Assessing influential trade effects via high-frequency market reactions

Meihui Guo, Yi Ting Guo, Chi Jeng Wang, Liang Ching Lin

Research output: Contribution to journalArticlepeer-review


In the literature, traders are often classified into informed and uninformed and the trades from informed traders have market impacts. We investigate these trades by first establishing a scheme to identify the influential trades from the ordinary trades under certain criteria. The differential properties between these two types of trades are examined via the four transaction states classified by the trade price, trade volume, quotes, and quoted depth. Marginal distribution of the four states and the transition probability between different states are shown to be distinct for informed trades and ordinary liquidity trades. Furthermore, four market reaction factors are introduced and logistic regression models of the influential trades are established based on these four factors. Empirical study on the high-frequency transaction data from the NYSE TAQ database show supportive evidence for high correct classification rates of the logistic regression models.

Original languageEnglish
Pages (from-to)1458-1471
Number of pages14
JournalJournal of Applied Statistics
Issue number7
Publication statusPublished - 2015 Jul 3

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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