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Worse Than Chance? Performance and Confidence Among Professionals and Laypeople in the Stock Market


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In two studies, stock market professionals (N1 = 22, N2 = 21) and laypeople (N1 = 29, N2 = 34) provided thirty-day forecasts for twenty stocks and estimated the size of their own errors as well as their own and the other group's mean errors. Both groups predicted that the errors made by professionals would be half the size of the errors made by laypeople. In reality, the errors of both groups were about the size predicted for the laypeople. Participants also estimated their ability to pick the best performing stock from two options. Both groups proved to be overconfident. Professional predictions were only successful 40% of the time, a performance below what could be expected from chance alone. Self reports and correlations between forecasts and price movements sug- gested that the professionals based their predictions on specific information of the stocks without sufficient awareness of the unreliability of this information, while the laypeople used simple heuristics based on previous price movements. During the recent worldwide stock market turbu- lence, fortunes were rapidly made and just as rapidly lost. The great majority of participants did not antici- pate either the magnitude or the pace of these events. The bewilderment has been hard to hide. This paper addresses two questions raised by more than a few people in the aftermath: "How much do stock market professionals really know about the fu- ture development of stock prices, and how much do they think they know?" We first discuss psychological findings concerning expert judgments and experts' and laypeople's confidence in their own judgments.
Content may be subject to copyright.
The Journal of Behavioral Finance
Vol. 5, No. 3, 148-153
Copyright © 2004 by
The Institute of Psychology and Markets
Worse Than Chance? Performance and Confidence Among
Professionals and Laypeople in the Stock Market
Gustaf Torngren and Henry Montgomery
In two
stock market professionals (Nj
N2 =
21) and laypeople (Nj
N2 =
34) provided thirty-day forecasts for
stocks and estimated
size of their
own errors as well as their own and the other group's mean errors. Both groups pre-
dicted that the errors made
professionals would be half the size of the errors made
of both groups were about
size predicted for the
laypeople. Participants also estimated their ability to pick the best performing stock
from two options. Both groups proved to be overconfident. Professional predictions
were only successful 40% of the time, a performance below what could be expected
from chance alone. Self
and correlations between forecasts and price move-
ments suggested that the professionals based their predictions on specific information
of the stocks without sufficient awareness of the unreliability of this information,
while the laypeople used simple heuristics based on previous price movements.
During the recent worldwide stock market turbu-
lence, fortunes were rapidly made and just as rapidly
lost. The great majority of participants did not antici-
pate either the magnitude or the pace of these events.
The bewilderment has been hard to hide.
This paper addresses two questions raised by more
than a few people in the aftermath: "How much do
stock market professionals really know about the fu-
ture development of stock prices, and how much do
they think they know?" We first discuss psychological
findings concerning expert judgments and experts' and
laypeople's confidence in their own judgments.
Expert Judgment
People often turn to experts for guidance in coping
with complex and uncertain decision environments, es-
pecially those that could affect their standard of living
such as financial decision making. Although theoreti-
cal and empirical questions have been raised about the
efficacy of predicting fluctuations in the stock market,
a small but generously compensated industry is en-
gaged in providing investors with analysis and advice
aimed at differentiating winners from losers. This as-
sumes that an expert's knowledge helps investors make
better judgments and more accurate predictions, result-
ing in greater return on investment.
Gustaf Torngren is a Ph.D. candidate in psychology at Stock-
holm University.
Henry Montgomery is a professor of cognitive psychology at
Stockholm University.
Requests for reprinst should be sent
Gustaf Torngren, Depart-
ment of Psychology, Stockholm University, S-106 91 Stockholm,
Sweden. Email:
Research on expertise in general, however, does not
fully confirm this assumption. A simple linear model
can often provide more accurate predictions than ex-
perts are able to (Grove and Meehl [1996]). Moreover,
simple and basic training can provide non-experts with
sufficient knowledge to perform at the same level as
experts (Camerer and Johnson [1991]).
On the other hand, experts use information more
efficiently (Shanteau and Stewart [1992]). Because
experts are experienced in recognizing patterns, they
are able to analyze problems without using
cognitively more costly mental calculations. This ap-
parent contradiction has been labeled the "pro-
cess-performance paradox," and the question has
been "how can experts know so much but perform so
(Camerer and Johnson [1991]).
Studies assessing financial analyst predictions of-
ten give a rather gloomy picture of their validity. As
early as the 1930s, Cowles [1933] showed how stocks
recommended by a number of financial services in
the years between 1928 and 1932 were outperformed
by the average common stock. This trend continues
today. De Bondt [1991] analyzed the results of a lon-
gitudinal study (1952-1987), and found that the ex-
perts could have improved their results by using his-
torical means instead of specific evaluations.
Similarly, Malkiel [1999] found that, from 1988 to
the average equity mutual fund in the U.S. had
a 3.3% lower average annual return than the Standard
& Poor's index during the same period.
The degree of confidence people have in the cor-
rectness of their judgments and predictions is cru-
cially important in judgment and decision processes.
Researchers on human judgment consistently find
discrepancies between individuals' subjective proba-
bility estimates and the relevant objective probabili-
Typically, calibration between the two is poor.
Moreover, participants' actual performance is gener-
ally lower than what they expected. So their confi-
dence level tends to be higher than their actual perfor-
mance would warrant.
