Gilbert Fridgen, Roman Kräussl, Orestis Papageorgiou, Alessandro Tugnetti
{"title":"非金融产品的定价动态和羊群行为","authors":"Gilbert Fridgen, Roman Kräussl, Orestis Papageorgiou, Alessandro Tugnetti","doi":"10.1111/eufm.12506","DOIUrl":null,"url":null,"abstract":"This paper analyzes the sales of 875,389 art nonfungible tokens (NFTs) on the Ethereum blockchain to identify the key determinants influencing NFT pricing and market dynamics. We find that market liquidity and trade volume are strong predictors of NFT prices. Contrarily, social media activity negatively correlates with prices. Introducing an artist ranking system, our study reveals a “superstar effect”, with a few artists dominating sales, and herding behaviour within the NFT market.","PeriodicalId":501261,"journal":{"name":"European Financial Management ","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pricing dynamics and herding behaviour of NFTs\",\"authors\":\"Gilbert Fridgen, Roman Kräussl, Orestis Papageorgiou, Alessandro Tugnetti\",\"doi\":\"10.1111/eufm.12506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper analyzes the sales of 875,389 art nonfungible tokens (NFTs) on the Ethereum blockchain to identify the key determinants influencing NFT pricing and market dynamics. We find that market liquidity and trade volume are strong predictors of NFT prices. Contrarily, social media activity negatively correlates with prices. Introducing an artist ranking system, our study reveals a “superstar effect”, with a few artists dominating sales, and herding behaviour within the NFT market.\",\"PeriodicalId\":501261,\"journal\":{\"name\":\"European Financial Management \",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Financial Management \",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/eufm.12506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Financial Management ","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/eufm.12506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper analyzes the sales of 875,389 art nonfungible tokens (NFTs) on the Ethereum blockchain to identify the key determinants influencing NFT pricing and market dynamics. We find that market liquidity and trade volume are strong predictors of NFT prices. Contrarily, social media activity negatively correlates with prices. Introducing an artist ranking system, our study reveals a “superstar effect”, with a few artists dominating sales, and herding behaviour within the NFT market.
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