thesis managerial overconfidence - tilburg university
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Master thesis in Finance Managerial Overconfidence in the Netherlands Tilburg University, August 23, 2010
Name: M.M.W.J. Verberne
Administration number: S996398
Faculty name: Faculty of Economics and Business
Study program: Financial Management
Supervisor: Drs. J. Grazell, Department of Finance
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Abstract Managers tend to overstate their ability and consider themselves above average. This
managerial overconfidence can have important implications for financial markets. This
paper shows the degree of managerial overconfidence in the Netherlands and shows
how it contributes to the investment – cash flow sensitivity. The overconfidence of a
manager is measured using the moment on which the CEO exercises the stock options
he owns in his firm. This paper uses a multiple case study with the data of five Dutch
listed firms from the AEX. The relation between overconfidence and investment – cash
flow sensitivity is expected to be positive but the coefficients from the multiple linear
regressions do not confirm this hypothesis.
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Summary A large body of research in economics and psychology suggests that managers are
irrational and that their decisions are often subject to systematic behavioral influences.
Managers do posses some personal characteristics, for example that they tend to
overstate their ability and consider themselves above average. This managerial
overconfidence can have some important implications for financial markets.
The research question that will be answered in this paper is: “What is the degree
of managerial overconfidence in the Netherlands and how does managerial
overconfidence contribute to the investment – cash flow sensitivity in the Netherlands?”
This research question will be answered using a replication approach; some parts of the
research of Malmendier and Tate (2005a) will be replicated.
The research method of a multiple case study will be used in which first will be
focused on five companies separately where after the cases will be analyzed
simultaneously to look for common patterns or significant variations. The data of five
Dutch listed firms from the AEX for the years 2003 to 2009 are used and are all found
in the financial statements of the concerning companies. The results of the case study do
not say anything about all the managers in the Netherlands but the case study is used to
form a picture of the degree of managerial overconfidence in the Netherlands.
First, the overconfidence measure Holder 67 is constructed. A manager is
classified as overconfident if he does not exercise stock options in his own firm that are
more than 67% in-the-money. From these calculations it can be assumed that many
CEOs in the Netherlands are overconfident. The degree of overconfidence is high in the
Netherlands.
Second, a linear regression is done with investment as dependent variable and
cash flow, Q ratio, firm size and overconfidence as independent variables. Interaction
terms for cash flow with the independent variables are added to the regression. The
regression findings do not support all the hypotheses. The coefficient for the interaction
term of cash flow and overconfidence is negative and statistically significant. This
implicates that the hypothesis that investment – cash flow sensitivity increases in
overconfidence is not confirmed by the regression results. The data do not support the
hypothesis.
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Table of contents 1 INTRODUCTION.......................................................................................................................5 2 LITERATURE REVIEW ...........................................................................................................7
2.1 SYSTEMATIC BEHAVIORAL INFLUENCES .................................................................................7 2.2 INVESTOR OVERCONFIDENCE.................................................................................................8 2.3 MANAGERIAL OVERCONFIDENCE ...........................................................................................9 2.4 CORPORATE POLICIES..........................................................................................................10 2.5 PECKING-ORDER THEORY ....................................................................................................11 2.6 CORPORATE DECISIONS .......................................................................................................12 2.7 INVESTMENT – CASH FLOW SENSITIVITY ..............................................................................16 2.8 MANAGERIAL COMPENSATION.............................................................................................21
3 RESEARCH METHOD............................................................................................................24 3.1 MULTIPLE CASE STUDY .......................................................................................................24 3.2 REPLICATION APPROACH .....................................................................................................25
4 HYPOTHESES..........................................................................................................................28 4.1 BASELINE ...........................................................................................................................28 4.2 INTERACTION TERMS...........................................................................................................29
5 DATA.........................................................................................................................................31 5.1 SAMPLE ..............................................................................................................................31 5.2 VARIABLES.........................................................................................................................31
6 TEST AND RESULTS ..............................................................................................................33 6.1 LINEAR MODEL ...................................................................................................................33 6.2 DESCRIPTIVE STATISTICS.....................................................................................................34 6.3 MODEL SUMMARY ..............................................................................................................34 6.4 REGRESSION SPECIFICATION ................................................................................................ 35 6.5 RESIDUAL ANALYSIS ..........................................................................................................35 6.6 INTERPRETING COEFFICIENTS BASE REGRESSION ..................................................................36 6.7 INTERPRETING COEFFICIENTS WITHIN-CASE..........................................................................39 6.8 INTERPRETING COEFFICIENTS CROSS-CASE ...........................................................................41
7 CONCLUSION..........................................................................................................................45
A SCATTER DIAGRAMS ............................................................................................................II B RESIDUAL ANALYSIS........................................................................................................... III C F-TEST.......................................................................................................................................V D T-TEST BASE REGRESSION ................................................................................................ VI E T-TEST TOTAL REGRESSION............................................................................................ VII F REGRESSION RESULTS BASE REGRESSION.................................................................VIII G REGRESSION RESULTS WITHIN-CASE............................................................................ IX H REGRESSION RESULTS CROSS-CASE ............................................................................XIV
REFERENCES................................................................................................................................. XV
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1 Introduction
A large body of research in economics and psychology suggests that managers are
irrational and that their decisions are often subject to systematic behavioral influences.
