This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns(CR)by employing various wavelet techniques.We concentrate on the recently published cryptocurrency uncertainty inde...This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns(CR)by employing various wavelet techniques.We concentrate on the recently published cryptocurrency uncertainty index(UCRY)and the top eight cryptocurrencies by market capitalization from December 30,2013,to June 30,2023.Our results showed that the UCRY index strongly predicted CR.In particular,the UCRY index has a leading position at all frequencies for all cryptocurrencies in our sample.Additionally,when the impacts of economic policy uncertainty and the volatility index are eliminated,the significant comovement of UCRY-CR remains unchanged for the short-,medium-,and long-term investment horizons.Therefore,we conclude that the UCRY-CR relationship is both persistent and pervasive.Our study contributes toward the literature on the relationships between cryptocurrencies and market uncertainties,as well as toward investors who use uncertainty indices to design investment strategies for their portfolios.展开更多
This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—cove...This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.展开更多
This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies.To identify the most suitable pairs and generate trading signals formulated from a reference asset for a...This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies.To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index,the study employs linear and nonlinear cointegration tests,a correlation coefficient measure,and fits different copula families,respectively.The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions,assessing its returns and risks.The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.展开更多
This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively i...This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively impacts cryptocurrencies.Moreover,the results indicate rapid divergence from counterfactual predictions,and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse.Cryptocurrency is reacting strongly to the uncertainty caused by insolvency.In relative terms,the collapse of FTX has been highly detrimental to Solana and Ethereum.Furthermore,the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred.FTX collapsed owing to a mismatch between the assets and liabilities.The industry is still mostly unregulated,and regulators must act quickly,highlighting the need for outstanding innovation and decentralized and trustless technology adoption.展开更多
Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting it...Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.展开更多
Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked popu...Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked populations,the lack of bank controls,and inflation.This study investigates the global patterns of cryptocurrency adoption using Generalized Linear Models and Spatial Autoregressive Models.This research introduces a novel perspective on global cryptocurrency adoption using spatial models.Our findings reveal that cryptocurrency adoption is significantly influenced by economic instability,infrastructure availability,and spatial dynamics,with higher adoption rates in countries with limited access to traditional financial systems.展开更多
Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,...Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.展开更多
Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.How...Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.However,more research is needed to identify the factors driving cryptocurrency adoption and understand its impact.We use social network analysis(SNA)to identify the influencing factors and reveal the impact of each on cryptocurrency adoption.Our analysis initially revealed 44 influential factors,which were later reduced to 25 factors,each exerting a different influence.Based on the SNA,we classify these factors into highly,moderately,and least influential categories.Discomfort and optimism are the most influential determinants of adoption.Moderately influential factors include trust,risk,relative advantage,social influence,and perceived behavioral control.Price/value,facilitating conditions,compatibility,and usefulness are the least influential.The factors affecting cryptocurrency adoption are interdependent.Our findings can help policymakers understand the factors influencing cryptocurrency adoption and aid in developing appropriate legal frameworks for cryptocurrency use.展开更多
Crypto assets have become increasingly popular in recent years due to their many advantages,such as low transaction costs and investment opportunities.The performance of crypto exchanges is an essential factor in deve...Crypto assets have become increasingly popular in recent years due to their many advantages,such as low transaction costs and investment opportunities.The performance of crypto exchanges is an essential factor in developing crypto assets.Therefore,it is necessary to take adequate measures regarding the reliability,speed,user-friendliness,regulation,and supervision of crypto exchanges.However,each measure to be taken creates extra costs for businesses.Studies are needed to determine the factors that most affect the performance of crypto exchanges.This study develops an integrated framework,i.e.,fuzzy best-worst method with the Heronian function—the fuzzy measurement of alternatives and ranking according to compromise solution with the Heronian function(FBWM’H-FMARCOS’H),to evaluate cryptocurrency exchanges.In this framework,the fuzzy best-worst method(FBWM)is used to decide the criteria’s importance,fuzzy measurement of alternatives and ranking according to compromise solution(FMARCOS)is used to prioritize the alternatives,and the Heronian function is used to aggregate the results.Integrating a modified FBWM and FMARCOS with Heronian functions is particularly appealing for group decision-making under vagueness.Through case studies,some well-known cryptocurrency exchanges operating in Türkiye are assessed based on seven critical factors in the cryptocurrency exchange evaluation process.The main contribution of this study is generating new priority strategies to increase the performance of crypto exchanges with a novel decision-making methodology.“Perception of security,”“reputation,”and“commission rate”are found as the foremost factors in choosing an appropriate cryptocurrency exchange for investment.Further,the best score is achieved by Coinbase,followed by Binance.The solidity and flexibility of the methodology are also supported by sensitivity and comparative analyses.The findings may pave the way for investors to take appropriate actions without incurring high costs.展开更多
