Advertisement

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)

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.

Keywords

Excess Return Share Price Dividend Yield Trading Rule Investor Sentiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Narang, K.: Inside the Black Box The Simple Truth About Algorithmic Trading. John Wiley & Sons, Inc., New York (2009)CrossRefGoogle Scholar
  2. 2.
    Ghandar, A., Michalewicz, Z., Schmidt, M., To, T.-D., Zurbruegg, R.: Computational intelligence for evolving trading rules. IEEE Trans. Evolutionary Computation 13(1), 71–86 (2009)CrossRefGoogle Scholar
  3. 3.
    Ghandar, A., Michalewicz, Z., Zurbruegg, R.: A case for learning simpler rule sets with multiobjective evolutionary algorithms. In: RuleML Europe, pp. 297–304 (2011)Google Scholar
  4. 4.
    Jin, Y., Sendhoff, B.: Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(3), 397–415 (2008)CrossRefGoogle Scholar
  5. 5.
    Lee, C.-C.: A self-learning rule-based controller employing approximate reasoning and neural net concepts. International Journal of Intelligent Systems 6(1), 71–93 (1991)CrossRefGoogle Scholar
  6. 6.
    Ray, Ball: Anomalies in relationships between securities’ yields and yield-surrogates. Journal of Financial Economics 6(2-3), 103–126 (1978)CrossRefGoogle Scholar
  7. 7.
    Basu, S.: Investment performance of common stocks in relation to their price-earnings ratios: A test of the efficient market hypothesis. The Journal of Finance 32(3), 663–682 (1977)CrossRefGoogle Scholar
  8. 8.
    Beaver, W., Lambert, R., Morse, D.: The information content of security prices. Journal of Accounting and Economics 2(1), 3–28 (1980)CrossRefGoogle Scholar
  9. 9.
    Bhandari, L.C.: Debt/equity ratio and expected common stock returns: Empirical evidence. The Journal of Finance 43(2), 507–528 (1988)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Barberis, N., Shleifer, A., Vishny, R.: A model of investor sentiment. Journal of Financial Economics 49(3), 307–343 (1998)CrossRefGoogle Scholar
  11. 11.
    Kavajecz, K., Odders-White, E.: Technical Analysis and Liquidity Provision. Rev. Financ. Stud. 17(4), 1043–1071 (2004)CrossRefGoogle Scholar
  12. 12.
    Wilder, J.: New Concepts in Technical Trading Systems. Trend Research (1978)Google Scholar
  13. 13.
    Cordón, O., Gomide, F.A.C., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic fuzzy systems. new developments. Fuzzy Sets and Systems 141(1), 1–3 (2004)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm. Tech. Rep. (2001)Google Scholar
  15. 15.
    Fama, E.F.: Components of investment performance. Journal of Finance 27(3), 551–567 (1972)Google Scholar

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

Personalised recommendations