A new recognition algorithm for “head-and-shoulders” price patterns

Terence Tai Leung Chong and Poon Ka-Ho
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Poon Ka-Ho: Department of Economics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

Studies in Nonlinear Dynamics & Econometrics, 2017, vol. 21, issue 5, 18

Abstract: Savin et al. [Savin, G., P. Weller, and J. Zvingelis. 2007. “The Predictive Power of “Head-and-Shoulders” Price Patterns in the US Stock Market.” Journal of Financial Econometrics 5: 243–265.] and Lo et al. [Lo, A. W., H. Mamaysky, and J. Wang. 2000. “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation.” Journal of Finance 55: 1705–1765.] analysed the predictive power of head-and-shoulders (HS) patterns in the U.S. stock market. The algorithms in both studies ignored the relative position of the HS pattern in a price trend. In this paper, a filter that removes invalid HS patterns is proposed. It is found that the risk-adjusted excess returns for the HST pattern generally improve through the use of our filter.

Keywords: head-and-shoulders pattern; kernel regression; technical analysis (search for similar items in EconPapers)
JEL-codes: G02 G14 G17 (search for similar items in EconPapers)
Date: 2017
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Working Paper: A New Recognition Algorithm for “Head-and-Shoulders” Price Patterns (2014)
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DOI: 10.1515/snde-2015-0066

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