This tendency toward overconfidence has been
found in both laboratory and naturalistic settings.
Studies have typically examined people's confidence
in their ability to answer general knowledge questions
Slovic, and Lichtenstein [1977], Keren
[1988]), but similar results have also been found in fi-
nancial settings (Lichtenstein and Fischoff [1977],
Russo and Schoemaker [1992]).
Studies that examine professionals in their deci-
sion-making environment produce a split picture as to
the level of overconfidence. Experienced estate
agents (Northkraft and Neale [1987]), physicians
(Christensen-Szalanski and Bushyhead [1981],
Detmer, Fryback, and Gassner [1978]), and business
managers making forecasts of such economic indica-
tors as input/output prices (Aukutsionek and Belianin
[2001]) have all been shown to make biased probabil-
ity judgments that are poorly calibrated.
Other studies, however, have shown that some ex-
perts provide well calibrated judgments. Bridge play-
ers predicting making contracts (Keren [1997]), expe-
rienced weather forecasters (Murphy and Winkler
[1984]), and horse race bettors (Johnson and Bruce
[2001]) have all proven their ability to make realistic
predictions about the accuracy of their judgments.
Certain factors have been shown to contribute to
overconfidence. According to Bradley [1981], people
with a high degree of perceived expertise in the area of
a general knowledge question are likely to have unreal-
istically high expectations of the probability of answer-
ing correctly. Davies, Lohse, and Kotterman [1994]
found that more information, even when redundant,
boosted the degree of overconfidence among finance
students predicting stock market fluctuations. Olsen
[1997] found that professional investment managers
tended to overestimate probabilities of outcomes that
were positive to the respondent and to underestimate
undesired outcomes, which we refer to as desirability
bias. Cultural differences (Yates, Lee, and Shinotsuka
[1996]) and gender (Estes and Hosseini [1988]) have
also been shown to influence overconfidence levels.
The cause of these effects has been the subject of an
intense debate during the past ten years. Until the early
1990s, cognitive bias was the dominant explanation for
the overconfidence phenomenon. In this account,
miscalibration is a result of cognitive biases that occur
as a byproduct of
heuristics used in the information
processes underlying people's judgments.
More recently, however, proponents of ecological
models have challenged this approach (see Gigerenzer,
Hoffrage, and Kleinbolting [1991], Juslin [1994], and
Bjorkman [1994] for examples). These models view
heuristics differently,
as a
"fast and frugal" way of using
the structure of information in its own decision environ-
ment to make reasonable judgments and decisions un-
der realistic conditions. An example of a "fast and fru-
heuristic is recognition, which implies the
inference of higher values to objects that are recognized
Tested as a stock-picking de-
it was actually proven quite "frugal." Portfolios
based solely on company recognition by foreign
laypeople actually outperformed professionally man-
aged funds and the market index (Gigerenzer et al.
[1999]). However, in a replication of this study con-
ducted during a period of bearish market behavior
(rather than the bull market that prevailed during the
the results were reversed (Boyd
Despite the criticism by proponents of the ecologi-
cal approach, the concept of overconfidence is still on
the judgment and decision making agenda. Recent
studies in naturalistic settings (Aukutsionek and
Belianin [2001]), as well as experimental studies de-
signed to avoid the problems of underrepresentation,
have still reported overconfidence (Brenner et al.
Griffin and Tversky [1992]).
This article continues the small volume of research
on perceived and actual accuracy in predictions of
stock prices (see, e.g., Davies, Lohse, and Kotterman
Olsen [1997], and De Bondt [1991]). Contrary
to previous research, we compared professional judg-
ments with laypeople judgments, and we asked our
participants to estimate their own group's accuracy as
well as the other group's accuracy. Although many
studies suggest that professionals do not outperform
laypeople who use simple strategies (Grove and Meehl
[1996]), there seems to be a lack of research on how
these two groups perceive their own accuracy and the
accuracy of the other group.
It seems reasonable that the market would work
more efficiently if laypeople and professionals could
judge each other's predictive abilities more accurately.
Overconfidence can cause excessive trading, which
can be risky to financial well being (Barber and Odean
[2000]). To better understand the background of the
two groups' performance with respect to their actual
and perceived accuracy, we explored how each group
makes predictions. Professionals often use information
that (more or less erroneously) supports their identity
as experts, such as specific information about stocks.
We conducted two studies: a thirty-day study that
ended on March 10, 2001, and a replication that took
place between March
and April 7,2002. All
ipants in both studies submitted their contributions on
the same day, which was vital because stock prices
change continuously. Study
was based on twenty-two
stock market professionals and twenty-nine laypeople.
Study 2 was based on twenty-one stock market profes-
sionals and thirty-four laypeople.
"Stock market professionals"
that regularly and on a professional basis engage in the
evaluation of and/or investment in
This includes
portfolio managers, analysts, brokers, and investment
counselors. One person at each company was contacted
by the author, informed about the study, and asked to
find colleagues with working duties who fulfilled the
criteria. With one exception, all the professionals were
men, and they had a mean experience of twelve years.
The laypeople were undergraduate students in psy-
chology at Stockholm University. In Study 1, six par-
ticipants were male and twenty-three were female. In
Study 2, ten participants were male and twenty-four
were female. It is likely that most of the professionals
in Study
also took part in Study 2 (uninformed of the
results of Study 1), while the laypeople were not the
same in the two studies.
