The authors hope to bridge the gulf between technical analysis and quantitative
finance by developing an automated, computer-based, systematic and scientific
approach to such analysis. Based on the smoothing technique known as
“nonparametric kernel regression,” this new approach identifies
regularities in time series of prices by extracting nonlinear patterns from
noisy data. The authors find that certain technical patterns when applied to a
large number of U.S. stocks from 1962 to 1996 provide incremental information,
especially for Nasdaq stocks. The new approach suggests that traditional
technical analysis can be improved by using automated algorithms and that
traditional patterns, such as the head-and-shoulders pattern, may not be
optimal. Moreover, patterns that are optimal for detecting statistical anomalies
need not be optimal for indicating trading profits, and vice versa. In an
additional “discussion” section, Narasimhan Jegadeesh provides
valuable analysis and commentary regarding the authors' methodology, findings,
and conclusions.
Technical analysis has been used by traders and investors for many decades, but it has
not received the academic acceptance that fundamental analysis has. One of the main
obstacles is the highly subjective nature of technical analysis. The authors examine and
evaluate complicated technical trading strategies that are hard to define and implement
objectively.
The general goal of technical analysis is to identify regularities in the time series of
prices by extracting nonlinear patterns from noisy data. The authors propose using
smoothing estimators to extract nonlinear relationships by averaging out the noise. They
start with quantitative definitions of 10 patterns that are commonly used by
technicians, such as the head-and-shoulders, triangle top, and rectangle bottom
patterns. They then smooth the price data using kernel regressions and construct an
algorithm for automating the detection of technical patterns. The algorithm contains
three steps: (1) Define each technical pattern in terms of its geometrical properties,
such as local maximums and minimums; (2) construct a kernel estimator of a given time
series of prices so that its extreme can be determined numerically; and (3) analyze the
kernel estimator for occurrences of each technical pattern. The final steps are simply
applications of kernel regression, but the first step is controversial because it
requires the skill and judgment of a professional technical analyst.
The authors examine five pairs of technical patterns that are among the most popular
patterns of traditional technical analysis: head-and-shoulders and inverse
head-and-shoulders, broadening tops and bottoms, triangle tops and bottoms, rectangle
tops and bottoms, and double tops and bottoms. These 10 patterns, among other technical
indications, are chosen to demonstrate the power of smoothing techniques because these
patterns are difficult to quantify analytically.
The authors apply the kernel regression approach to identify technical patterns in the
daily returns of individual NYSE/Amex and Nasdaq stocks from 1962 to 1996. They also
split the data into NYSE/Amex stocks and Nasdaq stocks and into seven five-year
subperiods to reduce the effects of nonstationarities induced by changing market
structure and institutions. In each five-year period, 10 stocks from each of five
market-capitalization groups are randomly selected. This procedure produces a sample of
50 stocks for each subperiod. The empirical results show that for the entire
1962–96 period, the most common patterns are double tops and double bottoms,
followed by head-and-shoulders and inverted head-and-shoulders patterns. Most of the 10
patterns are more frequent for larger stocks than for smaller ones, and they are
relatively evenly distributed over the five-year subperiods.
The authors' algorithms allow them to recognize patterns objectively by using a computer
rather than visually. They then compare the postpattern distribution of stock returns
with the unconditional return distribution to evaluate the effectiveness of technical
analysis. They find that certain technical patterns provide useful information,
especially for Nasdaq stocks. Although this finding does not imply that technical
trading rules can be used for identifying profitable investment opportunities, it raises
the possibility that technical analysis can add value to the investment process. The
authors conclude that traditional technical analysis can be improved by using automated
algorithms, and theirs is one of many possible techniques.
In a separate discussion section by Narasimhan Jegadeesh, he states that the authors'
findings are an important contribution to the literature on technical trading. The
results indicate that by lessening the subjectivity in technical pattern identification
by using an algorithm for pattern recognition, technical analysis can be a useful
adjunct to fundamental trading strategies. The evidence, however, does not support the
use of technical analysis as a tool to independently identify profitable trading
opportunities. Jegadeesh recommends that future research be conducted to build on the
study's use of an algorithmic approach to technical pattern recognition. Such research
might identify profitable trading rules and then use the algorithm in choosing patterns
that can predict future returns.