Managers do posses some personal characteristics, for example that they tend to
overstate their ability and consider themselves above average. This managerial
overconfidence can have some important implications for financial markets. This paper
focuses on the degree of managerial overconfidence in the Netherlands using an
overconfidence measure based on the stock options that a CEO owns in his own
company and the moment on which he exercises his stock options.
The research question that will be answered is: “What is the degree of managerial
overconfidence in the Netherlands and how does managerial overconfidence contribute
to the investment – cash flow sensitivity in the Netherlands?” This research question
will be answered using a replication approach; some parts of the research of
Malmendier and Tate (2005a) will be replicated.
Managerial overconfidence is expected to strengthen the investment – cash flow
sensitivity. Overconfident managers are inclined to invest more when there are abundant
internal funds, because they overvalue their own corporate projects, leading to the
overinvestment problem. But overconfident managers are inclined to invest less when
there are not enough internal resources, because they are reluctant to issue undervalued
equity from the market, leading to the underinvestment problem.
As a result, managerial overconfidence can lead to investments in negative net
present value projects and the reluctance to finance positive net present value projects
when this requires external financing. Managerial and shareholders’ interest are
misaligned resulting in a destruction of shareholder value. It is therefore important to
research the consequences of managerial overconfidence and especially the contribution
of managerial overconfidence to the investment – cash flow sensitivity.
The research method of a multiple case study will be used in which first will be
focused on the five companies separately where after the cases will be analyzed
simultaneously to look for common patterns or significant variations. The data of five
Dutch listed firms from the AEX for the years 2003 to 2009 are used and are all found
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in the financial statements of the concerning companies. The case study is used to form
a picture of the degree of managerial overconfidence in the Netherlands but does not say
anything about all the managers in the Netherlands.
This paper contributes to the current literature considering the degree of
managerial overconfidence in the Netherlands in stead of the United States. It uses
hand-collected data and therefore gives a deeper look at some specific companies in
stead of using general data from a large sample of companies.
The remainder of this paper is organized as follows. Section 2 gives the literature
review in which the current research concerning managerial overconfidence will be
elaborated. Several theories related to managerial overconfidence will also be explained
in this literature survey. Section 3 explains the research method that is used for this
paper and the approach that is taken. In section 4, the hypotheses are clarified and
section 5 explains the data that are used. Section 5 also gives the variables needed for
testing the hypotheses. The hypotheses will be tested using a regression specification in
section 6. The last section gives the conclusion of this paper.
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2 Literature review
2.1 Systematic behavioral influences
Some literature argues that decision makers are rational in the sense that they
make no systematic errors. But a large body of research in economics and psychology
suggests the contrary to this assumption namely that decision makers are irrational and
that their decisions are often subject to systematic behavioral influences (“biases”). An
example of a systematic behavioral influence is loss aversion as stated by Kahneman
and Tversky (1979); meaning that individuals feel losses more deeply than they do feel
gains. The happiness about winning 100 euro is not as great as the pain of loosing 100
euro. Their paper also shows that risk attitudes depend on the ‘framing’ of the particular
choice situation; when decision makers think of gains, they are often risk averse but
when they think of losses, they are often risk loving.
Loss aversion can lead to the status quo bias mentioned by Samuelson and
Zeckhauser (1988) in which an individual prefers to do nothing over taking a risky
project, implicating that he is reluctant to implement change and innovation,
overestimating the cost of initiating a project and overestimating the risk inherent in a
new project.
But perhaps the most robust finding in the psychology of judgment is that people
are overconfident, as stated by De Bondt and Thaler (1995). When people say that they
are 90% sure that an event will happen or that a statement is true, they may only be
correct 70% of the time. Overconfidence has come to be viewed as an important factor
in financial markets, because it exists in many aspects of human behavior; both in
investor behavior and managerial behavior. The investor overconfidence will shortly be
mentioned below, but the main focus will be on managerial overconfidence.
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2.2 Investor overconfidence
Investor overconfidence has emerged to explain asset-pricing theories such as
short-term continuation (momentum) and a long-term reversal in stock returns. These
theories are inconsistent with rational and efficient markets and are the consequence of
a disproportionate amount of risk borne by investors. In accordance with the momentum
strategy, investors buy stocks with high returns over the previous 3 to 12 months and
sell stocks with poor returns over the same time period; earning profits of about one
percent per month (Jegadeesh and Titman, 1993). Some authors argue that these
momentum profits arise because of inherent biases in the way that investors interpret
information. In accordance with the theory of long-term reversal, stocks are ranked on
three- to five- year past returns and past winners tend to be future losers, and vice versa
(DeBondt and Thaler, 1985). Their paper attributes this long-term reversal to the fact
that investors overreact to past information. So both asset-pricing theories are explained
by the overconfidence of investors.
According to Chuang and Lee (2006), the investor overconfidence can have some
negative consequences, namely that investors overreact to private information and
ignore publicly available information. Further, they argue that overconfident investors
trade more aggressively in subsequent periods and that their excessive trading
contributes to excessive volatility. Another negative consequence is that overconfident
investors underestimate risk and trade more in riskier securities.
But there are also some positive consequences, as Ko and Huang (2007) argue in
their paper. They argue that overconfident investors overinvest in information
acquisition and this overinvestment results in security prices that are closer to their true
values, and therefore make markets more efficient. Indeed, investor overconfidence can
have both negative and positive consequences and this also holds for managerial
overconfidence.