The questionnaire used twenty stocks from
well-known blue-chip companies (Ericsson, Volvo,
etc.) and from a broad spectrum of industries listed on
the Stockholm stock
Participants were given
the name, industry, and monthly percent price change
for each stock for the previous twelve months and
asked to predict the rate of change for the share price
over the next thirty days. They also picked one of two
stocks shown on each page that they expected to per-
form better during the same period, and they estimated
the probability (between 50%-100%) that they would
choose the best performing stock. Participants rated on
a ten-point scale the extent to which they used each of
four strategies (previous monthly results, other knowl-
intuition, or guessing), with "not at all" and "to a
very high degree" as extremes. Finally, participants es-
timated the mean errors of their own predictions (in
percent), as well as the collective mean errors of the
professionals and laypeople.
We calculated mean values for both groups to illus-
trate the participants' own expectations and their expec-
tations for the two
shows significant dif-
ferences between the professionals' and the laypeople's
estimates of their mean errors: t (43) = 3.22, p < 0.05
(Study 1); t (52) = 4.39, p
0.05 (Study 2). Thus, the
stock market professionals had higher expectations than
the laypeople about their ability to make accurate pre-
Clearly these expectations
not warranted.
The differences in performance, displayed in the actual
mean errors, were small but marginally significant in
Study 1, / (46) = 2.29, p < 0.05. Interestingly, both
groups shared the erroneous belief that the professionals
would produce better results.
also shows a good consistency between the
estimations of participants' own performance and the
estimated performance of other group members. Fur-
thermore, both professionals and laypeople seem to
with the expected performances of the two groups.
The predictions made by stock market professionals
were negatively correlated to the actual outcomes in
both studies. The correlations, -0.17 in Study 1 and
-0.16in Study
were significant: r(20)=-2.75,p<0.5,
and t
(20) =
For laypeople, no correlation
was noted. Respondents as a whole were too optimistic
when predicting stock
In Study
the average es-
timation for
twenty stocks was
by the profes-
sionals and
the stocks
during the thirty days. In Study 2, the estima-
tions were
professionals and
by the
laypeople. The actual outcome was
In estimating their ability to pick the winner be-
tween two stocks, the participants were again too opti-
mistic. The laypeople were moderately overconfident,
predicting a 58% rate of correctness in Study 1 and
in Study 2. The actual rate was closer to 50%,
which could be the result of chance alone. But the pro-
fessionals displayed an even higher degree of overcon-
fidence, rating their probabilities of success at 63% in
Study 1 and 67% in Study 2.
Remarkably, as Table 2 shows, the performance of
stock market professionals is repeatedly worse than the
performance of laypeople: t (44) =
p < 0.10
(Study 1); t (52) =
p < 0.05 (Study 2). In addi-
tion, their performance was below the 50% expected
from chance alone: t (20) = -2.63, p< 0.05 (Study 1); t
(20) = -2.90, p < 0.05 (Study 2). The best calibration
occurred when participants estimated their probabili-
ties of having chosen the best performing stock to be in
line with chance. Higher confidence judgments did not
reflect more correct predictions for either the profes-
sionals or for the laypeople.
Finally, participants were asked to rate the extent to
which they used four strategies
to make
their judgments.
Not surprisingly,
as Table 3
experts perceived
that they used strategies based on expert knowledge.
The laypeople reported that they were mainly guessing:
in Study
tween professionals and laypeople). They also reported
previous results for
t (49) =
0.05 in Study 1; / (53) =
p < 0.05 in Study 2.
The professionals, however, considered knowledge
(other than monthly performance over the last twelve
months) as the most important influence when making
Table 1. Estimated and Actual Mean Errors in Percent for Stock Price Predictions
Study 1 Study 2
Professionals Laypeople Professionals
Estimated error in own predictions 8.67
Estimated error of professionals 4.3
Estimated error of laypeople 8
Actual error 10.84
Table 2. Calibration Data on Probability Estimations of Picking the Better Performing Stock Out of Two
Study 1 Study 2
Rated probability
Professionals Laypeople Professionals
Mean prob. rating
Mean correctness
0.50 (147)
0.48 (49)
0.49 (43)
0.60 (10)
0.61 (8)
- (0)
Note: Figures in parentheses denote the number of estimations behind the mean in each cell.
Table 3. Reported Use of Judgment Strategies
Previous month's results
Other knowledge
Study 1
Study 2
Note: Values represent ratings on a scale from 1-10, where
= "not at all," and 10 = "to a very high degree."
0.05 in Study 2.
Further analysis of the forecasts showed that the
laypeople were influenced by the historical price
movements of the stocks. More specifically, the results
of the past month apparently served as an anchor for
their extrapolations. This was shown as a positive cor-
relation between predictions and stock results for the
past month across stocks in both studies: The mean
correlations were 0.18 and 0.28, respectively. Both
mean correlations differed significantly from zero: t
(28) =
p < 0.05 and t (33) = 4.72, p < 0.05. This
was not true for the professionals, and the differences
were significant: t (50) = -2.07, p < 0.05 in Study 1; t
(54) = -2.77, p
0.05 in Study 2.
