Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Optimal Control of Execution Costs for Portfolios
Computing in Science and Engineering
Optimal Control of Execution Costs for Portfolios
Computing in Science and Engineering
Optimal Security Liquidation Algorithms
Computational Optimization and Applications
(In)Stability properties of limit order dynamics
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Reinforcement learning for optimized trade execution
ICML '06 Proceedings of the 23rd international conference on Machine learning
Execution costs in financial markets with several institutional investors
FEA '07 Proceedings of the Fourth IASTED International Conference on Financial Engineering and Applications
Optimal Portfolio Execution Strategies and Sensitivity to Price Impact Parameters
SIAM Journal on Optimization
Optimal Control of Trading Algorithms: A General Impulse Control Approach
SIAM Journal on Financial Mathematics
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The dramatic growth in institutionally managed assets, coupled with the advent of Internet trading and electronic brokerage for retail investors, has led to a surge in the size and volume of trading. At the same time, competition in the asset-management industry has increased to where fractions of a percent in performance can separate the top funds from those in the next tier. In this environment, portfolio managers have begun to explore active management of trading to boost returns. Controlling execution cost can be viewed as a stochastic dynamic optimization problem because trading takes time, stock prices exhibit random fluctuations, and execution prices depend on trade size, order flow, and market conditions. In this article, the authors apply stochastic dynamic programming to derive trading strategies that minimize the expected cost of executing a portfolio of securities over a fixed time period. That is, they derive the optimal sequence of trades as a function of prices, quantities, and other market conditions. To illustrate the practical relevance of these methods, the authors apply them to a hypothetical portfolio of 25 stocks. They estimate the methods' price-impact functions using 1996 trade data and derive the optimal execution strategies. The authors also perform several Monte Carlo simulations to compare the optimal strategy's performance to that of several alternatives.