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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Number of pages19
JournalJournal of Financial Markets
Early online date27 Aug 2019
DOIs
Publication statusE-pub ahead of print - 27 Aug 2019

Keywords / Materials (for Non-textual outputs)

  • trading volume
  • permanent component
  • transitory component
  • market quality
  • time series models
  • state-space modeling
  • high-frequency trading

Fingerprint

Dive into the research topics of 'A state-space modeling of the information content of trading volume'. Together they form a unique fingerprint.

Cite this