Skip to main content
SpringerLink
Log in

Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data

  • Conference paper
Artificial Neural Nets and Genetic Algorithms

Abstract

In this paper, the stock index S&P 500 is used to test the predicting performance of genetic programming (GP) and genetic programming neural networks (GPNN). While both GP and GPNN are considered universal approximators, in this practical financial application, they perform differently. GPNN seemed to suffer the overlearning problem more seriously than GP; the latter outdid the former in all the simulations.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. S.-H. Chen and C.-W. Tan. Measuring Randomness by Rissanen’s Stochastic Complexity: Applications to the Financial Data, pages 200–211. World Scientific, 1996.

    Google Scholar 

  2. S.-H. Chen and C.-H. Yeh. Bridging the gap between nonlinearity tests and the efficient market hypothesis by genetic programming. In Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, pages 34–39. IEEE Press, 1996.

    Google Scholar 

  3. A.I. Esparcia-Alcazar and K.C. Sharman. Evolving recurrent neural network architectures by genetic programming. In Proc. Genetic Programming 1996 Conference. Stanford, CA, U.S.A., July 28–31 1996.

    Google Scholar 

  4. J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, MA, 1992.

    MATH  Google Scholar 

  5. S. Makridakis. Accuracy measure: Theoretical and practical concerns. International Journal of Forecasting, 9:527–529, 1993.

    Article  Google Scholar 

  6. F. Wong. Neurogenetic computing technology. NeuroVest Journal, 2(4):12–15, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, National Chengchi University, Taipei, Taiwan, 11623

    S.-H. Chen & C.-C. Ni

Authors
  1. S.-H. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C.-C. Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Wien

About this paper

Cite this paper

Chen, SH., Ni, CC. (1998). Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_87

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-6492-1_87

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83087-1

  • Online ISBN: 978-3-7091-6492-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation