文档介绍：THE JOURNAL OF FINANCE • VOL. LV, NO. 4 • AUGUST 2000
Foundations of Technical Analysis:
Computational Algorithms, Statistical
Inference, and Empirical Implementation
ANDREW W. LO, HARRY MAMAYSKY, AND JIANG WANG*
ABSTRACT
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 . stocks from 1962 to 1996 to evaluate the
effectiveness of technical analysis. paring 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.
ONE OF THE GREATEST GULFS between academic finance and industry practice
is the separation that exists between technical analysts and their academic
critics. In contrast to fundamental analysis, which was quick to be adopted
by the scholars of modern quantitative finance, technical analysis has been
an orphan from the very start. It has been argued that the difference be-
tween fundamental analysis and technical analysis is not unlike the differ-
ence between astronomy and astrology. Among some circles, technical analysis
is known as “voodoo finance.” And in his influential book A Random Walk
down Wall Street, Burton Malkiel ~1996! concludes that “***@u#nder scientific
scrutiny, chart-re