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2.3 Managerial overconfidence
Overconfidence does also have some important implications for financial markets
in the sense of managerial overconfidence. Managers do posses some personal
characteristics that can be linked to overconfidence. Managers are not rational but are
subject to some specific characteristics following from the psychology literature. The
“better-than-average” effect (Alicke, 1995) explains that managers tend to overstate
their ability; more than 50% says to be better than average. Managers consider
themselves above average in their ability to get along with others. Another characteristic
of managers is that they underestimate the volatility of random events. They also have
the tendency to attribute good outcomes to their own actions and bad outcomes to bad
luck (Miller and Ross, 1975). This self-serving attribution of outcomes strengthens
overconfidence.
Managerial overconfidence is defined by several authors in the literature. All the
different definitions that these authors use do have a similar aspect, namely that
managerial overconfidence is about a general miscalibration in beliefs. Miscalibration is
the tendency to overestimate the precision of one’s information. The predictions about a
certain issue differ from the actual outcome. It can be correlated to the “better-than-
average” effect and the illusion of control. The different definitions of managerial
overconfidence will be mentioned in this literature survey.
Managerial overconfidence is hard to measure because it is an abstract term. Still
some measures are found by authors to measure this behavioral influence. First it can be
measured by studying the moments that the Chief Executive Officer (CEO) exercises
his options (Malmendier and Tate, 2005a) and second by studying the future estimations
of the Chief Financial Officer (CFO) (Ben-David, Graham and Harvey, 2007). The first
measure will be used in this paper and will be elaborated further on in this paper. The
second measure uses a questionnaire about expectations of the S&P 500 under financial
executives to measure overconfidence. With the answers of this questionnaire,
individual probability distributions were created based on the CFOs 10th and 90th
percentile estimations. This probability distribution is narrow when the CFO is
confident about his predictions.
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For the purpose of this paper, it is important to examine whether the
overconfidence of the CEO matters or the overconfidence of the CFO matters to check
for managerial overconfidence. The paper of Malmendier and Tate (2005a) and many
other papers use the actions of the CEO as overconfidence measure, while only the
paper of Ben-David, Graham and Harvey (2007) uses the actions of the CFO as
overconfidence measure. Since the main body of the research focuses on the CEO of the
company and since the CEO is the main executive of the firm, this research also focuses
on the actions of the CEO instead of that of the CFO.
2.4 Corporate policies
Ben-David, Graham and Harvey (2007) associate CFO overconfidence with a
variety of corporate policies. First, overconfident managers underestimate cash flow
volatility, which can lead to lower discount rates used to value cash flows. This again
can lead to overinvestment; investment in negative NPV projects that are considered to
be positive NPV projects. Second, overconfident managers believe that the firm and the
equity of the firm are undervalued by investors, as a result leading to preference of
internally generated funds. The internally generated funds will be used to invest and
will not go directly to the shareholders, resulting in lower dividend payments. Third,
they find that the debt leverage increases. The increased debt leverage can cause a debt
overhang problem; more about this problem further on in this literature review.
Another part of the paper of Ben-David, Graham and Harvey (2007) is related to
the overconfidence of CFOs in their predictions. They document that expected market
returns and confidence bounds depend on recent past market returns and on returns of
the CFO his own firm. Executives are more confident following periods of high market
return and less confident following low market returns periods.
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2.5 Pecking-order theory
Overconfident managers prefer to use internally generated funds, because they
believe the equity of the firm is undervalued by investors. This is related to the pecking-
order theory, in which internal funds are used first, then debt is issued and equity is
raised last. The vast majority of investment is funded by retained earnings, with net
external financing amounting to less than 30% of capital expenditures in most years.
According to Myers and Majluf (1984) firms prefer to issue internal to external funds.
But when there are no internal funds available, the firm has to raise debt or equity to be
able to finance projects.
Debt is preferred over equity, because debt has lower information costs. The
information costs for equity include the transfer of special knowledge to all investors.
There is information asymmetry between the management of the firm and the investors.
The management is expected to know more about the value of the company than the
investors do. In case of positive managerial information, the investors are expected to
undervalue the securities of the firm.
Debt is also preferred over equity because of the signaling role of equity. The
issuance of shares by the manager gives the market a signal that the equity is
overvalued. It gives a signal that the managerial information is negative. Issuing shares
can therefore lead to a decline in the stock price of the firm. Both the information
asymmetry and the signaling effect can lead to the pecking order in which internal funds
are used first. When external finance is needed, debt is preferred over equity.
But there is substantial evidence that firms do not follow a strict pecking order, as
firms often issue equity even when borrowing is possible. Leary and Roberts (2005)
argue that while the mispricing theory of Myers and Majluf (1984) gives a reason for
equity issuances, it does not necessarily result in a pecking order. The manager’s choice
of financing will also depend on the fact whether the firm is overvalued or undervalued
by investors. Firms might also have low leverage because they are not able to issue
additional debt and are therefore forced to rely on equity financing.
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2.6 Corporate decisions
The managerial overconfidence can have some influences on the corporate
decisions that have to be made; overconfident managers will make decisions that differ
from decisions of rational managers. This in turn can lead to a destruction of
shareholder value. There are three main factors that trigger overconfidence: the illusion
of control, a high degree of commitment to good outcomes and the fact that corporate
decisions include rare events in the life of the company and are therefore hard to
compare across individuals (Alicke et al. 1995). The corporate decisions include capital
structure decisions, payout decisions and investment decisions. The influences of
managerial overconfidence on these types of decision-making will be elaborated below.