Our results indicate clear differences between
stock market professionals and laypeople. However,
the two groups did not differ clearly with respect to
forecast accuracy. Both groups expected the profes-
sionals to outperform the laypeople by a large
margin, but the professionals made as many errors as
the laypeople. When participants estimated their
probabilities of success in picking a winner between
two stocks, the tendency was the same. Both groups
were overconfident, but the professionals overesti-
mated their ability by a greater margin.
In addition to being more overconfident than
laypeople, the professionals' performance was
icantly worse than the 50% correctness rate expected
from chance alone. Furthermore, there was no appar-
ent connection between certainty of having picked
the right stock and actual success for either group.
In expressing their perceived use of different judg-
ment strategies, the two groups differed. Professionals
mainly relied on knowledge, while the laypeople based
their judgments primarily on chance and on the previ-
ous monthly results.
It seems clear that the benefits of experience
and knowledge, which professionals ostensibly
have greater access to, are overrated. Our results
suggest that professionals do not have advantages
over laypeople. It is possible, however, that the
judgments participants were required to make here
did not fully correspond to real-life investment de-
cisions. This might help explain the exaggerated
expectations laypeople had for professionals, but
surely the professionals should have been able to
ascertain whether their backgrounds would help
them gain greater accuracy.
Our results agree with prior research that exper-
tise (Bradley [1981]) and access to information
(Davies, Lohse, and Kotterman [1994]) can result
in overconfidence. The results show that profes-
sionals perceived their judgments as having been
based mainly on "knowledge." It seems reasonable
that basing predictions on "knowledge" would gen-
erate higher levels of confidence than "guessing,"
the primary strategy used by laypeople.
Another factor previously shown to contribute to
professional investor overconfidence is the desirability
bias (Olsen [1997]). For stock market professionals,
bull markets are desirable because they offer the possi-
bility of receiving higher bonuses, and research has
suggested that optimism can be career-promoting
(Hong and Kubik [2003]). These factors contribute to
the recognized surplus of recommendations to "buy"
versus "sell" (Carleton, Chen, and Steiner [1998]).
One of our most intriguing findings is the low level
of correctness when professionals were required to
pick a winner from two stocks. The results suggest that
a chimpanzee, a symbol of randomness in economic
contexts, could have outperformed the stock market
professionals in this study. And chimps do not possess
cognitive advantages. On the contrary, the repeated and
significant deviations from the 50% level of correct-
ness predicted by chance as well as the significant
(negative) correlations between the professionals' pre-
dictions and the outcomes suggest that the profession-
als did use some common judgment strategies.
We attempted to determine whether the profession-
predictions were correlated with the volatility of
the stocks or other industry variables, but no clear-cut
evidence was found.
One possible explanation for our results (which was
not ruled out by our study) is that the high expectations
on the performance of the professional could have been
fulfilled under different circumstances, as both studies
were carried out under bear market conditions (falling
It would be interesting to examine the re-
sults under bull market conditions.
In summary, despite the ecologically realistic set-
both groups displayed overconfidence, and it
was most evident among the stock market profession-
The results suggest that the information-based pre-
dictions of the professionals did not outperform the
simple heuristics used by laypeople.
This research has received financial support from
Sparbankemas Forskningstiftelse.
Aukutsionek, S.P., and A.V. Belianin. "Quality of Forecasts and
Business Performance: Study of Russian
Economic Psychology, 22, (2001), pp. 661-^92.
Barber, B and Odean, T (2000), Trading Is Hazardous To Your
Wealth: The Common Stock Investment Performance of Indi-
vidual Investors, Journal of Finance, 55, (2000), pp.773-806.
Bjorkman, M. "Internal Cue Theory: Calibration and Resolution of
Confidence in General Knowledge." Organizational Behavior
and Human Decision Processes, 58, (1994), pp. 386-405.
Boyd, M. "On Ignorance, Intuition, and Investing: A Bear Market
Test of the Recognition Heuristic." Journal of Psychology and
Financial Markets 2, (2001), pp. 150-156.
Bradley, J.V. "Overconfidence in Ignorant Experts." Bulletin of
Psychonomic Society, 17, (1981), pp. 82-84.
Brenner, L., D. Koehler, V. Liberman, and A. Tversky. "Overconfi-
dence in Probability and Frequency Judgments: A Critical Ex-
amination." Organizational Behavior and Human Decision
Processes, 65, (1995), pp. 212-219.
Camerer, C.F., and E. Johnson. "The Process-Performance Paradox
in Expert
How Can Experts Know So Much and Pre-
dict So Badly?" In K.A. Ericsson and J. Smith, eds.. Toward a
General Theory of Expertise: Prospects and Limits. Cam-
bridge: Cambridge University Press, (1991), pp. 195-217.
Carleton, W.T, C.R. Chen, and T.L. Steiner. "Optimism Biases
Among Brokerage and Non-Brokerage Firms' Equity Recom-
mendations: Agency Costs in the Investment Industry." Finan-
cial Management, 27, (1998), pp. 17-30.
Christensen-Szalanski, J.J.J., and J.B. Bushyhead. "Physicians' Use
of Probabilistic Information in a Real Clinical Setting." Journal
of Experimental Psychology: Human Perception and Perfor-
mance, 7, (1981), pp. 928-935.
"Can Stock Market Forecasters
pp. 309-324.
