Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system
Date
2012
Authors
Ghandar, A.
Michalewicz, Z.
Zurbrugg, R.
Editors
Coello, C.A.C.
Cutello, V.
Deb, K.
Forrest, S.
Nicosia, G.
Pavone, M.
Cutello, V.
Deb, K.
Forrest, S.
Nicosia, G.
Pavone, M.
Advisors
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Conference paper
Citation
Proceedings of the 12th International Conference on Parallel Problem Solving from Nature, held in Taormina, Italy, 1-5 September, 2012 / C.A. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia and M. Pavone (eds.): pp.42-51
Statement of Responsibility
Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbruegg
Conference Name
International Conference on Parallel Problem Solving from Nature (12th : 2012 : Taormina, Italy)
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.
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© Springer-Verlag Berlin Heidelberg 2012