This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities.We build a theoretical mod...This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities.We build a theoretical model for the reporting decision structure of a private bank or cryptocurrency exchange and show that an inferior ability to detect money laundering(ML)increases the ratio of reported transactions to unreported transactions.If a representative money launderer makes an optimal portfolio choice,then this ratio increases further.Our findings suggest that cryptocurrency exchanges will exhibit more excessive reporting behavior under this regulation than private banks.We attribute this result to cryptocurrency exchanges’inferior ML detection abilities and their proximity to the underground economy.展开更多
In this study,we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions,selling,or acceptance as a means of payme...In this study,we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions,selling,or acceptance as a means of payment.Our analysis focuses on traditional firms whose core business is unrelated to blockchain or cryptocurrency.We find that the aggregate market reaction around these events is slightly positive but statistically insignificant for most event windows.However,when we perform heterogeneity analyses,we observe significant differences in market reaction between events with high(larger CARs)and low cryptocurrency exposure(lower CARs).Multivariate regressions show that the level of exposure to cryptocurrency("skin in the game")is a critical factor underlying abnormal returns around the event.Further analyses reveal that economically meaningful acquisitions of BTC or ETH(relative to firm’s total assets)drive the observed effect.Our findings have important implications for managers,investors,and analysts as they shed light on the relationship between cryptocurrency adoption and firm value.展开更多
Many types of cryptocurrencies,which predominantly utilize blockchain technology,have emerged worldwide.Several issuers plan to circulate their original cryptocurrencies for monetary use.This study investigates whethe...Many types of cryptocurrencies,which predominantly utilize blockchain technology,have emerged worldwide.Several issuers plan to circulate their original cryptocurrencies for monetary use.This study investigates whether issuers can stimulate cryptocurrencies to attain a monetary function.We use a multi-agent model,referred to as the Yasutomi model,which simulates the emergence of money.We analyze two scenarios that may result from the actions taken by the issuer.These scenarios focus on increases in the number of stores that accept cryptocurrency payments and situations whereby the cryptocurrency issuer designs the cryptocurrency to be attractive to people and conducts an airdrop.We find that a cryptocurrency can attain a monetary function in two cases.One such case occurs when 20%of all agents accept the cryptocurrency for payment and 50%of the agents are aware of this fact.The second case occurs when the issuer continuously airdrops a cryptocurrency to a specific person while maintaining the total volume of the cryptocurrency within a range that prevents it from losing its attractiveness.展开更多
In recent years,the tendency of the number of financial institutions to include crypto-currencies in their portfolios has accelerated.Cryptocurrencies are the first pure digital assets to be included by asset managers...In recent years,the tendency of the number of financial institutions to include crypto-currencies in their portfolios has accelerated.Cryptocurrencies are the first pure digital assets to be included by asset managers.Although they have some commonalities with more traditional assets,they have their own separate nature and their behaviour as an asset is still in the process of being understood.It is therefore important to summarise existing research papers and results on cryptocurrency trading,including available trading platforms,trading signals,trading strategy research and risk management.This paper provides a comprehensive survey of cryptocurrency trading research,by covering 146 research papers on various aspects of cryptocurrency trading(e.g.,cryptocurrency trading systems,bubble and extreme condition,prediction of volatility and return,crypto-assets portfolio construction and crypto-assets,technical trading and others).This paper also analyses datasets,research trends and distribution among research objects(contents/properties)and technologies,concluding with some promising opportunities that remain open in cryptocurrency trading.展开更多
