLOBALIZATION

The central bank's digital currency (CBDC) is a digital form of the country's currency that is regulated by the country's central currency. Although the central bank is the issuer of digital currency, this type of money carries certain risks for users / consumers, the financial / banking system and the state. However, the nature of the risks and their potential impact will be significantly different from the risks of circulation of individual cryptocurrencies. [1-4]. For commercial banks, the CBDC is a type of money that competes with bank accounts. In recent years, banks have begun to pay more attention to ensuring that their accounts can be used for money transfers, including among individuals. The growth rate of money transfers is compared with the growth rate of cash and card payment transactions at ATMs. Part of these remittances is payment for goods and services. If citizens are willing to use e-money for everyday needs, then the CBDC can also perform the function of storing value (such as cash and current accounts in banks). The competitiveness of the CBDC compared to banking systems may depend on the design of the central bank's retail digital currency. In recent years, significant changes have taken place in the field of money circulation, both in Kazakhstan and around the world. Many people today use non-cash forms of payment for goods and services. The idea of creating a national digital currency today. The day is one of the most topical topics.

A Cryptocurrency Risk–Return Analysis for Bull and Bear Regimes

In this article, the authors develop a new analytical lens through which to examine the risk–return profiles of bitcoin, litecoin, ripple, and ethereum. Their focus is to understand better the price behavior of individual cryptocurrencies and their influence on one another. To achieve this, they segment each cryptocurrency’s time series of returns into disparate bull and bear regimes. They then examine the nature and extent of overlap between these regimes and whether they change over time. They also collect and plot several indicative distributed-denial-of-service attacks against the time series to investigate their possible impact on regime change episodes. Their findings shed light on previously unexplored systemic risk indicators within the cryptomarket as a whole and on the relationship between specific cryptocurrency pairs. These findings enhance the risk management toolkit for investors by revealing potential price behavior contagion patterns between cryptocurrencies pertinent to blended portfolio management. Furthermore, the authors’ approach serves as a blueprint for additional research into regime-type overlap within the cryptomarket. TOPICS:Real assets/alternative investments/private equity, exchanges/markets/clearinghouses, financial crises and financial market history Key Findings ▪ Periods of overlapping regimes increase over time. The increase indicates a rise in cryptomarket systemic risk and an associated reduction in the diversification value of a blended portfolio of cryptocurrencies. ▪ Bitcoin exhibits the most favorable risk measures across both bull and bear regimes, including the lowest proclivity for extreme events during bear regimes. In contrast, ripple displays the overall riskiest profile across both regime types. ▪ Bitcoin’s regime type has the most meaningful impact on the risk–return profile of other cryptocurrencies, namely, litecoin and ripple. However, this relationship does not hold in reverse, a likely consequence of bitcoin’s market dominance and relative maturity.