2.6.1 Capital structure decisions
The manager has to make capital structure decisions meaning decisions about the
combination of debt and equity in the firm. The research of Hackbarth (2009) argues the
influences of managerial overconfidence on the capital structure decisions. He argues
that the managerial traits can have both positive and negative consequences; there are
two counterbalancing effects.
The first effect is that overconfident managers choose higher debt levels. This
effect has a negative consequence in the sense that it reinforces the underinvestment
problem; also called the debt overhang problem in which positive net present value
projects cannot be financed due to existing debt. On the other hand, these high debt
levels do restrain managers from diverting funds, which increases firm value, as also
argued in the paper of Hackbarth (2007). Fairchild (2006) also argues that managerial
overconfidence is not necessarily bad for the shareholders with respect to capital
structure decisions. It has a positive effect by inducing higher managerial effort.
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The second effect from the paper of Hackbarth (2009) is that overconfident
managers are inclined to invest earlier, what, in contrast with reinforcing the
underinvestment problem, actually alleviates the underinvestment problem.
The research of Oliver (2005) also examines the empirical relation between
capital structure and managerial overconfidence. He found that when managerial
confidence is higher, firms have higher levels of debt, confirming the argument that
overconfident managers will tend to issue more debt.
2.6.2 Payout decisions
The manager has to make payout decisions meaning decisions about whether or
not to pay dividend to the shareholders and decisions about the amount of dividend to
pay. As mentioned earlier in this literature review, overconfident managers are inclined
to use internally generated funds to invest in projects, instead of paying these funds to
shareholders in the form of dividend payments; resulting in lower dividend payments.
Cordeiro (2009) argues that managers are less inclined to pay dividends because they
think they can earn more by investing the funds in a project. This effect will be
strengthened by the preference of using internal funds. Deshmukh, Goel and Howe
(2009) agree that the level of dividend payout is lower in firms managed by
overconfident CEOs.
2.6.3 Investment decisions
Instead of paying the money to the shareholders, the manager can decide to use
the money for investment decisions. Investments can be subdivided into replacement
investments and extension investments. With replacement investments, capital
equipment is bought to replace the old capital equipment. Extension investments are
growth investments meant to increase the production capacity and are thus meant for
increasing the turnover of the company. These growth investments can be subdivided
into internal growth investments and external growth investments. The former one is
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referred to as Greenfield investments and the latter one is referred to as mergers and
acquisitions. The influences of managerial overconfidence on these two extension
investments will be elaborated below.
2.6.3.1 Greenfield investments
Greenfield investments are internal growth investments made for extension of the
turnover of the company with existing and new products on existing and new markets.
There is some talk of autonomic growth. Managerial overconfidence can have some
influences on the decisions about the Greenfield investments that have to be made.
Malmendier and Tate (2005a) argue that managerial overconfidence can account
for corporate investment distortions, because overconfident managers overestimate the
returns to their investment projects and view external funds as unduly costly.
Alternative explanations for investment distortions are the misalignment of managerial
and shareholders’ interests (Jensen and Meckling, 1976) and asymmetric information
between corporate insiders and the capital market (Myers and Majluf, 1984). Under the
former explanation, also called the agency problem, the manager is self-interested and
overinvests for his own private benefits. Under the latter explanation, that the capital
market is imperfect, the manager acts in the interest of the shareholders, but limits
external financing because he thinks his company shares are undervalued; external
funds are viewed as unduly costly. In both cases, the manager is willing to invest more
when there are abundant internal sources; there is overinvestment. More about the
agency problem and the capital market imperfections will be explained further on in this
paper.
But Gervais, Heaton and Odean (2002) found a positive role of managerial
overconfidence in investment decisions. Risk-averse rational managers are inclined to
postpone decisions to undertake a project longer than is best for the shareholders.
Overconfident managers on the contrary are less likely to postpone decisions to
undertake projects because they underestimate the risk of the concerning project. They
undertake projects more quickly which is better for the shareholders. This effect results
in the fact that shareholders prefer an overconfident manager with less ability to a
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rational manager with greater ability. They found that managerial overconfidence can
increase the value of the firm.
2.6.3.2 Mergers and acquisitions
Mergers and acquisitions are external growth investments in which the company
decides to takeover another company or entities of a large organization. Managerial
overconfidence can have some influences on the decisions about the mergers and
acquisitions that have to be made.
Malmendier and Tate (2008) did some research with respect to merger decisions.
They recognize that overconfident CEOs overpay for target companies and undertake
value-destroying mergers. This is because of the fact that overconfident CEOs
overestimate their ability to generate returns. The effects for merger decisions are
strongest if they have access to internal financing. Malmendier and Tate (2008) also
found that acquisitions are 65% more likely when the manager of the firm is
overconfident. This is the same result as Ben-David et al. (2007) found in their paper.
They show that overconfident managers are inclined to make more acquisitions. The
acquisitions intensity on the long run increases when overconfidence increases.
