Bachlor Thesis

Behavioural biases : overconfidence
and optimism and their influence on
corporate dividend policy
Paulina Krawczyk
Supervisor: Profesor Philip Valta
Bachelor Thesis
Bachelor in Business Administration
December 2015
L’auteur de ce projet est le seul responsable de son contenu, qui n’engage en rien la responsabilité de
l’Université ou celle du professeur chargé de sa supervision. Par ailleurs, l'auteur atteste que le contenu est
de sa propre rédaction, en dehors des citations parfaitement identifiées, empruntées à d'autres sources.
Paulina Krawczyk GSEM1

Table of contest:
Executive Summary 3
Introduction 4
Can we assume that managers are fully rational? 5
Biases 6
Section I: Overconﬁdence 9
Definition 9
CEO’s overconfidence 9
Measures of overconfidence – solutions 10
Malmendier and Tate’s approach 10
Ben - Davids approach 12
Section II: Optimism 14
Definition 14
Optimistic CEOs 14
Campbell optimism measures 15
Otto’s approach 17
Section III: Overconﬁdence, optimism and dividend policy 19
Dividend policy 19
Dividend policy and overconfidence 22
Dividend policy and optimism 22
Conclusion 24
References 25

Executive Summary
The goal of my Bachelor Thesis is to show how behavioural biases might influence corporate
financial decision. I concentrate in my project on the two most quoted and most robust biases:
optimism and overconfidence and on their influence on the dividend policy.
The work is divided in five parts. The first part is an introduction, where I describe the
development of cognitive psychology and its influence on the world of finance and
economics. I also ask if we should assume that the CEO, whose goal is to maximise firm’s
profit is always fully rational and does not suffer from behavioural distortions. At the end of
the section I give a definition of bias.
The second part covers overconfidence, in particular the overconfidence of CEOs. The idea in
this section is to first explain overconfidence from a psychological point of view and then
show how one might measure that irrational behaviour in economics and finance. Assessment
of psychological treats is not an evident task, especially in a corporate setting, where both
internal and external factors have an impact on the CEO’s decisions. However I present two
evolution methods than help link irrational behaviour to corporate decision-making.
In the next section I proceed similarly with the optimism bias: I start with a description of
optimistic CEOs and then propose an explanation for the gap between rational and irrational
decisions. Then I suggest that overconfidence and optimism bias may have the same influence
on corporate decision making.
The fourth section applies the presented bias measure methodology on dividend policy.
In conclusion I sum up my work and ask open questions about the development of
behavioural finance and its impact on corporate decision-making.

Introduction
The theory of efficient markets assumes that agents behave in a rational way. That theory is
widely approved in economics, finance, psychology, political science and as well in
philosophy. Described by Adam Smith in the Wealth of Nation (1776), the theory considers
that in the decision making process agents take into consideration available information,
probabilities and potential costs in order to chose the self-determined best action. That’s the
classical XVIII century theory, which gave a solid base for the development of economics
studies.
The theory was questioned 200 years later. Researchers started to ask themselves how we can
say that individuals make choices in their own interest and what it means precisely. In XX
century the attractions of the rational choice approach, its empirical failings in psychology
and economics have promoted an intense interest in new approaches. A wide range of
alternative models were suggested. They are called “real life models” and complete the
classical theory of rational decision making. Let’s take the example of two great researchers:
Kaheman and Tversky who established the Prospect Theory (1979) – theory which helped to
develop new field of study of behavioural economics and behavioural finance. According to
Kaheman and Tversky agents do not behave in a rational way and they systematically violate
the Expected Utility Theory. They tend to overweight small probabilities and underweight
large probabilities. Individuals make decisions by comparing alternatives and prefer avoiding
losses over acquiring gains. The other example which is often used to question rational
decision making is an experience called Allais Paradox (1953), which explains how people
violate the Independence Axiom. Also related is Robin’s Calibration Theorem (1997), which
describes agent’s attitude over small and large stakes. There are many more examples which
talk about the divergence from the classical rational theory and introduce cognitive
psychology into the world of economics and finance.
The new behavioural financial concepts grow beginning 2000, when the dot-com bubble
seems to be in its blow-off phase. Financial analysts and economists started to realise that the
agent’s behaviour is far away from being rational. They introduce into their analysis the
concepts of anchoring, hindsight bias, confirmation bias, overconfidence and herd behaviour.
Researchers begin to realise that introducing the aspects of cognitive psychology into asset
pricing models is something essential. They follow the hypothesis by empirical studies.
Firstly, Hersh Shefrin (1994) introduces the name of “noise traders” for agents making
cognitive errors. Mair Statman (2008) explains an behavioural asset pricing model in which
expected returns are high not only when the objective risk is high, but also when the
subjective risk is high. Fama and French (2004) summarise the theory and review the
empirical studies of the CAMP. They conclude that CAMP fails to fully explain the relation

between risk and return, which invalids most of its applications. Behavioural finance takes its
place in the asset pricing modelling.
In this paper I am going to investigate behavioural finance in the field of corporate finance.
Is it important to consider cognitive aspects of CEO’s in their decision-making or can we
assume that they are fully rational and they only need to face the irrational investor’s
decision? What are the biases? How shall we measure cognitive treats? What are their
influences on corporate manager’s decisions? Does it influence only certain decisions, like
investment cash-flow? I would like to answer these questions by investigating managerial
overconfidence and optimism biases and at the end look at their influence on dividend policy.
Can we assume that managers are fully rational?
Even though we perceive the CEO’s role in measuring a company’s performance as crucial,
when the performance is poor, we rarely blame the CEO. Usually we look for an explanation
in market conditions, political policy or economic bubbles. As far as the CEO is concerned we
do believe that they behave in a rational way and their goal is to maximise the company’s
profit, given their leadership position and compensation. What is more in companies there
exist many mechanisms designed to keep CEOs focused on value maximisation, like paying
them with a stock options or saddling them with debt. Nowadays, researchers and analysists
tend to relate corporate decisions to personal characteristics of the top decision maker inside
the firm, instead of looking at the firm – its characteristics or environmental characteristics.
One of the first ones who took into consideration manager’s irrationality was Roll (1986). He
was studying corporate takeovers and found that economists should abandon the hypothesis of
manager’s rationality. According to him there is no evidence to indicate that every individual
behaves as if they were rational economic agents. He claims that market prices reflect rational
behaviour because of averages: in actuality the market is characterised by grossly irrational
individual behaviour that cancels out in the aggregate, leaving at the end a small behavioural
component, a rational core, that all individuals have in common. On the example of takeovers
he suggested that economists should stop disregarding psychological studies and started to
analyse managerial biases. When managers think about taking over another firm, they conduct
a valuation analysis of that firm and they take synergies into consideration. Roll suggests that
if they are overconfident, they will be too quick to launch a bid when their valuations exceeds
the market price of the target. It leads to excessive trading. Also in the case of takeovers the
arbitrage strategies are complicated to implant as the managerial decisions concern assets,
also human assets which are difficult to make an arbitrage with. What is more the hypothesis
that irrational managers will learn from bad decisions is not valid, as the takeovers do not
happen very often. We need to add as well a tournament mechanism in the company. That
assumption could be generalised. During the CEO’s cadence some of the decisions are taken

