RT Journal Article SR Electronic T1 Price-Pattern Recognition Using a Local Polynomial
Regression JF The Journal of Trading FD Institutional Investor Journals SP 37 OP 43 DO 10.3905/jot.2012.7.2.037 VO 7 IS 2 A1 Sheng-Yang Wang A1 Guoyi Zhang YR 2012 UL https://pm-research.com/content/7/2/37.abstract AB 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.TOPICS: Statistical methods, technical analysis, exchanges/markets/clearinghouses