social influence on predictions of simulated stock prices

Journal of Behavioral Decision Making

J. Behav. Dec. Making, 22: 271–279 (2009)

Published online 13 November 2008 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/bdm.625

*Correspondence to: Maria AndersSweden. E-mail: Maria.Andersson@

Copyright # 2008 John Wiley &

Social Influence on Predictions of SimulatedStock Prices

MARIA ANDERSSON*, TED MARTIN HEDESSTROMand TOMMY GARLING

Department of Psychology, University of Gothenburg, Sweden

ABSTRACT

Herding in financial markets refers to that investors are influenced by others. This studyaddresses the importance of consistency for herding. It is suggested that, in financialmarkets perceptions of consistency are based on repeated observations over time.Consistency may then be perceived as the agreement across time between investors’predictions. In addition, consistency may be related to variance over time in eachinvestor’s predictions. In an experiment using a Multiple Cue Probability Learningparadigm, 96 undergraduates made multi-trial predictions of future stock prices giveninformation about the current price and the predictions made by five fictitious others.Consistency was varied between the others’ predictions (correlation) and within theothers’ predictions (variance). The results showed that the predictions were signifi-cantly influenced by the others’ predictions when these were correlated. No effect ofvariance was observed. Hence, participants were influenced by the others when theywere in agreement, regardless of whether they varied their predictions over trials or not.Copyright # 2008 John Wiley & Sons, Ltd.

key words social influence; prediction; financial markets; herding

Investors in financial markets frequently make similar decisions. This phenomenon is referred to as herding

(for a review, see Hirshleifer & Teoh, 2003). It is a possible cause of market booms and bursts. For this reason,

the popular press often holds investors’ tendency to herd as responsible. However, research points in opposing

directions; while some studies confirm the existence of herding in financial markets (e.g., Guedj &Bouchaud,

2005), others do not (e.g., Drehmann, Oechssler, & Roider, 2005).

When investors are making the same decisions as others, it may result from indirect influences (Drehmann

et al., 2005; Sias, 2004) including common knowledge (Grinblatt, Titman, & Wermers, 1995), fads (Sias,

2004), or common investment styles (Wermers, 2000). It may also result from investors’ tendency to imitate

other investors. However, it is a challenge for naturalistic observations of behavior in financial markets to

distinguish such a direct influence from an indirect influence. An experimental approach is more informative

in this respect. Previous experiments have investigated participants’ sequential choices when these are based

son, Department of Psychology, University of Gothenburg, PO Box 500, SE-40530 Goteborg,psy.gu.se

Sons, Ltd.

272 Journal of Behavioral Decision Making

on private information and information about the choices made by others preceding them (Anderson, 2001;

Anderson &Holt, 1997; Celen &Kariv, 2004). The results indicate that the participants tend to disregard their

private information and instead imitate the others.

In financial markets, when observation of others’ choices provides valid information, it is rational for

investors to follow the others without regard to their private information. This form of herding is referred to as

information cascades (Bikhchandani, Hirshleifer, & Welch, 1992; Devenow & Welch, 1996). A focus of

experiments aimed at demonstrating information cascades is to assess the degree of rationality of herding in

investment decisions. For a general discussion of herding and information cascades, see Smith and Sorensen

(2000).

In the present research, our general aim is to understand the herding phenomenon. Our focus is therefore

on herding irrespective of whether considered to be rational or not. In the following we briefly review factors

known to moderate or mediate social influence which potentially have bearings on explanations of herding.

In research on social influence it is assumed that in many areas of social life, people’s views are influenced

by the views of others. Such social influences may be normative or informative (Deutsch & Gerard, 1955). In

the former case the motive is to conform with others due to external social pressure or internalized norm

systems, whereas in the latter case the motive is to acquire useful information from others. For instance, in

Festinger’s (1954) theory of social comparison processes, a tenet is that when one disagrees with a number of

apparently unrelated sources that are in agreement and there is no obvious explanation for their agreement, it

is sensible to infer that others are accurate, and thus to be influenced by them. Nevertheless, such a rule may

be overgeneralized, leading to an influence from others even though the others are not accurate.

A general empirical finding is that a majority has a stronger influence than a minority (Bond & Smith,

1996; Brown, 2000;Martin, Gardikiotis, &Hewstone, 2002). A substantial number of studies have also found

that the influence increases with the number of others in the majority (e.g., Arbuthnot &Wayner, 1982; Bond,

2005; Clark & Maass, 1990; Hodges & Geyer, 2006; Lascu, Bearden, & Rose, 1995; Mugny & Papastamou,

1980; Nemeth, Wachtler, & Endicott, 1977). It has furthermore been found that the degree of consistency

within a group affects influence of both minorities and majorities (e.g., Arbuthnot et al., 1982; Bray, Johnson,

& Chilstrom, 1982;Moscovici, Lage, &Naffrechoux, 1969; Nemeth et al., 1977;Wood, Lundgren, Ouellette,

Busceme, & Blackstone, 1994).