only once, like large investment decisions or choosing investment partners. In fact major
decisions are not repeated often, which means that executives do not have the possibility to
learn from bad decisions.
At the beginning managerial and economical press which was supposed to study the role of
CEOs focused on the directly observable characteristics, like age, sex or education, as those
variables could affect corporate decisions. Nowadays most of the researchers follow Roll’s
hypothesis and consider in their empirical studies behavioural biases, which make the
mangers behave irrationally, because even if companies have mechanisms to keep managers
focused on value maximisation, CEOs tend to think they are proceeding correctly and if they
are wrong it is difficult to change their mind. That is why the development in the study of
CEO’s behavioural biases is so essential.
Biases
Cognitive biases, or beliefs, are described as a type of error in thinking that occurs when
people are processing and interpreting information in the world around them. They often
originate from an attempt to simplify information processing, like a rule of thumb that helps
agents to make sense of the world and reach decisions with relative speed. What is the reason
of biases? It is psychologically proved that heuristic or mental shortcuts can often lead to such
errors. What is more social pressure, individual motivations, emotions and limits on the
mind’s ability to process information can also contribute to cognitive biases. They can often
lead to bad judgments and as follow wrong decision-making. However we should not always
consider them as bad. Psychologists argue that many of them may serve an adoptive purpose
– they allow us to reach decisions quickly, which might be very helpful when one is forced to
make decisions under pressure. Relevant examples of bias for this paper include:
Representativeness: Is described as a situation in which agents take decisions based on
stereotypes and prejudice. The problem was investigated by Kahmen and Tversky (1974) and
refers to the problem of underweighting of base rates of some situations or characteristics,
which were generated by already existed model or situation:
”Many of the probabilistic questions with which people are concerned belong to one of the
following types: What is the probability that object A belongs to class B? What is the
probability that event A originate from process B? What is the probability that process B will
generate event A? In answering such questions, people typically rely on the representativeness
heuristic, in which probabilities are evaluated by the degree to which A is representative of B,
that is, by the degree to which A resembles B. For example, when A is highly representative of
B, the probability that A originates from B is judged to be high. On the other hand, if A is not
similar to B, the probability that A originates from B is judged to be low.”

Availability Heuristic: it refers to the problem when decision is taken in an “easy and quick
way”, when agents take into consideration only readily available information (usually relying
on memories), without investigating other alternatives or procedures. Again the problem is
described by Tversky and Kahneman (1974):
"There are situations in which people assess the frequency of a class or the probability of an
event by the ease with which instances or occurrences can be brought to mind. For example,
one may assess the risk of heart attack among middle-aged people by recalling such
occurrences among one's acquaintances. Similarly, one may evaluate the probability that a
given business venture will fail by imagining various difficulties it could encounter. This
judgmental heuristic is called availability. Availability is a useful clue for assessing frequency
or probability, because instances of large classes are usually reached better and faster than
instances of less frequent classes. However, availability is affected by factors other than
frequency and probability. Consequently, the reliance on availability leads predictable biases."
The problem has been followed by the researches and for example Chiodo (2002) found that
overestimation of the subjective probabilities can cause overreaction and under reaction of
expectations and, subsequently, asset pricing.
Anchoring: is a heuristic in which decisions are made based on an “anchor”, which means that
agents start their decisions with some initial, possibly arbitrary, value and then they adjust to
it. Tversky and Kahneman conducted an experiment in which subjects were asked to estimate
the percentage of United Nations’ countries that are African. Before giving a guess, they were
asked to generate a random number between 0-100. As it appeared later their guessed were
affected by the generated number. Agents how gave number 10, estimated to probability at
25%, and whose compared to 60, gave the estimation at 45%.
In psychology the biases problem is well known and described, but when the psychological
theories came into financial literature, they were met with scepticism. Some scientists argued
that behaviourists use psychological literature to find support for their assumptions which will
make their arguments matter, without using empirical studies; that for instance they use
contradicting biases, like optimism and pessimism to support the same argument in their
research. It is not surprising that the demand for empirical studies has emerged. The empirical
studies have for goal to identify which exact bias, if any, influences decision-making and in
which way. They have to deal with many challenges, because it is not obvious how to
measure the gap between rationality and irrationality, especially when we consider corporate
decision making. Campbell (2000) discusses the “technical” problem of empirical research,
arguing that behavioural models cannot be tested using aggregate consumption or market
portfolios, because rational utility maximisation neither consumes aggregate consumption,
nor holds a market portfolio. As stated previously, Roll (1986) claimed population might be
considered as grossly irrational individuals. The idea of measuring behavioural biases will be

to treat each decision and individual separately to measure the distortions. As a result testing
behavioural models is difficult without detailed information on the individual trading
information of market participants. That sort of information is confidential and difficult to get.
The other problem in empirical studies might be time scale: can we measure the CEO’s biased
decisions over one year and assume that the next year they will be rational, since CEO is
learning from his mistakes? The effect of learning is often muted by errors of application:
when a bias is explained, agents often understand it, but then immediately proceed to violate
it again in a specific application. Finally, there is a difficulty to link the biased CEO’s
decisions with the changes in corporate decisions, like investment cash-flow, the behaviour
towards shareholders or the financial preferences. The key question is: how and which biases
influence CEO’s decisions, and which kinds of decision?
As psychologists use the psychological tests to evaluate biases – like cognitive reflection test
– in corporate finance some interesting, solutions have been proposed as well. In future
sections I will present how researchers suggested to deal with the numerical measures of
behavioural biases. I will focus on overconfidence and optimism as these two are mostly
quoted. They are as well robust and strong and have been measured on many samples. Since
these biases are very individual there is a little risk that they will be unbiased through learning
(Baker and Wurgler 2013). Furthermore I will be investigating their influence on the dividend
policy.

