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Enhancing Profitability through Interpretability in Algorithmic Trading with a Multiobjective Evolutionary Fuzzy System
Adam Ghandar1, Zbigniew Michalewicz1,2,3, and Ralf Zurbruegg4
1School of Computer Science, University of Adelaide, Adelaide, SA 5005, Australia
2Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland
3Polish-Japanese Institute of Information Technology, ul. Koszykowa 86, 02-008, Warsaw, Poland
4Business School, University of Adelaide, Adelaide, SA 5005, Australia
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.
LNCS 7492, p. 42 ff.
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