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
This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with t...This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with two benchmark models:the multivariate t copula and the dynamic conditional correlation(DCC)GARCH model.Moreover,this study examines whether the Bitcoin meltdown of 2013,selloff of 2018,COVID-19 pandemic,2021 crash,and the Russia-Ukraine conflict impact the linkage with conventional currencies.The results indicate that for both currency baskets,R-vine beats the benchmark models.Hence,the dependence is better modeled by providing sufficient information on the shock transmission path.Furthermore,the cross-market linkage slightly increases during the Bitcoin crashes,and reaches significant levels during the 2021 and 2022 crises,which may indicate the end of market isolation of the virtual currency.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 fo展开更多
The valuation of cryptocurrencies is important given the increasing significance of this potential asset class.However,most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factor...The valuation of cryptocurrencies is important given the increasing significance of this potential asset class.However,most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources.In this study,a robust cryptocurrency valuation method is developed using up-to-date datasets.Using various panel regression models and moving-window regression tests,the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1,2009 to April 30,2023.The research shows the importance of sentiments and suggests that the fear and greed index can indicate when to make a cryptocurrency investment,while Google search interest in cryptocurrency is crucial when choosing the appropriate type of cryptocurrency.Moreover,consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins,while their impacts on cryptocurrencies with stablecoins are insignificant.Other fundamental factors,such as the type of supply and the presence of smart contracts,do not have a significant influence on cryptocurrency.Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors,portfolio managers,and policymakers in assessing the utility level of each cryptocurrency.展开更多
Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets con...Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.展开更多
Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavi...Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.展开更多
A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions.Given the anonymity of a user of a crypt...A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions.Given the anonymity of a user of a cryptocurrency,such digital currencies may be used for many different types of both lawful and illicit activities.The main purpose of this paper is to examine the extent to which ethical considerations associated with the use of cryptocurrencies affect the valuations attached to such currencies.The examination is based on a text analytic approach that involves measuring the extent to which ethical and unethical words are used in a discussion related to Bitcoin on Twitter to determine if there is a connection between ethics and cryptocurrency valuations.We find the frequency of an unethical discussion about Bitcoin is negatively associated with its price.In contrast,the frequency of an ethical discussion is positively associated with its price.展开更多
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This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with two benchmark models:the multivariate t copula and the dynamic conditional correlation(DCC)GARCH model.Moreover,this study examines whether the Bitcoin meltdown of 2013,selloff of 2018,COVID-19 pandemic,2021 crash,and the Russia-Ukraine conflict impact the linkage with conventional currencies.The results indicate that for both currency baskets,R-vine beats the benchmark models.Hence,the dependence is better modeled by providing sufficient information on the shock transmission path.Furthermore,the cross-market linkage slightly increases during the Bitcoin crashes,and reaches significant levels during the 2021 and 2022 crises,which may indicate the end of market isolation of the virtual currency.
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 fo
文摘The valuation of cryptocurrencies is important given the increasing significance of this potential asset class.However,most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources.In this study,a robust cryptocurrency valuation method is developed using up-to-date datasets.Using various panel regression models and moving-window regression tests,the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1,2009 to April 30,2023.The research shows the importance of sentiments and suggests that the fear and greed index can indicate when to make a cryptocurrency investment,while Google search interest in cryptocurrency is crucial when choosing the appropriate type of cryptocurrency.Moreover,consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins,while their impacts on cryptocurrencies with stablecoins are insignificant.Other fundamental factors,such as the type of supply and the presence of smart contracts,do not have a significant influence on cryptocurrency.Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors,portfolio managers,and policymakers in assessing the utility level of each cryptocurrency.
基金supported by the European Union’s H2020 Coordination and Support Actions CA19130 under Grant Agreement Period 2.
文摘Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.
基金provided by Marie Sklodowska-Curie ITN Horizon 2020-funded project INSIGHTS(call H2020-MSCA-ITN-2017,grant agreement n.765710)NWO—Nederlandse Organisatie voor Wetenschappelijk Onderzoek(Award Number:KIVI.2019.006 HUMAINER AI project)。
文摘Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.
文摘A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions.Given the anonymity of a user of a cryptocurrency,such digital currencies may be used for many different types of both lawful and illicit activities.The main purpose of this paper is to examine the extent to which ethical considerations associated with the use of cryptocurrencies affect the valuations attached to such currencies.The examination is based on a text analytic approach that involves measuring the extent to which ethical and unethical words are used in a discussion related to Bitcoin on Twitter to determine if there is a connection between ethics and cryptocurrency valuations.We find the frequency of an unethical discussion about Bitcoin is negatively associated with its price.In contrast,the frequency of an ethical discussion is positively associated with its price.
