Abstract:
\nWe propose a state space modelling approach for decomposing high frequency trading volume into liquidity-driven and information-driven components. Based on 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. This disparity is explained by high frequency trading activity, which leads to the elimination of pricing inefficiencies at high frequencies.
\nJEL Classification: G12; G14; G15<\/p>\n
Keywords: trading volume; expected component; unexpected component; volatility; liquidity;
\nmarket toxicity, unobserved components time series models; high-frequency data.<\/p>\n","protected":false},"excerpt":{"rendered":"
Abstract: We propose a state space modelling approach for decomposing high frequency trading volume into liquidity-driven and information-driven components. Based on 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. [\u2026] <\/p>\n