Another factor known to affect social influence is task difficulty (see Bond & Smith, 1996, for review). In

general, when people face a more difficult task and lack knowledge, the informative influence from others

tends to increase. Normative influence may be observed in simple perceptual tasks, such as in the experiments

by Asch (1956).

In the difficult task of deciding how to make sound financial investments, investors are to some extent

likely to follow a majority because they believe the majority is accurate. This type of informative majority

influence has been referred to as the use of a consensus heuristic (Martin, Hewstone, &Martin, 2007a). In line

with this, Shiller (2000) refers to herding as ‘‘mindless’’ behavior in financial markets. The results of a recent

experiment (Andersson, Lee, Hedesstrom, & Garling, 2007) support the hypothesis that majority influences

in financial markets are mediated by heuristic processing.

The present study aims at investigating how predictions of stock prices are influenced by invalid

predictions made by a consistent majority. As noted above, consistency among others has been shown to

increase their influence. It is furthermore implied by the definition of herding that one follows others who are

in agreement with each other. Consistency may be defined in different ways. In Andersson et al. (2007)

consistency was confounded with the majority classification. Two or three others (the majority) were in

agreement on each trial, but the majority was however not the same from trial to trial. Therefore, it was not

possible to conclude whether the influence was due to the consistency among the others or to the fact that the

others constituted a majority.

In financial markets it is likely that perceptions of consistency are based on repeated observations over

time. Consistency may then be perceived as the agreement across time between investors’ predictions. In

Copyright # 2008 John Wiley & Sons, Ltd. Journal of Behavioral Decision Making, 22, 271–279 (2009)

DOI: 10.1002/bdm

M. Andersson et al. Social Influence on Predictions 273

addition, consistency may be related to variance over time in each investor’s predictions. Investors who are in

agreement with others and do not vary their predictions substantially from time to time would appear more

reliable, and they are therefore likely to exert a stronger influence.

In an experiment we adopt a multiple cue probability learning (MCPL) paradigm (Busemeyer, Byun,

Delosh, & McDaniel, 1997; Cooksey, 1996) to investigate multi-trial predictions of fictitious future stock

prices given information about a current stock price and others’ predictions. The current stock price and the

others’ predictions are cues that are utilized in predicting the future stock price. Consistency was varied as

variance of and correlation between the others’ predictions across trials.

In the present experiment neither the others’ predictions nor the current stock price are valid cues. When

the others predictions are perceived not to be in agreement and to vary substantially from trial to trial, we

assume that participants intuitively judge that the current price is a more valid cue than the others’

predictions. On the other hand, when the participants detect that the others’ predictions are in agreement and

do not vary considerably from trial to trial, we assume that they judge that the others’ predictions are more

valid cues than the current price. It is thus hypothesized that the participants’ predictions would correlate with

the others predictions when these are correlated and therefore not correlate with the current price. The reverse

is hypothesized to be the case when the others’ predictions are uncorrelated. It is further hypothesized that the

correlation with the others’ predictions decreases with their variance.

Since consistency in the others’ predictions is defined as variance in and correlation between their

predictions across trials, a number of trials would be necessary for the participants to detect the consistency

between and within the others’ predictions.1 After an initial block of learning trials, performance is measured

in two blocks of trials. Due to the difficulties in perceiving correlations and variances, the effect of

consistency is expected to increase across these blocks.

METHOD

ParticipantsThe participants were 96 undergraduates (64 women and 32 men) at University of Gothenburg volunteering

to participate in return for SEK 50 (approximately US$ 7). They were recruited through sign-up sheets and

electronic mails. The women’s mean age was 28.1 years (SD¼ 10.8) and the men’s mean age 29.9 years

(SD¼ 10.1).

DesignEqual numbers of participants with sex and age balanced were randomly assigned to six between-groups

conditions forming a 2 (Correlation: No vs. Yes)� 3 (Variance: Low vs. Medium vs. High)� 50 (Trial)

factorial design with trial as a repeated-measures factor.

