{"title":"The Structure of Cryptocurrency Returns","authors":"Amin Shams","doi":"10.2139/ssrn.3604322","DOIUrl":null,"url":null,"abstract":"This paper documents a persistent structure in cryptocurrency returns and analyzes a broad set of characteristics that explain this structure. The results show that similarities in size, trading volume, age, consensus mechanism, and token industries drive the structure of cryptocurrency returns. But the highest variation is explained by a “connectivity” measure that proxies for similarity in cryptocurrencies’ investor bases using their trading location. Currencies connected to other currencies that perform well generate sizably higher returns than the cross-section both contemporaneously and in the future. I examine three potential channels for these results. First, evidence from new exchange listings and a quasi-natural experiment shows that unobservable characteristics cannot explain the effect of connectivity. Second, decomposition of the order flows suggests that connectivity captures strong exchange-specific commonalities in crypto investors’ demand that also spills over to other exchanges. Finally, analysis of social media data suggests that these demand shocks are a first order driver of cryptocurrency returns, largely because they can be perceived as a sign of user adoption.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"16 7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3604322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
This paper documents a persistent structure in cryptocurrency returns and analyzes a broad set of characteristics that explain this structure. The results show that similarities in size, trading volume, age, consensus mechanism, and token industries drive the structure of cryptocurrency returns. But the highest variation is explained by a “connectivity” measure that proxies for similarity in cryptocurrencies’ investor bases using their trading location. Currencies connected to other currencies that perform well generate sizably higher returns than the cross-section both contemporaneously and in the future. I examine three potential channels for these results. First, evidence from new exchange listings and a quasi-natural experiment shows that unobservable characteristics cannot explain the effect of connectivity. Second, decomposition of the order flows suggests that connectivity captures strong exchange-specific commonalities in crypto investors’ demand that also spills over to other exchanges. Finally, analysis of social media data suggests that these demand shocks are a first order driver of cryptocurrency returns, largely because they can be perceived as a sign of user adoption.