Section I: Overconfidence
Definition
What does overconfidence mean? In the psychological literature the expression: “better than
average” is used. We might take as an example an experiment of Svenson (1981) who asked
subjects about their driving competences. The majority of subjects believed they were more
skilful and less risky than average – 82% of a sample of students placed themselves among
the top 30% safest drivers. However overconfidence may be regarded not only as a “better
than average” effect. After reading papers from the Psyching database with the keyword
“overconfidence” (365 results), Moore and Healy (2007) distinguished three different
definitions of overconfidence. The first one is the overestimation of one’s ability,
performance, level of control, or chance of success. The second one is when people believe
themselves better than others, they rank themselves better than median. It is called over
placement. The last way to look at the overconfidence is to examine subjects with numerical
answers and look if they are excessively regarding the accuracy of answer. That experiment
shows that subjects very often give to their answers the confidence intervals which are too
narrow, it leads to overvaluation of the correctness of theirs answers. Usually most of the
researchers do not take into consideration the difference between these definitions.
CEO’s overconfidence
Taking into consideration psychological papers, I may assume how the overconfident CEO
will be behaving. If we look at the “better than average” effect, I will expect that
overconfident CEO will be sure of his abilities of management and will find himself better
than other average managers in the company. They might assume that they have better skills
in picking investment policies or merger targets and that their decisions are usually correct.
When we look at the overestimation of expected payoffs we might consider that
overconfidence CEO’s will be expecting higher outcome of his investment decisions. There is
also a probability that overconfident CEO will be picking more risky and challenging
projects, as they under valuate the expected variance. And if we examine the CEO’s
confidence intervals we might identify that they are too narrow as they are too assured of their
decision-making. These are only my hypothesis given the overconfidence explanation;
however some of them are proved by the empirical research and some of the researchers
proposed smart results to these problems. To respond these questions fist of all we need to
establish how to measure overconfidence and than investigate which CEO’s decisions it
influences.

Measures of overconfidence – solutions
In behavioural finance the most demanding problem is to measure a derivation between
rational (none biased) behaviour, and irrational (biased) behaviour. The first idea which comes
to mind is to use in such a research psychological questionnaires, which is not a bad solution
and it is quite often used in searches. However, using psychological survey requires a lot of
time, because to measure the biases one needs at least 5-7 years time’s intervals (that is based
on a mean of few researchers, used in my paper). What is more, considering time’s intervals
we are facing a problem of tournament in the companies, when the CEO changes, one needs
to start a survey from the beginning. Even is psychological tests might take lots of time, they
are quite efficient in terms of eliminating company’s characteristics. Psychological surveys
are usually very personal and they only concentrate on the individual treats. That eliminates in
the survey the characteristics of company, which might influence CEO’s decisions – like
corporate culture, financial preferences or shareholders influence’s. On the other hand these
surveys should be created in such a way to exclude CEO’s environmental aspects, like
family’s history, education or financial status, as these are the variables which could impact
CEO’s overconfidence. As the cognitive questionnaires are concerned the numerical answers
tests are very efficient to measure the biases. These are the tests which help to measure the
confidence intervals which I had already described. Further I will present an example of a
survey lead by Ben-Davis (2007) to measure overconfidence. That survey is robust as it helps
to eliminate other biases which could correlate with overconfidence, like optimism.
The other solution, than cognitive tests will be to use numerical surveys, which success will
depend on the precision of measure of bias. As it was already mentioned in the introduction,
working on real data in behavioural finance is very challenging. We need detailed information
on personal trading actions, which are usually confidential. Nonetheless, some researchers
came up with some clever approaches. I will start this paragraph with Malmendier and Tate
(2005) approach. They argued that CEO’s characteristics lead to distortions in corporate
investment polices. What is very intriguing and revolutionary they looked at the detailed
information on the stock ownership and set of option package to define overconfidence bias.
Malmendier and Tate’s approach
Stock options, which give a recipient the right to buy a share of stock at a pre-specified
exercise price for a pre-specified term, have emerged as the largest component of
compensation for US executives (Hall and Liebman 1998, Hall and Murphy 2002). They are
interesting as a form of compensation since they provide a direct link between an executive
and a company’s stock performance. However executive stock–options differ in critical ways
from the standard “priced” options formulas, which are freely tradable and traders may hedge
the risk of option by short-selling. Executive options face the problem of under-diversification

