Dynamic Trade Informativeness

54 Pages Posted: 1 Oct 2018 Last revised: 22 Feb 2022

See all articles by Bart Z. Yueshen

Bart Z. Yueshen

INSEAD - Finance

Marcin Zamojski

University of Gothenburg, Centre for Finance

Jinyuan Zhang

UCLA Anderson School of Management

Date Written: February 20, 2022

Abstract

This paper develops a structural model to examine high-frequency price dynamics. The key innovation is to allow trades’ permanent price impact to be time-varying—dynamic trade informativeness. A distribution-free filtering technique pins the real-world data to the model. The filtered series significantly recover the efficient price innovation through the dynamics of trade informativeness, highlight general intraday trends across stocks, improve trades’ explanatory power for future returns, distinguish informativeness from trades’ aggressiveness, gauge informed investors’ patience, and capture systematic patterns around scheduled and unscheduled events. The framework contributes to the better utilization of high-frequency trading data.

Keywords: trade informativeness, price impact, filtering, high-frequency data

JEL Classification: C58, G14

Suggested Citation

Yueshen, Bart Zhou and Zamojski, Marcin and Zhang, Jinyuan, Dynamic Trade Informativeness (February 20, 2022). Available at SSRN: https://ssrn.com/abstract=3119538 or http://dx.doi.org/10.2139/ssrn.3119538

Bart Zhou Yueshen (Contact Author)

INSEAD - Finance ( email )

Boulevard de Constance
F-77305 Fontainebleau Cedex
France

Marcin Zamojski

University of Gothenburg, Centre for Finance ( email )

Box 640
Gothenburg, 405 30
Sweden

Jinyuan Zhang

UCLA Anderson School of Management ( email )

Los Angeles, CA
United States

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