Davies, F.D., G.L. Lohse, and J.E. Kotterman. "Harmful Effects of
Seemingly Helpful Informationon Forecasts of StockEamings."
Journal of Economic Psychology, 15, (1994), pp. 253-267.
Economists Know About
ket?'Journal of Portfolio Management, 17,(1991),pp. 84-92.
Detmer, D.E., D.G. Fryback, and K. Gassner. "Heuristics and Biases
in Medical Decision Making." Journal of Medical Education,
(1978), pp. 682-683.
Dunning, D., D. Griffin, J. Milojkovic, and L. Ross. "The Overconfi-
dence Effect in Social Prediction." Journal of
Social Psychology, 58, (1990), pp.
R., and J. Hosseini. "The Gender Gap on Wall Street: An
Empirical Analysis of Confidence in Investment Decision
Making." Journal of Psychology, 122, (1988), pp. 577-590.
B., P. Slovic, and S. Lichtenstein. "Knowing With Cer-
tainty: The Appropriateness of Extreme Confidence." Journal
of Experimental Psychology: Human Perception and Perfor-
mance, 3, ('1977), pp. 552-564.
Gigerenzer, G., B. Borges, D. Goldstein, and A. Ortman. "Can Ig-
norance Beat the Stock Market?" In G. Gigerenzer, P.M.
Todd, and the ABC Research Group, eds.. Simple Heuristics
that Make us Smart. Oxford: Oxford University Press, 1999.
Gigerenzer, G., U. Hoffrage, and H. Kleinbolting. "Probabilistic
Mental Models: A Brunswickian Theory of Confidence." Psy-
98, (1991), pp. 506-528.
Gigerenzer, G,, P,M, Todd, and the ABC Research Group, eds. Sim-
ple Heuristics
Smart. Oxford: Oxford University
Griffin, D,, and A, Tversky, "The Weighing of Evidence and the De-
terminants of Confidence," Cognitive Psychology, 24, (1992),
Grove, W,M,, and P,E, Meehl, "Comparative Efficiency of Infor-
mal (Subjective, Impressionistic) and Formal (Mechanical,
Algorithmic) Prediction Procedures: The Clinical-Statistical
Controversy," Psychology, Public Policy and Law, 2, (1996),
Hong, H,, and J,D, Kubik, "Analyzing the Analysts: Career Con-
cerns and Biased Earnings Forecasts." Journal of Finance, 58,
Johnson, J,, and A, Bruce, "Calibration of Subjective Probability
Judgments in a Naturalistic Setting," Organizational Behavior
and Human Decision Processes, 85, (2001), pp, 265-290,
Juslin, P, "The Overconfidence Phenomena as a Consequence of In-
formal Experimenter Guided Selection of Almanac Items," Or-
ganizational Behavior and Human Decision Processes, 57,
pp, 226-246.
Keren, G, "On the Ability of Monitoring Non-Veridical Perceptions
and Uncertain Knowledge: Some Calibration Studies," Acta
Psychologica, 67, (1988), pp, 95-119,
Keren, G, "On the Calibration of Probability
Some Criti-
cal Comments and Alternative Perspectives," Journal of Behav-
ioral Decision Making, 10, (1997), pp, 269-278,
Klein, G, "Naturalistic Decision Making: Where Are We Going?"
In C,E, Zsambok and S,G, Klein, eds,. Naturalistic Decision
Making. Mahwah, NJ: L, Erlbaum Associates, (1997), pp,
Lichtenstein, S,, and B,
"Do Those Who Know More Also
Know More About How Much They Know?" Organizational
Behavior and Human Decision Performance, 20, (1977), pp,
Lichtenstein, S,, B,
Philips, "Calibration of Proba-
bilities: The State of the Art
and A, Tversky, eds,. Judgment under uncertainty: Heuristics
and biases. Cambridge: Cambridge University Press,
Malkiel, B,G, A Random
Street. New York: W,W,
Norton and Company, 1999,
Murphy, A,H,, and R,L, Winkler, "Probability Forecasting in Meteo-
rology," Journal of the American Statistical Association, 79,
pp, 489-500,
Northkraft, G,B,, and M,A, Neale, "Expert, Amateurs and Real Es-
An Anchoring and Adjustment Perspective on Property
Pricing Decisions," Organizational Behavior and Human Deci-
sion Processes, 39, (1987), pp, 84-97,
Olsen, R, "Desirability Bias Among Professional Investment Man-
Journal of Behavioral Decision Making, 10, (1997),
Russo, J,E,, and PJ,H, Schoemaker, "Managing Overconfidence,"
Sloan Management
33, (1992), pp, 7-17,
Shanteau, J,, and T, Stewart, "Why Study Expert Decision
Making? Some Historical Perspectives and Comments," Or-
ganizational Behavior and Human Decision Processes, 53,
pp, 95-106,
J,F,, J,-W, Lee, and H, Shinotsuka, "Beliefs about Overcon-
fidence, Including its Cross-National Variation," Organiza-
tional Behavior and Human Decision Processes, 65, (1996),
... Risk attitude studies have found that two types of risk attitude exist: risk proneness and risk aversiveness. Those in the category of being risk prone have been reported to be overconfident, whilst those averse to risk have been said to be conscientious (Lauriola and Levin, 2001;Menkhoff, Schmidt, and Brozynski, 2006;Törngren, and Montgomery, 2004). Risk proneness is positively related to the following traits: 1. Disinhibition and susceptibility to boredom (Lauriola and Levin, 2001); 2. Arousal, experience and sensation seeking (Hunter and Kemp, 2004;Wong and Carducci, 1991); and 3. Emotional stability and openness to experience (Lauriola and Levin, 2001). ...
