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
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
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
The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets.This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold a...The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets.This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19.Through the application of various statistical techniques,including cointegration tests,vector autoregressive models,vector error correction models,autoregressive distributed lag models,and Granger causality analyses,we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies.Our findings reveal that during the COVID-19 pandemic,gold is a strong safe-haven for Bitcoin,Litecoin,and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash,EOS,Chainlink,and Cardano.In contrast,gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero.Additionally,Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19,while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether,Bitcoin Cash,EOS,and Monero.Furthermore,the Granger causality analysis indicates that before the COVID-19 pandemic,the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets;however,during the COVID-19 period,the direction of causality shifted,with cryptocurrencies exerting influence on the gold and crude oil markets.These findings provide subtle implications for policymakers,hedge fund managers,and individual or institutional cryptocurrency investors.Our results highlight the need to adapt risk exposure strategies during financial turmoil,such as the crisis precipitated by the COVID-19 pandemic.展开更多
The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment,economic conditions,and economic uncertainty can p...The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment,economic conditions,and economic uncertainty can predict tail risk interconnectedness.Combining the Conditional Autoregressive Value-at-Risk(CAViaR)model with the time-varying parameter vector autoregressive(TVP-VAR)approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time.During crises and significant events,transmission bursts and tail risks change.Based on both in-and out-of-sample forecasts,we find that the information contained in investor sentiment,economic conditions,and uncertainty includes significant predictive content about the tail risk connectedness of cryptocurrencies.展开更多
Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusia...Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors?Using natural language processing techniques applied to Twitter data,this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors.The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors.In particular,cryptocurrency enthusiasts’tweets became more neutral and,surprisingly,less negative.This result appears to be primarily driven by a deliberate,collectivist effort to promote positivity within the cryptocurrency community(“wagmi”).Considering the more nuanced emotional content of tweets,it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors.Moreover,cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash,with a relative increase in tweet frequency of approximately one tweet per day.An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.展开更多
This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries,addressing concerns about potential spillover effect...This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries,addressing concerns about potential spillover effects,especially the high volatility and lack of regulation in the cryptocurrency market.The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets.A daily time series dataset from January 1,2017,to December 31,2021,was employed to analyze the contagion effect.Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market;however,the percentage spillover effect is still low but growing.Specifically,evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets,at least in the short run.Evidence in South Africa,Nigeria,and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market.Similarly,we found no evidence of a spillover effect from the African financial market to the cryptocurrency market.The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market.Specifically,the DCC-GARCH revealed a greater integration in both markets,especially in the long run.The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.展开更多
Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to ...Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to estimate implied volatility.Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy.The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index(RSI)covering multiple time resolutions as a factor,as momentum is often used by investors and speculators in their trading decisions,and in contrast to volatility,RSI can distinguish between bull and bear markets.To the best of our knowledge,prior studies have not included RSI as a predictive factor in modeling IV.Instead of using a simple linear regression as in previous studies,we use a machine learning regression algorithm,namely random forest,to model a nonlinear IV.Previous studies apply DVIF modeling to options on traditional financial assets,such as stock and foreign exchange markets.Here,we study options on the largest cryptocurrency,Bitcoin,which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets.Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation.Our dataset includes short-maturity options with expiry in less than six days,as well as a full range of moneyness,both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building.Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options.The nonlinear machine learning random forest algorithm also performed better than a simple linear regression.Compared to prevailing option pricing models that employ stochastic variables,our DIVF model does not include stochastic factors but exhibits reasonably good performance.It is also easy to compute due to the availability of real-time RSIs.Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.展开更多
Turbulent market conditions,well-publicized advantages,and potential individual,social,and environmental risks make blockchain-based cryptocurrencies a popular focus of the public and scientific communities.This paper...Turbulent market conditions,well-publicized advantages,and potential individual,social,and environmental risks make blockchain-based cryptocurrencies a popular focus of the public and scientific communities.This paper contributes to the literature on the future of crypto markets by analyzing a promising cryptocurrency innovation from a customer-centric point of view;it explores the factors influencing user acceptance of a hypothetical social network-backed cryptocurrency in Central Europe.The research model adapts an internationally comparative framework and extends the well-established unified theory of acceptance and use of the technology model with the concept of perceived risk and trust.We explore user attitudes with a survey on a large Hungarian sample and analyze the database with consistent partial least square structural equation modeling methodology.The results show that users would be primarily influenced by the expected usefulness of the new technology assuming it is easy to use.Furthermore,our analysis also highlights that while social influence does not seem to sway user opinions,consumers are susceptible to technological risks,and trust is an important determinant of their openness toward innovations in financial services.We contribute to the cryptocurrency literature with a future-centric technological focus and provide new evidence from an under-researched geographic region.The results also have practical implications for business decision-makers and policymakers.展开更多
