Optimal order placement in limit order markets. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Cont, Rama; Kukanov, Arseniy
• Is Part Of: Quantitative finance. Volume 17:Issue 1(2017)
• Page Start: 21
• Page End: 39
• Publication Date: 2017-01-02
• Subjects: Limit order markets -- Optimal order execution -- Execution risk -- Order routing -- Algorithmic trading -- Supervised learning -- Machine learning -- Transaction costs -- Stochastic approximation -- Robbins–Monro algorithm; C61 -- G31; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2016.1190030
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : To execute a trade, participants in electronic equity markets may choose to submit limit orders or market orders across various exchanges where a stock is traded. This decision is influenced by characteristics of the order flows and queue sizes in each limit order book, as well as the structure of transaction fees and rebates across exchanges. We propose a quantitative framework for studying this order placement problem by formulating it as a convex optimization problem. This formulation allows the study of how the optimal order placement decision depends on the interplay between the state of order books, the fee structure, order flow properties and the aversion to execution risk. In the case of a single exchange, we derive an explicit solution for the optimal split between limit and market orders. For the general case of order placement across multiple exchanges, we propose a stochastic algorithm that computes the optimal routing policy and study the sensitivity of the solution to various parameters. Our algorithm does not require an explicit statistical model of order flow but exploits data on recent order fills across exchanges in the numerical implementation of the algorithm to acquire this information through a supervised learning procedure.