and are not tradable and, furthermore, the sale of executive stock-options may be restricted.
These are the mechanisms put on the CEO to engage risk-seeking behaviour.
Malmendier and Tate (2005) use an analysis of execution of stock options to evaluate the
overconfidence bias. They study a sample of 477 large publicly traded US companies from
1980-1994, which had to as well appear at least four times at one of the Forbes lists. Their set
of data provides them with detailed information on the stock ownership and options package
and they obtain the CEO’s portfolio picture. To investigate the CEO’s transactions on his
personal account and on corporate account, they supplied the data set with extra items, like
investment on capital expenditures, cash-flow as earnings before extraordinary items, capital
as property. To have a final sample, they supplement the data with personal information about
executives (their education background, employment experience). I assume that they use
these variables to show a more detailed picture of CEO.
They say that rational CEOs should exercise in-the-money options or sell stock on a pre-
committed schedule, as it helps to avoid sending negative signals to the market. What is more
executive stock-options as said before face the under diversification problem and the human
capital is invested, which confirms that they should be exercised before expiation or when it
exceeds some level of in-the-money. Looking at the data they notice that some of executives
do exactly the opposite, they keep or even buy more company stock-options instead of selling
it, which shows that they overestimate the company’s performance under their executive
period (1 type of overconfidence according to Moole and Haely definition). That helped them
to define three measures of overconfidence, or proxies based on “delta”: Holder 67,
Longholder and Net Buyer.
Holder 67 refers to the CEO who keeps the stock-options after the vesting period and when
their stock-option exceeds 67% in-the-money. Vesting is a period over which executive has
the ability to realise his right to exercise stock-options — in most cases it is 5 years.
Malmendier and Tate use the Hall and Murphy (2002) framework to decide a reasonable
benchmark for the percentage in-the-money at above which executive should exercise options
and they fix it at 67%. Holder 67 is constructed as follow: they are looking at the CEOs who
had 67% in-the-money during the vesting period. They identify the CEO and look if they
wasted the possibility of executing the option during or after the vesting period. If the CEO
failed to exercise the option they are investigating if they subsequently exhibit the same
behaviour at least one more time during their tenure as CEO. If that is right the CEO is
classified as an overconfident. From the subsample of firms (337 observations), 58 CEOs
were classified as overconfident using the Holder 67 measure.
Constructing Longholder measure Malmedier and Tate targeted the expiration date of option,
rather than looking at vesting period. The CEO was classified as an overconfident if they were

holding the option till the last year of its duration. In this subsample of 661 CEOs, 85 were
considered as overconfident.
In the last measure called Net Buyer, they looked at the CEOs’ tendency to purchase
additional stock-options despite their high exposure to company risk. Of 158 CEOs used in a
subsample, 97 of them were considered as Net Buyers.
The rationale behind these behavioural measures is that the overconfident executive perceives
a firm’s stock as under value by the market and is less likely to exercise stock-options, than a
rational executive. Furthermore, overconfident CEOs believe in a good performance of
company under their tenure. Malmendier and Tate also looked at some alternatives which
could explain the late exercising of stock-options and they excluded: inside information,
signalling, risk aversion and corporate governance, which strengthen the interpretation of
their measures as proxies for overconfidence.
I am using the example of Malmendier and Tate’s approach to present a link between stock-
options exercising and overconfidence. Their solution for empirical studies is nowadays used
to show the relations between overconfidence and corporate polices. However Malmendier
and Tate’s solution does not exclude other biases which might make agents behave the same
way. For example agents who are optimistic might also overestimate the performance of the
company. That is why overconfidence and optimism in their research are considered very
close and similar to each other.
Their insight into stock-options exercises as an assumption of overconfidence is interesting
and is coherent with the Hall and Murphy framework. However access to that sort of data is
difficult and the bias can not be measured for every firm.
As mentioned above the other possibility to measure overconfidence will be to run an
empirical, “numerical answer” survey. I will use now a different approach to identify a
behavioural bias.
Ben - Davids approach
Ben-Davids (2007) says that overconfidence is an overestimation of one’s beliefs or
underestimation of variance of risky processes. These two definitions of overconfidence help
to investigate the bias by focusing on the confidence intervals and by looking at the variance
of predicted results. In his research he was using a data collected by Duke University between
March 2001 and June 2009, which was investigating each quarter around 2000-3000 financial
officers. The aggregate results of data are accessible on www.cfosurvay.org. The question
asked in the survey was as following:
“On the next year, I expect the S&P 500 return will be:

a). There is 1 in 10 chance the actual return will be less than …%
b). I expect the return to be ….%
c). There is 1 in 10 chance that actual return will be grater than … %”
He also used a double-siring procedure to separate the long and short term overconfidence
(ST - based on the one-year return and LT - based on the 10 year return). Given the responses
he measures the confidence intervals of given predictions (10% and 90%) into probability
distribution. Looking at the probability distribution, if it is a wide one, we may assume the
subjective uncertainty and a narrow one, may reflect a subjective confidence. In addition, we
can calculate the variance of distribution - if the predicted variance distribution is bigger than
the real variance distribution, we can assume that the agent is overconfident.
Ben-Davids went further than Malmendier and Tate’s in his research. He tried to separate
optimism and overconfidence bias, which very often go together. Agents, who sees their
company’s future in bright colours, might feel as well more confident in their decision-
making. To eliminate optimism form the overconfidence he asked one more question in his
survey:
“a). Rate your optimism about the US economy on a scale from 0-100, with 0 being the least
optimistic and 100 being the most optimistic.
b), Rate your optimism about the financial prospects for your company on a scale from 0-100,
with 0 being the least optimistic and 100 being the most optimistic. “
These questions helped him to specify the optimism about the US economy and optimism
about the firm performance. Then, as in the first question, he compares the predictions with
the reality. As Malmendire and Tate he also excludes some other variables which could
influence the executives predictions, like past market and firm performance and personal
characteristics. These variables are included in his final sample.
Ben-Davids’ approach is also different because it is based on the predictions, which goes well
with the theory that overconfidence causes the overestimation of expected returns. An
advantage in his approach is the fact that he eliminates (by asking more specific questions)
the optimism bias from the overconfidence, so it makes his research more robust. However
making a survey requires lots of time and the data is not easy to be collected.
Ben-Davids and Malmendier and Tates measures of overconfidence will be used furthermore
in this paper to elaborate the influence of biases on the dividend policy. Now we will have a
look at optimism, how it is defined, and what is the idea to measure it.

