Does trade clustering reduce trading costs? Evidence from periodicity in algorithmic trading

Dmitriy Muravyev, Joerg Picard

Research output: Contribution to journalArticlepeer-review

Abstract

We study how trading activity affects liquidity and volatility by introducing two periodicities in trading activity. First, trades and quote updates are much more frequent within the first 100 ms of a second than during its remainder. Second, trading activity often spikes at intervals of exactly one second. For these two periodicities, higher trade and quote intensities lead to higher volatility, but they do not significantly affect stock liquidity. These periodicities are likely caused by algorithms that trade predictably by repeating instructions in loops with round start times and time increments. Such predictable behavior may provide an example of behavioral biases in trading algorithms.

Original languageEnglish (US)
Pages (from-to)1201-1229
Number of pages29
JournalFinancial Management
Volume51
Issue number4
DOIs
StatePublished - Dec 1 2022
Externally publishedYes

Keywords

  • algorithmic biases
  • algorithmic trading
  • trading seasonality

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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