MaterialsBooklets were prepared to present 50 trials of current prices of a fictitious stock. Following an instruction

page each page in the booklet represented one trial. Numbers corresponding to the prices were randomly

sampled from a normal distribution (M¼ SEK 500, SD¼ SEK 92, range SEK 303 – SEK 717) and presented

under all conditions. Deviations from SEK 500 were thus random errors, rendering the expected value of the

1Note that the others’ correlated predictions may differ from each other in different ways on each trial, but that they will tend to followeach other across trials.


DOI: 10.1002/bdm


price equal to SEK 500. Predictions made by five fictitious others were presented on the same page as the

current price. The sequences of numbers corresponding to the others’ predictions were randomly sampled

from three normal distributions with the same mean as the price and either low variance (SD¼ 48, range SEK

374–SEK 645), medium variance (SD¼ 92, range SEK 241–SEK 802), or high variance (SD¼ 133, range

SEK 141–SEK 868). The others’ predictions were either highly correlated with each other (r> .95) or

uncorrelated (r< .20). All the others’ predictions were uncorrelated (r< .20) with the price. Four random

sequences of the others’ predictions were presented to different participants under each condition.

ProcedureThe participants were appointed through electronic mails to come to the laboratory. Upon arrival they were

seated in separate cubicles and given the booklet to fill out at their own pace. An experimenter was present to

supervise the participants. A session lasted for approximately 25 minutes.

In the general instructions the participants were informed that they would be presented the current price of

a fictitious stock on each page and asked to predict the price of the stock the day after by writing a price on a

line at the bottom of the page.2 On the following page they would be presented the price on the next day, thus

making it possible for them to assess the accuracy of their own prediction as well as the accuracy of the

others’ predictions. The participants were told that in addition to the current stock price, they would also each

time be shown the predictions made by the same five others (identified by letters) who previously had

participated in the experiment under identical conditions. The participants were finally instructed to go

through the pages in the order they appeared and not look back at previous pages.

After having completed the predictions, the participants were requested to answer three questions on a

single page in the booklet. The first question read ‘‘To what extent did you perceive that the others’

predictions varied across trials?’’ (referred to as awareness of variation in the others’ predictions), the second

question ‘‘Did you believe that the others were in agreement when they made their predictions?’’ (awareness

of agreement among the others’ predictions), and the third question ‘‘To what extent were you able to predict

how much the price of the stock was going up or down the next day?’’ (accuracy beliefs). Answers to these

questions were given on 9-point rating scales ranging from never (1) to always (9). Participants were finally

debriefed and paid.

RESULTS

Manipulation checksThe mean responses to the three post-experimental questionnaire questions are shown in Table 1. As may be

seen, the means for awareness of agreement and awareness of variance differ between conditions, whereas for

accuracy beliefs there are no apparent difference between the means. Three separate 2 (Correlation: No vs.

Yes)� 3 (Variance: Low vs. Medium vs. High) analyses of variance (ANOVAs) were conducted. On the

ratings of awareness of agreement among the others’ predictions, at the significance level of a¼ .05 a

significant main effect of correlation was revealed, F(1, 89)¼ 55.57, p< .001, v2partial¼ .36, substantiating

that participants under the conditions where the others’ predictions were correlated believed that the others

were more in agreement than did the participants under the conditions where others’ predictions were uncor-

related (Mcorrelated¼ 6.8 vs. Muncorrelated¼ 3.9). With respect to awareness of variation among the others’

predictions, significant main effects of both correlation, F(1, 89)¼ 19.23, p< .001, v2partial¼ .16, and

2After stating their prediction of the stock price the participants were asked to rate their confidence in the prediction by writing a numberbetween 0 (completely uncertain) and 100 (completely certain) on the same page. Since the confidence ratings appeared to be largelyinsensitive to the variables systematically varied in the experiment, the results are not reported.


DOI: 10.1002/bdm

Table 1. Mean answers to post-experimental questions

Low variance Medium variance High variance

Correlation Correlation Correlation

No Yes No Yes No Yes

Accuracy beliefs 4.4 4.1 4.4 4.2 3.3 4.1Awareness of agreement 4.3 6.2 4.4 7.1 3.3 7.1Awareness of variation 5.8 4.2 6.2 5.1 7.3 5.3

Note: The answers were given as frequency ratings on scales ranging from 1 to 9.


variance, F(1, 89)¼ 4.49, p¼ .014, v2partial¼ .07, were observed. The participants in the conditions where

the others’ predictions were correlated believed that the others’ predictions varied less than did the participants

in the conditions where the others’ predictions were uncorrelated (Mcorrelated¼ 4.9 vs. Muncorrelated¼ 6.4).

Awareness of variation was greater for the participants in the condition with high variance than in the condition

with medium variance and greater in the condition with medium variance than in the condition with low

variance in the others’ predictions (Mhigh¼ 6.3 vs. Mmedium¼ 5.6 vs. Mlow¼ 5.0).

