Instantaneous order impact and high-frequency strategy optimization in limit order books

TL;DR

This paper develops a Markov decision process model for high-frequency trading in limit order books, optimizing order placement by considering recent order impact and LOB shape, leading to superior execution strategies.

Contribution

It introduces a joint model of order impact and LOB shape using a Markov chain, and derives an optimal order placement policy that outperforms existing strategies.

Findings

The proposed policy significantly outperforms other strategies in ultra high-frequency data.

Market orders are used aggressively during adverse mid-price movements.

Limit orders are optimally placed based on LOB state and recent order type.

Abstract

We propose a limit order book (LOB) model with dynamics that account for both the impact of the most recent order and the shape of the LOB. We present an empirical analysis showing that the type of the last order significantly alters the submission rate of immediate future orders, even after accounting for the state of the LOB. To model these effects jointly we introduce a discrete Markov chain model. Then on these improved LOB dynamics, we find the policy for optimal order choice and placement in the share purchasing problem by framing it as a Markov decision process. The optimal policy derived numerically uses limit orders, cancellations and market orders. It looks to exploit the state of the LOB summarized by the volume at the bid/ask and the type of the most recent order to obtain the best execution price, avoiding non-execution and adverse selection risk simultaneously. Market orders are used aggressively when the mid-price is expected to move adversely. Limit orders are placed under favorable LOB conditions and canceled when non-execution or adverse selection probability is high. Using ultra high-frequency data from the NASDAQ stock exchange we compare our optimal policy with other submission strategies that use a subset of all available order types and show that ours significantly outperforms them.