Skip to main content
SpringerLink
Log in

Evolutionary Induction of Trading Models

  • Chapter
Evolutionary Computation in Economics and Finance

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 100))

Abstract

Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed, and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterizing rules obtained with alternate fitness criteria.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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. Allen F., Karajalainen R. (1999) Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics 51(2), 245–71.

    Article  Google Scholar 

  2. Arthur W. B., Holland J. H., LeBaron B., Palmer R., Tayler P. (1997) As set Pricing Under Endogenous Expectations in an Artificial Stock Market. In Arthur W. B., Durlauf S. and Lane D. (Eds.), The Economy as an Evolving Complex System II, Addison-Wesley, pp. 15–44.

    Google Scholar 

  3. Bauer R. J. Jr. (1994) Genetic Algorithms and Investment Strategies. John Wiley and Sons, NY

    Google Scholar 

  4. Bhattacharyya S., Pictet O. V., Rumbach G. (1998) Representational Semantics for Genetic Programming Based Learning in High-Frequency Financial Data. In: Koza J. R. (Ed.) Proceedings of the Third Annual Genetic Programming Conference. Wisconsin, MD, Morgan Kaufmann, 11–16

    Google Scholar 

  5. Bhattacharyya S., Mehta K. (1999) Genetic Learning in Financial Time Series Data: Focus on Fitness Measures. In Review. 1999

    Google Scholar 

  6. Brock W. A. (1996) Asset Pricing Behavior in Complex Environments. In: Arthur W. B., Lane D., Durlauf S. (Eds.) The Economy as an Evolving Complex System II

    Google Scholar 

  7. Brock W., Lakonishok J., LeBaron B. (1992) Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47(5), 1731–1764

    Article  Google Scholar 

  8. Cootner P. H. (1962) Stock Prices: Random vs. Systematic Changes. Industrial Management Review 3(2), 24–45

    Google Scholar 

  9. Dacorogna M. M., Müller U. A., Jost C, Pictet O. V., Olsen R. B., Ward J. R. (1995) Heterogeneous Real-Time Trading Strategies in the Foreign Exchange Market. The European Journal of Finance 1, 383–403

    Article  Google Scholar 

  10. DeJong K., Spears W. M., Gordon D. F. (1993) Using Genetic Algorithms for Concept Learning. Machine Learning 13, 161–188

    Article  Google Scholar 

  11. Goldberg D. E. (1989) Genetic Algotrithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA

    Google Scholar 

  12. Goodhart C, O’Hara M. (1995) High-Frequency Data in Financial Markets: Issues and Applications. London School of Economics and Johnson Graduate School of Management, Cornell University

    Google Scholar 

  13. Guillaume D. M., Dacorogna M. M., Dave R. D., Müller U. A., Olsen R. B., Pictet O. V. (1997) From the Bird’s Eye to the Microscope: A Survey of New Stylized Facts of the Intra-daily Foreign Exchange Markets. Finance and Stochastics 1, 95–129

    Article  MATH  Google Scholar 

  14. Harrald P. G., Kamstra M. (1997) Evolving Artificial Neural Networks to Combine Financial Forecasts. IEEE Transactions on Evolutionary Computation 1(1), 40–52

    Article  Google Scholar 

  15. Janikow C. Z., DeWeese S. (1998) Processing Constraints in Genetic Programming with CGP2.1. In: Koza J. R. et al. (Eds.) Proceedings of the Third Annual Conferenceon Genetic Programming. Wisconsin, Madison, Morgan Kaufmann, 173–180

    Google Scholar 

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

    MATH  Google Scholar 

  17. LeBaron B. (1991) Technical trading rules and regime shifts in foreign exchange, Santa Fe Institute Working Paper 91–10–044

    Google Scholar 

  18. Mehta K., Bhattacharyya S.. Combining Rules Learnt Using Genetic Algorithms for Financial Forecasting. In Angeline P. (Eds.) Proceedings of the 1999 Congress on Evolutionary Computation (CEC-99), Washington D.C, IEEE, 1245–1252

    Google Scholar 

  19. Michalewicz Z. (1994) Genetic Algorithms + Data Structures = Evolution Programs, 2nd Edition, 1994, Springer-Verlag

    Book  MATH  Google Scholar 

  20. Mitchell M. (1996) An Introduction to Genetic Algorithms, 1996, MIT Press

    Google Scholar 

  21. Montana D. J. (1995) Strongly Typed Genetic Programming. Evolutionary Computation, 3(2), 199–230

    Article  Google Scholar 

  22. Müller U. A., Dacorogna M. M., Dave R. D., Pictet O. V., Olsen R. B., Ward J. R. (1993) Fractals and Intrinsic Time-A Challenge to Econometricians. Invited presentation at: the XXXIXth International AEA Conference on Real Time Econometrics, 14–15 Oct 1993 in Luxembourg, and at: the 4th International Parallel Problem Solving from Nature - Applications in Statistics and Economics Workshop, 22–26 Nov 1993 in Ascona (Switzerland)

    Google Scholar 

  23. Müller U. A. (1989) Indicators for Trading Systems. Technical Report UAM.1989–08–10, Olsen and Associates, Zurich

    Google Scholar 

  24. Neely C, Weiler P., Ditmar R. (1997) Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach. Journal of Financial and Quantitative Analysis 32(4), 405–427

    Article  Google Scholar 

  25. Oussaidene M., Chopard B., Pictet O. V., Tomassini M. (1996) Parallel Gen-tic Programming and Its Application for Trading Model Induction. Parallel Computing (Netherlands), 23(8), 1997, 1183–1198

    Article  MATH  Google Scholar 

  26. Pantazopoulos K. N., Tsoukalas L. H., Bourbakis N. G., Brun M. J., Houstis E. N. (1998) Financial Prediction and trading Strategies Using Neurofuzzy Approaches. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 28(4), 520–531

    Article  Google Scholar 

  27. Pictet O. V., Docorogna M. M., Chopard B., Shirru M. O. R., Tomassini M. (1995) Using Genetic Algorithms for Robust Optimization in Financial Applications. In: Parallel Problem Solving from Nature-Applications in Statistics and Economics Workshop, Germany

    Google Scholar 

  28. Pictet O. V., Dacorogna M. M., Müller U. A., Olsen R. B., Ward J. R. (1992) Real-time Trading Models for Foreign Exchange Rates. Neural Network World 2(6), 713–744

    Google Scholar 

  29. Weigend A. S., Abu-Mostafa Y. S., Refenes A. P. N. (1996) Decision Technologies for Financial Engineering: Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets (NNCM-96). World Scientific, Singapore

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information and Decision Sciences, College of Business Administration, University of Illinois at Chicago, USA

    Siddhartha Bhattacharyya & Kumar Mehta

Authors
  1. Siddhartha Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kumar Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan, R. O. China

    Shu-Heng Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bhattacharyya, S., Mehta, K. (2002). Evolutionary Induction of Trading Models. In: Chen, SH. (eds) Evolutionary Computation in Economics and Finance. Studies in Fuzziness and Soft Computing, vol 100. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1784-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1784-3_17

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2512-1

  • Online ISBN: 978-3-7908-1784-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation