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Article

Price-Pattern Recognition Using a Local Polynomial
Regression

Sheng-Yang Wang and Guoyi Zhang
The Journal of Trading Spring 2012, 7 (2) 37-43; DOI: https://doi.org/10.3905/jot.2012.7.2.037
Sheng-Yang Wang
is a graduate student at the University of New Mexico in Albuquerque, NM.
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  • For correspondence: lambwang1@gmail.com
Guoyi Zhang
is an assistant professor in the Department of Mathematics and Statistics at the University of New Mexico in Albuquerque, NM.
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  • For correspondence: gzhang123@gmail.com
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Abstract

Technical analysis, also known as “charting,” has received con - siderable attention for many decades. Much of the work of technical analysts has relied on the ability to recognize patterns when displayed pictorially. Some researchers have investigated the use of nonparametric methods to identify price patterns. Subjective bandwidth selection and boundary problems are two main issues when applying the Nadaraya–Watson kernel estimator to pattern recognition. To fill the gap, we propose a complete data-driven technical analysis algorithm with the application of a nonparametric local linear estimator. We incorporate trading volume together with trading price to define the patterns. Empirical implementation on S&P 500 Index stocks indicates that the proposed algorithm is very informative and promising.

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Price-Pattern Recognition Using a Local Polynomial
Regression
Sheng-Yang Wang, Guoyi Zhang
The Journal of Trading Mar 2012, 7 (2) 37-43; DOI: 10.3905/jot.2012.7.2.037

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Price-Pattern Recognition Using a Local Polynomial
Regression
Sheng-Yang Wang, Guoyi Zhang
The Journal of Trading Mar 2012, 7 (2) 37-43; DOI: 10.3905/jot.2012.7.2.037
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