Authors: Wei Cui, Anthony Brabazon, Michael O'Neill
Addresses: Financial Mathematics and Computation Cluster, School of Business, University College Dublin, Ireland. ' Financial Mathematics and Computation Cluster, School of Business, University College Dublin, Ireland. ' Natural Computing Research and Applications Group, School of Computer Science and Informatics, University College Dublin, Ireland
Abstract: Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument. Investors wishing to execute large orders face a tradeoff between market impact and opportunity cost. Trade execution strategies are designed to balance out these costs, thereby minimising total trading cost. Despite the importance of optimising the trade execution process, this is difficult to do in practice due to the dynamic nature of markets and due to our imperfect understanding of them. In this paper, we adopt a novel approach, combining an evolutionary methodology whereby we evolve high-quality trade execution strategies, with an agent-based artificial stock market, wherein the evolved strategies are tested. The evolved strategies are found to outperform a series of benchmark strategies and several avenues are suggested for future work.
Keywords: algorithmic trading; trade execution; artificial stock markets; evolutionary computation; grammatical evolution; financial markets; market impact; opportunity cost; agent-based systems.
International Journal of Financial Markets and Derivatives, 2011 Vol.2 No.1/2, pp.4 - 31
Published online: 28 Feb 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article