Behavioural Finance and Private Banking
Behavioural Biases
Thorsten Hens & Isabella Kooij
Department of Banking and Finance
Spring 2022
Finance
Behavioral
Finance
Psychology
Insights from Behavioral Finance
•
•
•
People often do not act rationally
Systematic errors distort perception and behavior
Effects on financial decisions and financial markets
Emotions at the stocks market
I think I see a trend.
Let’s watch the
market for a bit
longer
BUY
SELL
Good I didn’t wait
any longer
J
If I wait any longer I
wont’t profit form
the trend anymore.
BUY!
I will use this
opportunity to buy
more.
Great! At this price I
double my investment
Why does the
government not
intervene?
Whatever, I buy again. It
is cheaper than last
time anyways.
BUY
I can’t believe it! The price is
down by 50%. This has to be
the all-time low.
L
I tell you, it will
go down again!
J
I knew all the
time that it would
recover!
What’s going on now?
Enough! I sell and won’t ever
touch stocks again
Such a good decision to sell everything!
I knew it…
3
Emotions at the stocks market
J
BUY
J
BUY
SELL
L
Buy High Sell Low ?
4
Classroom Experiment
Participants:
Rules:
Winner:
Audience of this talk.
Write down a number between 0 and 100.
The player who is closest to 2/3 of the average number.
Average: 26.4
2/3 of Average: 17.6
Winning number:
18
Recap - Overconfidence Bias
• People are often not able to assess
themselves correctly
• Overestimating oneself in terms of
knowledge and one's own abilities
• Accuracy and reliability of own
forecasts is overestimated
Recap - Herding
• Investors base their behavior on
that of other investors
• Too little attention is paid to
fundamental values
• Investors invest or disinvest "as a
herd "
• High volatility and large price
fluctuations
• Emergence of speculative bubbles
7
Behavioral Biases in the Decision making process
Information
Selection
Information
Processing
Feedback
Decision
Behavioural Biases
8
Behavioral Biases in the Decision making process
Information
Selection
Information
Processing
Feedback
Decision
Behavioural Biases
12
CONFIRMATION BIAS
Behavioural Biases
13
Confirmation Bias
• Tendency to interpret new
evidence as confirmation of one's
existing beliefs or theories
• Ignoring or underweighting
inconsistent information.
• Overweighting of supporting
information
• Bias in information selection
Behavioural Biases
http://jamesclear.com/wpcontent/uploads/2015/09/confirmation-bias.jpg
14
Confirmation Bias
Jonas, Schulz-Hardt, Frey and Thelen (2001)
•
All Participants:
– Read an introductory article on the topic
– Make a preliminary decision: Should health insurance cover alternative healing methods or should it
only cover traditional medical treatments?
•
Pool of 16 one-page articles, e.g.
– "The success of alternative healing methods cannot be ignored. Therefore, alternative treatments
should also be paid by health insurance."
– "In the absence of an unequivocal explanation of how certain methods work, it would be
irresponsible to call such a method therapeutic. Thus, alternative treatments should not be paid by
health insurance.”
•
Group A (simultaneous search):
– 1. Choose the articles you want to read
– 2. Read the chosen articles
•
Group 2 (sequential search):
– 1. Choose from two articles
one “supporting” and one “conflicting”
– 2. read
– 3. choose from two new articles
– 4. read
Behavioural Biases
15
in the simultaneous search condition (M = 3.17,
?(34) = 3.46, p < .01. As a consequence, the overall
Confirmation
Bias:
An Example
hosen articles
was also higher
in sequential
informa-
simultaneous information search, all information ti
sented together; thus, the decision maker always has
of the available pieces of information as well as his
Table 1
Means and Standard Deviations for Information Search Dependent on
Information Search Procedure in Experiment 1
Information
Supporting
Conflicting
Confirma tion
bias"
Search procedurea
M
SD
M
SD
M
SD
Simultaneous information search
Sequential information search
3.17
5.28
1.89
1.78
2.39
2.94
1.79
1.76
0.78
2.33
1.56
2.81
a
« = 18 in each condition. b The confirmation bias corresponds to the difference between the number of
chosen supporting and the number of chosen conflicting articles.
Source: Jonas, E., Schulz-Hardt, S., Frey, D., and Thelen, N. (2001). Confirmation bias in sequential information search
after preliminary decisions: An expansion of dissonance theoretical research on selective exposure to information.
Journal of Personality and Social Psychology, 80(4), 557–571. https://doi.org/10.1037//0022-3514.80.4.557
Behavioural Biases
16
Social Media and Confirmation Bias
• Do you see a relation between social media and the
confirmation bias?
