International Review of Financial Analysis

13 (2004) 217–225

Technical analysis as the representation

of typical cognitive biases

Piotr Zielonka

Warsaw University SGGW, Rakowiecka 26/30, 02-528 Warsaw, Poland

Accepted 13 February 2004

Abstract

The present research provides a justification for the popularity of the technical analysis. It finds

that financial analysts firmly discriminate between two types of technical signals—those based on

typical cognitive biases and ‘‘empty’’ signals that sound like a technical analysis but are without any

connotation with psychological inclinations.

At the same time that they treat them differently, different analysts rate these items very similarly.

These results suggest that the popularity of technical analysis is associated with its relation to the

typical cognitive biases of humans.

D 2004 Elsevier Inc. All rights reserved.

JEL classification: D84; G14

Keywords: Behavioral finance; Psychological biases; Technical analysis

1. Introduction

In spite of a long list of publications showing that market movement is random

(Alexander, 1961; Cootner, 1964; Malkiel, 1996; Samuelson, 1965) or at least very

difficult to predict (Lo & MacKinlay, 1999), considerable effort is devoted to forecasting

future stock prices.

One of the forecasting tools very popular among practitioners is technical analysis.

When the keyword ‘‘technical analysis’’ is put into the Internet seek engine Google,

326,000 URLs are located as opposed to only 49,700 URLs for ‘‘portfolio theory.’’

Technical analysis is the examination of past price movements to forecast future price

movements. As previous research shows, it is more an art than an objective analytical tool,

1057-5219/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.irfa.2004.02.007

E-mail addresses: bio@wp.pl, zielonka@delta.sggw.waw.pl (P. Zielonka).

P. Zielonka / Int. Rev. Financ. Analy. 13 (2004) 217–225218

and its efficiency has not been proven (Fama & Blume, 1966; Jensen & Bennington,

1970). Technical analysis is open to interpretation. Many times, two technicians will look

at the same chart and paint two different scenarios or see two different patterns, and both

would be able to come up with logical support to justify their positions. Moreover,

technical analysts’ opinions are often extremely ambiguous, like ‘‘the stock seems

positioned to break through resistance at US$65, but a stop loss should be placed at

US$54.’’

In addition, even if stock prices completely followed a random walk, people would be

able to convince themselves that there are patterns having predictive value. In laboratory

experiments, subjects are reported to have found patterns in purely random sequences of

stock prices (Roberts, 1959; Warneryd, 2001).

An extensive body of research examining the efficiency of technical analysis on the

Polish stock market has shown negative results. No efficient technical analysis tools were

found (Janiec, 2000); neither was there any evidence showing that any replicable technical

scheme works for an extended period of time.

The following fundamental question arises: if there is no evidence that technical

analysis can result in exceptional profits, why is it so popular? Since technical analysis

started at the beginning of the 20th century when the possibilities of testing its

efficiency were very limited, it has become more and more popular, as it offers an

unlimited set of tools and signals and seems to be an interesting method of market

analysis. However, recent studies since the 1950s have shown that its efficiency is

very low.

So why does technical analysis still remain so popular? The first answer may be

related to tradition and education. Investors have used it for over a century, and new

market participants learn it from more advanced market participants. The second answer

is related to some common cognitive biases that affect people’s behavior. It has been

proven that stock prices approximately follow a random walk pattern most of the time,

and psychologists have described a number of ways in which people deal with

randomness. Additionally, market participants may be subject to herd behavior. Perhaps

these psychological mechanisms are the invisible fundamentals of technical analysis.

Below is a list of four common psychological inclinations affecting stock market

participants:

1. The gambler’s fallacy is observed in a casino, where after a long run of red, the roulette

player is inclined to believe that black will be next. In financial markets, the gambler’s

fallacy manifests itself in predicting a trend reversal. If a trend continuation is predicted,

then the behavior is called antiregressive (Wagenaar, 1988).

2. Misperception of regression to the mean disregards the statistical fact that in a long

series of a chance process, extreme values are likely to be followed by less extreme

values (Tversky, 1974). In financial markets, the misperception of regression to the

mean manifests itself in predicting a trend continuation.

