This paper examines the interaction of decision model complexity and utility in a computational intelligence system for algorithmic trading. An empirical analysis is undertaken which makes use of recent developments in multiobjective evolutionary fuzzy systems (MOEFS) to produce and evaluate a Pareto set of rulebases that balance conflicting criteria. This results in strong evidence that controlling portfolio risk and return in this and other similar methodologies by selecting for interpretability is feasible. Furthermore, while investigating these properties we contribute to a growing body of evidence that stochastic systems based on natural computing techniques can deliver results that outperform the market. © 2012 Springer-Verlag.
CITATION STYLE
Ghandar, A., Michalewicz, Z., & Zurbruegg, R. (2012). Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7492 LNCS, pp. 42–51). https://doi.org/10.1007/978-3-642-32964-7_5
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