Enhancing Profitability through Interpretability in Algorithmic Trading with a Multiobjective Evolutionary Fuzzy System

  • Adam Ghandar
  • Zbigniew Michalewicz
  • Ralf Zurbruegg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)


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.


Excess Return Share Price Dividend Yield Trading Rule Investor Sentiment 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Adam Ghandar
    • 1
  • Zbigniew Michalewicz
    • 1
    • 2
    • 3
  • Ralf Zurbruegg
    • 4
  1. 1.School of Computer ScienceUniversity of AdelaideAdelaideAustralia
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Polish-Japanese Institute of Information TechnologyWarsawPoland
  4. 4.Business SchoolUniversity of AdelaideAdelaideAustralia

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