PredictionsThe squared product-moment correlations (r2) between the price and the predictions were computed for each

participant in two blocks which each consisted of 20 trials, excluding the learning phase consisting of the

initial 10 trials. Multiple correlations (R2) were also computed with the predictions as the dependent variable

and the price and the predictions of the five others as the independent variables. The difference R2�r2 is used

as a measure of the influence of the others’ predictions. All the following statistical analyses are performed on

Fisher’s zr transformed values.

The effects of the price on the predictions are displayed in Table 2. A 2 (Correlation: No vs. Yes)� 3

(Variance: Low vs. Medium vs. High)� 2 (Block) analysis of variance (ANOVA) with block as repeated

measures factor was performed. The results revealed a significant main effect of correlation, F(1, 90)¼ 5.23,

p¼ .024, v2partial¼ .04. As expected, the participants under the conditions where others’ predictions were

uncorrelated relied more on the price than did the participants under the conditions where the others’

predictions were correlated (Muncorrelated¼ .921 vs. Mcorrelated¼ .615). No main effect of variance was

observed, F(2, 90)< 1. The means were approximately similar under all conditions (Mlow¼ .748 vs.

Mmedium¼ .766 vs. Mhigh¼ .791). Block showed a non-significant tendency for participants to follow the

Table 2. Mean Fisher zr transformed correlations with price across blocks under conditions with low, medium, or highvariance with or without correlation between others’ predictions



Block No Yes No Yes No Yes

1 0.846 0.767 0.911 0.728 1.042 0.5612 0.793 0.587 0.870 0.554 1.066 0.494


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Table 3. Mean Fisher zr transformed correlations with others’ predictions across blocks under conditions with low,medium, or high variance with or without correlation between others’ predictions



Block No Yes No Yes No Yes

1 0.514 0.898 0.517 0.779 0.473 1.0482 0.608 0.999 0.656 1.045 0.479 1.038


price more in the first block than in the second block (Mblock1¼ .809 vs. Mblock2¼ .727), F(1, 90)¼ 3.20,

p¼ .077, v2partial¼ .04. No interactions with block were significant.

The mean correlations between the participants’ and the others’ predictions (assessing the impact of the

others’ predictions) are displayed in Table 3. A 2 (Correlation: No vs. Yes)� 3 (Variance: Low vs. Medium

vs. High)� 2 (Block) ANOVAwith block as a repeated-measures factor yielded a significant main effect of

correlation, F(1, 90)¼ 11.77, p¼ .001, v2partial¼ .10. As expected, the participants under the conditions

where the others’ predictions were correlated were significantly more influenced by the others’ predictions

than were those in the conditions where others’ predictions were uncorrelated (Mcorrelated¼ .968 vs.

Muncorrelated¼ .541). No significant effect of variance was found (Mlow¼ .755 vs. Mmedium¼ .749 vs.

Mhigh¼ .759), F(2, 90)< 1. A significant main effect of block was revealed, F(1,90)¼ 5.96, p¼ .017,

v2partial¼ .05, indicating that the others’ influence increased across blocks (Mblock1¼ .705, Mblock2¼ .804).

No interaction involving block was significant.

DISCUSSION

As expected, the results showed that the participants relied less on the price and were more influenced by the

others’ predictions when correlated than when uncorrelated. Although block had main effects, there were no

interactions with block. Thus, it seemed to be sufficient with a learning block for the participants to detect the

correlation between the others’ predictions.

Variance in the others’ predictions had not the expected effect on the influence of the others’ predictions.

The manipulation check suggested that the participants observed the differences in variance. These

differences were perhaps still ignored if the correlation among the others’ predictions is the most important

component. Another possibility is that participants interpreted the differences in variance as differences in

risk taking and reacted differently to this interpretation. Some of the participants might have preferred to

follow those with large variance because they considered them to be risk takers, others those with low

variance because they considered them to be risk averse. The net effect would be to counteract any effect of

variance.

In Andersson et al. (2007) consistency was confounded with the number of others who made the same

predictions on each trial. The present results therefore more clearly demonstrate the possible role of

correlation or agreement over time among other investors for herding to occur in financial markets.

Andersson et al. (2007) also showed that monetary rewards for following amajority increased its influence. In

contrast, the same monetary rewards for following a minority had no effect. A question for future research is

whether a minority would have an influence if its members are in agreement over time. Previous research

suggests that this would be the case. However, according to dual-process theories of social influence (e.g.,

Moscovici, 1985) and previous results demonstrating a majority influence (Martin et al., 2002; Martin &

Hewstone, 2003; Martin et al., 2007a,b), the consensus heuristic will only be used if the others constitute a


DOI: 10.1002/bdm


majority. Predictions by others who constitute a minority will, according to the theory, be critically assessed,

thus they will probably only be followed if their predictions are accurate.

