Using high-frequency transaction and Limit Order Book (LOB) data, we extend the identification dimensions of High Price Impact Trades (HPITs) by using LOB matchedness.
HPITs are trades associated with disproportionately large price changes relative to their proportion of volume. Authors find that a higher presence of HPITs leads to a decline in volatility due to more contrarian trades against uninformed traders, but this decline varies with information environments and liquidity levels. Further, they show that more HPITs lead to higher price efficiency for stocks with greater public disclosure and higher liquidity. Their empirical results provide evidence that HPITs mainly reflect fundamental-based information in a high public information environment, and belief-based information in a low public information environment.
Click here to read the article.