3. The anchoring effect shows that most people initially tend to base their estimations or

assessment of information on a first source of reference value (anchor) and

subsequently adapt this to the real value. Empirical research shows that the adjustment

process is regularly cut short and is incomplete. The original value (anchor) is afforded

P. Zielonka / Int. Rev. Financ. Analy. 13 (2004) 217–225 219

too much weight (Goldberg & Nitzsch, 2001; Slovic & Lichtenstein, 1971; Tversky &

Kahneman, 1971). Investors overestimate the importance of a recently quoted or

otherwise significant stock price. This price may then serve as an anchor for further

probability assessments.

4. Herd behavior reflects the immense power of social pressure on individual judgment

(Asch, 1952). Many people accept the perceived authority of others on stock market

valuation and stop trusting their own judgments if the majority of market participants

hold a different opinion.

The present research was designed to determine if technical analysis signals are

associated with some common cognitive biases. A questionnaire was administered to

24 Polish financial analysts, and there were three groups of items within the

questionnaire:

5 regular technical analysis signals representing four common psychological biases;

5 technical-like signals created by the author of the questionnaire, representing the same

four biases: and

5 technical-like signals also created by the author of the questionnaire, not representing

any psychological biases.

Hypothesis 1: A majority of the financial analysts will highly rate signals belonging to the

first and the second groups and will assign no predictive value to the signals from the third

group.

If Hypothesis 1 is supported, then it can be concluded that some technical analysis

signals represent common cognitive biases, and this may be responsible for the great

popularity of technical analysis.

Hypothesis 2: A majority of the financial analysts will agree on the impact of particular

items for future stock prices.

If this hypothesis is supported, then it can be concluded that the analysts are subject to

some psychological biases, inasmuch as they agree on the predictive value of unproven

measures.

2. Method

The study was carried out in Warsaw in January–February 2002. The respondents were

24 financial analysts or dealers employed by banks and Polish capital market institutions.

A convenience sample was used. Each participant was administered a 24-item question-

naire. As noted, there were three groups of items within the questionnaire. Each group

consisted of eight items.

The first group consisted of regular technical analysis signals representing four

common psychological inclinations. Each inclination was represented by two signals.

Usually, one from a pair of signals was a predictor of a stock fall (� ), whereas the other

signal was a predictor of a stock rise (+).

P. Zielonka / Int. Rev. Financ. Analy. 13 (2004) 217–225220

(1) Gambler’s fallacy

– Rising WIG1 index breaks its main trend line (� );

– After a big rise, WIG index creates a ‘‘head and shoulders’’ formation (� ).

Both signals above are believed by technicians to precede a rapid trend reversal.

(2) Misperception of the regression to the mean

– Falling WIG index breaks successive levels of support (� );

– Rising WIG index confirms its main trend line (+).

The two signals above are regarded as signifying a continuation of an existing trend.

(3) Anchoring effect

– Rising WIG index repeatedly bounces from a resistance level—rising WIG fails to

surpass some barrier (� );

– Rising WIG index breaks strong psychological barriers (+).

According to technical analysts, both signals indicate that some stock price (or market

index) levels are especially significant for making predictions.

(4) Herd behavior

– A WIG index fall accompanied with a decreasing popularity of stock market (� );

– Dramatic rise in the value of a buy order (+).

Both signals above are believed to represent the behavior of a large group of investors,

which, according to technicians, have a strong impact on the future movements of

stock prices.

The second group consisted of technical-like signals created by the author of the

questionnaire, representing the same four psychological inclinations (two items for each

inclination).

(1) Gambler’s fallacy

– Rising WIG fails to surpass 4/5 of the last peak (� );

– After the index falls, a big overbalance of demand occurs (+).

(2) Anchoring effect

– Falling WIG index breaks October effect peak (� );

– Rising WIG breaks local peak of January effect (+).

