A State-Space Modeling of the Information Content of Trading Volume
Journal of Financial Markets, Forthcoming
51 Pages Posted: 29 Jan 2018 Last revised: 4 Sep 2019
Date Written: January 22, 2018
We propose a state-space modeling approach for decomposing trading volume into its liquidity-driven and information-driven components. Using a set of high-frequency S&P 500 stock data, we show that informed trading is linked with a reduction in volatility, illiquidity, and toxicity/adverse selection. We observe that our estimated informed trading component of volume is a statistically significant predictor of one-second stock returns; however, it is not a significant predictor of one-minute stock returns. This disparity is explained by high-frequency trading activity, which eliminates pricing inefficiencies at low latencies.
Keywords: trading volume, permanent component, transitory component, market quality, time series models, state-space modeling, high-frequency trading
JEL Classification: G12, G14, G15
Suggested Citation: Suggested Citation