Mean-Variance Optimal Adaptive Execution

Electronic trading of equities and other securities makes heavy use of “arrival price” algorithms, that balance the market impact cost of rapid execution against the volatility risk of slow execution. In the standard formulation, mean-variance optimal trading strategies are static: they do not modify the execution speed in response to price motions observed during trading. We show that substantial improvement is possible by using dynamic trading strategies, and that the improvement is larger for large initial positions. We develop a technique for computing optimal dynamic strategies to any desired degree of precision. The asset price process is observed on a discrete tree with a arbitrary number of levels. We introduce a novel dynamic programming technique in which the control variables are not only the shares traded at each time step, but also the maximum expected cost for the remainder of the program; the value function is the variance of the remaining program. The resulting adaptive strategies are “aggressive-in-the-money”: they accelerate the execution when the price moves in the trader’s favor, spending parts of the trading gains to reduce risk. ∗Work performed when the author was at the Institute of Theoretical Computer Science, ETH Zurich/Switzerland, and was supported by UBS AG. [email protected] ∗∗New York University Courant Institute and Quantitative Brokers, [email protected]