Machine Learning in a Dynamic Limit Order Market

60 Pages Posted: 10 Jul 2020 Last revised: 27 Jul 2020

See all articles by Richard Philip

Richard Philip

University of Sydney Business School

Date Written: July 27, 2020

Abstract

We use a novel machine learning approach to tackle the problem of limit order management. Applying our framework to data, we show that the most important variable for a trader to consider is the price level of their order, followed by the queue sizes of the order book, volatility and finally queue position. Further, we show the option to cancel a limit order is valuable and contributes approximately 15% of a limit order's total expected value. This paper takes an important step towards describing pervasive features and dynamics that exist in financial markets.

Keywords: Limit order markets, machine learning, queue size, optimal limit order

JEL Classification: G10, G20, D40

Suggested Citation

Philip, Richard, Machine Learning in a Dynamic Limit Order Market (July 27, 2020). Available at SSRN: https://ssrn.com/abstract=3630018 or http://dx.doi.org/10.2139/ssrn.3630018

Richard Philip (Contact Author)

University of Sydney Business School ( email )

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

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