Since the emergence of Bitcoin,cryptocurrencies have grown significantly,not only in terms of capitalization but also in number.Consequently,the cryptocurrency market can be a conducive arena for investors,as it offer...Since the emergence of Bitcoin,cryptocurrencies have grown significantly,not only in terms of capitalization but also in number.Consequently,the cryptocurrency market can be a conducive arena for investors,as it offers many opportunities.However,it is difficult to understand.This study aims to describe,summarize,and segment the main trends of the entire cryptocurrency market in 2018,using data analysis tools.Accord-ingly,we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies,and one that looks for associations between the clustering results,and other factors that are not involved in clustering.Particularly,the methodology involves applying three different partitional clustering algorithms,where each of them use a different representation for cryptocurrencies,namely,yearly mean,and standard deviation of the returns,distribution of returns that have not been applied to financial markets previously,and the time series of returns.Because each representation provides a different outlook of the market,we also examine the integration of the three clustering results,to obtain a fine-grained analysis of the main trends of the market.In conclusion,we analyze the association of the clustering results with other descriptive features of cryptocurrencies,including the age,technological attributes,and financial ratios derived from them.This will help to enhance the profiling of the clusters with additional descriptive insights,and to find associations with other variables.Consequently,this study describes the whole market based on graphical information,and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly,and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period,we found that the market can be typically segmented in few clusters(five or less),and even considering the intersections,the 6 more populations account for 75%of the market.Regarding the associations between the clusters and descriptive features,we find associations between some clusters with volume,market capitalization,and some financial ratios,which could be explored in future research.展开更多
Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(C...Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.展开更多
The importance of cryptocurrency to the global economy is increasing steadily,which is evidenced by a total market capitalization of over$2.18T as of December 17,2021,according to coinmarketcap.com(Coin,2021).Cryptocu...The importance of cryptocurrency to the global economy is increasing steadily,which is evidenced by a total market capitalization of over$2.18T as of December 17,2021,according to coinmarketcap.com(Coin,2021).Cryptocurrencies are too confusing for laymen and require more investigation.In this study,we analyze the impact that the effective reproductive rate,an epidemiological indicator of the spread of COVID-19,has on both the price and trading volume of eight of the largest digital currencies—Bitcoin,Ethereum,Tether,Ripple,Litecoin,Bitcoin Cash,Cardano,and Binance.We hypothesize that as the rate of spread decreases,the trading price of the digital currency increases.Using Generalized Autoregressive Conditional Heteroskedasticity models,we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region.These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies.展开更多
In recent years,cryptocurrency has become gradually more significant in economic regions worldwide.In cryptocurrencies,records are stored using a cryptographic algorithm.The main aim of this research was to develop an...In recent years,cryptocurrency has become gradually more significant in economic regions worldwide.In cryptocurrencies,records are stored using a cryptographic algorithm.The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media.Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels.Therefore,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet sentiments.This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and Ethereum.The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.展开更多
In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and...In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.展开更多
The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in ...The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in the price-formation process.This study examines five cryptoassets with different backgrounds,namely Bitcoin,Ethereum,Litecoin,XRP,and Dogecoin between 2016 and 2022.It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events.The findings show that the price and return dynamics of all the studied assets,except for Dogecoin,emerge from complex interactions between fundamental and speculative components,includ-ing episodes of price bifurcations.Bitcoin shows the strongest fundamentals,with on-chain activity and economic factors driving the fundamental part of the dynam-ics.Investor attention and off-chain activity drive the speculative component for all studied assets.Among the fundamental drivers,the analyzed cryptoassets present their coin-specific factors,which can be tracked to their protocol specifics and are economi-cally sound.展开更多
Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has furth...Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.展开更多
文摘This study investigates how the uncertainty surrounding cryptocurrency affects cryptocurrency returns(CR)by employing various wavelet techniques.We concentrate on the recently published cryptocurrency uncertainty index(UCRY)and the top eight cryptocurrencies by market capitalization from December 30,2013,to June 30,2023.Our results showed that the UCRY index strongly predicted CR.In particular,the UCRY index has a leading position at all frequencies for all cryptocurrencies in our sample.Additionally,when the impacts of economic policy uncertainty and the volatility index are eliminated,the significant comovement of UCRY-CR remains unchanged for the short-,medium-,and long-term investment horizons.Therefore,we conclude that the UCRY-CR relationship is both persistent and pervasive.Our study contributes toward the literature on the relationships between cryptocurrencies and market uncertainties,as well as toward investors who use uncertainty indices to design investment strategies for their portfolios.