Section II: Optimism
Definition
Wenstein (1980) defines optimism as the unrealistic tendency of individuals to underestimate
the likelihood they will experience adverse events, such as skin cancer or a car accident. He
calls it a positive illusion. Taylor and Brown (1988) describe optimism as unrealistically
positive self-evaluation, exaggerated perception of control. They believe that it is maintained
through interpretation and retrieval of information, cognitive drift, acknowledging pockets of
incompetence (i.e. accepting a lack of talent in certain areas of life while maintaining belief in
a general competence) and negative self-schemata which enable people to simply avoid
situations in which they lack talent – in other words they construct a self enhancing world in
which to live. Optimism bias is especially pronounced when the problem is perceived
infrequently, for example becoming bankrupt or experiencing a nervous breakdown (Griffin
and Murray, 2008). On the other hand when the event is frequent the optimism bias perceived
is relatively prevalent. There exist two approaches in psychology to asses optimism bias. In
first one participants are explicitly instructed to rate the likelihood of experiencing some
adverse event. The direct approach demonstrates that some agents feel less likely to
experience some adverse events. The second method, which is indirect agents are asked the
same question, plus one additional question. They are asked to estimate the likelihood that
other individuals will experience some adverse events at the end a difference is calculated
between the two estimations. The indirect method is more often used and gives more robust
results.
The psychological studies on optimism bias made optimism an interesting subject of study for
corporate finance. On the subject of managerial optimism, Heaton (2002) states: “managers
are more optimistic when they systematically overestimate the probability of good firm
performance and underestimate the probability of bad firm performance”. March and Shapira
(1987) say that managers are more optimistic about the outcomes they can control and Gilson
(1989) shows that agents are more optimistic about the outcomes to which they are highly
committed. That implies that managers will generally be highly optimistic about the firm’s
success, because their wealth and reputation depend on it. As in the previous section, I will
make a hypothesis about how optimism might influence corporate managers decisions, then I
will demonstrate Campbell’s and Otto’s approaches to measure optimism bias.
Optimistic CEOs
Since optimistic CEOs tend to overestimate a probability of positive events I will assume that
optimistic CEOs will have a tendency to think that the projects they choose will be very
profitable or they might think that the mergers will bring positive effects. We can note that
these assumptions are very similar to the ones about overconfidence. Can we say that

optimism and overconfidence have the same effects? Is there any difference between
optimism and overconfidence? The main difference is that overconfidence will be making
changes in variance - it causes the underestimation of variance, and optimism causes
overestimation of the mean. However the effects of both biases will be similar and we can
easily consider both biases causing the same results. Ben-David (2007) finds that: “In
particular, it is plausible that managers who exhibit overconfidence are also optimistic about
the future of their firms. Alternatively, it is possible that managers who anticipate a bright
future for their firm feel more confident.” I find the Ben-Davids statement very wise, further I
will write about Otto’s paper, where he shows a correlation between optimism and
confidence. Most of the researches use overconfidence and optimism alternatively and they
establish similar measures for both biases. A very good example of how overconfidence and
optimism are so close to each other is a paper by Colin Campbell (2010), where he uses
Malmenier’s and Tate’s overconfidence proxies to establish an optimism measure.
Campbell optimism measures
Campbell (2010) investigates a relation between optimism and forced turnover. He asks if
optimistic CEOs are more likely to be fired. To examine that problem he tests that hypothesis
using CEOs in the ExecuComp database between 1992 and 2005 and uses Malmendier’s and
Tate’s overconfidence proxies modified to infer a hight or low optimism level. They draw
three optimism measures.
1. Optimism measure based on stock option holding/exercise decision
The first measure is based on the overconfidence measures of Malmendier and Tate.
Campbell proceeds in a similar way to Malmendier and Tate, looking at the stock option
exercise decisions. He takes as a given 67% money's cutoff for a full sample of CEOs to
indicate optimistic CEOs. Then he identifies high and low optimistic measures. To define high
optimism he looks at the CEOs who hold stock options that are more than 100% in the money.
To complete the hight optimism he defines low optimism as the CEOs who exercise stock
options that are less than 30% in the money and do not hold any other exercisable options that
are greater than 30% in the money. Given both definitions of high and low optimism he calls
moderate optimism CEOs who hold or exercise options between 30-100% in the money.
Having three definitions of optimism he classifies the CEOs.
His approach is very similar to Malmendier’s who also defined three measures of
overconfidence. Since keeping options more than 67% into money is not rational Campbell
assumes that sort of behaviour is due to optimistic bias. However in his research he lays out
some potential concerns about the stock exercise measure. First, CEO options decisions might
depend on the expectations of their board of directors pressure. Second, CEOs might have

inside information. However later Campbell creates a model which is robust and these
concerns are not valid anymore.
2. Optimism based on net stock purchases
Net stock purchases equal purchases minus sales, both in units of shares. Campbell calls a
CEO highly optimistic if in a given year their net purchases are in the top quantile of
distribution of net purchases by all CEOs and those purchases increase their ownership of
firm stock by at least 10%. The CEOs are classified as low optimistic if they are in a bottom
quantile of net stock distribution. CEOs in between low and high net purchases are classified
as moderately optimistic. In this measure of optimism we may be concerned that the buy or
sell stock decisions might depend on the inside information or on the board of directors or
even on the CEO’s personal taxes information. The idea will be to construct a model to take
into consideration those matters.
3. Optimism Measures based on firm investment level.
This kind of measure helps avoid personal decisions, because it does not depend on
managerial compensations and incentives. Return on investment is defined as a profit made
on the sale of a security or other asset divided by amount of the year’s investment, expressed
in an annual percentage rate. Highly optimistic CEOs are classified if they are placed in the
top quantile of firms sorted on industry-adjusted investment rates for two consecutive years.
Already Malmendier and Tate (2005) showed that there exists a link between behavioural
biases and investment decisions, however that sort of measure is a subject of criticism, as
investment decisions might depend on many external factors, such as the market situation,
interest rates or internal — company — factors like cash flow.
Campbell’s results show that in most cases the percentage of each sort of optimism measures
in average is about 33%. We might conclude that if a CEO is hight optimistic according to
option holding exercise, they will also be optimistic according to investment rate indicator.
That means the the bias will be visible in the CEO’s stock option exercises and in his
investment policy at the same moment.
Campbell’s measures are very coherent with Malmendier’s and Tate’s approach. Obviously
robustness of those proxies will depend on the way one constructs the model to investigate an
implication between optimism and a managerial decisions.
The other measure of optimism I want to present in my paper is based on the earnings
forecasts. Using that kind of measure we will assume that the CEOs overestimate perceived
earnings. The empirical research is proposed by Otto in his paper CEO optimism and
incentive compensation, 2012.

