Optimal Placement in a Limit Order Book

Larrard, Xin GuoAdrien de; Ruan, Zhao

doi:10.2139/ssrn.2318220

Cited by 17 publications

(24 citation statements)

References 85 publications

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“…Blanchet and Chen (2013) derived a continuous-time model for the joint evolution of the mid price and the bid-ask spread. Several papers such as Guo et al (2013), Cont and Kukanov (2013), and Maglaras et al (2015) have been working on optimizing trading decisions in the context of a queueing model for the limit order book. More specifically, Guo et al (2013) proposed a model to optimally place orders, given price impact.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several papers such as Guo et al (2013), Cont and Kukanov (2013), and Maglaras et al (2015) have been working on optimizing trading decisions in the context of a queueing model for the limit order book. More specifically, Guo et al (2013) proposed a model to optimally place orders, given price impact. Cont and Kukanov (2013) From the empirical front, there is a significant body of literature conducting empirical analyses of the dynamics of limit order books in major exchanges.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Model for Queue Position Valuation in a Limit Order Book

Moallemi

Yuan

2016

SSRN Journal

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Model for Queue Position Valuation in a Limit Order Book

Moallemi

Yuan

2016

SSRN Journal

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“…The latter, on the other hand, could be viewed as the former when the execution risk is removed, as shown by Guo et al [34].…”

Section: Optimal Placementmentioning

confidence: 99%

“…Guo et al [34] propose a correlated random walk model for the optimal placement problem. Their starting point is the characteristics of the LOB rather than the whole LOB and they focus on modeling the ask price.…”

Section: Correlated Random Walkmentioning

confidence: 99%

Optimal Placement in a Limit Order Book

Guo¹

2013

Theory Driven by Influential Applications

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Please scroll down for article-it is on subsequent pagesINFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Abstract Algorithmic trading refers to the automatic and rapid trading of large quantities with orders specified and implemented by an algorithm. Roughly speaking, algorithmic trading is based on two different time scales: the daily or weekly scale and a smaller (10-to 100-second) time scale. These two time scales essentially reflect the two steps by which the traders slice and place orders. The first step is to optimally slice big orders into smaller ones on a daily basis with the goal to minimize the price impact and/or to maximize the expected utility; the second step is to optimally place the orders within seconds. The former is the well-known optimal execution problem, and the latter is the much less studied optimal placement problem. This paper reviews several simple models and approaches for the optimal placement problem. Several most relevant statistical issues are presented, together with a brief discussion on the key differences between the system of limit order book and the multiclass queues with reneging.

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“…Another approach has been to model the process through which an order is filled as a dynamic random process (Cont 2011, Cont and leading to a formulation of the optimal execution problem as a stochastic control problem. This formulation has been studied in various settings with limit orders (Bayraktar andLudkovski 2011, Gueant andLehalle 2015) or limit and market orders (Guilbaud and Pham 2013, Huitema 2012, Guo et al 2013, Li 2013) but its complexity makes it computationally intractable unless restrictive assumptions are made on price and order book dynamics. For example, these studies commonly assume that a trader places a single limit order for one unit at a time and its execution probability is given by a simplified function of distance to best quotes.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Order Placement in Limit Order Markets

Cont

Kukanov

2012

SSRN Journal

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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 to study 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 algoirthm 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.

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Optimal Placement in a Limit Order Book

Cited by 17 publications

References 85 publications

A Model for Queue Position Valuation in a Limit Order Book

A Model for Queue Position Valuation in a Limit Order Book

Optimal Placement in a Limit Order Book

Optimal Order Placement in Limit Order Markets

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