David vs Goliath (You against the Markets), A Dynamic Programming Approach to Separate the Impact and Timing of Trading Costs

TL;DR

This paper introduces a stochastic dynamic programming model that separates trading costs into Market Impact and Market Timing, providing practical tools for optimal trade execution and insights into market dynamics.

Contribution

It presents a novel dynamic programming framework that decomposes trading costs and extends to various market conditions, enhancing understanding and decision-making in trading.

Findings

Model effectively separates impact and timing costs.

Numerical methods enable optimal trade execution under complex market laws.

Decomposition reveals the zero-sum nature of trading costs.

Abstract

We develop a fundamentally different stochastic dynamic programming model of trading costs. Built on a strong theoretical foundation, our model provides insights to market participants by splitting the overall move of the security price during the duration of an order into the Market Impact (price move caused by their actions) and Market Timing (price move caused by everyone else) components. We derive formulations of this model under different laws of motion of the security prices, starting with a simple benchmark scenario and extending this to include multiple sources of uncertainty, liquidity constraints due to volume curve shifts and relating trading costs to the spread. We develop a numerical framework that can be used to obtain optimal executions under any law of motion of prices and demonstrate the tremendous practical applicability of our theoretical methodology including the powerful numerical techniques to implement them. Our decomposition of trading costs into Market Impact and Market Timing allows us to deduce the zero sum game nature of trading costs. It holds numerous lessons for dealing with complex systems, wherein reducing the complexity by splitting the many sources of uncertainty can lead to better insights in the decision process.