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 analysis.
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 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 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.