... First, one can say that the present study confirmed that personality features (implicit and explicit) have a strong impact on fund performance. It supported earlier research findings that posit that there is a relationship between personality and investment performance (Durand et al.., 2008(Durand et al.., , 2013Hunter and Kemp, 2004;Lauriola and Levin, 2001;Menkhoff et al.., 2006;Törngren, and Montgomery, 2004;Wong and Carducci, 1991). The present study also showed that implicit motives, here measured by Zulliger and Wartegg tests, have been a neglected area within personality studies in the financial area. ...
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The article presents empirical results of forty fund managers‘ implicit and explicit motives in relation to five year risk adjusted investment performance. The first step of the study was to create composite variables from three personality tests: Zulliger, Wartegg and Personality Research Form (PRF). Three composite variables were created of the implicit and explicit personality variables: financial risk attitude, stress tolerance and complex problem solving. The finding of the present work was that financial risk attitude, stress tolerance and complex problem solving explained 53.8% of the investors‘ five year performance. All three composite variables correlated significantly to a five year period consisting of a rising and descending market. The conclusion of the study was that fund managing is suitable for certain personality characters, implicit motives have been neglected in behavioral finance, fund companies should review their assessment procedures and individual thinking and distress management should be fostered to enhance performance.
... Meanwhile, financial losses are rather attributed to environmental circumstances, like unfavorable macroeconomic developments or simply bad luck, with the investors degree of overconfidence remaining constant [58], [59]. Also, professionals were found to be as likely as laypersons to express overconfidence in making economic decisions as well as in re-evaluating the quality of their own previous decision in hindsight [60]. ...
Successful design of human-in-the-loop control systems requires appropriate models for human decision makers. Whilst most paradigms adopted in the control systems literature hide the (limited) decision capability of humans, in behavioral economics individual decision making and optimization processes are well-known to be affected by perceptual and behavioral biases. Our goal is to enrich control engineering with some insights from behavioral economics research through exposing such biases in control-relevant settings. This paper addresses the following two key questions: 1) How do behavioral biases affect decision making? 2) What is the role played by feedback in human-in-the-loop control systems? Our experimental framework shows how individuals behave when faced with the task of piloting an UAV under risk and uncertainty, paralleling a real-world decision-making scenario. Our findings support the notion of humans in Cyberphysical Systems underlying behavioral biases regardless of -- or even because of -- receiving immediate outcome feedback. We observe substantial shares of drone controllers to act inefficiently through either flying excessively (overconfident) or overly conservatively (underconfident). Furthermore, we observe human-controllers to self-servingly misinterpret random sequences through being subject to a "hot hand fallacy". We advise control engineers to mind the human component in order not to compromise technological accomplishments through human issues.
... We are neither surprised by the strong general belief in the role of expertise in the financial market and the professionals' stronger confidence in financial expertise. Previous research has shown that professionals and non-professionals alike expect more skilful decision from experts compared to lay investors (Törngren and Montgomery, 2004;Peterson et al., 2015). It is argued that for this reason nonprofessionals search for the advice of investment experts (Huber et al., 2010) and put their trust and money in the hands of fund managers (Carlander et al., 2013). ...
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Investment beliefs, serving as a bridge between high-level objectives and practical decision making, are increasingly implemented in the investment industry. The present web-based study compares the beliefs of Swedish professional (N=64) and non-professional (N=278) investors, testing the links between investment beliefs and portfolio risk-taking in both samples. The results expose significant differences between the beliefs of professionals and others, also showing that the portfolio risk-taking of non-professionals is susceptible to self-confidence and emotional effects while the professionals respond to investment beliefs and risk attitude. The results confirm that disclosure of investment beliefs may reduce tensions between stakeholders and investment managers for the industry's benefit.
... overconfidence and optimism about future performance, to analysts' intention to maintain a good relationship with company executives. Another explanation for an overestimated earnings forecast is the relative advantage on information of analysts (Torngren & Montgomery, 2004). Having greater amount of resources, access to information such as data systems and opportunities to communicate with corporate usually leads to analysts placing more emphasis on firm-specific issues rather than macroeconomic conditions, resulting in extra forecast errors during uncertain macroeconomic conditions (Chopra, 1998;Amiram et al., 2014). ...
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This thesis examines the impact of brokerage size on earnings forecast accuracy and the behaviour of analysts under varying market conditions. In particular, periods with high market uncertainty are used, with over 2008 to 2011 serving as a proxy of the crisis period. Earnings forecasts of large brokerages are found to outperform during non-crisis periods. Possible evidence and explanations are provided in order to facilitate an understanding of the tendency of imitating forecasts during crisis periods, which mitigates the difference in forecasting ability of large and smaller brokerages. Also, large brokerages are more likely to issue optimistic forecasts. Collectively, this confirms the strong evidence that large brokerage forecasts are less accurate than smaller ones during crisis periods after controlling for analyst and firm-related characteristics. 2
... The results of the present study supported earlier research findings within behavioral finance that suggested a relationship between personality and investment performance (Durand,et al., 2008;2013;Hunter & Kemp, 2004;Lauriola & Levin, 2001;Menkhoff, et al., 2006;Törngren, & Montgomery, 2004;Wong & Carducci, 1991). As described in the introduction, ascending and descending financial markets represent each other's opposite in many ways. ...
