Reducing Transaction Costs with Low-Latency Trading Algorithms

14 Pages Posted: 17 Sep 2015

See all articles by Sasha Stoikov

Sasha Stoikov

Cornell Financial Engineering Manhattan

Rolf Waeber

Cornell University - School of Operations Research and Industrial Engineering

Date Written: September 16, 2015

Abstract

We formulate a trade execution problem at the market microstructure level and solve it using dynamic programming. The objective is to sell a single lot of an asset in a short time horizon T, using the imbalance of the top of book bid and ask sizes as a price predictor. The optimization problem takes into account the latency L of the trading algorithm, which affects the prices at which the asset is traded. The solution divides the state space into a "trade" and a "no-trade" region. We calculate the cost of latency per lot traded and demonstrate that the advantage of observing the limit order book can dissipate quickly as execution latency increases. In the empirical section, we show that our optimal policy significantly outperforms a TWAP algorithm in liquidating on-the-run U.S. treasury bonds, saving on average approximately 1/3 of the spread per share if trades are executed with low latency (1 millisecond).

Keywords: Optimal asset liquidation, algorithmic trading, transaction costs, market microstructure, high-frequency trading, optimal stopping, trade execution, latency, cost of latency, dynamic programming.

Suggested Citation

Stoikov, Sasha and Waeber, Rolf, Reducing Transaction Costs with Low-Latency Trading Algorithms (September 16, 2015). Available at SSRN: https://ssrn.com/abstract=2661618 or http://dx.doi.org/10.2139/ssrn.2661618

Sasha Stoikov (Contact Author)

Cornell Financial Engineering Manhattan ( email )

2 W Loop Rd
New York, NY New York 10044
United States

HOME PAGE: http://www.sashastoikov.com

Rolf Waeber

Cornell University - School of Operations Research and Industrial Engineering ( email )

206 Rhodes Hall
Ithaca, NY 14853
United States

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