文摘This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.
基金financial support of the grant GAČR 22-19617 S“Modeling the structure and dynamics of energy,commodity,and alternative asset prices.”。
文摘This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies.To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index,the study employs linear and nonlinear cointegration tests,a correlation coefficient measure,and fits different copula families,respectively.The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions,assessing its returns and risks.The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
文摘This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively impacts cryptocurrencies.Moreover,the results indicate rapid divergence from counterfactual predictions,and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse.Cryptocurrency is reacting strongly to the uncertainty caused by insolvency.In relative terms,the collapse of FTX has been highly detrimental to Solana and Ethereum.Furthermore,the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred.FTX collapsed owing to a mismatch between the assets and liabilities.The industry is still mostly unregulated,and regulators must act quickly,highlighting the need for outstanding innovation and decentralized and trustless technology adoption.
基金financial support from the National Natural Science Foundation of China(Grant Nos.71971079,72271087,and 71871088)the Major Projects of the National Social Science Foundation of China(Grant No.21ZDA114)+1 种基金the National Social Science Foundation of China(Grant No.19BTJ018)the Hunan Provincial Natural Science Foundation of China(Grant No.21JJ20019).
文摘Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.
文摘Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked populations,the lack of bank controls,and inflation.This study investigates the global patterns of cryptocurrency adoption using Generalized Linear Models and Spatial Autoregressive Models.This research introduces a novel perspective on global cryptocurrency adoption using spatial models.Our findings reveal that cryptocurrency adoption is significantly influenced by economic instability,infrastructure availability,and spatial dynamics,with higher adoption rates in countries with limited access to traditional financial systems.
文摘Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.
文摘Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.However,more research is needed to identify the factors driving cryptocurrency adoption and understand its impact.We use social network analysis(SNA)to identify the influencing factors and reveal the impact of each on cryptocurrency adoption.Our analysis initially revealed 44 influential factors,which were later reduced to 25 factors,each exerting a different influence.Based on the SNA,we classify these factors into highly,moderately,and least influential categories.Discomfort and optimism are the most influential determinants of adoption.Moderately influential factors include trust,risk,relative advantage,social influence,and perceived behavioral control.Price/value,facilitating conditions,compatibility,and usefulness are the least influential.The factors affecting cryptocurrency adoption are interdependent.Our findings can help policymakers understand the factors influencing cryptocurrency adoption and aid in developing appropriate legal frameworks for cryptocurrency use.
文摘Crypto assets have become increasingly popular in recent years due to their many advantages,such as low transaction costs and investment opportunities.The performance of crypto exchanges is an essential factor in developing crypto assets.Therefore,it is necessary to take adequate measures regarding the reliability,speed,user-friendliness,regulation,and supervision of crypto exchanges.However,each measure to be taken creates extra costs for businesses.Studies are needed to determine the factors that most affect the performance of crypto exchanges.This study develops an integrated framework,i.e.,fuzzy best-worst method with the Heronian function—the fuzzy measurement of alternatives and ranking according to compromise solution with the Heronian function(FBWM’H-FMARCOS’H),to evaluate cryptocurrency exchanges.In this framework,the fuzzy best-worst method(FBWM)is used to decide the criteria’s importance,fuzzy measurement of alternatives and ranking according to compromise solution(FMARCOS)is used to prioritize the alternatives,and the Heronian function is used to aggregate the results.Integrating a modified FBWM and FMARCOS with Heronian functions is particularly appealing for group decision-making under vagueness.Through case studies,some well-known cryptocurrency exchanges operating in Türkiye are assessed based on seven critical factors in the cryptocurrency exchange evaluation process.The main contribution of this study is generating new priority strategies to increase the performance of crypto exchanges with a novel decision-making methodology.“Perception of security,”“reputation,”and“commission rate”are found as the foremost factors in choosing an appropriate cryptocurrency exchange for investment.Further,the best score is achieved by Coinbase,followed by Binance.The solidity and flexibility of the methodology are also supported by sensitivity and comparative analyses.The findings may pave the way for investors to take appropriate actions without incurring high costs.
