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

Jian Yao, Jun Chen, Ke Xu, Zhaoyang Zhao, Tao Yu and Bill C. Giessen
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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

A chapter in Practical Fruits of Econophysics, 2006, pp 131-135 from Springer

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.

Keywords: S&P500 Futures; Optimization of N-ϕ Prediction Method; Price Cycle Periods; Market Prediction (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-28915-9_23

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DOI: 10.1007/4-431-28915-1_23

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