Cryptocurrency Market Research Articles

Investor attention and market activity: evidence from green cryptocurrencies

Purpose This paper aims to investigate the relationship between investor attention and market activity (return, volatility and volume) using a sample of 14 clean energy cryptocurrencies (hereafter green cryptocurrency), namely, Chia, Cardano, Stellar, Tron, Ripple, Nano, IOTA, EOS, Bitcoin Green, Alogrand, Hedara, Polkadot, FLOW and Tezos. Design/methodology/approach This paper use 26040 crypto-day observations and a range of econometric techniques, including Dynamic Granger causality, Panel vector autoregression (VAR), Impulse response function and the decomposition of forecast error variance. Findings Based on 26040 crypto-day observations, this paper finds a bidirectional Granger causal relationship between investor attention and all measures of market activity, namely, return, absolute volatility, squared volatility and volume. The panel VAR and impulse response function demonstrate that market activity in the green crypto ecosystem, especially volatility and volume, is considerably responsive to changes in investor attention proxied by Google search volume (hereafter Google search volume (GSV)). The findings also demonstrate a significant asymmetric effect of return and volume on investor attention since past negative shocks “or bad news” in return and volume are more likely to grab the investor’s attention. All in all, our study emphasizes the crucial role of investor attention in the green crypto ecosystem. Originality/value (i) The research is the first to shed light on investor attention in the green cryptocurrency market. (ii) The paper uses a wide range of green cryptocurrencies to offer a comprehensive picture of the green cryptocurrency ecosystem. (iii) This paper is the first to use the panel Granger causality to investigate investor attention in the cryptocurrency market which provides several advantages over the conventional Granger causality approach. (iv) This paper is the first to provide novel empirical evidence on the prevalent influence of investor attention in the green crypto market.

The Role of Volunteers vs. Investors and Speculators in the Cryptocurrency Market: A Comparative Study of Reputation and Value Building

The cryptocurrency market, known for its volatility and rapid growth, is influenced by various actors, including investors, speculators, and volunteers. Investors and speculators often seek short-term profits, sometimes disregarding the long-term fundamentals of projects, leading to significant market instability and reputational damage. Conversely, volunteers contribute consistently to the development, governance, and sustainability of crypto projects, focusing on long-term value creation. This paper examines the contrasting roles of these groups and provides real-world examples where speculators and investors have caused harm to the market, while volunteers have played a vital role in building and preserving value.

Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market

The role of cryptocurrencies within the financial systems has been expanding rapidly in recent years among investors and institutions. It is therefore crucial to investigate this phenomenon and develop statistical methods able to capture their interrelationships, the links with other global systems and, at the same time, the serial heterogeneity. Here we introduce hidden Markov regression models for jointly estimating quantiles and expectiles of cryptocurrency returns using regime-switching copulas. The proposed approach allows us to focus on extreme returns and describe their temporal evolution by introducing time-dependent coefficients, evolving according to a latent Markov chain. Moreover to model their time-varying dependence structure, we consider elliptical copula functions defined by state-specific parameters. Maximum likelihood estimates are obtained via an expectation-maximization algorithm. The empirical analysis investigates the relationship between daily returns of five cryptocurrencies and major world market indices.

Stablecoins for Transferring Value in the Cryptocurrency Market

Stablecoins have gained prominence as an effective means to mitigate the inherent volatility in the cryptocurrency markets by providing a stable and reliable medium for value preservation and transfer. This research paper investigates the attributes that render stablecoins a preferable vehicle for value retention and transaction within these markets. By employing a quantitative methodology, this study collected data through surveys, with a sample size of 714 individuals chosen via purposive sampling techniques. To analyze the data, both descriptive and inferential statistical methods were applied. The demographic profiles of the sample were summarized using descriptive statistics such as frequencies and percentages. For examining the relationships between demographic factors (such as gender, age, education, income, and place of residence) and the perceived efficacy of stablecoins, inferential techniques including Chi-square tests and logistic regression were utilized. The logistic regression analysis specifically assessed how these variables influenced the likelihood of participants favoring stablecoins for financial transactions. The results indicate that the preference for stablecoins as a means of holding or transferring value within the cryptocurrency domain in Thailand varies significantly across different demographic groups, specifically influenced by factors such as gender, education, income, and place of residence. These outcomes provide insightful implications concerning the demographic influences on the adoption and utilization of stablecoins in financial activities. Keywords: stablecoin, holding, transferring, value, cryptocurrency market

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.