文摘This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities.We build a theoretical model for the reporting decision structure of a private bank or cryptocurrency exchange and show that an inferior ability to detect money laundering(ML)increases the ratio of reported transactions to unreported transactions.If a representative money launderer makes an optimal portfolio choice,then this ratio increases further.Our findings suggest that cryptocurrency exchanges will exhibit more excessive reporting behavior under this regulation than private banks.We attribute this result to cryptocurrency exchanges’inferior ML detection abilities and their proximity to the underground economy.
基金National Council for Scientific and Technological Development–CNPq(Grant#313033/2022-6)and the Silicon Valley Community Foundation for providing financial support to conduct this research throughout the University Blockchain Research Initiative(UBRI).
文摘In this study,we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions,selling,or acceptance as a means of payment.Our analysis focuses on traditional firms whose core business is unrelated to blockchain or cryptocurrency.We find that the aggregate market reaction around these events is slightly positive but statistically insignificant for most event windows.However,when we perform heterogeneity analyses,we observe significant differences in market reaction between events with high(larger CARs)and low cryptocurrency exposure(lower CARs).Multivariate regressions show that the level of exposure to cryptocurrency("skin in the game")is a critical factor underlying abnormal returns around the event.Further analyses reveal that economically meaningful acquisitions of BTC or ETH(relative to firm’s total assets)drive the observed effect.Our findings have important implications for managers,investors,and analysts as they shed light on the relationship between cryptocurrency adoption and firm value.
文摘Many types of cryptocurrencies,which predominantly utilize blockchain technology,have emerged worldwide.Several issuers plan to circulate their original cryptocurrencies for monetary use.This study investigates whether issuers can stimulate cryptocurrencies to attain a monetary function.We use a multi-agent model,referred to as the Yasutomi model,which simulates the emergence of money.We analyze two scenarios that may result from the actions taken by the issuer.These scenarios focus on increases in the number of stores that accept cryptocurrency payments and situations whereby the cryptocurrency issuer designs the cryptocurrency to be attractive to people and conducts an airdrop.We find that a cryptocurrency can attain a monetary function in two cases.One such case occurs when 20%of all agents accept the cryptocurrency for payment and 50%of the agents are aware of this fact.The second case occurs when the issuer continuously airdrops a cryptocurrency to a specific person while maintaining the total volume of the cryptocurrency within a range that prevents it from losing its attractiveness.
文摘In recent years,the tendency of the number of financial institutions to include crypto-currencies in their portfolios has accelerated.Cryptocurrencies are the first pure digital assets to be included by asset managers.Although they have some commonalities with more traditional assets,they have their own separate nature and their behaviour as an asset is still in the process of being understood.It is therefore important to summarise existing research papers and results on cryptocurrency trading,including available trading platforms,trading signals,trading strategy research and risk management.This paper provides a comprehensive survey of cryptocurrency trading research,by covering 146 research papers on various aspects of cryptocurrency trading(e.g.,cryptocurrency trading systems,bubble and extreme condition,prediction of volatility and return,crypto-assets portfolio construction and crypto-assets,technical trading and others).This paper also analyses datasets,research trends and distribution among research objects(contents/properties)and technologies,concluding with some promising opportunities that remain open in cryptocurrency trading.
基金Funding was provided by EIT Digital(Grant no 825215)European Cooperation in Science and Technology(COST Action 19130).
