Reinforcement learning for optimized trade execution
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Intention-Disguised algorithmic trading
FC'10 Proceedings of the 14th international conference on Financial Cryptography and Data Security
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In this paper, we address the importance of efficient execution in electronic markets. Due to intense competition for profit opportunities, trading costs can represent a significant portion of overall return. They must be taken into account both when a specific trade is being executed, and when a general investment strategy is being designed. We empirically demonstrate that by combining market orders (which offer immediate execution regardless of price) and limit orders (which offer uncertain execution at a specified price), we are able to obtain a superior average price than by using market orders only. Our analysis highlights the trade-off between expected price improvement from limit orders and the risk of non-execution. We show how to determine the optimal limit order price in a simplified setting and suggest how this approach can be generalized to a complete solution. All of our experimental results are obtained on an extensive collection of NASDAQ limit order data.