A deep learning approach to estimating fill probabilities in a limit order book. Issue 11 (2nd November 2022)

• Record Type: Journal Article
• Authors: Maglaras, Costis; Moallemi, Ciamac C.; Wang, Muye
• Is Part Of: Quantitative finance. Volume 22:Issue 11(2022)
• Page Start: 1989
• Page End: 2003
• Publication Date: 2022-11-02
• Subjects: Fill probabilities -- Limit order book -- Neural network -- Simulation; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2022.2124189
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : Deciding between the use of market orders and limit orders is an important question in practical optimal trading problems. A key ingredient in making this decision is understanding the uncertainty of the execution of a limit order, that is, the fill probability or the probability that an order will be executed within a certain time horizon. Equivalently, one can estimate the distribution of the time-to-fill. We propose a data-driven approach based on a recurrent neural network to estimate the distribution of time-to-fill for a limit order conditional on the current market conditions. Using a historical data set, we demonstrate the superiority of this approach to several benchmark techniques. This approach also leads to significant cost reductions while implementing a trading strategy in a prototypical trading problem.