{"title":"加密资产市场存在价值溢价吗?","authors":"Luca J. Liebi","doi":"10.2139/ssrn.3718684","DOIUrl":null,"url":null,"abstract":"Investigating a collection of 652 cryptoassets, I find that cryptoasset returns increase with increasing active addresses to network value ratio, a proxy for the value anomaly. Cryptoassets with a high active address to network value ratio yield on average 2.1 percentage points higher weekly returns compared to cryptoassets with low active addresses to network value ratio, and comparable size. Fama-Macbeth regressions indicate that the active addresses to network value ratio, combined with size, and momentum capture the cross-sectional variation of cryptoasset returns. Adding the value factor to existing factor models helps to explain average cryptoasset returns.","PeriodicalId":319022,"journal":{"name":"Economics of Networks eJournal","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Is There a Value Premium in Cryptoasset Markets?\",\"authors\":\"Luca J. Liebi\",\"doi\":\"10.2139/ssrn.3718684\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Investigating a collection of 652 cryptoassets, I find that cryptoasset returns increase with increasing active addresses to network value ratio, a proxy for the value anomaly. Cryptoassets with a high active address to network value ratio yield on average 2.1 percentage points higher weekly returns compared to cryptoassets with low active addresses to network value ratio, and comparable size. Fama-Macbeth regressions indicate that the active addresses to network value ratio, combined with size, and momentum capture the cross-sectional variation of cryptoasset returns. Adding the value factor to existing factor models helps to explain average cryptoasset returns.\",\"PeriodicalId\":319022,\"journal\":{\"name\":\"Economics of Networks eJournal\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Economics of Networks eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3718684\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Economics of Networks eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3718684","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigating a collection of 652 cryptoassets, I find that cryptoasset returns increase with increasing active addresses to network value ratio, a proxy for the value anomaly. Cryptoassets with a high active address to network value ratio yield on average 2.1 percentage points higher weekly returns compared to cryptoassets with low active addresses to network value ratio, and comparable size. Fama-Macbeth regressions indicate that the active addresses to network value ratio, combined with size, and momentum capture the cross-sectional variation of cryptoasset returns. Adding the value factor to existing factor models helps to explain average cryptoasset returns.