The Conduits of Price Discovery: A Machine Learning Approach

EFA 2021; AFA 2022

48 Pages Posted: 14 Oct 2020 Last revised: 13 Jan 2022

See all articles by Amy Kwan

Amy Kwan

University of New South Wales (UNSW)

Richard Philip

University of Sydney Business School

Andriy Shkilko

Wilfrid Laurier University - Lazaridis School of Business and Economics

Date Written: October 13, 2020

Abstract

When examining information flow into prices, empirical literature usually focusses on direct conduits such as order submissions. Meanwhile, theory suggests that market conditions should have substantial additional effects. Empirical analyses of such effects are methodologically challenging and therefore uncommon. We bypass these challenges using a machine learning technique that allows for multiple conditioning variables. Consistent with theory, price discovery is notably affected by such conditions as the state of the limit order book, price history, bid-ask spread, and order arrival frequency. The state of the book and price history stand out as conduits, whose magnitudes rival that of order submissions.

Keywords: price discovery, order submission strategies, machine learning

JEL Classification: G14, G15

Suggested Citation

Kwan, Amy and Philip, Richard and Shkilko, Andriy, The Conduits of Price Discovery: A Machine Learning Approach (October 13, 2020). EFA 2021; AFA 2022, Available at SSRN: https://ssrn.com/abstract=3710491 or http://dx.doi.org/10.2139/ssrn.3710491

Amy Kwan

University of New South Wales (UNSW) ( email )

Kensington
High St
Sydney, NSW 2052
Australia

Richard Philip

University of Sydney Business School ( email )

Cnr. of Codrington and Rose Streets
Sydney, NSW 2006
Australia

Andriy Shkilko (Contact Author)

Wilfrid Laurier University - Lazaridis School of Business and Economics ( email )

LH 4050
75 University Ave. W.
Waterloo, Ontario N2L3C5
Canada
519.884.0710 ext. 2462 (Phone)
519.884.0201 (Fax)

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