By studying the trading behavior of particularly aggressive investors, we provide new evidence on the joint determination of trading volume and asset prices that is consistent with the presence of informational advantages such as those theorized by Wang (1994). Using a unique Chinese data set of the most active daily market participants for each stock, we uncover the importance of a specific component of aggregate volume - we demonstrate that volume associated with particularly aggressive investor buying (selling) predicts large positive (negative) abnormal returns around key announcement dates. Critically, an advantage of our data is that we can also directly identify several plausible channels through which such an informational advantage could arise. Specifically, the abnormal returns are largest (in absolute terms) following announcement dates in the presence of aggressive pre-event traders who share the same geographic location as the firms in which they trade, and these effects are the most pronounced for stocks with the lowest analyst coverage or the smallest capitalizations. Further, we also find that particularly active traders located near relevant counterparties in an M&A transaction, new bank loan facility, or a key political change also exhibit informational advantages.
{"title":"Informed Trading Volume and Asset Prices: The Role for Intensive Traders","authors":"C. Lundblad, Zhishu Yang, Qi Zhang","doi":"10.2139/ssrn.2776322","DOIUrl":"https://doi.org/10.2139/ssrn.2776322","url":null,"abstract":"By studying the trading behavior of particularly aggressive investors, we provide new evidence on the joint determination of trading volume and asset prices that is consistent with the presence of informational advantages such as those theorized by Wang (1994). Using a unique Chinese data set of the most active daily market participants for each stock, we uncover the importance of a specific component of aggregate volume - we demonstrate that volume associated with particularly aggressive investor buying (selling) predicts large positive (negative) abnormal returns around key announcement dates. Critically, an advantage of our data is that we can also directly identify several plausible channels through which such an informational advantage could arise. Specifically, the abnormal returns are largest (in absolute terms) following announcement dates in the presence of aggressive pre-event traders who share the same geographic location as the firms in which they trade, and these effects are the most pronounced for stocks with the lowest analyst coverage or the smallest capitalizations. Further, we also find that particularly active traders located near relevant counterparties in an M&A transaction, new bank loan facility, or a key political change also exhibit informational advantages.","PeriodicalId":224284,"journal":{"name":"Second Annual Volatility Institute at NYU Shanghai (VINS) Conference - 2016 (Archive)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116643827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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