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Multiobjective optimization of technical market indicators

Published:08 July 2009Publication History

ABSTRACT

This paper deals with the optimization of technical indicators for stock market investment. Price prediction is a problem of great complexity and usually some technical indicators are used to predict the markets trends. The main difficulty in the use of technical indicators lies in deciding the parameters values. We proposed the use of Evolutionary Algorithms (EAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. This paper extends the work presented on previous works by including additional indicators and applying them to more complex problems. In this way the Moving Averages Convergence-Divergence (MACD) indicator and the Relative Strength Index (RSI) oscillator have been selected to obtain the buying/selling signals. The experimental results indicate that our EAs offer a solution to the problem obtaining results that improve those obtained through technical indicators with their standard parameters.

References

  1. Álvarez González, A. Análisis Bursátil con fines especulativos: Un enfoque moderno. Limusa. Mexico. 2005.Google ScholarGoogle Scholar
  2. Oxelgein, L. and Wihlborg, C., Macroeconomic Uncertainty Wiley and Sons, New York, 1988.Google ScholarGoogle Scholar
  3. Reilly F.K., Investment Analysis and Portfolio Management Driden Ed., Chicago, 1989.Google ScholarGoogle Scholar
  4. Murphy, J.J. Technical Analysis of Financial Markets, Prentice Hall Press, NYIF, 1999.Google ScholarGoogle Scholar
  5. Graham, B., The intelligent investor, Collins Business, 2003.Google ScholarGoogle Scholar
  6. Coello, C., Lamont, G.B., Van Delhhuizen D.A., Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Allen F., Karjalainen R. Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics. Vol 5. Pp. 245--275. 1999Google ScholarGoogle Scholar
  8. Arifovic J., Evolutionary Algorithms in Macroeconomic Models. Macroeconomic Dynamics, Cambridge University Press, vol. 4(3), pags. 373--414, September 2000.Google ScholarGoogle Scholar
  9. Yan, W. and Clack, C.D. Evolving Robust Solutions for Hedge Fund Stock Selection in Emerging Markets. GECCO'07, London, England, United Kingdom, ACM 978-1-59593-697-3/07/0007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Fernández-Blanco, P., Bodas-Sagi, D.J., Soltero, F.J., and Hidalgo, J.I. Technical market indicators optimization using evolutionary algorithms. In Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Computation, Atlanta, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Elder, A., Trading for a Living: Psychology, Trading Tactics, Money Management, John Wiley & Sons Inc, 1993.Google ScholarGoogle Scholar
  12. Bogle, J., Bogle on Mutual Funds: New Perspectives for the Intelligent Investor, Dell, 1994.Google ScholarGoogle Scholar
  13. London Stock Exchange, http://www.londonstockexchange.com/en-gb/pricesnews/prices/system/detailedprices.htm?ti=OSB3Google ScholarGoogle Scholar
  14. Graziano, J.P., Análisis Técnico estadístico. Principales indicadores y su aplicación al ISR®. Investigación y Desarrollo. Bolsa de Comercio de Rosario, 2001.Google ScholarGoogle Scholar
  15. CBOE Volatility Index, http://www.cboe.com/micro/vix/introduction.aspxGoogle ScholarGoogle Scholar
  16. Dow Jones Industrial Average, http://www.djaverages.com/Google ScholarGoogle Scholar
  17. Mantegna, R.N. and Stanley, H.E., An introduction to ecophysics. Correlations and Complexity in Finance Cambridge University Press. Cambridge 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ferri, R., The ETF Book: All You Need to Know About Exchange-Traded Funds, Wiley, 2007.Google ScholarGoogle Scholar
  19. Bowman, B., Debray, S.K., and Peterson, L.L. Reasoning about naming systems. ACM Trans. Program. Lang. Syst., 15, 5 (Nov. 1993), 795--825. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Ding, W., and Marchionini, G. A Study on Video Browsing Strategies. Technical Report UMIACS-TR-97-40, University of Maryland, College Park, MD, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Fröhlich, B. and Plate, J. The cubic mouse: a new device for three-dimensional iput. In Proceedings of the SIGCHI conference on Human factors in computing systems Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. (CHI '00) (The Hague, The Netherlands, April 1-6, 2000). ACM Press, New York, NY, 2000, 526--531.Google ScholarGoogle Scholar
  23. Lamport, L. LaTeX User's Guide and Document Reference Manual. Addison-Wesley, Reading, MA, 1986.Google ScholarGoogle Scholar
  24. Sannella, M.J. Constraint Satisfaction and Debugging for Interactive User Interfaces. Ph.D. Thesis, University of Washington, Seattle, WA, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library

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