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This study investigates how the personality factors of financial risk attitude, stress tolerance and complex problem solving predict investment performance in relation to rising versus descending market periods. The performance of 40 professional fund managers was measured over a total period of five years. In the rising market the three personality factors predicted 39% of the performance. In the descending market period the risk attitude and stress tolerance factors predicted 46.4% of the performance. The conclusions of the present study are as follows: a) Small differences were found in prediction strength between the personality factors of rising and descending markets. b) The financial risk attitude was the most important factor in both bull and bear markets. c) Stress tolerance is important in both periods, but especially in descending markets. d) Motivation for complex problem solving is important in rising markets. e) The three personality factors seem to be robust predictors of fund manager performance in all market phases.
... Even professionals who make forecasts are unable to overcome these inherent epistemological difficulties. Indeed, professional forecasts of complex phenomena, for example, the performance of individual stocks in the stock market, the direction of the global economy, the outcome of sporting matches or of political events are notoriously unreliable (Torngren and Montgomery 2004;Andersson et al. 2005;Tetlock 2017). In any given year, a global biological catastrophe is a low-probability occurrence and so, based on a lifetime of experience, it seems fine to ignore, but the cumulative risk and catastrophic consequences suggest this understandable approach is imprudent. ...
Natural and intentional biological risks threaten human civilization, both through direct human fatality as well as follow-on effects from a collapse of the just-in-time delivery system that provides food, energy and critical supplies to communities globally. Human beings have multiple innate cognitive biases that systematically impair careful consideration of these risks. Residents of low-income countries, especially those who live in rural areas and are less dependent upon global trade, may be the most resilient communities to catastrophic risks, but low-income countries also present a heightened risk for biological catastrophe. Hotspots for the emergence of new zoonotic diseases are predominantly located in low-income countries. Crowded, poorly supplied healthcare facilities in low-income countries provide an optimal environment for new pathogens to transmit to a next host and adapt for more efficient person-to-person transmission. Strategies to address these risks include overcoming our natural biases and recognizing the importance of these risks, avoiding an over-reliance on developing specific biological countermeasures, developing generalized social and behavioral responses and investing in resilience.
... In sum, 7 out of 10 companies in class 2 lost value on the stock market during the second quarter. In comparison, the success rate at stock picking by a hybrid AI system was reported with on average 55.19 to 60.69% [26], and experts had a success rate worse than chance [27]. Thus, the clustering allows a data-driven stock picking with a high chance of success for a short position. ...
Conference Paper
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Stocks of the German Prime standard have to publish financial reports every three months which were not used fully for fundamental analysis so far. Through web scrapping, an up-to-date high-dimensional dataset of 45 features of 269 companies was extracted, but finding meaningful cluster structures in a high-dimensional dataset with a low number of cases is still a challenge in data science. A hybrid of a swarm with a SOM called Databionic swarm (DBS) found meaningful structures in the financial reports. Using the Chord distance the DBS algorithm results in a topographic map of high-dimensional structures and a clustering. Knowledge from the clustering is acquired using CART. The cluster structures can be explained by simple rules that allow predicting which future stock courses will fall with a 70% probability.
This article discusses some issues connected with studies of the behavioural factors when making financial decisions. Therefore, it is possible to take into account factors that are inexplicable in traditional models. The main goal of our research was verification of the hypothesis market participants make financial decisions based on their experiences, intuition, stereotypes, illusions, emotions and not only on the criterion of financial gain and rational assumptions. After all, such diverse behaviour en masse influences the financial system as a whole. The practical significance of reported here study is the identification of errors in the application of classical economic theory and the possibilities of their further elimination. An effective behavioural model to avoid negative consequences is the primary tool in making financial decisions. In the first part, the author analyses the theoretical basis of her study. The second part examines the main problems associated with classical economic theory and presents the main mistakes in making financial decisions. Particular attention the author paid to the study of the behaviour of investors and managers. The third part described the research of behavioural mechanisms in making financial decisions with specific examples and implementation of the use of mathematical models.
