Optimal Execution Horizon

33 Pages Posted: 5 Jun 2015

See all articles by David Easley

David Easley

Cornell University - Department of Economics; Cornell University - Department of Information Science

Marcos Lopez de Prado

Harvard University

Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management

Multiple version iconThere are 2 versions of this paper

Date Written: July 2015


Execution traders know that market impact greatly depends on whether their orders lean with or against the market. We introduce the OEH model, which incorporates this fact when determining the optimal trading horizon for an order, an input required by many sophisticated execution strategies. This model exploits the trader's private information about her trade's side and size, and how it will shift the prevailing order flow. From a theoretical perspective, OEH explains why market participants may rationally “dump” their orders in an increasingly illiquid market. We argue that trade side and order imbalance are key variables needed for modeling market impact functions, and their dismissal may be the reason behind the apparent disagreement in the literature regarding the functional form of the market impact function. We show that in terms of its information ratio OEH performs better than participation rate schemes and VWAP strategies. Our backtests suggest that OEH contributes substantial “execution alpha” for a wide variety of futures contracts. An implementation of OEH is provided in Python language.

Keywords: liquidity, flow toxicity, broker, VWAP, market microstructure, adverse selection, probability of informed trading, VPIN, OEH

Suggested Citation

Easley, David and de Prado, Marcos Lopez and O'Hara, Maureen, Optimal Execution Horizon (July 2015). Mathematical Finance, Vol. 25, Issue 3, pp. 640-672, 2015, Available at SSRN: https://ssrn.com/abstract=2614738 or http://dx.doi.org/10.1111/mafi.12045

David Easley (Contact Author)

Cornell University - Department of Economics ( email )

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Cornell University - Department of Information Science ( email )

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Marcos Lopez De Prado

Harvard University

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Maureen O'Hara

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States
607-255-3645 (Phone)
607-255-5993 (Fax)

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