Technical analysis, also known as "charting," has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analy- sis. One of the main obstacles is the highly subjective nature of technical analy- sis-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 we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical dis- tribution of daily stock returns to the conditional distribution-conditioned on spe- cific technical indicators such as head-and-shoulders or double-bottoms-we find that over the 31-year sample period, several technical indicators do provide incre- mental information and may have some practical value.
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