Date: 22 February 2019, Friday
\nTime: 13:40-14:40
\nPlace: Faculty of Business Administration,
\n\u00dcmit Berkman Seminar Room (MA-330)<\/p>\n
“A high frequency analysis of the information content of trading volume”
\nby
\nKhaladdin Rzayev
\nThe University of Edinburgh<\/p>\n
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":"
Date: 22 February 2019, Friday Time: 13:40-14:40 Place: Faculty of Business Administration, \u00dcmit Berkman Seminar Room (MA-330) “A high frequency analysis of the information content of trading volume” by Khaladdin Rzayev The University of Edinburgh Abstract: We propose a state space modelling approach for decomposing high frequency trading volume into [\u2026] <\/p>\n