Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system

Authors:
Adam Ghandar;Zbigniew Michalewicz;Ralf Zurbruegg
Affiliations:
School of Computer Science, University of Adelaide, Adelaide, SA, Australia;School of Computer Science, University of Adelaide, Adelaide, SA, Australia,Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland,Polish-Japanese Institute of Information Techn ...;Business School, University of Adelaide, Adelaide, SA, Australia
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Year:
2012

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Abstract

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.