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This article explores the utility, validity and reliability of three psychological tests in predicting fund managers investment performance. Two of the psychological tests-the Zulliger and Wartegg tests-represented implicit psychological motives. The third test-the Personality Research Form (PRF)-represented explicit psychological motives. Investment performance was measured by analyzing the five-year risk adjusted performance of forty professional fund managers in Finland. The finding of the present work was that the Zulliger Test predicted 45.4 %, the PRF predicted 25.4 % and the Wartegg Test predicted 8.3 % of the investment performance. The combined three-test prediction percentage of investment performance was 55.0 %. One of conclusions of this study is that implicit motives have been neglected in behavioral finance research. Key Words: Projective Personality Measures, Personality Measures, Investment Decision, Test Validity, Test Reliability
Purpose The purpose of this study was to gain a better understanding of pension fund managers investment thinking when confronted with challenging investment decisions. The study focuses on the theoretical question of how dual thinking processes in experts’ investment decision-making emerge. This question has attracted interest in economic psychology but has not yet been answered. Here, it is explored in the context of pension funds. Design/methodology/approach The sample included 22 pension fund managers. The authors explored their decision-making by applying the critical incident interview technique, which entailed collecting investment decisions that fund managers retrieved from recent memory (Flanagan, 1954). Questions concerned the investment situation, the decision-making process and the challenges and uncertainties the fund managers faced. Findings Many of the 61 critical incidents examined concerned challenging (mostly stock) investments based on extensive analysis (e.g. reliance on external analysts for advice; analysis of massive amounts of hard company and stock market information; scrutiny of company reports and personal meetings with CEOs). However, fund managers to a high degree based their decisions on soft information judgments such as experience and qualitative judgements of teams. The authors found heuristics, intuitive thinking, biases (sunk cost effects) and social influences in investment decision-making. Research limitations/implications The sample is small and not randomly selected. Practical implications The authors suggest anti-bias training and better acquaintance with human forecasting limitations for pension fund managers. Originality/value Pension fund managers’ investment thinking has not previously been investigated. The authors show the types of investment situations in which analytical and intuitive thinking and biases occur.
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This article describes the origins and contributions of the naturalistic decision making (NDM) research approach. NDM research emerged in the 1980s to study how people make decisions in real-world settings. Method: The findings and methods used by NDM researchers are presented along with their implications. The NDM framework emphasizes the role of experience in enabling people to rapidly categorize situations to make effective decisions. The NDM focus on field settings and its interest in complex conditions provide insights for human factors practitioners about ways to improve performance. The NDM approach has been used to improve performance through revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support decision making and related cognitive functions.
Calibration of probability judgments has attracted in recent years an increasing number of researchers as re¯ected by an expanding number of articles in the literature on judgment and decision making. The underlying funda-mental question that stimulated this line of research concerns the standards by which probability judgments could (or should) be assessed and evaluated. The most common (though certainly not exclusive) accepted criterion is what has been termecalibration', the roots of which can be traced in the well-known Brier score (Brier, 1950) and subsequent modi®cations (e.g. Murphy, 1973; Yates, 1982, 1988). Two main criteria that evolved from this line of research are customarily referred to as calibration and resolution. Calibration (or reliability) supposedly measures the accuracy of probability judgments whereas resolution measures the diagnosticity (or discriminability) of these judgments. The two major substantive and pervasive ®ndings (e.g. Lichtenstein, Fischho€, and Phillips, 1982; Keren, 1991) are overcon®dence and the interaction between the amount of overcon®dence and diculty of the task, the so-called hard±easy e€ect. Several problems have been raised with regard to research on calibration, and in this commentary l would like to focus on three of them. First, calibration studies assume (implicitly or explicitly) that probabilities are subjective (e.g. Lichtenstein, Fischho€, and Phillips, 1982) yet evaluate them by a frequentistic criterion (Gigerenzer, 1991; Keren, 1991). The validity of such a procedure remains controversial. A second problem concerns the possible tradeo€ between calibration and resolution. Yates (1982) noted that calibration and resolution are not completely independent of each other, and Keren (1991) claimed that the requirements for maximizing calibration (i.e. minimizing the discrepancies between probability judgments and the corresponding reality) and achieving high resolution may often be incompatible. A similar point has been recently made by Yaniv and Foster (1995), who studied the evaluation of interval judgments. A third problem concerns the analysis and interpretation of calibration studies. Speci®cally, Erev, Wallsten, and Budescu (1994) have eloquently described the importance of regression toward the mean in interpreting calibration studies. Similar conclusions have been reached independently by Pfeifer (1994). In a nutshell, the contribution of the papers by Erev et al. and Pfeifer is in pointing out that both overcon®dence and the hard±easy e€ect may, at least to some degree, be an artifact due to regression toward the mean. In re¯ecting on the articles in this special volume, I will focus on these three issues and examine how they are treated by the di€erent authors. I will end this commentary by raising the question of what has been learned from thirty years of research on calibration of probabilities, and will o€er a brief (and somewhat skeptical) answer to the question. RANDOM ERROR MODELS A common underlying thread of several papers to which this commentary is addressed (i.e. Budescu, Erev, Wallsten (Parts I and II); Juslin, Olsson, and BjoÈ rkman; Wallsten, Budescu, Erev, and Diederich) is the phenomenon of regression-toward-the-mean (or in the more general case, reversion to the mean). They cite, and heavily hinge on, the paper by Erev, Wallsten, and Budescu (1994). Notwithstanding, and certainly not undermining, the importance of the contribution by Erev et al. (1994) and Pfeifer (1994), it is important to stress two points.
This study replicates recent tests of the recognition heuristic as a device for selecting stock portfolios. The heuristic represents a lower limit to the search for information, since simple name recognition is the least one can know about anything. Gigerenzer and others conducted original experiments in this field at the Max Planck Institute for Psychological Research's Center for Adaptive Behavior and Cognition (the "ABC Research Group"). The ABC Group's tests support the use of the heuristic in a bull market environment. This study, conducted in a down market, reaches a different conclusion: Not only can a high degree of company name recognition lead to disappointing investment results in a bear market, it can also be beat by pure ignorance. Virtually the only finding of the ABC Group's study that we match here is that Americans are not very good at picking American stocks to outperform the market.