Otto’s approach
Otto (2012) investigates the relation between optimism and CEO compensations. He assumes
that the more optimistic the CEO is, the higher their compensation will be. To measure
optimism bias, he uses an approach similar to Campbell’s, Malmendier’s and Tate’s. He also
establishes another approach based on the earnings forecasts, called EPS Forecasts.
Earnings forecasts are very powerful. Even small deviations can send a stock higher or lower.
If a company exceeds its consensus estimates, it is usually rewarded with an increase in stock
price. If the company sometimes falls under the consensus or only meets expectations, the
share price might fall. In the short term the difference between earnings forecasts and real
numbers might be one of the factors which influence share prices.
Companies release earnings forecasts each year, around 70 % of these forecasts are available
in the Execucomp database. Otto starts his analysis with all company-issued EPS forecasts
announced on Execucomp between 1996 and 2005. Than he compares the forecasts with the
ex post realised EPS. “If the forecast was issued in the form of an EPS range (62% of all
forecasts), then the dummy variable takes the value one if the lower bound of the range
exceeds the EPS that were eventually realised. If the forecast was a point estimate (38% of all
forecasts), then the dummy variable takes the value one if this point estimate exceeds the ex
post realised EPS.“ (Otto, CEO optimism and incentive compensation, 2012). Otto introduces
two more variables in his paper: HighForecast and WidthForecast. HighForecast is a dummy
variable which is zero if all EPS forecasts are lower than realised EPS. That means that a
higher value of HighForecast will imply a higher value of optimism. ForecastWidth measures
that average the width of the EPS ranges that are forecast by CEOs. By calculating the
confidence intervals of forecast we may assume that whether the CEO is confident about their
forecasts or not. What is more interesting, he calculates the correlation between
ForecastWidth and HighForecast. The correlation coefficient implies a positive relation
between optimism and confidence. That means the more optimistic CEOs are also more
confident in their earnings forecasts.
Otto’s approach is similar to Ben-Davids’ approach for optimism which is also based on
predictions. I find that the advantage of Otto’s approach is that each CEO wants to give the
predictions witch will be very close to the real EPS, because as mentioned before even small
derivations between predictions and real EPS might influence share prices. That is why
executives do not see any resort to manipulate the forecasts. Furthermore, large amounts of
EPS forecasts are available in open databases, like the Execucomp database.
Having established solutions to measure cognitive treats, let us focus on how they might
influence corporate decisions. Many researchers connect overconfidence or optimism with
managerial decisions, like investment judgements, mergers and acquisitions choices or

financing policies. I would like to ask in next section if these two biases should be taken into
consideration during the analysis of dividend polices, if they impact the dividend distribution
and can explain the variations in dividend payouts.

Section III: Overconfidence, optimism and dividend policy
Dividend policy
What makes a dividend policy such a complicated decision to make by the CEOs?
First of all, dividends payment may be executed in different ways: shareholders can be paid in
cash, they might be given extra shares or even properties as dividend payment. The choice of
payment is already complicated, as dividends in cash are highly taxed, but they are still
mostly preferred by shareholders. There is also the crucial question of how much they should
be paid, since the total amount of money is significative. As Allen and Michaely, 2003 say:
“For example, in 1999 corporations spent more than $350b on dividends and repurchases and
over $400b on liquidating dividends in the form of cash spent on mergers and acquisitions”.
Dividend payments also touch the problem of equity and social responsibility. Let us have a
look at the consolidated income statements of Novartis for 2012 and 2011.

In October 2011 Novartis announced that more than 2000 employees would be fired. That
decision is reflected in the operating income of 2012 which is higher that the one from 2011
(most of the operating expenses are wages). More interstingly, in 2012 the part attributed to
shareholders is higher than the one from 2011. One may assume that Novartis had chosen to
fire employees in order to keep the dividends high and satisfy shareholders.
The other problem with dividend polices is the fact that they imply some consistency over
time (Allen, Michaely, 2003), which implies that companies try to find a pattern in the
dividend payment. The most crucial and seemingly most complex fact in dividend policy is
that it interacts with other major corporate decisions and understanding it may help
understand other decisions, like mergers and acquisitions, asset pricing, or financial
budgeting. Allen and Michaely (2003) make six empirical observations:
Table 1. Source Prof. Michel Ferray, Changement Stratégique et Management des Ressources Humains, University of Geneva
2012 USD millions 2011 USD millions
Net sales 56673 58566
Other revenues 888 809
Cost of goods sold -18756 -18983
Gross proﬁt 38805 40392
Marketing and Sales -14353 -15079
Research and Development -9332 -9583
General and Administration -2937 -2970
Other Income 1187 1354
Other expenses -1859 -3116
Operating Income 11511 10998
Income from associated
companies
552 528
Intrest expenses -724 -751
Other ﬁnancial income and
expenses
-96 -2
Income before taxes 11243 10773
Taxes -1625 -1528
Net Income 9618 9245
Attribuable to shareholders
of Novartis
9595 9113

“(1) Large, established corporations typically pay out a significant percentage of their
earnings in the form of dividends and repurchases.
(2) Historically, dividends have been the predominant form of payout. Share re-purchases
were relatively unimportant until the mid-1980s, but since then have become an important
form of payment.
(3) Among firms traded on organised exchanges in the USA, the proportion of dividend-
paying firms has been steadily declining. Since the beginning of the 1980s, most firms have
initiated their cash payment to shareholders in the form of repurchases rather than dividends.
(4) Individuals in high tax brackets receive large amounts in cash dividends and pay
substantial amounts of taxes on these dividends.
(5) Corporations smooth dividends relative to earnings. Repurchases are more volatile than
dividends.
(6) The market reacts positively to announcements of repurchase and dividend increases, and
negatively to announcements of dividend decreases.”
What is more important, Allen and Michaely define the corporate decisions which should be
taken into consideration, while fixing a dividend payments.
“(i) Taxes If dividends are taxed more heavily than capital gains, and investors cannot use
dynamic trading strategies to avoid this higher taxation, then minimising dividends is optimal
(ii) Asymmetric information If managers know more about the true worth of their firm,
dividends can be used to convey that information to the market, despite the costs associated
with paying those dividends (However, we note that with asymmetric information, dividends
can also be viewed as bad news. Firms that pay dividends are the ones that have no positive
NPV projects in which to invest).
(iii) Incomplete contracts If contracts are incomplete or are not fully enforceable, equity
holders may, under some circumstances, use dividends to discipline managers or to
expropriate wealth from debt holders.
(iv) Institutional constraints If various institutions avoid investing in non or low- dividend-
paying stocks because of legal restrictions, management may find that it is optimal to pay
dividends despite the tax burden it imposes on individual investors.
(v) Transaction costs If dividend payments minimise transaction costs to equity- holders
(either direct transaction costs or the effort of self control), then positive dividend payout may
be optimal.” (Handbook of Economics of Finance, chapter 7).

