Information Environments and High Price Impact Trades: Implication for Volatility and Price Efficiency

Georges Dionne () and Xiaozhou Zhou ()
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Xiaozhou Zhou: Université du Québec à Montréal (UQAM), Postal: Finance Department, ESG UQAM, https://professeurs.uqam.ca/professeur/zhou.xiaozhou/

No 19-3, Working Papers from HEC Montreal, Canada Research Chair in Risk Management

Abstract: We include trade matchedness to Limit Order Book (LOB) as an extended identification dimension of High Price Impact Trades (HPITs). HPITs are trades associated with disproportionately large price changes in comparison with their volume proportion and are related to informed trades. We show that the introduction of matchedness provides finer informed trading identification, and we find that a stronger presence of HPITs leads to a decline in volatility due to more contrarian informed trades, but this decline varies with information environments. Finally, we conclude that HPITs mainly reflect belief-based (fundamental-based) information in a high (low) public disclosure environment.

Keywords: Price efficiency; Price discovery; Limit Order Book; Trade size clustering; Stealth trading (search for similar items in EconPapers)
JEL-codes: C22 C41 C53 G11 (search for similar items in EconPapers)
Pages: 67 pages
Date: 2019-06-18, Revised 2019-11-04
New Economics Papers: this item is included in nep-mst and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:crcrmw:2019_003

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