(3) Misperception of the regression to the mean

– The fall of WIG index accompanied by a government crisis (� );

– WIG index’s increase accompanied by a drop of the unemployment rate (� ).

(4) Herd behavior

– Diminishing rate of a WIG fall accompanied by a vanishing trade turnover (+);

– Over 2 months’ rise of WIG accompanied by a high trading volume (+).

The third group consisted of technical-like signals (created by the author of the

questionnaire) that did not represent any psychological inclinations.

1 WIG is the main Polish stock exchange index—a total return index encompassing all shares listed on the

main market, calculated since 1991.

P. Zielonka / Int. Rev. Financ. Analy. 13 (2004) 217–225 221

– Drop of chemical companies’ prices;

– Horizontal, typically sinusoidal, WIG index movement;

– Rising WIG index creates longer and longer horizontal shelves;

– A fan formation support line moves upward;

– Diminishing dynamics of price rise in textile branch;

– An alternate large and small daily trade volume;

– Second moving average convergence/divergence (MACD)2 derivative goes negative;

– WIG index creates horizontal small amplitude sinusoid curve.

The technical analysis signals were assigned to particular psychological inclinations by

the author of the research. In a few cases, it is not clear if a technical signal represents only

one bias. For instance, it is not clear whether the item ‘‘Rising WIG index breaks strong

psychological barriers’’ represents the anchoring effect or herd behavior. However, it should

be emphasized that the problem of proper assignment is not crucial for the results of this

research. What is important is the distinction between the items representing any psycho-

logical inclination (the first and the second group) and the empty items (the third group).

The cover page of the questionnaire stated that the survey was designed to better

understand the opinions of experts on the implementation of technical analysis. This

remark allowed the respondents to feel more like experts, whose opinion was needed for

some further research rather than merely the persons to be examined. The respondents

were assured of confidentiality. Demographic information was collected on the respond-

ents’ age, educational level, sex, and employment status.

The respondents to the questionnaire were asked to assign a score of � 3, � 2, � 1, 0,

+ 1, + 2, or + 3 to each item, depending on how they estimated the impact of the item for

the future (within a few weeks) behavior of stock prices (the WIG index). The analysts

were asked what market movement they would expect if a certain technical signal

occurred, with a higher score indicating a stronger positive impact of the item, resulting

in a stock price rise. Thus, ‘‘� 3’’ meant that the item was expected to evoke a strong

stock price drop, while ‘‘ + 3’’ meant a strong rise. Zero meant that the item was regarded

by the participant as a neutral measure, having no effect on stock price changes.

All 24 respondents filled out the questionnaires. On average, less than 3% of the

questionnaire items remained unscored. No regularity was noticed among unscored items.

Statistical analysis of the scores was done with the use of the StatSoft computer program

Statistica 5.5.

3. Results

To examine which of the 24 items received the highest, lowest, and median marks,

indicating respondents’ opinions on its power to affect stock prices, a cluster analysis was

carried out (Hartigan, 1975) in two stages. A horizontal hierarchical tree plot and a graph

2 A technical indicator that signals overbought and oversold conditions by measuring the intensity of public

sentiment. TheMACD indicator is created by calculating the difference between two exponential moving averages.

A third exponential moving average is plotted on top of the MACD as a trigger line to provide buy and sell signals.

P. Zielonka / Int. Rev. Financ. Analy. 13 (2004) 217–225222

of the amalgamation schedule (Ward’s method amalgamation rule, Euclidean distances)

indicated three main clusters. K-means clustering (constant intervals) divided all 24 items

into the three clusters, as presented in Fig. 1.

Cluster 1 consists of eight signals, which, according to respondents, have little or no

impact on future stock prices. All eight in this cluster were technical-like signals created

by the author of the questionnaire, not representing any psychological inclinations.

Cluster 2 consists of eight items rated as highly important predictors of stock price

declines. All the technical and technical-like questionnaire items representing cognitive

biases associated with a stock price decline are included in this cluster.

Cluster 3 consists of eight signals rated by questionnaire respondents as highly important

predictors of stock price rises. All the technical and technical-like questionnaire items

representing cognitive biases associated with stock price rises are included in this cluster.

