{"title":"测试大体积分类算法","authors":"Allen Carrion, Madhuparna Kolay","doi":"10.2139/ssrn.3746731","DOIUrl":null,"url":null,"abstract":"We document that the existing evidence that bulk volume trade classification (BVC) measures informed trading arises largely due to mis-specified tests. Simulations show that these tests detect spurious relationships in data containing only uninformed liquidity trades. We also assess the performance of BVC order imbalances in the NASDAQ HFT dataset, showing that BVC order imbalances underperform conventional order imbalance measures in detecting informed trading. The component of order flow designated by BVC as passive informed trading fails to predict returns with the correct sign. On balance, our evidence supports the use of conventional order imbalance measures to identify informed trading.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Testing the Bulk Volume Classification Algorithm\",\"authors\":\"Allen Carrion, Madhuparna Kolay\",\"doi\":\"10.2139/ssrn.3746731\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We document that the existing evidence that bulk volume trade classification (BVC) measures informed trading arises largely due to mis-specified tests. Simulations show that these tests detect spurious relationships in data containing only uninformed liquidity trades. We also assess the performance of BVC order imbalances in the NASDAQ HFT dataset, showing that BVC order imbalances underperform conventional order imbalance measures in detecting informed trading. The component of order flow designated by BVC as passive informed trading fails to predict returns with the correct sign. On balance, our evidence supports the use of conventional order imbalance measures to identify informed trading.\",\"PeriodicalId\":18611,\"journal\":{\"name\":\"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"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.3746731\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3746731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We document that the existing evidence that bulk volume trade classification (BVC) measures informed trading arises largely due to mis-specified tests. Simulations show that these tests detect spurious relationships in data containing only uninformed liquidity trades. We also assess the performance of BVC order imbalances in the NASDAQ HFT dataset, showing that BVC order imbalances underperform conventional order imbalance measures in detecting informed trading. The component of order flow designated by BVC as passive informed trading fails to predict returns with the correct sign. On balance, our evidence supports the use of conventional order imbalance measures to identify informed trading.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