• What is different/similar for traditional and social
media w.r. to the confirmation bias?
Behavioural Biases
17
Confirmation Bias
Examples:
• Government decisions
• Polarization in opinions and conspiracy theories
–
“Belief in conspiracy theories: Basic principles of an emerging research domain” Van Prooijen and
Douglas (2018)
–
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6282974/
• Investing
– Active vs. passive investing
Behavioural Biases
18
Confirmation Bias: Consequences and Counter Measures
• Once a belief or opinion has been formed, it can be very resistive to
change, even in the face of compelling evidence that it is wrong
• Can the confirmation bias be benefitial?
– Example: New England Puritans could establish a society
• Is the confirmation bias really a bias?
– People use inference startegies that identify potential rewards and
avoid errors, but not to satisfy the logic of science
• Counter measures:
à Actively search for information contradicting your view
à Be aware of how you search for information
•
•
Good overview:
https://www.britannica.com/science/confirmation-bias
For a discussion see:
Nickerson, R. S. (1998). Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.
Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175
Behavioural Biases
19
ATTENTION BIAS AND AVAILABILITY
Behavioural Biases
20
Attention Bias
• Tendency to prioritize the
processing of certain types of
stimuli over others.
• attention is focused on just a
subset of the available
information
• Some options and pieces of
information are ignored.
https://medium.com/an-idea-for-you/8-ways-to-improve-your-focus-bf7c691413
Behavioural Biases
21
Attention Bias
• Experiment: https://youtu.be/vJG698U2Mvo
• When people focus too much on one task something unexpected
can happen, that they may not notice
• Security campaign of the City of London 2008: Link
Behavioural Biases
22
Experiment 1, 24 radiologists (mean age = 48, range =
menter spent roughly 10 min teaching these observers
28–70) had up to 3 min to freely scroll through each of
how to identify lung nodules. This experiment began
five chest CTs, searching for nodules as we tracked their
with a practice trial, during which the experimenter took
eye position. The five trials contained an average of 10
time to point out several nodules. The experimenter then
nodules, and the observers were instructed to click on
encouraged the observer to try to find nodules on his or
nodule locations with the computer mouse. In the final
her own. Once the observer was able to detect at least
trial, we inserted a gorilla with a white outline into the
one nodule, the practice trial was concluded, and the
lung (see Fig. 1). A typical stack of images from a chest
experimental trials began. As in Experiment 1, a subset
CT contains 100 to 500 slices. In the current study, the
(12) of observers completed the study on a slightly
stack that contained the gorilla had 239 slices.
smaller screen. We observed
noetdifference
in gorilla or
1850
Drew
al.
Nine radiologists were tested at Brigham and Women’s
nodule detection as a result of equipment differences.
final trial?” Twenty
of the 24 radiologists
to report
Hospital in Boston, Massachusetts, and 15 expert examinExperiment
3 was a failed
control
experiment intended to
seeingata gorilla.
This was
due
to the gorilla
being
ers from the American Board of Radiology were tested
ensure
thatnot
the
gorilla
was, in
fact,dif-visible. Twelve naive
ficult to perceive:
All 24 radiologists
a meeting of that organization in Louisville, Kentucky.
observers
(mean agereported
= 37.3,seeing
rangethe
= 21–54) were shown
gorilla when they were asked if they noticed anything
The gorilla measured 29 × 50 mm. Because of equipment
movies
that
progressed
from
the
top
unusual in Figure 1 after completing the experiment (seeto the bottom of the
differences, the image size was slightly different also
at the
same
chest CT 3).
case that was used as the final trial in
the results
for Experiment
two sites, and consequently the size of the gorilla differed
Experiments
1 and
2. The to
gorilla
was inserted into the
The radiologists
had ample
opportunity
find the
who
missed
the gorilla
slightly (Boston: 0.9 × 0.5 degrees of visual gorilla.
angle;On average,
moviesthose
in the
same
location
on spent
50% of the 20 trials, and
s viewingobservers
the five slices
containing
(range
= 1.1–12
Louisville: 1.3 × 0.65 degrees of visual angle). To5.8avoid
were
askeditto
judge
whether the gorilla was
s) and spent an average of 329 ms looking at the gorilla’s
large onset transients, we had the gorilla fade into
and
present
or
absent
on
each
trial.
A circular cue indicated
location. Furthermore, eye tracking revealed that of the
out of visibility over five 2-mm-thick slices (Fig. 1).