These results confirm Hypothesis 1; however, an ANOVA analysis of the mean absolute

values of scores for each of the three types of signals was used to precisely verify this.

The post hoc T-Tukey (HSD) test showed that the respondents discriminated between

the technical-like signals that trigger cognitive biases (TLS-P) and the technical-like

signals which did not represent any biases (TLS-N): P < .001. The respondents also

discriminated between the real technical signals (TA) and technical-like signals that

represented cognitive biases (TA-L biases): P < .001.

Fig. 1. The plot of the means for each of the three clusters. All eight technical-like signals not based on

psychological traps belong to Cluster 1. All the technical and technical-like signals based on psychological traps

representing a stock fall are included into Cluster 2. All the technical and technical-like signals based on

psychological traps representing a stock rise are included into Cluster 3.

Fig. 2. Mean rates of the predictive value (absolute values of the scores) of the three types of signals. TS—

technical signals; TLS-P—technical-like signals based on psychological traps; TLS-N—technical-like signals not

based on psychological traps. ANOVA F=163.78, p<0.0000.

P. Zielonka / Int. Rev. Financ. Analy. 13 (2004) 217–225 223

As Fig. 2 shows, the respondents assigned the highest predictive value to the real

technical signals (TS) and slightly lower values to the technical-like signals that

represented cognitive biases (TLS-P). At the same time, they assigned close to zero

predictive value to technical-like signals that did not represent any cognitive bias (TLS-N).

3.1. Interjudge agreement

The standard deviations of the scores assigned to each questionnaire item were between

0.5 and 1.3, which can be considered relatively small values. The small standard

deviations confirm that the analysts were in general agreement about the predictive value

of different signals of unproven efficiency. The likely explanation for the general

agreement among analysts is that they are subject to some psychological biases typical

for all humans. The results of this research confirm Hypothesis 2. The demographic

features of the respondents did not correlate in any way with their responses.

4. Discussion

In spite of much evidence of its low or zero efficiency for stock market forecasting,

technical analysis remains one of the most popular analytical tools among practi-

tioners. Its popularity may have been derived partly from habitual behavior. However,

P. Zielonka / Int. Rev. Financ. Analy. 13 (2004) 217–225224

habit alone cannot account for the popularity of technical analysis, considering its

complete lack of efficiency. Thus, there must be a more fundamental reason for this

persistence.

The present research provides an explanation for the popularity of technical

analysis. The popularity may well be caused by the fact that technical signals

represent typical cognitive biases such as the gambler’s fallacy. Financial analysts

firmly discriminated between two types of technical signals—those based on typical

cognitive biases and empty signals that sound like technical analysis but are without

any connotation of psychological inclinations. Both the ‘‘official’’ technical signals

and the technical-like signals created by the author and representing cognitive biases

were assigned a high predictive value by the financial analysts. On the other hand,

the empty technical-like signals created by the author and that did not correspond to

any cognitive inclinations were assigned a low predictive value. The official technical

analysis signals were scored significantly higher than technical-like signals represent-

ing cognitive biases. Presumably, this difference can be explained by the fact that

financial analysts are familiar with the official technical signals and unfamiliar with

technical-like signals created by the author. However, even new technical-like signals

which represent cognitive biases were scored fairly highly. Thus, one can create

relatively easily one’s own technical signals which will be perceived by technical

analysts as good predictors of stock market behavior. The only necessary condition is

that these new technical-like signals have to correspond to common cognitive

inclinations.

The believers in technical analysis for almost two centuries have discovered various

patterns on stock charts and have formulated their predictions accordingly. It appears as if

the patterns which have survived correspond to psychological heuristics. In particular,

those patterns which are the most popular among practitioners trigger the most persistent

cognitive inclinations, such as the gambler’s fallacy, anchoring effect, or the misperception

of the regression to the mean. This is in line with many findings (cf., e.g., Stephan & Kiell,

2000) showing that not only lay people but also assorted experts are subject to judgmental

heuristics and biases.

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