Also Doukas and Petmezas (2007) did some research with respect to merger and
acquisition decisions; they examined whether managerial overconfidence plays an
important role in explaining the performance of mergers. The merger announcement
effects for the shareholders of target firms are that they earn significant and positive
abnormal returns, due to the high premium that is paid by the acquirer, while the
shareholders of the acquiring firms experience negative to zero abnormal returns
following the announcement. The combined entity earns a positive abnormal return. The
question they answer in their paper is whether overconfident managers act in the interest
of their shareholders when they engage in mergers, given the fact that these mergers
have a negative wealth effect for the shareholders of their firm. They recognize that
overconfident managers are likely to make acquisitions quickly and frequently, because
these managers feel more superior and do therefore believe that these serial investment
decisions are in the best interest of their shareholders. The measure of overconfidence
used in their paper is therefore the number of acquisitions within a very short time
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interval. The serial investments within a short time period are predicted to encourage
acquisitions that generate lower announcement returns. Thus, overconfident managers
believe they act in the interest of their shareholders, while this is not necessarily true.
The merger announcement effects are consistent with the “hubris” hypothesis,
introduced by Roll (1986). A particular individual bidder, manager, has the opportunity
to make only a few takeover offers during his career and he will therefore not refrain
from bidding because he has made some errors in the future. He will be convinced of
the thought that the market values the target firm too low and that his own valuation is
the right one. So, the “hubris” hypothesis suggests that the management of the acquiring
firm overvalues their ability to create value once they take control of the firm’s assets.
Managers engage in acquisitions with an excessive optimism about their ability to
create value.
2.7 Investment – cash flow sensitivity
Some theories link the investment – cash flow sensitivity to the misaligned
incentives between managers and shareholders, other theories link it to the capital
market imperfections or to the size of the firm. But when the incentives of the manager
and the shareholders are perfectly aligned and when there is no information asymmetry,
the manager may still invest not optimally because he is overconfident. The different
theories will be explained below.
2.7.1 Misaligned incentives
The agency theory of Jensen (1986) analyses the conflicts of interest that can
occur between the agents of the firm, the managers who are not the owners, and the
shareholders. These conflicts of interest can occur when the manager has to make
payout decisions concerning the amount of dividend to pay to the shareholders or the
number of stock to repurchase. The amount of dividend to be paid to the shareholders is
dependent on the amount of available free cash flow. Free cash flow is cash flow in
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excess of that required to fund all projects that have positive net present values when
discounted at the relevant cost of capital. When the company has a lot of free cash flow,
the conflicts of interest between the manager and the shareholders are especially severe.
In the agency approach, the free cash flow is costly. The shareholders may
suppose the managers to expand firm size to favor their own interests rather than the
interests of the shareholders. The managers may be inclined to invest the money at
below the cost of capital or waste it on organizational inefficiencies. The managers may
be inclined to retain free cash flows and invest it in projects that increase managerial
benefits like compensation or power and reputation. But the shareholders prefer the
manager to payout the money as a dividend, because the projects that increase
managerial benefits often may be negative net present value projects. According to
Jensen (1986), managers would rather invest in negative NPV projects than pay out the
free cash flow to shareholders, because the dividend payout reduces the resources that
are under the control of the manager. When the manager has fewer resources under his
control, he is dependent of the external capital market. When obtaining new capital, the
manager will incur the monitoring of the capital market or the possibility that the funds
will not be available or only at too high prices. But in a firm with more free cash flow,
the manager can circumvent the discipline of the capital market and pursue value-
destroying investments.
The free cash flow hypothesis of Jensen (1986) suggests that the manager will be
encouraged by market pressures to distribute free cash flow as a dividend to
shareholders. The “control hypothesis” of debt suggests that debt has the benefit of
motivating managers to be efficient. With the fixed interest payments of debt, the
manager is bonded to the promise to pay out future cash flow. When he fails to do so,
the shareholder recipients of the debt have the right to take the firm into bankruptcy
court. Leverage increasing transactions that bond the firm to pay out free cash flows
increase shareholder value and mitigate the agency problem. Stock prices of firms with
positive free cash flow should increase over time. So debt and dividend are presented as
substitutes for controlling the agency problem.
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2.7.2 Capital market imperfections
Modigliani and Miller (1958) found that the financial structure of a firm is
irrelevant to corporate investment decisions. External funds provide a perfect substitute
for internal capital. This view assumes that capital markets are perfect. But theory has
questioned the substitutability of internal and external capital as did the paper of
Fazzari, Hubbard and Petersen (1988). They argue that the internal and external capital
markets are not perfect substitutes and that investment may depend on financial factors
such as the availability of internal finance, access to new debt or equity finance, or the
functioning of particular credit markets. Their view assumes that the capital markets are
imperfect and that there is information asymmetry.
The asymmetric information approach is typified by Myers and Majluf (1984).
They argue that not all market participants have the same access to information;
management is assumed to know more details about the firm’s value than potential
investors. Investors will under price risky securities, and driving a wedge between
internal and external finance by raising the cost of external finance. The management is
reluctant to issue undervalued securities to the under-informed capital market. As a
consequence, firms may refuse to issue stock and therefore pass up valuable investment
opportunities. A firm with more free cash flow will be perceived as less risky by
investors because it has to rely less on the costly external finance. In this approach the
free cash flow is beneficial, because it prevents from the underinvestment problem.
Fazzari, Hubbard and Petersen (1988) argue that firms that are more financially
constraint have higher investment – cash flow sensitivity. There will be
underinvestment when external finance is more costly than internal finance. But Kaplan
and Zingales (1997) argue that a reverse causality is not necessarily true. They actually
found that firms that are less financially constraint exhibit greater investment – cash
flow sensitivity. This indicates that a higher investment – cash flow sensitivity cannot
be interpreted as evidence that a firm is more financially constraint.