Since the present results are in some respects similar to the results of the previous research on social

influence (e. g., Bond, 2005), they represent an extension of previous findings. The recent research on

social influence has primarily focused on the connection between heuristic vs. systematic processing and

social influence, usually measured as attitude change. A commonly used paradigm (e.g., Erb, Bohner, Rank,

& Einwiller, 2002; Martin et al., 2002; Martin et al., 2003; Martin et al., 2007a,b) is to assess participants’

attitudes after they are exposed to different messages that are delayed in time. An initial message is endorsed

by a group of others, and then measurements are made of how much attitude certainty is influenced by a

subsequent counter-message arguing the opposite position. This task implies that participants are presented

with clear information about the group’s position. The present experiment used a different and probably more

difficult task, since participants themselves had to infer the degree of consistency in the group. Furthermore,

focus was not on attitude change but on predictions of uncertain outcomes. Despite this the present results

show that a majority of others was influential. Thus, participants seemed to use a consensus heuristic,

probably as a result of heuristic processing. This interpretation is in line with interpretations of most results

on majority influence in previous studies, which suggests that the same processes involved in social influence

may generalize to investment predictions.

In Moscovici’s conversion theory (1985), one important characteristic for minority influence to occur is

consistency in its opinion. Drawing upon this notion, an interesting implication to investigate in additional

studies would be whether the variance across trials in the others’ predictions would have an effect if a

minority is in agreement.

Another area of related research is the study of advice effects on judgment and decision making. The

review of Bonacio and Dalal (2006) suggests that in general advisors have less influence than they should

have given their knowledge. This ‘‘egocentric bias’’ on the part of advisees appears on the surface to be

inconsistent with the present results. In the present experiment, the others appeared to have more influence

than they should have given that they made inaccurate predictions. Thus, the present results do not seem to be

consistent with Budescu, Rantilla, Yu, and Karelitz (2003), who showed that information has an influence in

proportion to its reliability.

Finally, the question needs to be raised whether the present results can be generalized to actual financial

markets. The tasks devised in the experiments are realistic in terms of statistical properties. However, these

tasks do not tap knowledge of the stock market which may be an important factor in actual stock investments.

It should also be pointed out that in naturalistic investment situations, it may be difficult to judge which others

constitute a herd. Furthermore, the present experiments primarily focused on the informational aspects of

herding, not on reputational or normative concerns. One avenue to investigate the generalizability of the

present findings is to test their invariance across different investment tasks in laboratory experiments.

A remaining issue for future research concerns how consistency is perceived in a stock market. Anderson

et al.’s (2007) definition of consistency as agreement on a single occasion downplays the fact that stock

investments are made repeatedly over time. It may therefore to a larger extent involve a building up of

confidence in others. Both variance in and agreement among others’ predictions over time seem relevant.

However, the statistical measures of variance and correlation may not fully capture these concepts. Therefore,

future research should be directed towards developing better such measures.

ACKNOWLEDGEMENTS

This research has been financially supported by a grant from the Swedish Foundation for Strategic

Environmental Research (MISTRA) to the program ‘‘Behavioral Impediments to Sustainable Investments’’.


DOI: 10.1002/bdm


Thanks are due to Henrik Svedsater for comments and assistance, and to Boel Siljebrat and Lisa Ohman for

assistance in collecting the data.

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Authors’ biographies:

Maria Andersson is a Ph.D. student in the Department of Psychology at University of Gothenburg, Goteborg, Sweden.Her dissertation focuses on social influence processes in financial markets.

TedMartrin Hedesstrom has a Ph. D. in Psychology and is now an Assistant Professor in the Department of Psychologyat University of Gothenburg, Goteborg, Sweden. He is currently doing research on issues related to sustainableinvestments in financial markets.

Tommy Garling has a Ph.D. in Psychology from Stockholm University and is a Professor in the Department ofPsychology at University of Gothenburg, Goteborg, Sweden. His research interests include topics in judgment anddecision making, economic psychology, and environmental psychology.

.

Authors’ addresses:

Maria Andersson, Ted Martin Hedesstrom, and Tommy Garling, Department of Psychology, University ofGothenburg, PO Box 500, SE-405 30 Goteborg, Sweden.


DOI: 10.1002/bdm

social influence on predictions of simulated stock prices

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