The
the
possible
location
of
the
gorilla
20 radiologists who did not report the gorilla, 12 looked on each trial. The
total volume of the rectangular box that could hold
theat themovies
presented
at avisible.
rate ofThe
35 or 70 ms per frame
directly
gorilla’swere
location
when it was
mean
time
on the gorillawithin
in this subjects).
group was 547 ms.
gorilla was more than 7,400 mm3, roughly the size
of dwell
a
(manipulated
Attention Bias of Experts
Are experts better in avoiding biases?
Figure 2b shows the eye positions of a radiologist who
clearly fixated the gorilla but did not report it.
Experiment 2
None of the 25 naive observers reported noticing the
gorilla.
As was the case with the radiologists
in Experiment
Fig.
2. Computed-tomography
image
containing the embedded gorilla
1, all of the naive observers reported seeing the gorilla
(a)when
and eye-position
plot
of
a
radiologist
who did not report seeing the
shown Figure 1. The results support the idea
gorilla
(b). In
(b),
circles
represent
recorded at 1-ms
(Memmert,
2006)
thatthe
experts
are somewhat
less eye
pronepositions
to
IB than novices are (Fisher’s exact test: p = .0497; see Fig.
intervals.
3a). However, unlike in Memmert’s study, our two groups
showed a sizable difference in performance on the primary task. As expected, radiologists were much better at
detecting lung nodules (mean detection rate = 55%) than
were naive observers (12%), t(47) = 12.3, p < .001 (see
Fig. 3b).
Eye movement data followed the pattern seen with the
Source: Drew, Lo undWolfe (2013): “The Invisible Gorillaradiologists.
Strikes Again:
Inattentional
Blindness
The Sustained
naive observers
spent an average
of in Expert Observers”
Fig. 1. Illustration of the slices showing the gorilla in the final4.9
trial
of Experiments
1 and in
2. which
The opacity
of thewas
gorilla
increased from 50% to 100%
s searching
the frames
the gorilla
visible
and then decreased back down to 50% over the course of 5 slices
a stack
and within
an average
of of
157239.
ms looking at the gorilla’s location.
Although both measures showed that radiologists who
missed the gorilla spent slightly more time searching in
Fig. 2. Computed-tomography image containing the embedded gorilla
its vicinity than did the naive observers, neither differ(a) and eye-position plot of a radiologist who did not report seeing the
ence was significant, t(43) = 1.26, p = .22, and t(43) =
Results
Exper
None
gorilla.
1, all o
when
(Memm
IB than
3a). Ho
showe
mary t
detecti
were n
Fig. 3b
Eye
radiolo
4.9 s se
and an
Althou
missed
its vici
ence w
1.23, p
looked
the gor
Experiment 1
Exper
The nodule detection task was challenging, even for
expert radiologists. The overall nodule detection rate was
55%. While engaged in this task, the radiologists freely
Althou
seeing
experim
Availability Bias
• Tendency to overestimate the
probabilities of events that
come readily to mind
• Concrete, imaginable and
exciting information is more
easily perceived and stored than
abstract or statistical data (more
“available”)
Behavioural Biases
24
Availability Bias
Behavioural Biases
25
Availability Bias in Finance
•
Barber and Odean (2008): “All that glitters: The effect of attention on the
buying behaviour on individual and on institutional investors”
– “ individual investors are net buyers of attention-grabbing stocks, e.g.,
stocks in the news, stocks experiencing high abnormal trading volume,
and stocks with extreme one-day returns”
•
Barber, Huang, Odean, Schwarz (2020): Attention-Induced Trading and
Returns: Evidence from Robinhood Users
– “Robinhood investors engage in more attention-induced trading than
other retail investors, and Robinhood outages disproportionately reduce
trading in high-attention stocks. The evidence is consistent with
Robinhood attracting relatively inexperienced investors. However, we
show that it can also be partially driven by the app’s unique features.
– Consistent with models of attention-induced trading, intense buying by
Robinhood users forecast negative returns.”
Behavioural Biases
26
Attention and Availability Bias
• Where have you experienced the attention
and availability bias in your life?
• In these situations: was it helpful or
problematic?
Behavioural Biases
27
Attention and Availability Bias: Consequences and Counter Measures
• Stories told in the market:
–
–
–
–
–
IT-revolution
Fight for natural resources (e.g. oil, gas, water ...)
Aging of the society
Globalization
AI
• Investors believe in the story if they see a price pattern supporting
it
• Stories that are more easily available get more attention
Behavioural Biases
28
Attention and Availability Bias: Consequences and Counter Measures
• Can the bias be benefitial?