Taxes, or transaction cost should be taken into consideration while realising dividend.
However can we consider that executives do not behave rationally and consider behavioural
biases like overconfidence and optimism, which could influence dividends payments? In the
next section I would like to show how both biases might influence dividend payments and the
CEO’s preferences about the dividend policy.
Dividend policy and overconfidence
Ben-David, Graham, Harvey (2007) test how miscalibrated (irrational) CEOs take financing
decisions. They showed that miscalibrated managers think that investors underestimate the
value of project and therefore they believe that their firms’ equity is undervalued by market.
For an overconfident CEO external securities to finance projects seem very expensive,
thought they will prefer to use internal cash to finance them. All of that will influence
dividend policy. Since internal cash is used to pay investment projects, there will be less of it
to finance dividend payments and therefore overconfident CEOs are less likely to pay
dividends because they hoard cash for future investments.
Deshmukh and Goel (2013) go even further in their analysis. They model a dividend
distribution as a trade off between minimising the cost of retaining excess cash in the firm and
minimising the expected cost of external financing for future investment. According to it a
CEO should be worried about the cost of external financing as shareholder interests are taken
into consideration. Deshmukh and Goel investigate the differences in dividend payments by
rational and irrational CEOs and take into consideration growth opportunities, cash flow and
firm size. Their results show that dividends paid by overconfident CEOs are smaller in
companies with a higher growth rate than those paid by overconfident CEOs in companies
with a smaller growth rate. Moreover, the positive relation between dividend payments and
cash flow is stronger for overconfident CEOs. If current cash-flows are hight an overconfident
will increase the dividend payment. They explain that it is caused by the strong perception of
current cash-flows, which influence a stronger prediction of future cash-flows. They also
study the effect of asymmetric information on the overconfident CEOs’ dividend payments. It
seems that there should be a negative relation between asymmetric information and dividend
payment, however they conclude that the strength of the effect does not appear to depend on
the overconfidence bias.
Dividend policy and optimism
While most papers concentrate on the dividend amount payments and relation with
behavioural biases, professor Christa H.S Bouwman decided to look closer into the market
reaction on dividend announcements, in the case of investors who don’t know which manager
is rational and which one is not. Her main prediction is that: “the market reacts more
positively to a given dividend change by an optimistic manager.” Her assumption implies that
when rational and irrational managers announce the same dividend change, the market will
look more assuredly into the dividend change announced by optimistic manager. On the other

hand, her assumption does not imply that optimistic managers will pay higher dividends than
rational ones. In her study she investigates only positive dividend changes.
Her empirical research is very interesting. The prior question, which need to be studied
carefully is to determine the announcement day. It needs to include the same type of dividend
change and avoid announcements which took place around confounding events. First, she
takes all the dividend announcements in the CRSP (Center in Research in Security Prices).
Then, to deﬁne ﬁnal sample she uses the following criteria:
“a). the firm paid an ordinary quarterly cash dividend in U.S dollars, in a current and previous
quarter
b). Other distribution events (stock splits, mergers, stock dividends, rights offerings, etc.)
were not declared between the declaration of the previous dividend and four days after the
declaration of the current dividend.
c). There were no ex-distribution dates between the ex-distribution dates of the previous and
current dividends. “ Than the market reaction is measured using the modified market model
(Brawn and Warner, 1985), where daily returns for each sample firm are calculated by
deducting the equal- or value-weighted index return from firm’s return. She is summing the
returns over three days and gets the Cumulative Abnormal Returns.
To measure optimism bias Bouwman used Malmendier’s and Tate’s observation sample and
their proxies. For each observation she associate the CRSP dividend observation and
calculates the Cumulative Abnormal Returns. Her results are as follow: “the coefficients on
the optimism measures suggest that, ceteris paribus, the three-day cumulative abnormal
returns are 0.7% to 1.7% higher when an optimistic CEO announces a dividend increase than
when her rational counterpart announces such an increase.
”

Conclusion
The goal of my Bachelor Thesis was to show the impact of behavioural biases on the
corporate financial decisions. Since behavioural finance literature sees its peak performance
since 2000, there appeared interesting solutions to run empirical studies. Malmendier’s and
Tate’s suggest to look at the stock-option execution to find if executives suffer of
overconfidence or optimism biases. Ben-Davids asks in his survey numerical questions,
which help him to estimate confidence intervals and measure overconfidence biases.
Campbell suggests three different measures of optimism: based on stock-options exercise,
based on net purchases and on the firm’s investment level. Finally, Otto looks at the EPS
forecasts to decide it an executive is optimistic about future firm performance. These
solutions are revolutionary in a world of corporate finance and they might help complete
financial decisions analyses. As shown in the final example, behavioural biases might
influence dividend policy. However, we need to mention that other studies proved that
overconfidence and optimism influence as well: mergers and acquisitions (Malmendier and
Tates 2008 or Schneider and Spalt, 2010), investment polices (Malmendier and Tates 2005),
capital structure (Hackbarth, 2009) or IPO prices (Ljungqvist and Wilhelm, 2005).
The subject of behavioural biases is very comprehensive, some might still say that it is very
subjective. However, I think that the period when behavioural finance was not considered
serious has passed and now it occupies financial press and has been introduced as a course in
top Business Universities. That is very crucial, because as with every new developing area of
study there are some open questions, which still needs to be resolved.
In my introduction I pointed out some other biases than optimism and overconfidence. It
would be interesting to investigate an influence of representativeness or availability heuristics
on corporate decisions.
The other problem concerns the measures of overconfidence and optimism. As psychological
surveys are very precise, they take a lot of time. On the other hand Malmendier’s and Tate’s
solution to determine overconfidence or optimism require precise data on stock option
exercises, which is difficult to obtain. Do there exist some other ideas to approach the issue of
biases measures? Some solution available to everyone, so that the analysis of behavioural
biases on corporate decisions making can become something daily?
As behavioural biases touch crucial financial decisions, is there any way to avoid these
biases? Can we control the executives, like with the stock-option remuneration, which
supposed to keep a CEO focused on the profit maximisation? Is it possible to introduce some
sort of regulation to avoid biased decision making?  

