This summary contains everything you need to know about the course Behavioural and Experimental Finance. It starts of with a summary of the enitre course as taught in the lectures, and later gives all the Ernie assignments as well as the answers to them. The perfect summary for anyone who has diffi...
Behavioural and Experimental
Finance Summary
This summary will fist summarise the entire course as thought in the lectures. However,
sometimes a small exception is made when it made more sense to include it in a previous or
later lecture.
Next, I will go over all 5 assignments and their answer. These answers are the same as
provided by the teacher. They do vary for your course ID number, so don’t copy paste them
but use them as a reference point instead.
All lectures are given a title. These titles do not correspond to what the teacher has said. It is
just what I felt was logical at the time to name the lectures.
The only thing that was not covered in this summary was the Q&A session. After watching it,
there was no real new information that wasn’t yet covered in a previous lecture summary.
,Lecture 1 Introduction
Behavioural finance is about decision making. You look at what influences a decision.
Factors included are bounded rationality and perception of a decision.
Richard Thaler
Experimental finance is about looking if behaviour changes or remains the same over
different situations when you change just one variable.
Vernon Smith
Behavioural economics set its roots during the 90s. Before that, we assumed rational
traders. The behavioural framework introduced showed irrational traders who survived the
markets because of limited arbitrage, whereas arbitrage was assumed non-existent
beforehand.
Theories we have for decision making:
- Expected utility: take the weighted probability of all possible situations to calculate and
compare the expected utility you will get from each possible decision
- prospect theory: there are a gain and a loss domain, and the theory is that we are risk
averse when it comes to the gain domain but risk seeking when it comes to the loss domain.
Example of this is the following game. You have to choose A or B for both situations:
- Situation 1: A = 1/3 chance of getting €600 and 2/3 chance of getting €0 or B = 100%
chance of getting €200
- Situation 2: A = 1/3 chance of losing €600 and 2/3 chance of losing €0 or B = 100%
chance of losing €200.
Even though the probabilities of these options are the same, you probably chose B in
situation 1 and A in situation 2. This shows you are willing to take more risk if a loss is at
stake than if a gain is at stake. See figure 1.
figure 1: prospect theory
,Let’s take a starting point for this course. Before we look at behavioural or experimental
finance, let’s first look at traditional finance.
- Capital market: allocation of the economy’s capital stock
- efficient market hypothesis: prices always fully reflect all available information
- weak form: prices reflect all historical information
- semi-strong form: prices reflect all current public information
- strong form: prices reflect all current information, including private/insider
information
there should be no excess returns on the long-run: you cannot consistently outperform
the market
- we assume utility maximising agents who act with rational expectations, so the stock prices
reflect the fundamental value.
Efficient market hypothesis (EMH) has three main pillars:
- rational behaviour: all agents are assumed rational
- uncorrelated errors: even if there are people in the market who are not rational and make
‘mistakes’ or outperform it in some way, this is not correlated with other people and the
irrational people are outliers.
- unlimited arbitrage: if people have more information than the market they can exploit this
information and make money
only one of these three pillars has to hold in order for EMH to hold as well.
The Behavioural finance models relax the EMH assumptions.
- agents fail to update their beliefs correctly, which is not consistent with Bayes’ law
- agents follow Bayes’ law correctly, but behave choices that are inconsistent with subjective
expected utility
- agents are not always rational
- in practice there are often correlated errors: people when they think the economy is going
bad, will follow that train of thought.
- there are limits to arbitrage. Arbitrage is often costly and there is risk associated to it, so
people don’t utilise it fully (e.g. fundamental risk, noise trader risk, synchronisation risk etc.)
There are two main pillars to behavioural finance
- limits to arbitrage
- psychology, which shows deviations from full rationality
The rest of the lecture is about how the course is organised in general, so not a lot of useful
information here.
, Lecture 2 Mainstream Finance
Behavioural is always some kind of deviation from a benchmark.
- what is considered rational and how does behaviour differ from that?
In lecture 1, we saw how people are assumed rational and how even if they are not those
people are outliers and there is no correlation between them. On the aggregate level, the
averages follow rational behaviour.
The homo economicus describes a human being that is completely rational, who would act
in such a way that a market could be left alone without interventions. Some key aspects of
the homo economicus:
- act out of self-interest
- are assumed to have rational behaviour
- act in a way that is personal utility maximising
- react to constraints
- have fixed and known preferences that don’t change over time
- have full information and act in accordance to it.
Basically you could summarize it to this:
- they only care about their own well-being. They only do something nice when it maximises
their utility as well.
- they are only interested in non-social material goods
- they are extremely autistic and have no social pleasures, i.e. feelings are not included.
- beliefs, actions and preferences are all assumed constant
Economists use utility functions to deal with outcomes. Utility functions assign a value to an
outcome, and that way you can rank certain outcomes. You can describe preferences by
assigning a higher number to them. It makes items you cannot usually compare comparable.
It considers all bundles available and allows you to select the best method. We assume that
a higher wealth also indicates a higher utility, although this process is not linear. Utility
functions are often concave, meaning there is marginally diminishing utility for higher levels
of wealth.
In order to deal with probabilities, you can use Bayes’ rule. This rule says prices are based on
all information and change as soon as information is updated. An example of this is the
following game. You have a bag containing 5 poker chips. There are two possible scenarios:
- the bag has 1 red chip and 4 white chips, which is considered the good company’s bag
- the bag has 3 red chips and 2 white chips, which is considered the bad company’s bag
Prior to the game, there is a 50/50 chance you have the good company’s bag or the bad
company’s bag. Now let’s say you draw one chip out of the bag, and it turns out to be a red
chip. What is the updated probability that you face the good company’s bag?
drawing a red chip makes it more likely you face the bad company’s bag. Now, all 4 other
chips have to be white.
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