{"title":"Intraday Volume-Return Nexus in Cryptocurrency Markets: A Novel Evidence From Cryptocurrency Classification","authors":"L. Yarovaya, D. Zięba","doi":"10.2139/ssrn.3711667","DOIUrl":null,"url":null,"abstract":"This paper analyses the volume-return relationships across top 30 most traded cryptocurrencies from the April 2013 to June 2019 using a high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which cryptocurrency built on, consensus algorithm, to name but a few. We identified significant bidirectional causalities between trading volume and returns at high-frequency intervals, however, those linkages are disappearing with increased frequencies of data. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency market providing useful insights from cryptocurrency categorisation.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Asset Pricing Models (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3711667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This paper analyses the volume-return relationships across top 30 most traded cryptocurrencies from the April 2013 to June 2019 using a high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which cryptocurrency built on, consensus algorithm, to name but a few. We identified significant bidirectional causalities between trading volume and returns at high-frequency intervals, however, those linkages are disappearing with increased frequencies of data. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency market providing useful insights from cryptocurrency categorisation.