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2.7.3 Underinvestment – overinvestment tradeoff
The combined effect of the misaligned incentives and the capital market
imperfections can have a large effect on the demand for internal financing. Heaton
(2002) delivers both the agency problem and the problem of asymmetric information in
a single framework implying an underinvestment-overinvestment tradeoff from
managerial overconfidence related to free cash flow. On the one hand, the manager is
more willing to invest when there are enough internal resources. This may lead to the
overinvestment problem in which overconfident managers may be inclined to invest in
negative net present value projects, because they overvalue their own corporate projects.
The free cash flow is costly because it makes it easier to undertake negative net present
value projects mistakenly perceived to be positive. On the other hand, the manager is
less willing to invest when there are not enough internal resources. An overconfident
manager can feel undervalued by the market and is reluctant to issue risky securities.
This may lead to the underinvestment problem in which overconfident managers may
be reluctant to finance positive net present value projects.
2.7.4 Firm size
The theory of investment – cash flow sensitivity can also be linked to the size of the
firm. There is the general agreement that smaller firms have less access to external
capital markets and should therefore be more affected by the availability of internal
funds. Smaller firms have higher investment – cash flow sensitivity. Larger firms have
better access to external finance. There are three reasons for this. First, larger firms face
lower transaction costs in raising external finance. Transaction costs encompass among
others the application fee, underwriting fee, underwriting spread, rating fee, prospectus
cost, legal fee and the advisory fee. Second, there is less information asymmetry,
because for large firms there is more public information available. The last reason is
that larger firms have more institutional shareholdings which monitor the firm closely.
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Larger firms should be less affected by the availability of internal funds and thus have
lower investment – cash flow sensitivity.
So the previous explanations say that the investment – cash flow sensitivity is a
good measure of access to capital markets. A higher sensitivity means that firms rely
more on internal cash flow and this would stand for bad access to capital markets. This
scenario would hold for smaller firms. However, Kaddapakkam, Kumar and Riddick
(1998) found that the investment – cash flow sensitivity is generally highest for large
firms and investments are least sensitive to cash flows in the case of small firms. They
do not reject the general agreement but relate this finding to the conclusion that the
investment – cash flow sensitivity is not a good measure of access to capital markets.
This is in conformity with the finding that a higher investment – cash flow sensitivity
cannot be interpreted as evidence that a firm is more financially constraint. A possible
explanation for the finding that the investment – cash flow sensitivity is highest for
large firms is that larger firms have greater flexibility in timing investments and may
defer investments until internal funds are available.
2.7.5 Overconfidence
According to Malmendier and Tate (2005a), overconfidence has a reinforcing
effect on the investment – cash flow sensitivity. The more overconfident the manager is,
the less likely he is to finance projects externally.
The paper of Malmendier and Tate (2005a) provides some extra evidence that
personal characteristics other than overconfidence are also related to investment – cash
flow sensitivity. Examples of these personal characteristics are educational and
employment background, birth cohort, and accumulation of titles within the company.
All these characteristics reinforce the investment – cash flow sensitivity and are
important for a better understanding of corporate decision making.
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2.8 Managerial compensation
The compensation of a manager can consist of several components, namely base
salary, annual bonus plans and stock options. These components will be defined below.
The purpose of managerial compensation is to give managers incentives to perform well
and align their interests with the interests of the shareholders.
2.8.1 Base salary
The base salary of the manager is a certain amount of cash. This base salary is
determined through benchmarking and is typically related to the size of the company
and the industry. The process of determining the base salary through benchmarking
becomes less important because the base salaries comprise a declining percentage of
total compensation.
2.8.2 Annual bonus plan
When the manager has reached a certain performance threshold, he will end up in
the “incentive zone” and he gets pay for his performance on top of his base salary. This
pay is called a bonus. It can consist of an amount of cash or a number of shares of the
company. The annual bonus plans have become an increasingly large portion of the total
compensation.
Most companies use more than one performance measure. In many cases, an
accounting measure is used as a performance measure. The problem with accounting
measures is that they are backward-looking and based on the short-run. Therefore, the
share price of the company is an ideal performance measure, because it reflects the
value of current and future value contributing projects; it is objective and forward
looking. The problem with the share price as a performance measure is that the
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management is not completely responsible for the movement of the share price, because
changes in capital structure will have an impact on the share price.
A compensation contract for managers that links the pay to the performance of
the firm can mitigate agency problems. The research of the agency theory (Jensen and
Meckling, 1976) emphasizes the importance of the alignment of interests between the
CEO and the shareholders of the firm.
A long-term incentive plan is like a bonus plan, but performance is now based on
the past 3 – 5 years. The long-term incentive plans are introduced in the UK from 1995
and are designed to increase the sensitivity of the compensation of the manager to the
performance of the firm. Restricted stock can be a part of this long-term incentive plan.
This restricted stock cannot be traded and cannot be sold for a number of years, about 3
– 5 years, which mitigates the agency problems on the long run.
2.8.3 Stock options
Both the base salary and the annual bonus plans can consist of stock options.