The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility.The study uses high-frequency panel data from 2020 to 2022 to examine...The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility.The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers,such as daily leverage,signed volatility and jumps.Several known autoregressive model specifications are estimated over different market regimes,and results are compared to equity data as a reference benchmark of a more mature asset class.The panel estimations show that the positive market returns at the high-frequency level increase price volatility,contrary to what is expected from the classical financial literature.We attributed this effect to the price dynamics over the last year of the dataset(2022)by repeating the estimation on different time spans.Moreover,the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’future volatility,unlike what emerges from the same study on a cross-section of stocks.This result signals a structural difference in a nascent cryptocurrency market that has to mature yet.Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.展开更多
This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market,using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1,...This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market,using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1,2017 to July 1,2022.The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods.Additionally,we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns.However,when positive investor sentiment prevails in the cryptocurrency market,the holiday effect weakens,implying that positive investor sentiment attenuates the holiday effect.Robustness tests based on the Bitcoin market generate consistent results.Moreover,this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon.These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market.展开更多
As the crypto-asset ecosystem matures,the use of high-frequency data has become increasingly common in decentralized finance literature.Using bibliometric analysis,we characterize the existing cryptocurrency literatur...As the crypto-asset ecosystem matures,the use of high-frequency data has become increasingly common in decentralized finance literature.Using bibliometric analysis,we characterize the existing cryptocurrency literature that employs high-frequency data.We highlighted the most influential authors,articles,and journals based on 189 articles from the Scopus database from 2015 to 2022.This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses.It shows knowledge expansion through authors’collaboration in cryptocurrency research with co-authorship analysis.We identify four major streams of research:(i)return prediction and measurement of cryptocurrency volatility,(ii)(in)efficiency of cryptocurrencies,(iii)price dynamics and bubbles in cryptocurrencies,and(iv)the diversification,safe haven,and hedging properties of Bitcoin.We conclude that highly traded cryptocurrencies’investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis.This study also provides recommendations for future studies.展开更多
This study investigated the extent of currency competition within the cryptocurrency market through the Hayek’s concept of the denationalization of money.Hayek’s original analysis primarily centered on competition r...This study investigated the extent of currency competition within the cryptocurrency market through the Hayek’s concept of the denationalization of money.Hayek’s original analysis primarily centered on competition revolving around the medium of the exchange function.This study posited that cryptocurrencies compete across diverse monetary functions,particularly concerning their roles as speculative stores of value and exchange media.This assertion provided insight into the distinction between Hayek’s envisaged private currencies and the cryptocurrency paradigm.Utilizing an extensive dataset encompassing 101 cryptocurrencies spanning from 2016 to 2022,an empirical exploration was conducted to scrutinize the progression and intensity of competition within the broader cryptocurrency market and its submarkets.These findings reveal a robust competition among unpegged cryptocurrencies,predominantly contending for speculative investment purposes.Similarly,there is pronounced competition among stablecoins as stable stores of value.In contrast,competition is much less pronounced concerning the medium of the exchange function,potentially entailing network effects and the emergence of monopolistic tendencies within this specific submarket.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
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.
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.
基金the financial support of the Chaire Fintech AMF—Finance Montréal,Canada.Contract number 0007.
文摘The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets.This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19.Through the application of various statistical techniques,including cointegration tests,vector autoregressive models,vector error correction models,autoregressive distributed lag models,and Granger causality analyses,we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies.Our findings reveal that during the COVID-19 pandemic,gold is a strong safe-haven for Bitcoin,Litecoin,and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash,EOS,Chainlink,and Cardano.In contrast,gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero.Additionally,Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19,while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether,Bitcoin Cash,EOS,and Monero.Furthermore,the Granger causality analysis indicates that before the COVID-19 pandemic,the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets;however,during the COVID-19 period,the direction of causality shifted,with cryptocurrencies exerting influence on the gold and crude oil markets.These findings provide subtle implications for policymakers,hedge fund managers,and individual or institutional cryptocurrency investors.Our results highlight the need to adapt risk exposure strategies during financial turmoil,such as the crisis precipitated by the COVID-19 pandemic.
文摘The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment,economic conditions,and economic uncertainty can predict tail risk interconnectedness.Combining the Conditional Autoregressive Value-at-Risk(CAViaR)model with the time-varying parameter vector autoregressive(TVP-VAR)approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time.During crises and significant events,transmission bursts and tail risks change.Based on both in-and out-of-sample forecasts,we find that the information contained in investor sentiment,economic conditions,and uncertainty includes significant predictive content about the tail risk connectedness of cryptocurrencies.
文摘Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors?Using natural language processing techniques applied to Twitter data,this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors.The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors.In particular,cryptocurrency enthusiasts’tweets became more neutral and,surprisingly,less negative.This result appears to be primarily driven by a deliberate,collectivist effort to promote positivity within the cryptocurrency community(“wagmi”).Considering the more nuanced emotional content of tweets,it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors.Moreover,cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash,with a relative increase in tweet frequency of approximately one tweet per day.An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.
文摘This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries,addressing concerns about potential spillover effects,especially the high volatility and lack of regulation in the cryptocurrency market.The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets.A daily time series dataset from January 1,2017,to December 31,2021,was employed to analyze the contagion effect.Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market;however,the percentage spillover effect is still low but growing.Specifically,evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets,at least in the short run.Evidence in South Africa,Nigeria,and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market.Similarly,we found no evidence of a spillover effect from the African financial market to the cryptocurrency market.The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market.Specifically,the DCC-GARCH revealed a greater integration in both markets,especially in the long run.The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.
