{"title":"加密货币清洗交易:直接vs.间接估计","authors":"Brett Hemenway Falk, Gerry Tsoukalas, Niuniu Zhang","doi":"arxiv-2311.18717","DOIUrl":null,"url":null,"abstract":"Recent studies using indirect statistical methods estimate that around 70% of\ntraded value on centralized crypto exchanges like Binance, can be characterized\nas wash trading. This paper turns to NFT markets, where transaction\ntransparency, including analysis of roundtrip trades and common wallet\nactivities, allows for more accurate direct estimation methods to be applied.\nWe find roughly 30% of NFT volume and between 45-95% of traded value, involve\nwash trading. More importantly, our approach enables a critical evaluation of\ncommon indirect estimation methods used in the literature. We find major\ndifferences in their effectiveness; some failing entirely. Roundedness filters,\nlike those used in Cong et al. (2023), emerge as the most accurate. In fact,\nthe two approaches can be closely aligned via hyper-parameter optimization if\ndirect data is available.","PeriodicalId":501487,"journal":{"name":"arXiv - QuantFin - Economics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Crypto Wash Trading: Direct vs. Indirect Estimation\",\"authors\":\"Brett Hemenway Falk, Gerry Tsoukalas, Niuniu Zhang\",\"doi\":\"arxiv-2311.18717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent studies using indirect statistical methods estimate that around 70% of\\ntraded value on centralized crypto exchanges like Binance, can be characterized\\nas wash trading. This paper turns to NFT markets, where transaction\\ntransparency, including analysis of roundtrip trades and common wallet\\nactivities, allows for more accurate direct estimation methods to be applied.\\nWe find roughly 30% of NFT volume and between 45-95% of traded value, involve\\nwash trading. More importantly, our approach enables a critical evaluation of\\ncommon indirect estimation methods used in the literature. We find major\\ndifferences in their effectiveness; some failing entirely. Roundedness filters,\\nlike those used in Cong et al. (2023), emerge as the most accurate. In fact,\\nthe two approaches can be closely aligned via hyper-parameter optimization if\\ndirect data is available.\",\"PeriodicalId\":501487,\"journal\":{\"name\":\"arXiv - QuantFin - Economics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2311.18717\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2311.18717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Crypto Wash Trading: Direct vs. Indirect Estimation
Recent studies using indirect statistical methods estimate that around 70% of
traded value on centralized crypto exchanges like Binance, can be characterized
as wash trading. This paper turns to NFT markets, where transaction
transparency, including analysis of roundtrip trades and common wallet
activities, allows for more accurate direct estimation methods to be applied.
We find roughly 30% of NFT volume and between 45-95% of traded value, involve
wash trading. More importantly, our approach enables a critical evaluation of
common indirect estimation methods used in the literature. We find major
differences in their effectiveness; some failing entirely. Roundedness filters,
like those used in Cong et al. (2023), emerge as the most accurate. In fact,
the two approaches can be closely aligned via hyper-parameter optimization if
direct data is available.