Stock options give the manager the right to buy a share at a pre-specified exercise price
for a pre-specified term. A typical stock option has expiration in 10 years. During the
first 3 years, the vesting period, the manager does not gain control over the stock
options yet. The exercise price, also called the grant price, is equal to the market value
at the time the option is awarded to the manager. The options cannot be traded, to
strengthen the incentive effects of this kind of compensation. Stock options are a form
of compensation with high incentive effects, but there is a problem with this form of
compensation. The incentive effects will disappear when the share price is below
exercise price.
The manager of a firm is highly exposed to the idiosyncratic risk of his company.
This risk is also called unsystematic risk and is specific to the company. It affects a very
small number of assets and is uncorrelated with the market returns. It can be eliminated
from a portfolio through diversification. The manager is undiversified and should
therefore minimize the number of company stock in his portfolio.
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Despite the fact that a manager is highly exposed to the idiosyncratic risk of his
company, there are several reasons why a manager would still want to be exposed to
this type of company risk through the holding of options or buying stock. These reasons
are mentioned in the paper of Malmendier and Tate (2005a). The first reason is the fact
that a manager may know more about his company than the market knows; there is
information asymmetry. When this inside information is positive, he knows that the
stock price will rise in the future and therefore wants to hold more options that can be
exercised in the future. Negative inside information will prevent him from holding stock
options or he will exercise early. The second possible reason is that the manager wants
to give a signal to the market that the future prospects of the firm are good; also called
the signaling effect. Holding more stock options would make investors think that the
manager is confident about the stock price and this again is positive for the stock price
itself. Another reason has to do with the risk tolerance of the manager. The moment the
manager exercises his stock options depends on his risk tolerance. A risk-averse
manager will exercise early given a high stock price, while a manager with a higher
risk-tolerance will exercise his options later.
The moment the manager exercises his stock options depends on his individual
wealth, the degree of risk aversion and diversification (Hall and Murphy, 2002). Given
the latter two, we expect him to exercise options early given a high stock price.
However, when a manager is overconfident, he overestimates his ability to generate
returns. An overconfident manager expects the company to perform better in the future
under his leadership. He expects the stock price to rise even more and will therefore not
exercise his options immediately given a high stock price, but wait until the stock price
has risen even more.
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3 Research method
3.1 Multiple case study
The research method that is going to be used in this paper is the method of a case
study. This method is suitable for this paper, because it puts more emphasis on the
reasons behind managerial overconfidence and answers the questions ‘why’ and ‘how’
instead of ‘what’ and ‘how much’. Another reason for using this method is that this
research needs hand-collected data and it would take too much time to collect the data
of a large sample of firms; large enough to do a survey. The cases analyzed in this paper
are Dutch listed firms from the Amsterdam Exchange index (AEX) and will be
perceived as ‘out there’, existing independently of each other. A quantitative
perspective is taken, so there is no need to investigate the industry as part of the case.
The case study has the advantage that it takes a look at the firms in detail, while other
research methods only use general data to answer a research question.
Investigating an issue, here managerial overconfidence, in more than one context
(i.e. case) is usually better than basing results on just one case. This paper therefore uses
a multiple case study in which will be looked at several companies in detail. A set of
cases (companies), that are similar to each other, is chosen whereupon the differences
between them will be analyzed and reasoned.
A within-case analysis will focus on the companies separately without trying to
bring in the findings or lessons from another case. The within-case analysis will check
whether or not the manager of the concerned company is overconfident and it will take
a look at the investment – cash flow sensitivity. After the within-case analysis, all the
companies will be analyzed simultaneously, also known as cross-case analysis, to look
for common patterns or significant variations across the cases. The results from the
cross-case analysis will be compared with the theory from the introduction of this paper,
where the influences of managerial overconfidence are given. It is expected that the
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same phenomenon, investment - cash flow sensitivity, occurs in the same
circumstances, namely when there is an overconfident manager.
Doing a multiple case study is not sufficient to support or reject the theory, but the
series of case studies permits the assessment of the theory. It is important to note here
that the results of case studies are not generalized to populations, but to theoretical
propositions. This means that it does not say anything about all the managers in the
Netherlands, but it does tell a lot about the power of the theory in the previous literature.
3.2 Replication approach
This paper takes a replication approach; some parts of the research of Malmendier
and Tate (2005a) will be replicated in this paper. As mentioned in the literature review
of this paper, Malmendier and Tate (2005a) argue that managerial overconfidence can
account for corporate investment distortions. They use the data of Forbes 500 CEOs.
This paper differs from that, because it does not do a survey under a large number of
firms, but uses the research method of a case study as mentioned above, using the data
of only a few firms. Their empirical analysis consists of two steps: the construction of
an overconfidence measure and the analysis of two predictions.
3.2.1 Construction of an overconfidence measure
A large part of the compensation that managers receive consists of stock and
options, which makes their human capital dependent on the performance of the firm.
Managers are not allowed to trade these stocks and options and are therefore not able to
diversify; there is under-diversification. Managers are expected to exercise their call
options immediately after the vesting period when the stock price is high. But when the
manager is overconfident about the performance of his firm, he will hold the options
until the expiration date, because he expects the stock price to rise even more.
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Therefore, the measure of overconfidence is the duration that the manager holds his
options.
The paper of Malmendier and Tate (2005a) uses three measures of managerial
overconfidence: Holder 67, Longholder and Net buyer. According to the Holder 67
measure, the manager is overconfident when the option is more than 67% in-the-money.
