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Behavioral Finance and Real Estate (BFRE) - Summary Lectures
A summary of the BFRE lectures of 2021 given at the Vrije Universiteit
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SUMMARY BFRE LECTURESMINOR RISK MANAGEMENT FOR FINANCIAL INSTITUTIONS
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,Lecture 1 – Introduction______________________________________________________________
Behavioral Finance is the study of how psychological phenomena impact financial behavior and
markets. Its approach is to examine systematic deviations from rational behavior and relax
assumptions of rationality and perfect capital markets.
The Homo Economicus is a self-regarding maximizer with unlimited and costless information
processing capacity and unbreakable willpower. He all relevant available information, no systematic
errors.
But the Homo Economicus doesn’t exist. We are Homo Sapiens:
- Wise
- Bounded rationality (we seek a decision that will be good enough, rather than the best
possible decision)
- Bounded awareness (when you make decisions and have all the relevant info, you
sometimes don’t use it)
- Bounded willpower (plans but not live up to them)
- Bounded self-interest (we care a lot about ourselves, but also about others)
There are two important pillars for every decision beliefs (outcomes, options, probabilities) and
preferences.
Traditional Finance says beliefs are rational and preferences are normatively acceptable, but people
deviate systematically from rational norms.
A heuristic is an experience-based rule of thumb or mental shortcut.
Why do we use heuristics? We have limited information, memory, information processing ability and
time. Our brain is trying to make sense of imperfect input all the time, so that’s why what you see is
what your brain predicts the reality to be (brainstorm and green needle). Context is important to
how you perceive things.
There are two types of thinking, which Kahneman described in his book:
System 1: intuitive and automatic (fast, effortless)
System 2: reflective and deliberate (slow, effort)
Priming is a phenomenon in which a recent experience activates thoughts that subconsciously
influence future thought and actions (often not able to be independently replicated). If in a class
people hear words related to elderly, like gray or old, those people walk slower to the next class
than people who didn’t hear those words (not replicable, so not true).
Semantic priming does work, for example: wash so_p, you will probably say soap, but if the word
“wash” is changed by “eat” eat so_p, you will probably say soup.
,Lecture 2 – Overconfidence and Optimism_______________________________________________
The spotlight effect is the irrational thought of thinking everybody sees something we are ashamed
of (like a stain of coffee on our shirt), but in reality, we overestimate how many people see it. The
IKEA effect means that we attach more value to the things we constructed ourselves. The halo
effect is thinking that the whole person is bad when he/she has a bad trait or has done something
bad.
Two important biases in decision making are:
Overconfidence (overprecision) excessive precision in one’s
beliefs. It is a bias in which subjective confidence is greater that
objective accuracy. Overconfident people are often surprised. In the
picture beneath you can see the uncertainty is much smaller.
Optimism bias in which the likelihood of positive outcomes of
actions is overestimated and the likelihood of negative outcomes
underestimated overestimation (one’s actual performance) and
overplacement (one’s performance relative to others). Optimistic
people are often disappointed. In the picture beneath, the graph
shifts to the right, which means that positive outcomes will be more
likely.
The subjects are asked to give a percentage about
how sure they are with their answers. The green
line is the perfect calibration, so if people say they
are 80% sure the answer is right, the answer will
be right 80% of the time. But we can see the
results of different studies are much lower than
this perfect calibration. We tend to overestimate
the accuracy of our believes.
One possible explanation for our tendency to be overconfident is the hindsight bias tendency for
people with outcome knowledge to believe falsely that they would have predicted the outcome.
Other explanations is that we have the desire to feel sure about ourselves and the desire to make
others feel sure about us. Also, confirmation bias, representativeness biases and anchoring.
There are two important aspects needed for people to overcome their overconfidence:
- Feedback: in order to learn, we need feedback.
- Accountability: feeling responsible to make good forecasts (weather forecasters)
, If we have a prediction task, we tend to think from the specifics of the case we have to predict
(inside view). This inside view tends to be a very inaccurate approach because it’s anchored on plan
and impressions. For a more accurate approach, we have to make a prediction by recognizing that
the case is a member of a broad category of cases (outside view).
Planning Fallacy tendency to hold a confident belief that one’s own project will proceed as
planned, even while knowing that the vast majority of similar projects fail (for example, divorce
rate). The possible explanations for this fallacy are:
- Focusing on plans
- Neglecting past experiences we tend to attribute failures in the past to external factors
(self-serving bias: tendency to attribute successes to internal personal factors and failures to
external situational factors beyond control)
The self-serving bias and the planning fallacy are possible explanations of optimism.
Lecture 3 – Other judgmental biases____________________________________________________
Representativeness (similarity) heuristic the probability that X belongs to set Y is judged on the
basis of how similar X is to the stereotype of Y. The probability that according to a description
subject X is an engineer is based on how similar subject X is to the stereotype of an engineer.
Another example: If people are told to choose 5 numbers between 1-40, they will probably spread
the choices evenly, so it seems random because that is what is representative of a random outcome.
People tend to choose higher spacing between numbers than lower spacing.
P(A|B) is the conditional probability of A, given B
P (B|A) is the conditional probability of B, given A
P(A&B) is their joint probability.
P(A|B) x P(B) = P(A&B) = P(A|B) x P(A)
P (B|A) x P(A)
Bayes’ Law: P(A|B) =
𝑃(𝐵)
The Bayes’ Law can be modified to fit into behavioral finance Bayesian Updating:
P (info | 𝐻 + ) x P(𝐻 + )
P(H+|info) = ;
P(info)
where H+ is the probability of the hypothesis being true and info is the information given.
There is a simple statistical rule P(A&B) ≤ P(A) and P(A&B) ≤ P(B)
If this rule is not applicated, we speak about conjunction fallacy P(A&B) > P(A) or P(A&B) > P(B)
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