Skip to main content

Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6291))

Abstract

This paper presents a stop-loss - maximum return (SLMR) trading strategy based on improving the classic moving average technical indicator with neural networks. We propose an improvement in the efficiency of the long term moving average by using the limited recursion in Elman Neural Networks, jointly with hybrid neuro-symbolic neural network, while still fully keeping all the learning capabilities of non-recursive parts of the network. Simulations using Eurostoxx50 financial index will illustrate the potential of such a strategy for avoiding negative asset returns and decreasing the investment risk.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gomes, C.: Maximus investment fund, Tech. report, GoBusiness (2010)

    Google Scholar 

  2. Bader, S., Hölldobler, S., Marques, N.: Guiding backprop by inserting rules. In: ECAI 2008 Workshop on Neural-Symbolic Learning and Reasoning, Greece, vol. 366, CEUR (2008)

    Google Scholar 

  3. Brock, W., Lebaron, B., Lakonishok, J.: Simple technical rules and stochastic properties of stock returns. Journal of Finance 47, 1731–1764 (1992)

    Article  Google Scholar 

  4. Elman, J.L.: Finding structure in time. Cognitive Science 14, 179–211 (1990)

    Article  Google Scholar 

  5. Feuring, T.: Learning in fuzzy neural networks. In: Proc. IEEE Int. Conf. Neural Networks, pp. 1061–1066 (1996)

    Google Scholar 

  6. d’Avila Garcez, A.S., Broda, K.B., Gabbay, D.M.: Neural-Symbolic Learning Systems — Foundations and Applications. In: Perspectives in Neural Computing, Springer, Berlin (2002)

    Google Scholar 

  7. Hölldobler, S., Kalinke, Y.: Towards a massively parallel computational model for logic programming. In: ECAI 1994 Workshop on Combining Symbolic and Connectionist Processing. pp. 68–77 (1994)

    Google Scholar 

  8. Marques, N.C.: An extension of the core method for continuous values: Learning with probabilities. In: New Trends in Artificial Intelligence, pp. 319–328. APPIA (2009)

    Google Scholar 

  9. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)

    Article  MATH  MathSciNet  Google Scholar 

  10. Marques, N., Gomes, C.: T.: An intelligent moving average. In: Proceedings of the 19th European Conference on Artificial Intelligence - ECAI 2010 (2010)

    Google Scholar 

  11. Sheikh, A.Z., Quiao, H.: Non-normality of market returns. The Journal of Alternative Investments 12(3) (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Marques, N.C., Gomes, C. (2010). Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy. In: Bi, Y., Williams, MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science(), vol 6291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15280-1_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15280-1_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15279-5

  • Online ISBN: 978-3-642-15280-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics