Machine Learning in a Dynamic Limit Order Market
60 Pages Posted: 10 Jul 2020 Last revised: 27 Jul 2020
Date Written: July 27, 2020
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: Suggested Citation