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Market Cycle Turning Point Forecasts by a Two-Parameter Learning Algorithm as a Trading Tool for S&P Futures

In: Practical Fruits of Econophysics

Author

Listed:
  • Jian Yao

    (Northeastern University)

  • Jun Chen

    (Northeastern University)

  • Ke Xu

    (Northeastern University)

  • Zhaoyang Zhao

    (Northeastern University)

  • Tao Yu

    (Northeastern University)

  • Bill C. Giessen

    (Northeastern University)

Abstract

Summary Among the long-term stationary (although complex) behavior characteristics of futures markets is a set of identifiable intermediate-length (2–21.5 days) price cycles. Using a two-parameter extrapolation technique, time and price objectives of these cycles are determined. The valley-to-valley time differences (wave-lengths) are more regular than those for top-to-top, with standard deviations of the former about 50% smaller than those of the latter. The substantial profitability in S&P futures trading based on these parameters can be further increased by including additional features.

Suggested Citation

  • Jian Yao & Jun Chen & Ke Xu & Zhaoyang Zhao & Tao Yu & Bill C. Giessen, 2006. "Market Cycle Turning Point Forecasts by a Two-Parameter Learning Algorithm as a Trading Tool for S&P Futures," Springer Books, in: Hideki Takayasu (ed.), Practical Fruits of Econophysics, pages 131-135, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-28915-9_23
    DOI: 10.1007/4-431-28915-1_23
    as

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