文摘Since the emergence of Bitcoin,cryptocurrencies have grown significantly,not only in terms of capitalization but also in number.Consequently,the cryptocurrency market can be a conducive arena for investors,as it offers many opportunities.However,it is difficult to understand.This study aims to describe,summarize,and segment the main trends of the entire cryptocurrency market in 2018,using data analysis tools.Accord-ingly,we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies,and one that looks for associations between the clustering results,and other factors that are not involved in clustering.Particularly,the methodology involves applying three different partitional clustering algorithms,where each of them use a different representation for cryptocurrencies,namely,yearly mean,and standard deviation of the returns,distribution of returns that have not been applied to financial markets previously,and the time series of returns.Because each representation provides a different outlook of the market,we also examine the integration of the three clustering results,to obtain a fine-grained analysis of the main trends of the market.In conclusion,we analyze the association of the clustering results with other descriptive features of cryptocurrencies,including the age,technological attributes,and financial ratios derived from them.This will help to enhance the profiling of the clusters with additional descriptive insights,and to find associations with other variables.Consequently,this study describes the whole market based on graphical information,and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly,and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period,we found that the market can be typically segmented in few clusters(five or less),and even considering the intersections,the 6 more populations account for 75%of the market.Regarding the associations between the clusters and descriptive features,we find associations between some clusters with volume,market capitalization,and some financial ratios,which could be explored in future research.
基金supported in part by the National Natural Science Foundation of China (62272078)the CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2021-035A)the Doctoral Student Talent Training Program of Chongqing University of Posts and Telecommunications (BYJS202009)。
文摘Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.
文摘The importance of cryptocurrency to the global economy is increasing steadily,which is evidenced by a total market capitalization of over$2.18T as of December 17,2021,according to coinmarketcap.com(Coin,2021).Cryptocurrencies are too confusing for laymen and require more investigation.In this study,we analyze the impact that the effective reproductive rate,an epidemiological indicator of the spread of COVID-19,has on both the price and trading volume of eight of the largest digital currencies—Bitcoin,Ethereum,Tether,Ripple,Litecoin,Bitcoin Cash,Cardano,and Binance.We hypothesize that as the rate of spread decreases,the trading price of the digital currency increases.Using Generalized Autoregressive Conditional Heteroskedasticity models,we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region.These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies.
文摘In recent years,cryptocurrency has become gradually more significant in economic regions worldwide.In cryptocurrencies,records are stored using a cryptographic algorithm.The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media.Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels.Therefore,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet sentiments.This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and Ethereum.The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.
基金support from the Fundamental Research Funds for the Central Universities(22D110913)Jingzhou Yan gratefully acknowledges the financial support from the National Social Science Foundation Youth Project(21CTJ013)+1 种基金Natural Science Foundation of Sichuan Province(23NSFSC2796)Fundamental Research Funds for the Central Universities,Postdoctoral Research Foundation of Sichuan University(Skbsh2202-18).
文摘In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.
基金financial support from the Czech Science Foundation under the 20-17295S“Cryptoassets:Pricing,Interconnectedness,Mining,and their Interactions”project and from the Charles University PRIMUS program(project PRIMUS/19/HUM/17)Jiri Kukacka gratefully acknowledges financial support from the Charles University UNCE program(project UNCE/HUM/035)supported by the Cooperatio Program at Charles University,research area Economics.
文摘The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in the price-formation process.This study examines five cryptoassets with different backgrounds,namely Bitcoin,Ethereum,Litecoin,XRP,and Dogecoin between 2016 and 2022.It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events.The findings show that the price and return dynamics of all the studied assets,except for Dogecoin,emerge from complex interactions between fundamental and speculative components,includ-ing episodes of price bifurcations.Bitcoin shows the strongest fundamentals,with on-chain activity and economic factors driving the fundamental part of the dynam-ics.Investor attention and off-chain activity drive the speculative component for all studied assets.Among the fundamental drivers,the analyzed cryptoassets present their coin-specific factors,which can be tracked to their protocol specifics and are economi-cally sound.
基金funding agencies in the public,commercial,or notfor-profit sectors.
文摘Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.