Can Bitcoin trigger speculative pressures on the US Dollar? A novel ARIMA-EGARCH-Wavelet Neural Networks

In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing debate regarding its effects on traditional financial markets, particularly the US dollar. This study investigates the relationship between Bitcoin and the US dollar, especially in the contexts of speculative attacks, where investors attempt to devalue a currency, and short squeezes, where rapid price rises force short sellers to quickly buy back assets to avoid further losses. The study employs a novel hybrid model combining an autoregressive moving average, Generalized Autoregressive Conditional Heteroskedasticity, and Wavelet Neural Networks techniques with neural networks approaches. The results suggest that significant trading activity in Bitcoin/US dollar, particularly during speculative attacks and short squeezes, can substantially impact the US dollar/EUR market, increasing price volatility as traders adjust their strategies. These adjustments, along with risk management strategies, drive higher trading volumes and further volatility. Our findings demonstrate that our novel hybrid model combined with Quantum Recurrent Neural Networks provides the most accurate predictions, offering valuable insights to inform trading strategies in both Bitcoin/US dollar and US dollar/EUR markets. This study has important implications for policymakers and market participants, emphasising the need to understand the relationship between Bitcoin and the US dollar for financial stability and effective policy formulation. It also highlights the necessity of advanced modeling techniques to accurately predict cryptocurrency market behavior.

Do News Reports and Google Searches Impact Bitcoin Prices? An Analysis of Granger Causality

Objective: to analyze the Granger causality relationship between the number of Bitcoin news articles, the number of Google searches related to the term "Bitcoin," and the price of the cryptocurrency. The study aims to understand how these variables influence each other using an econometric time series model. Theoretical framework: the paper is based on studies on financial market efficiency, cryptocurrencies, and Granger causality theory. It reviews research that explores the impact of media information and online searches on the price of financial assets, such as Bitcoin, while also discussing Bitcoin’s role as both a speculative asset and a transactional currency. Method: the construction of a time series with weekly data, from 2015 to 2018, covering Bitcoin price, the number of news articles, and Google searches. The Vector Autoregressive (VAR) model was used to conduct the Granger causality test and verify if there are causal relationships between these variables. Results and Discussion: the findings indicate a triple causality relationship among the studied variables. Bitcoin's price influences both the number of news articles and Google searches, and in turn, the articles and searches also influence Bitcoin's price. This suggests that increased interest and media coverage of Bitcoin drive its demand and price. Research implications: the research suggests that Bitcoin's price behavior is influenced not only by traditional economic factors but also by media information and the volume of online searches. This highlights the importance of informational factors in the cryptocurrency market and the influence of digital media on asset pricing. Originality/value: the study proposes integrating data from news articles and Google searches with Bitcoin price behavior, applying Granger causality analysis. Its contribution lies in demonstrating the importance of media in the dissemination and pricing of cryptocurrencies, providing a new perspective for the literature on digital assets and speculative behavior.

Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data

Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades.

Forecasting Cryptocurrency Markets: Predictive Modelling Using Statistical and Machine Learning Approaches

The rapidly evolving landscape of cryptocurrency markets presents unique challenges and opportunities. The significant daily variations in cryptocurrency exchange rates lead to substantial risks associated with investments in crypto assets. This study aims to forecast the prices of cryptocurrencies using advanced machine learning models. Among seven models that were tested for their prediction and validation efficiency, Neutral Networks performed the best with minimum error. Thus, Long Short-Term Memory (LSTM) neural networks were used for predicting future trends. LSTM model is well-suited for analyzing complex dependencies in financial data. Starting with historical data collection, data preprocessing, feature engineering, normalization and integrative binning, a comprehensive Exploratory Data Analysis (EDA) was conducted on 50 cryptocurrencies. Top performers were identified based on criteria such as trading volume, market capitalization, and price trends. The LSTM model was implemented using Python to predict 90-day price movements data to check intricate patterns and relationships. Model performance was validated by performance metrics such as MAE and RMSE. The findings align with the Adaptive Market Hypothesis (AMH) which suggests that cryptocurrency markets exhibit dynamic efficiency influenced by evolving market conditions and investor behavior. The study shows the potential of machine learning models in financial economics and their role in enhancing risk management strategies and investment decision-making processes.

Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian machine learning framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Developed a Bayesian machine learning framework for inferring the non-linear potential function from observed cryptocurrency prices. (iii) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes in the literature. (iv) Analysis of cryptocurrency market during various Bitcoin crash durations shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (v) The proposed cryptocurrency market indicators (attractors and repellers) was used to improve the prediction performance of state-of-art deep learning price prediction models.

