Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation
Technical analysis, also known as "charting", has been a part of financial practice for many decades, yet little academic research has been devoted to a systematic evaluation of this discipline. One of the main obstacles is the highly subjective nature of technical analysis---the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of US stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution---conditioned on specific technical indicators such as head-and-shoulders or double-bottoms---we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.
|Date of creation:||19 Mar 1999|
|Date of revision:|
|Publication status:||Published, Journal of Finance, 55:4, August 2000, 1705-1765.|
|Contact details of provider:|| Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA|
Web page: http://fmwww.bc.edu/CEF99/
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