Agent-Based Modelling with Wavelets and an Evolutionary Artificial Neural Network: Applications to CAC 40 Forecasting

  • Serge Hayward
Part of the New Economic Windows book series (NEW)


Analysis of separate scales of a complex signal provides a valuable source of information, considering that different financial decisions occur at different scales. Wavelet transform decomposition of a complex time series into separate scales and their economic representation is a focus of this study. An evolutionary / artificial neural network (E/ANN) is used to learn the information at separate scales and combine it into meaningfully weighted structures. Potential applications of the proposed approach are in financial forecasting and trading strategies development based on individual preferences and trading styles.


Discrete Wavelet Transform Continuous Wavelet Transform Sharpe Ratio Downside Risk Price Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dacorogna M. et al. (2001) An Introduction to High-Frequency Finance. Academic PressGoogle Scholar
  2. 2.
    Kaastra I, Boyd M (1996) Neurocomputing 10:215–236CrossRefGoogle Scholar
  3. 3.
    Mallat SG (1989) IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7):674–693MATHCrossRefADSGoogle Scholar
  4. 4.
    Hayward S (2005) Computational Economics 25(1–2):25–40MATHCrossRefGoogle Scholar
  5. 5.
    Leitch G, Tanner E (2001) American Economic Review 81:580–590Google Scholar
  6. 6.
    Hayward S (2006) in Practical Fruits of Econophysics, Ed. Takayasu H, Springer-Verlag: Tokyo. p. 99–106.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia 2006

Authors and Affiliations

  • Serge Hayward
    • 1
  1. 1.Department of Finance Ecole Supérieure de Commerce de DijonDijonFrance

Personalised recommendations