CARROT: Simultaneous prediction of anomalies from groups of correlated cryptocurrency trends

Cryptocurrencies are virtual currencies that exploit cryptography to perform secure financial transactions. They gained widespread popularity in recent years due to their decentralized nature, (pseudo-)anonymity, and ability to facilitate cross-border transactions without the need for intermediaries. However, their price on the market exhibits a huge volatility, that makes them prone to market anomalies. Therefore, predicting anomalies in cryptocurrency time series can be considered an important task for financial institutions, traders, and investors, to maximize their profit or minimize losses.In this paper, we propose a novel approach for predicting anomalies in cryptocurrency time series by exploiting temporal correlations among different cryptocurrencies. Our approach, called CARROT, is based on the idea that groups of cryptocurrencies exhibit similar trends, possibly due to common influencing factors. CARROT analyzes the temporal correlation between different cryptocurrencies, and identifies clusters showing similar patterns that can be useful for gaining insights into future anomalies. Subsequently, CARROT exploits multiple (i.e., one for each cluster) multi-target LSTM models to predict anomalies.Our experiments, performed on a dataset of 17 cryptocurrencies, proved that CARROT outperforms single-target LSTM models of up to 20%, as well as other approaches based on neural networks, i.e., MLP and CNN, in terms of macro F1-score. Therefore, the proposed approach can be considered as a promising tool for predicting anomalies in cryptocurrency time series data and can potentially be used to improve risk management and trading strategies in the cryptocurrency market.

Behavioral and Psychological Determinants of Cryptocurrency Investment: Expanding UTAUT with Perceived Enjoyment and Risk Factors

With their potential for high returns and expanding role in the financial landscape, cryptocurrency investments have garnered the attention of the financial press and investors. Applying an integrated research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study investigates the factors influencing individual investors’ attitudes toward cryptocurrency investments and their intention to continue investing. The model incorporates constructs such as performance expectancy, effort expectancy, social influence, perceived risk, perceived privacy, technology competency, perceived enjoyment, and prior experience. Data from 506 cryptocurrency investors located in the United States were collected through a 50-item questionnaire. The findings indicate that performance expectancy and perceived enjoyment positively impact attitudes toward cryptocurrency investments, which, in turn, influence the intention to continue investing. Perceived privacy positively affects performance expectancy, while technology competency enhances effort expectancy. These results offer valuable insights for policymakers and cryptocurrency exchanges to foster sustainable growth in the cryptocurrency market. Despite its contributions, the study acknowledges limitations, including a focus on current investors in the US and the exclusion of factors such as optimism and innovativeness. Future research should explore these aspects across different populations and regions to gain a more comprehensive understanding of cryptocurrency investment behavior.

Information flow in the FTX bankruptcy: A network approach

This paper investigates the cryptocurrency network of the FTX exchange during the collapse of its native token, FTT, to understand how network structures adapt to significant financial disruptions, by exploiting vertex centrality measures. Using proprietary data on the transactional relationships between various cryptocurrencies, we construct the filtered correlation matrix to identify the most significant relations in the FTX and Binance markets. By using suitable centrality measures - closeness and information centrality - we assess network stability during FTX’s bankruptcy. The findings document the appropriateness of such vertex centralities in understanding the resilience and vulnerabilities of financial networks. By tracking the changes in centrality values before and during the FTX crisis, this study provides useful insights into the structural dynamics of the cryptocurrency market. Results reveal how different cryptocurrencies experienced shifts in their network roles due to the crisis. Moreover, our findings highlight the interconnectedness of cryptocurrency markets and how the failure of a single entity can lead to widespread repercussions that destabilize other nodes of the network.

Herding behaviour in the cryptocurrency market: the role of uncertainty and return of classical financial markets

Herding behaviour in the cryptocurrency market: the role of uncertainty and return of classical financial markets

Skewness risk and the cross-section of cryptocurrency returns

This paper investigates whether investors can achieve higher returns by holding cryptocurrencies with lower asymmetry risk. First, using a non-parametric bootstrap resampling method, we found that cryptocurrencies with larger market capitalizations exhibit more left-skewed performance, while those with smaller market capitalizations display more right-skewed performance. This finding is consistent with the results of Jiang et al. (2020) in the stock market. Second, both portfolio-level analyses and cross-sectional regressions at the cryptocurrency level reveal a negative cross-sectional relationship between asymmetry risk and future returns in the cryptocurrency market. Additionally, our findings indicate that skewness in the cryptocurrency market is driven by idiosyncratic risk rather than systematic risk. This contrasts with Langlois (2020), who found that systematic skewness risk outweighs idiosyncratic risk in the stock market. Finally, in addition to the risk-return tradeoff theory, the limits-to-arbitrage theory also provides explanatory insight into these results. Collectively, our findings underscore the significant role of asymmetry risk in determining cryptocurrency prices.