– Ability to focus is crucial
– Attention is a scarce resource
– Sometimes decisions must be made fast (availability bias)
• Counter measure:
à Am I neglecting any important pieces of information?
à Should I invest more time to search for information?
à Am I overly relying on easily available information?
à Be aware of how you focus and potential blind spots
Behavioural Biases
29
Decision Mistakes
Information
Selection
Information
Processing
Feedback
Decision
Behavioural Biases
31
REPRESENTATIVENESS
Behavioural Biases
32
Representativeness
•
•
•
•
Judgements are often based on the degree to which an event A resembles
event B
The representative bias allows to make fast judgments in everyday life
Can lead to misjudgements
Probabilities for certain combinations of characteristics are
over/underestimated
Example:
• A fund manager has the skill to beat the market in 2 of 3 years. Which of the
following protocols is most likely:
a) BFBBB
b) FBFBBB
c) FBBBBB
p(a)=6.6%, p(b)=2.2%, p(c)=4.4%
Behavioural Biases
33
Representativeness
Experiment by Kahnemann and Tversky
•
Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student,
she was deeply concerned with issues of discrimination and social justice, and also participated in
anti-nuclear demonstrations.
•
Which is more probable?
–
–
a) Linda is a bank employee.
b) Linda is a feminist and bank employee
The majority rated the likelihood of Linda being "a bank employee and a feminist" much higher
Some critical comments: https://www.psychologytoday.com/us/blog/the-superhumanmind/201611/linda-the-bank-teller-case-revisited
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability
judgment. Psychological Review, 90(4), 293–315. https://doi.org/10.1037/0033-295X.90.4.293
Behavioural Biases
34
Representativeness: Problem of Stereotypes
Experiment (Kahneman and Tversky, 1973):
• 30 engineers and 70 lawyers
• “Jack is a 45-year-old man. He is married and has four children. He is generally
conservative, careful and ambitious. He shows no interest in political and
social issues and spends most of his free time on his many hobbies, which
include home carpentry, sailing, and mathematical puzzles.”
Is Jack an engineer or a lawyer?
• The probability that Jack is one of the 30 engineers in the sample of 100 is
______%.
• “Dick is a 30-year-old man. He is married with no children. A man of high
ability and high motivation, he promises to be quite successful in his field. He
is well liked by his colleagues.”
• The probability that Dick is one of the 30 engineers in the sample of 100 is
______%
•
People ignore base rates and only consider the case description
Behavioural Biases
35
Representativeness
• Problem of small samples
Behavioural Biases
36
Gambler’s Fallacy
• Number of runs until head shows
0.7"
Random"Walk"
Human"Percep=on"
0.6"
Probability*
0.5"
0.4"
0.3"
0.2"
0.1"
0"
1"
2"
3"
4"
5"
6"
7"
8"
9"
10"
Number*of*Runs*
Behavioural Biases
37
Hot Hand
• A player with a hot hand was one who had a better chance of
making a basket after or more successful shots than after have
missed a shot.
• Statistical analysis shows however that streak shooting did not exist
• Experiment:
– Toss a fair coin 100 times and record the sequence, e.g. HTHHTHT…
– Which series is more likely to have been produced by a non-random
process?
• HHHTTTHHTTHHTTHTHHTTT
• HTTHTHTHHTTHTTTHHTHHT
Behavioural Biases
38
Impact and Suggested Interventions
• Impact
– Belief among investors that companies with a good reputation are also
a good investment
– Using past performance as a predictor for future performance
– Influence of skill is underestimated
– Decision for active investment
• Suggested Interventions
– Critically question your own conclusions
– Apply statistical methods before making judgements
Behavioural Biases
39
Gambler’s Fallacy or Hot Hand?
were generally more confident than forecasters [F(1,30) "
65.83, MSe " 48.55, p # .001], perhaps because the forecasters, told that outcomes were somewhat predictable,
TON AND FISCHER
Figure 2. Percent confidence (±SE) in predicting/gambling as
a function of runs of success.
of 1–5 for gamblers [t(16)
turn in confidence for run
prevents the corresponding
tical significance [t(14) "
across run lengths of 1– 4
linear trend for both fore
.0001] and gamblers [t(16)
We also collated the dat
on confidence in prediction
ure. Figure 3 shows that,
were experienced, subjects
tions generally decreased.
A 2 (task) ! 5 (run len
these effects were statistica
failed predictions had a si
confidence [F(4,120) " 28
There was also a signif
[F(1,30) " 29.76, MS e "
length, confirming that the
less confident subjects we
was also a significant facto
erally less confident than
MSe " 95.57, p " .001].
