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Successful Price Cycle Forecasts for S&P Futures Using TF3, a Pattern Recognition Algorithms Based on the KNN Method

In: Practical Fruits of Econophysics

Author

Listed:
  • Bill C. Giessen

    (Northeastern University)

  • Zhaoyang Zhao

    (Northeastern University)

  • Tao Yu

    (Northeastern University)

  • Jun Chen

    (Northeastern University)

  • Jian Yao

    (Northeastern University)

  • Ke Xu

    (Northeastern University)

Abstract

Summary Basing on the perceived stationary internal structure of market movements on appropriate time scales, a series of interrelated pattern recognition programs was designed to compare specific features of current cycle “legs” with a selected universe of analogous prior market features periods which are then queried to obtain a prediction as to the future of the current cycle leg. Similarities are determined by a K-Nearest-Neighbor (KNN) method. This procedure yields good results in simulated S&P futures trading and demonstrates the hypothesized stationary of market responses to stimuli.

Suggested Citation

  • Bill C. Giessen & Zhaoyang Zhao & Tao Yu & Jun Chen & Jian Yao & Ke Xu, 2006. "Successful Price Cycle Forecasts for S&P Futures Using TF3, a Pattern Recognition Algorithms Based on the KNN Method," Springer Books, in: Hideki Takayasu (ed.), Practical Fruits of Econophysics, pages 116-120, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-28915-9_20
    DOI: 10.1007/4-431-28915-1_20
    as

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