文摘Modeling implied volatility(IV)is important for option pricing,hedging,and risk management.Previous studies of deterministic implied volatility functions(DIVFs)propose two parameters,moneyness and time to maturity,to estimate implied volatility.Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy.The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index(RSI)covering multiple time resolutions as a factor,as momentum is often used by investors and speculators in their trading decisions,and in contrast to volatility,RSI can distinguish between bull and bear markets.To the best of our knowledge,prior studies have not included RSI as a predictive factor in modeling IV.Instead of using a simple linear regression as in previous studies,we use a machine learning regression algorithm,namely random forest,to model a nonlinear IV.Previous studies apply DVIF modeling to options on traditional financial assets,such as stock and foreign exchange markets.Here,we study options on the largest cryptocurrency,Bitcoin,which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets.Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation.Our dataset includes short-maturity options with expiry in less than six days,as well as a full range of moneyness,both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building.Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options.The nonlinear machine learning random forest algorithm also performed better than a simple linear regression.Compared to prevailing option pricing models that employ stochastic variables,our DIVF model does not include stochastic factors but exhibits reasonably good performance.It is also easy to compute due to the availability of real-time RSIs.Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.
文摘Turbulent market conditions,well-publicized advantages,and potential individual,social,and environmental risks make blockchain-based cryptocurrencies a popular focus of the public and scientific communities.This paper contributes to the literature on the future of crypto markets by analyzing a promising cryptocurrency innovation from a customer-centric point of view;it explores the factors influencing user acceptance of a hypothetical social network-backed cryptocurrency in Central Europe.The research model adapts an internationally comparative framework and extends the well-established unified theory of acceptance and use of the technology model with the concept of perceived risk and trust.We explore user attitudes with a survey on a large Hungarian sample and analyze the database with consistent partial least square structural equation modeling methodology.The results show that users would be primarily influenced by the expected usefulness of the new technology assuming it is easy to use.Furthermore,our analysis also highlights that while social influence does not seem to sway user opinions,consumers are susceptible to technological risks,and trust is an important determinant of their openness toward innovations in financial services.We contribute to the cryptocurrency literature with a future-centric technological focus and provide new evidence from an under-researched geographic region.The results also have practical implications for business decision-makers and policymakers.
文摘The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility.The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers,such as daily leverage,signed volatility and jumps.Several known autoregressive model specifications are estimated over different market regimes,and results are compared to equity data as a reference benchmark of a more mature asset class.The panel estimations show that the positive market returns at the high-frequency level increase price volatility,contrary to what is expected from the classical financial literature.We attributed this effect to the price dynamics over the last year of the dataset(2022)by repeating the estimation on different time spans.Moreover,the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’future volatility,unlike what emerges from the same study on a cross-section of stocks.This result signals a structural difference in a nascent cryptocurrency market that has to mature yet.Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.
基金One of the authors(Jian Huang)received research support from Towson University,for this research.
文摘This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market,using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1,2017 to July 1,2022.The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods.Additionally,we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns.However,when positive investor sentiment prevails in the cryptocurrency market,the holiday effect weakens,implying that positive investor sentiment attenuates the holiday effect.Robustness tests based on the Bitcoin market generate consistent results.Moreover,this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon.These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market.
文摘As the crypto-asset ecosystem matures,the use of high-frequency data has become increasingly common in decentralized finance literature.Using bibliometric analysis,we characterize the existing cryptocurrency literature that employs high-frequency data.We highlighted the most influential authors,articles,and journals based on 189 articles from the Scopus database from 2015 to 2022.This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses.It shows knowledge expansion through authors’collaboration in cryptocurrency research with co-authorship analysis.We identify four major streams of research:(i)return prediction and measurement of cryptocurrency volatility,(ii)(in)efficiency of cryptocurrencies,(iii)price dynamics and bubbles in cryptocurrencies,and(iv)the diversification,safe haven,and hedging properties of Bitcoin.We conclude that highly traded cryptocurrencies’investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis.This study also provides recommendations for future studies.
文摘This study investigated the extent of currency competition within the cryptocurrency market through the Hayek’s concept of the denationalization of money.Hayek’s original analysis primarily centered on competition revolving around the medium of the exchange function.This study posited that cryptocurrencies compete across diverse monetary functions,particularly concerning their roles as speculative stores of value and exchange media.This assertion provided insight into the distinction between Hayek’s envisaged private currencies and the cryptocurrency paradigm.Utilizing an extensive dataset encompassing 101 cryptocurrencies spanning from 2016 to 2022,an empirical exploration was conducted to scrutinize the progression and intensity of competition within the broader cryptocurrency market and its submarkets.These findings reveal a robust competition among unpegged cryptocurrencies,predominantly contending for speculative investment purposes.Similarly,there is pronounced competition among stablecoins as stable stores of value.In contrast,competition is much less pronounced concerning the medium of the exchange function,potentially entailing network effects and the emergence of monopolistic tendencies within this specific submarket.
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.