SUBJECTIVE
and run length
was also si
action between choice and
MSe ! 111.26, p ! .039] p
The interaction reveals
predict that the wheel would
became less confident as t
ever, if they predicted tha
would be different from the
confident as the run length
Figure 1. Percent probability (±SE) of predicting the same color as the
last outcome.
a noticeable decrease in co
same outcome. Linear trend
dictions the same as the last
negative linear trend in conf
! 5 (run length) ANOVA on the confidence judged from a different standpoint: They may have been
#2.30, p " .05]. For predi
that run length (of successful past predic- dismayed by being unable to achieve higher expectations
would
be different, the posit
significant factor affecting confidence of success. This may have reduced their confidence relaicant across run lengths of 1–
0.25, MSe " 4.97, p # .001]. Figure 2 shows tive to the gamblers, who had no reason to expect better
tailed test], although it is
dence expressed by subjects increases ac- than chance performance.
e runs of success that they obtained in the
There was also an interaction effect between
run3. Percent confidence (±SE) in predicting/gambling as across run lengths of 1–5 [
Figure
subjects’ confidence in the
a function
as a significant linear trend in confidence as length and task [F(4,120) " 12.82, MSe " 4.97,
p # of runs of failure.
cording to whether they pre
run length of successes [F(1,30) " 14.70, .001]. Whereas both forecasters and gamblers showed an
Source: Ayton, P. and I. Fischer (2004)
recency; predictions consist
, p # .001]; subjects experiencing a run of increase in confidence over runs of success, the pattern
are more confident, and (at l
edictions increased their confidence in their for each is somewhat different. Whereas there isMS
a statise ! 6.67, p " .0001]. While both forecasters and
Behavioural
Biases
creasingly so 40
with run lengt
gamblers
showed
a
decrease
in
confidence
over
the
runs
n. The ANOVA also revealed that gamblers tically significant positive linear trend across run lengths
of failure,
y more confident than forecasters [F(1,30) " of 1–5 for gamblers [t(16) " 3.79, p # .01], the
down- the precise pattern for each is again somewhat sistent with the gambler’s fal
Gambler’s Fallacy or Hot Hand?
1374
AYTON AND FISCHER
Figure 5. (A) Probability of attributing sequences to basketball player scoring or coin toss. (B) Probability of attributing sequences
to football team scoring or roulette. (C) Probability of attributing sequences to tennis player serving or throw of a die.
Source: Ayton, P. and I. Fischer (2004)
less, plainly the two sequences are psychologically per- vestigated expectations to one of each type of process. In
ceived quite differently; subjects simultaneously
exhib- ourBiases
second experiment, we examined expectations for a
Behavioural
41
ited both positive and negative recency—the hot hand wider sample of sequence sources. The two tasks studied
fallacy and the gambler’s fallacy—for two binary se- in Experiment 1 differ in response format and modality
• Shall the nationality of a person found guilty of the crime be
revealed?
• Do you see any interferences with biases you learned about today?
Behavioural Biases
42
Anchoring
France has 67 million inhabitants. Of these, 2.2 million people live in Paris.
What is your estimate of the population of Nice?
• People use information as anchor
points
• Often people are not aware of the
anchor point of their estimate
• Estimates can be deliberately
manipulated by environmental
information
https://www.dreamstime.com/underwater-background-ship-anchor-lowered-to-bottom-drendering-image113789311
Behavioural Biases
43
Anchoring
Wheel of Fortune Experiment
•
Wheel of Fortune is spun:
Random number 1-100
Estimated percentage of African countries in
•
Estimate question: What is the
percentage of African countries in the
UN?
•
Estimated values are significantly
influenced by the random number
the UN
→ Random number unconsciously serves
as an anchor
→ Estimated value is distorted by
surrounding information
Behavioural Biases
Kahnemann & Tversky (1974)
44
Anchoring
Financial Decisions
• Investors assume that future index values will be (too) close to
current values
• Previous estimates are only reluctantly revised when new
information becomes known
• For equity investments, the purchase price is used as an anchor
point
• Investments are evaluated in relation to the price at which they
were acquired
Behavioural Biases
45
Anchoring
•
The anchoring effect describes the tendency to use a possibly
random value as an anchor point for an estimate.
•
This will bias the estimated value towards the anchor value
•
It can be useful for making a quick guess
•
Irrationally, investors often use the purchase price of a stock as an
anchor and adjust the valuation to it
•
If one is aware of the anchoring bias, biases in the evaluation can be
noticed and the adverse consequences can be reduced
Behavioural Biases
46