A state-space modeling of the information content of trading volume

Khaladdin Rzayev and Gbenga Ibikunle

Journal of Financial Markets, 2019, vol. 46, issue C

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: G12 G14 G15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finmar:v:46:y:2019:i:c:s1386418118302519

DOI: 10.1016/j.finmar.2019.100507

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