A Test of Technical Analysis: Matching Time Displaced Generalized Patterns

• Author: Jimmy Hilliard,James Squire,Adam Schwartz
• Date: 01 June 2013
• DOI: http://doi.org/10.1111/fima.12002
• Published date: 01 June 2013

A Test of Technical Analysis: Matching

Time Displaced Generalized Patterns

Jimmy Hilliard, Adam Schwartz, and James Squire∗

We use a least squares metric to match the return pattern of a target stock with that of an out-

of-sample-twin. The twin with the smallest metric is found by a comprehensive period-by-period

search of stocks in the Center for Research in Security Prices data set extending back to 1926. If

technical analysis has value, targets of twins producingthe highest returns in the twin postperiod

should also have the highest performance in the target postperiod. Using a randomly selected

sample of 66,000 return patterns, we find higher means for targets corresponding to the highest

returning twin quintile. We also use regressions to risk adjust target returns and find that twin

returns in the postmatch period significantly predict risk-adjusted target returns.

The historical price graph may be the most recognizable of all forms of information considered

by the ordinary investor. However, financial academics, relying on the logic of the efficient

markets theorem and exhaustive empirical investigations, largely contend that the information

contained in past stock prices is of little value to the investor. Conversely, technical analysts

embrace the idea that past patterns are informative. Malkiel (1981) states “ ...technical analysis

is anathema to the academic world. We love to pick on it. Our bullying tactics are prompted by

two considerations: (1) the method is patently false and (2) it easy to pick on. And while it may

seem a bit unfair to pick on such a sorry target, just remember it is your money we are trying

to save.” Technical analysts counter that academic studies can never capture every nuance of the

stock chart. In addition, if technical analysis is of no value, it is puzzling why it is still prevalent

in the marketplace. Why would a rational economic agent engage in such activityand why would

firms pay for this form of human capital? Lo, Mamaysky, and Wang (2000) characterize the

difference between technical analysis and quantitative finance as follows: “Technical analysis

is primarily visual, whereas quantitative finance is primarily algebraic and numerical.” And

“technical analysis has survived through the years, perhaps because its visual model of analysis

is more conducive to human cognitions....” Menkhoff (2010) f inds that the vast majority of

692 fund managers in five market (the United States, Germany, Switzerland, Italy, and Thailand)

trading countries rely heavily on technical analysis. He concludes that “At a forecasting horizon

of weeks, technical analysis is the most important form of analysis and up to this horizon it is

thus more important than fundamental analysis.”

The authors would like to thank Bill Christie (Editor) and an anonymous refereefor helpful comments and suggestions.

The paper has also benefited from the comments received at presentations at the Auburn University Seminar series

and the 2011 meeting of the Eastern Finance Association. Professor Hilliardacknowledges the support of Kathryn and

Raymond Harbert and Professor Schwartz acknowledges the support of the Lenfest grant program. We are responsible

for any errors.

∗Jimmy E. Hilliard is Professorof Finance and the Harbert Eminent Scholar at Auburn University in Auburn University,

AL. Adam Schwartz is the Lawrence Term Professor of Business Administration at the Williams School of Commerce,

Economics and Politics at Washington and Lee University in Lexington, VA. James C. Squire is Professor of Electrical

Engineering at Virginia Military Institute in Lexington, VA.

Financial Management •Summer 2013 •pages 291 - 314

292 Financial Management rSummer 2013

A significant problem in technical analysis is the precise mathematical definition and prespec-

ification of the patterns or rules. Neftci (1991) addresses the problem of the precise mathematical

definition of technical rules and notes that any well defined rule must pass the test of being

defined in Markov time. In short, at time t, the rule must give buy and sell signals without

using information from times τ>t. Further, he notes that according to the Wiener-Kolmogorov

prediction theory, vector autoregressions (VARs) should yield the optimal linear forecast. Hence,

any forecast that improves on VARs must be based on nonlinear methods.

Another problem confronting technical analysis is that of data snooping. Data snooping occurs

when the same data set is used repeatedly to investigate different models of pricing or selection

rules. Since there are no equilibrium models of technical analysis, efforts to find profitable

trading rules are necessarily ad hoc. Thus, the significance levels are suspect and almost certainly

overstated. Sullivan, Timmermann, and White (1999) use White’s (2000) reality check bootstrap

methodology to develop data snooping adjustments in the context of technical analysis.

Our approach satisfies the Neftci (1991) criterion and largely avoids the issue of data snooping

since we do not prespecify patterns or selection rules. Using a database of daily and monthly stock

returns from the Center for Research in Securities Prices (CRSP) stocks, we randomly choose

target stocks and, via exhaustive search, compare their (normalized) price pattern with the price

pattern of stocks at an earlier period. The stock and interval with the best matching pattern is

referred to as the “twin.” The selected twin can be a previous pattern of the target stock, but it is

more likely to be a pattern from some other stock in the universe.

There are four relevant intervalsin the target-twin paradigm: 1) the match period of the target, 2)

the match period of the twin, 3) the postmatch period of the twin, and 4) the postmatch period of

the target. All match and postperiods of the twin precede in time and do not intersect the match and

postperiods of the target. The out-of-sample performance of the target (target postmatch period)

is inferred from the out-of-sample, but from the known performance of the twin (twin postmatch

period). If the twin pattern is informative, twins that perform well in the twin postperiod will

significantly explain the performance of the target in the postmatch period. We use regressions

and the target/twin paradigm to test the hypothesis that price patterns are informative.

The paper is organized as follows. We review the literature in Section I. Section II develops

the model, while Section III documents the data and screens. Section IV outlines the regression

models and Section V provides our conclusions.

I. Background

An early form of technical analysis was formulated by Charles Dow (1851-1902), founder of

Dow Jones & Company and the Wall Street Journal. The Dow theory, popularized in Wall Street

Journal editorials, is developed in Robert Rhea’s book (1932) and in Murphy (1986). Following

Dow, Alfred Cowles (1933) demonstrated that the Dow Theory, as interpreted, would earn less

than a well diversified buy and hold portfolio. In more recent years, the use of technical trading

rules and pattern identification techniques have been proposed and tested extensively by a number

of scholars including Alexander (1961), Brock, Lakonishok, and LeBaron (1992), and Lo et al.

(2000) to name just a few.Alexander’s(1961) f ilter rule was first shown to produce returns greater

than buy and hold, but Fama and Blume (1966), using the 30 stocks in the Dow Jones Industrial

Average and the filter rule, found that when transaction costs were taken into account, only two

of the 30 stocks in the Dow Jones Industrial Average (DJIA) would have bested buy and hold.

Sweeney (1988), using the filter rule and transaction costs available to floor traders, finds that 14

of the surviving stocks in the Dow beat buy and hold for the period 1970-1982. Brock et al. (1992)