Intraday and daily dynamics of cryptocurrency

This paper examines and compares intraday and intraweek patterns in hourly and daily prices, returns, volumes and volatility of native cryptocurrencies, stablecoins and tokens traded on Bitstamp. We show that native cryptocurrencies and tokens share common intraday periodicity determined by the operating times of the NYSE, LSE and Hang Seng stock exchange markets. Periodic patterns are also documented in the returns on cryptocurrency market portfolio approximated by the PCA applied to intraday and intraweek cross-sectional correlation matrices of cryptocurrency returns. Stablecoins have distinct dynamics and their daily and hourly returns are uncorrelated with one another and with the returns on other cryptocurrencies. We introduce a functional CAPM to accommodate the periodic patterns and estimate it by regressing the functions of intraday and intraweek cryptocurrency returns on the market portfolio. We show that the return functions on Bitcoin, Ether, and Link satisfy affine relationships with the return functions of the market portfolio and their functional betas display periodic intraday and intraweek patterns.

Crypto network

The empirical literature has studied linkages in the cryptocurrency market because knowing how shocks pass from one currency to another helps policymakers and practitioners better counter their propagation in these and related markets. This paper contributes to this literature by proposing a methodology based on Granger causality and network analysis. Using the daily log-returns of 22 cryptocurrencies over the period 2018–2023, I develop a VAR model to infer unidirectional or bidirectional Granger causality among cryptocurrencies. These relationships are then transformed into a directed network and several centrality measures are calculated. The centrality measures are also observed over the years to understand the dynamics of the cryptocurrency network. I find out that each one unit increase in eigencentrality is associated with a 0.22 percent increase in log-returns. Cryptocurrencies are nontrivially connected, and in this sample Cardano, Dogecoin, Gridcoin, and Neo are amongst the most central in the network throughout the period. Some cryptocurrencies, such as Dogecoin or Neo, show decreasing centrality over the years, while others, such as Gridcoin, Litecoin, Namecoin, or Ripple, gain centrality. These results support the idea that the cryptocurrency market is no longer exclusively associated with Bitcoin and lay the groundwork for further study of shock propagation in financial markets.

Sustainable energy practices and cryptocurrency market behavior

We examine the effects of energy usage on the return, volatility and jump processes in the Bitcoin (BTC) and Ethereum (ETH) markets. Our main finding indicates that while BTC returns respond significantly to changes in electricity consumption, the effect of electricity consumption on ETH returns is negligible. We attribute this discrepancy to BTC's relative energy inefficiency, which contrasts with ETH's commitment to transitioning to a more energy-efficient mining protocol over our sample period. Additionally, we show that the effect of electricity consumption on cryptocurrencies is mitigated by media coverage and policy uncertainty regarding the state of the economy, environment, and cryptocurrency markets. Our results suggest that when trading cryptocurrencies, investors consider their relative energy efficiency and price electricity consumption differently, highlighting the influence of the transition towards more sustainable energy practices on investor preferences and investment decisions in cryptocurrency markets.

The influence of European MiCa regulation on cryptocurrencies

This research investigates the relationship between impending regulatory measures, specifically the introduction of European MiCa regulations, and their influence on cryptocurrency markets. Analysing stock market responses, we highlight significant shifts in liquidity, variance, and return dynamics post-MiCa-related announcements. Specifically, such announcements are associated with significant negative cryptocurrency returns and elevated liquidity. The findings reveal defined heterogeneity across different cryptocurrency sub-classes, each uniquely affected by its inherent attributes and susceptibility to regulatory changes. This work offers insights into the nature of market responses to regulatory intervention, providing an invaluable perspective on the equilibrium between cryptocurrency market behaviour and the evolving European regulatory landscape.

The legal nature of crypto assets and its reflection in the civil and tax legislation of the Russian Federation

The article analyzes the legal nature of crypto assets in Russian legislation and the specifics of their taxation, focusing on the need to form a legal framework that promotes effective protection of the rights of participants in the turnover of crypto assets.The methodological basis of the research is the dialectical method, the formal logical method, the formal legal method and the method of interpretation of law.The lack of a unified terminology base in the field of digital economy in Russian legislation creates additional difficulties in creating comfortable conditions for the development of the crypto asset market. Currently used various terms such as “virtual assets”, “digital assets”, “crypto assets”, “cryptocurrency” are not fixed in the current legislation. At the same time, the terms existing in Russian legislation do not allow for effective regulation in the field of taxation and civil turnover, since they intentionally exclude the most common crypto assets from their scope. Without clear and unambiguous legal regulation, it is difficult to ensure proper control and taxation of transactions with crypto assets, which can lead to significant tax losses and a decrease in budget revenues.Moreover, the concept of “digital currency” used in Russian legislation does not correspond to the generally accepted meaning in international practice and is mainly considered in an economic context, rather than in a legal one. Such a discrepancy in terminology creates additional difficulties for participants in the cryptocurrency market and creates obstacles to the formation of an effective legal framework for the regulation of the digital economy. According to the results of the study, it was concluded that the current approaches to the development of the terminological apparatus of the regulation of crypto assets have flaws, since they allow for a mixture of concepts and do not imply the formation of a clear hierarchy. The definition of “the digital asset” is proposed for the purpose of filling existing terminological gaps in the current legislation.