References
Allais; 1953; Le comportement de l’homme rationnel devant le risque: critique des
postulats et axiomes de l’école Américaine; Econometrica; Vol. 21 (4); pp. 503–546
Allen, Michaely; Payout policy; Handbook of the Economics of Finance, vol. 1; Elsevier; pp.
337-429 (Chapter 7)
Baker, Wurgler; 2013; Behavioral Corporate Finance: An Updated Survey; Handbook of
Economics of Finance; Chapter V, Elsevier,
Ben-David, Graham, Harvey; 2007; Managerial Overconfidence and Corporate Polices;
National Bureau of Economics Research;
Campbell; 2000; Asset pricing at the millennium; Journal of Finance 55; pp.1515–1567
Chiodo; 2004; Subjective Probabilities: Psychological Theories and Economic Applications;
Federal Reserve Bank of St. Louis Review; 86(1); pp. 33-47
Chiodo, Guidolin, Owyang, Shimoji; 2003; Subjective probabilities: Psychological Evidence
and Economic Application; Working Paper
Coval, Shumway; 2005; Do Behavioural Biases Affect Prices?; Journal of finance; Vol.
IX,No.1;
Deshmukh, Goel, Howe; 2013; CEO overconfidence and dividend policy; Journal of
Financial Intermediation; pp. 440-463
Fama, Kenneth; 2004; The capital Asset Pricing Model: Theory and evidence; Journal of
Economic Perspectives; vol. 18, no.3; pp. 25-46
Gilson; 1989; Management turnover and Financial Distress; Journal of Financial Economics;
Vol. 25; pp. 241-262
Hackbarth; 2009; Determinants of corporate borrowing: A behavioural perspective; Journal
of Corporate Finance; Vol. 15; pp. 389-411
Hall, Liebman; 1998; Are CEO’s really paid like bureaucrats?; Quarterly Journal of
Economics; pp 653-691
Hall, Murphy; 2000; Optimal exercise prices for executive stock options; American Economic
Review; pp 209-2014
Hall, Murphy; 2002; Stock options for undiversifed executives; Journal of Accounting and
Economics; pp 3-42

Harris, Griffin, Murray; 2008; Testing the limits of optimistic bias: event and person
moderators in a multilevel framework; J Pers Soc Psychol; Vol. 95(5); pp. 1225-12237
Heaton; 2002; Managerial Optimism and Corporate Finance; Financial Management; pp.
33-45
Kapla, Klebanov, Sorensen; 2008; Which CEO’s characteristics and abilities matter?; NBER
working paper series,
Ljungqvist, Wilhelm; 2005; Does prospect theory explain IPO market behaviour?; Journal of
Finance; Vol. 60; pp. 1759-1790
Malmendier, Tate; 2005; CEO Overconfidence and Corporate Investment; The Journal of
Finance; Vol. LX, No. 6
Mamendier, Tate; 2005; Does overconfidence affect corporate investment? CEO
overconfidence measure revisited; European Financial Management; Vol.11 (5); pp
649-659
Malmendier, Tate; 2008; Who makes acquisitions? CEO Overconfidence and corporate
investment; Journal of Finance; Vol. 60; pp. 2661-2700
March, Shapira; 1987; Managerial Plerspectives on Risk and Risk Taking; Management
Science; Vol. 33; pp. 1404-1418
Miller, Modigliani; 1961; The Journal of Business; Vol. 34; pp. 411-433
Moore, Healy; 2007; The trouble with overconfidence; Working Papers; Ohio State
University;
Oechssler, Roider, Schmitz; 2009; Cognitive abilities and behavioural biases; Journal of
Economic Behavior & Organization; Vol. 72; pp. 147–152
Otto; 2014; CEO Optimism and Incentive Compensation; Working Papers; HEC Paris
Oskamp, 1965; Overconfidance in case-study judgements; Journal of consulting psychology
studies; Vol. 29, no.3; pp. 261-265
Robin; 1999; Risk Aversion and Expected-Utility Theory: A Calibration Theorem; Working
Paper; Department of Economics University of California – Berkeley
Roll; 1986; The Hubris Hypothesis of Corporate Takeovers; The Journal of Business; Vol. 59;
No. 2, Part 1; pp. 197-216

Schneider, Spalt; 2010; Acquisition as lotteries: Do managerial gambling attitudes influence
takeover decision?; Working paper; University of Mannheim
Shefrin, Statman; 1994; Behavioural Capital Asset Pricing; Journal of Finance and
Quantitative Analysis; Vol. 29, Issue 3; pp. 323-349
Smith; 1776; Wealth of Nations;
Statman, Kenneth, Fisher, Anginer; 2008; Affects in a Behavioural Asset-Pricing Model;
Financial Analyst Journal; Vol. 64 Issue 2
Svenson;1981; Are we all less risky and more skilful than our fellow drivers; Acta
Psychological 47, pp. 143-148
Taylor, Brown; 1988; Illusion and well-being. A social psychological perspective on mental
health; Psychological Bulletin; Vol. 103; pp. 193-210.
Tversky, Kahneman; 1974; Judgement under Uncertainty: Heuristics and Biases; Science,
New Series; Vol. 185; pp. 1124-1131
Weinstein; 1980; Unrealistic optimism about future life events; Journal of Personality and
Social Psychology; Vol. 39; pp. 806 – 820.
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