Time and the Price Impact of a Trade

We use Hasbrouck (1991)'s vector autoregressive model for prices and trades to empirically test and assess the role played by the waiting time between consecutive transactions in the process of price formation. We find that as the time duration between transactions decreases, the price impact of trades, the speed of price adjustment to trade related information, and the positive autocorrelation of signed trades, all increase. This suggests that times when markets are most active are times when there is an increased presence of informed traders; we interpret such markets as having reduced liquidity. Time and the Price Impact of a Trade THE AVAILABILITY OF LARGE data sets on transaction data and powerful computational devices has generated a new wave of interest in market microstructure research and has opened new frontiers for the empirical investigation of its hypotheses. The microstructure literature is mainly devoted to the study of the mechanics of price formation, examining questions such as, " What are the determinants of the behavior of prices? " and " How is new information incorporated into prices? " 1. Hasbrouck's analysis (Hasbrouck (1991)) reveals that the change in prices depends on characteristics of trades (sign and size) and the market environment as measured by bid-ask spread, in addition to the current and past levels of prices. At time t, when a trade is performed, larger transaction size and spread imply a larger price revision after the trade. Furthermore, not only does volume affect prices, but it has a persistent impact on prices, which means volume conveys information. The intuition, supported by theoretical predictions, is that other trade related variables might also be informative. The primary objective of this paper is to show empirical evidence that the time between trades, which is a measure of trading activity, affects market price behavior. Moreover, by measuring how much and how fast prices respond to trades at any point during the trading day, we illuminate the dynamic behavior of some aspects of market liquidity that could be used to design optimal trading strategies. The background of this study goes back to Bagehot (1971), who first considered a scenario with heterogeneously informed traders. According to Bagehot, the specialist, possessing only publicly available information, faces informed and uninformed traders, but cannot distinguish between them. Uninformed traders are also called liquidity traders because their trading is either motivated by consumption needs and portfolio strategies or simply reflects personal …