Skip to main content
SpringerLink
Log in

Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading

  • Conference paper
Applications of Evolutionary Computation (EvoApplications 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6025))

Included in the following conference series:

Abstract

Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the simple “buy and hold” trading strategy. Many succeeding attempts have reported similar findings. There are a small handful of cases in which such work has managed to find rules that outperform buy-and-hold, but these have tended to be difficult to replicate. Recently, however, Lohpetch & Corne (2009) investigated work by Becker & Seshadri (2003), which showed outperformance of buy-and-hold. In turn, Becker & Seshadri’s work had made several modifications to Allen & Karjalainen’s work, including the adoption of monthly rather than daily trading. Lohpetch et al (2009) provided a replicable account of this, and also showed how further modifications enabled fairly reliable outperformance of buy-and-hold. It remained unclear, however, whether adoption of monthly trading is necessary to achieve robust outperformance of buy-and-hold. Here we investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions.

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

Access this chapter

Log in via an institution
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Natural Computing Series. Springer, New York (2005)

    Google Scholar 

  2. Koza, J.R.: Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  3. Angeline, P.J.: Genetic Programming’s Continued Evolution. In: Angeline, P., Kinnear, K. (eds.) Advances in Genetic Programming, vol. 2, pp. 89–110. MIT Press, Cambridge (1996)

    Google Scholar 

  4. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, San Francisco (1998)

    MATH  Google Scholar 

  5. Chen, S.H., Yeh, C.: Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming. J. Econ. Dyn. & Control 21, 1043–1063 (1996)

    Article  MathSciNet  Google Scholar 

  6. Fyfe, C., Marney, J.P., Tarbert, H.: Technical Trading versus Market Efficiency: A Genetic Programming Approach. Applied Financial Economics 9, 183–191 (1999)

    Article  Google Scholar 

  7. Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51, 245–271 (1999)

    Article  Google Scholar 

  8. Marney, J.P., Fyfe, C., Tarbert, H., Miller, D.: Risk Adjusted Returns to Technical Trading Rules: A Genetic Programming Approach. In: Computing in Economics and Finance, Soc. for Computational Economics, June 2001. Yale University, USA (2001)

    Google Scholar 

  9. Chen, S.H.: Genetic Algorithms and Genetic Programming in Computational Finance. Kluwer, Boston (2002)

    Google Scholar 

  10. Cheng, S.L., Khai, Y.L.: GP-Based Optimisation of Technical Trading Indicators and Profitability in FX Market. In: Proc. 9th ICONIP, vol. 3, pp. 1159–1163 (2002)

    Google Scholar 

  11. Farnsworth, G., Kelly, J., Othling, A., Pryor, R.: Successful Technical Trading Agents Using Genetic Programming, Tech. Rep. SAND2004-4774, Sandia Nat’l Labs (2004)

    Google Scholar 

  12. Potvin, J.Y., Soriano, P., Vallée, M.: Generating Trading Rules on the Stock Markets with Genetic Programming. Computers and Operations Research 31(7), 1033–1047 (2004)

    Article  MATH  Google Scholar 

  13. Pring, M.J.: Technical Analysis Explained. McGraw-Hill, New York (1980)

    Google Scholar 

  14. Ruggiero, M.A.: Cybernetic Trading Strategies. Wiley, New York (1997)

    Google Scholar 

  15. Murphy, J.: Technical Analysis of the Financial Markets. New York Inst. Finance, New York (1999)

    Google Scholar 

  16. Lo, A.W., Mamaysky, H., Wang, J.: Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. J. of Finance 55, 1705–1770 (2000)

    Article  Google Scholar 

  17. Marney, J.P., Miller, D., Fyfe, C., Tarbert, H.: Technical Analysis versus Market Efficiency: A Genetic Programming Approach. In: Computing in Economics and Finance, Society for Computational Economics, Barcelona, Spain (paper #169) (July 2000)

    Google Scholar 

  18. Neely, C.: Risk-adjusted, ex ante, optimal technical trading rules in equity markets. In: Working Papers 99-015D, Revised, Federal Reserve Bank of St. Louis (August 2001)

    Google Scholar 

  19. Becker, L.A., Seshadri, M.: Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules, Worcester Polytechnic Institute, Computer Science Technical Report WPI-CS-TR-03-09 (2003)

    Google Scholar 

  20. Becker, L.A., Seshadri, M.: Cooperative Coevolution of Technical Trading Rules, Worcester Polytechnic Institute, Computer Science Tech. Rep. WPI-CS-TR-03-15 (2003)

    Google Scholar 

  21. Becker, L.A., Seshadri, M.: GP-evolved technical trading rules can outperform buy and hold. In: Proc. 6th Int’l Conf. on Comp. Intell. & Natural Computing (2003)

    Google Scholar 

  22. Sharpe, W.F.: Mutual Fund Performance. Journal of Business 39(S1), 119–138 (1966)

    Article  Google Scholar 

  23. Marney, J.P., Tarbert, H., Fyfe, C.: Risk Adjusted Returns from Technical Trading: A Genetic Programming Approach. Appl. Financial Economics 15, 1073–1077 (2005)

    Article  Google Scholar 

  24. Lohpetch, D., Corne, D.: Discovering Effective Technical Trading Rules with Genetic Programming: Towards Robustly Outperforming Buy-and-Hold. In: Proc. NABIC 2009. IEEE Press, Los Alamitos (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of MACS, Heriot-Watt University, Edinburgh, UK

    Dome Lohpetch & David Corne

Authors
  1. Dome Lohpetch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Corne
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. School of Business, University College Dublin, Belfield, Dublin 4, UK

    Anthony Brabazon

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Wilhelm Schickard-Institut für Informatik, Universität Tübingen, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

  5. National University of Computer and Emerging Sciences (NUCES), A.K. Brohi Road, H-11/4, Islamabad, Pakistan

    Muddassar Farooq

  6. Fakultät für WiSo, HelmutSchmidt-Universität, Holstenhofweg 85, 22043, Hamburg, Germany

    Andreas Fink

  7. Department of Information Systems, Johannes Gutenberg-Universität, 55099, Mainz, Germany

    Jörn Grahl

  8. Mathematics and Computer Science Depatment, University of Richmond, 23173, Richmond, VA, USA

    Gary Greenfield

  9. Department of Informatics Engineering, Polo II - Pinhal de Marrocos, University of Coimbra, 3030 - 290, Coimbra, Portugal

    Penousal Machado

  10. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, UK

    Michael O’Neill

  11. Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR), Consiglio Nazionale delle Ricerche (CNR), Via P. Castellino 111, 80131, Naples, Italy

    Ernesto Tarantino

  12. School of Computing, Edinburgh Napier University, 10 Clinton Road, EH10 5DT, Edinburgh, UK

    Neil Urquhart

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lohpetch, D., Corne, D. (2010). Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12242-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12241-5

  • Online ISBN: 978-3-642-12242-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation