A High Frequency Analysis of the Information Content of Trading Volume
Abstract
We propose a state space modelling approach for decomposing high frequencytrading volume into liquidity-driven and information-driven components. Using a set of high
frequency S&P 500 stocks data, we show that informed trading increases pricing efficiency by
reducing volatility, illiquidity and toxicity/adverse selection during periods of non-aggressive
trading. We observe that our estimated informed trading component of volume is a statistically
significant predictor for one-second stock returns; however, it is not a significant predictor for
one-minute stock returns. We show that this disparity is explained by high frequency trading
activity, which eliminates pricing inefficiencies at high frequencies.