This benchmark is chosen using the Hall and Murphy (2002) framework as a theoretical
guide. The second measure Longholder classifies the manager as overconfident when he
ever holds an option until the last year of its duration. The problem with this measure is
that it could be the case that the option has never become in-the-money so that it has
never been profitable to exercise the option. Another problem with this measure is that
the options usually have duration of about 10 years and given the fact that the CEO of a
company changes over time, it is difficult to test the overconfidence with this measure.
The third measure Net Buyer classifies the manager as overconfident when he habitually
acquires company stock. The problem with the last measure is that it is most of the time
difficult to see if the company stock the manager holds is part of his compensation or if
the manager itself increased the number of stocks in his portfolio. Because of the
problems that can occur using the Longholder and the Net Buyer measure, this paper
only uses the Holder 67 measure as overconfidence measure.
It is important to take into account the vesting period of the concerning option.
The manager receives his stock options as compensation, but for 3 – 5 years he does not
gain control over the stock options. This period in which the manager is not allowed to
sell or exercise the options is called the vesting period. It could be the case that the
option is more than 67% in-the-money during the vesting period, but the manager is not
allowed to exercise the option. So the first thing to look at is whether or not the option is
exercisable in the concerning year.
Immediately after the vesting period, the manager is allowed to exercise his call
options. You would expect him to do this when the stock price is high. When the
exercise price of the option, also called grant price, is above the highest market price of
the current year, it has never been profitable to exercise the call option. Thus the second
thing to look at is whether or not it is valuable to exercise the option in the concerning
year and this is true when the exercise price is lower than the highest market price of
that year.
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When the vesting period is over and the exercise price of the option lies below the
price range of the market price, it has been profitable to exercise the option at least once
during the year. To check for the Holder 67 measure, the value at which the option is
more than 67% in-the-money has to be calculated. This is the exercise price plus 67%.
When this Holder 67 value lies below the highest market price of the current year, the
option has been more than 67% in-the-money at least once during the year and
therefore, the manager can be considered as overconfident.
3.2.2 Analysis of the prediction
The first prediction from the paper of Malmendier and Tate (2005a) is that the
sensitivity of investment to cash flow increases in overconfidence. The second
prediction is that overconfidence should matter most for firms that are equity-
dependent. This paper will only focus on the first prediction.
To test the first prediction, Malmendier and Tate use a regression specification
with investment as dependent variable of the regression specification and cash flow, the
ratio of market value of assets to book value of assets, some additional controls and the
overconfidence measure as independent variables. The additional controls consist of the
stock ownership of the manager in percentage of the shares outstanding, and the number
of vested options. The additional controls are not part of the regression specification in
this paper because these are not needed for answering the research question. In addition,
the variable size is added to be able to interpret the effects of it.
The data in the paper of Malmendier and Tate (2005a) are also supplemented with
personal information about CEO’s employment histories and educational backgrounds,
but that is beyond the intent of this paper.
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4 Hypotheses
The hypotheses given in this chapter show the expected relationship between the
dependent variable and the independent variables. The dependent variable is investment.
The explanation of the dependent and independent variables can be found in the next
chapter. The expected relationship will be compared with the coefficients from the
regression specification further on in this paper.
4.1 Baseline
The independent variables do all have some relation with the dependent variable
irrespective of the possible interaction that exists between the independent variables.
The hypotheses show whether the expected relations between the dependent and the
independent variables are positive or negative.
Hypothesis 1: The relation between cash flow and investment is positive. A larger
amount of cash flow results in higher investments.
A positive relationship is expected between the amount of cash flow and the
amount of investments; the investment – cash flow sensitivity is positive. When there
are more internal funds available, the amount of investments is larger. The manager is
willing to invest more when there are abundant internal sources and views external
funds as unduly costly. Cash flow has a large amount of explanatory power for
investment.
Another explanation is that current cash flow measures the success of past
investment decisions. A large amount of cash flow means that the investments done in
the past where good. This is an extra motivation to invest more in the future and thus
reflects a positive relation between cash flow and investment.
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Hypothesis 2: The relation between the Q ratio and investment is positive. A larger
Q ratio results in higher investments.
A positive relationship is expected between the Q ratio and the amount of
investments. The Q ratio shows what the market thinks about you. When the Q ratio is
greater than 1, the stock is overvalued; the value of the stock is more than the
replacements cost of the assets, meaning that the market likes the company. So the
investments should go up when the Q ratio is high.
Hypothesis 3: The relation between the size of the firm and investment is positive.
Larger firms make more investments.
There is a positive relationship expected between the size of the company and the
amount of investments. Larger firms make more investments than smaller firms,
because larger firms have better access to external finance.
Hypothesis 4: The relation between overconfidence and investment is positive.
Firms with more overconfident CEOs make more investments.
The expected relation between the level of overconfidence and the amount of
investments is positive. Overconfident CEOs are inclined to invest more because they
overestimate the returns to their investment projects.
4.2 Interaction terms
The correlation that exists between the different independent variables can have
different influences on investment. This is especially important for the correlation with
cash flow. The expected effects of the correlation of the different variables with cash
flow are given below. These interaction terms give the expected effects of the
concerning independent variable on the investment – cash flow sensitivity.
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Hypothesis 5: The interaction effect of cash flow and the Q ratio is positive.
The relation between the amount of cash flow and investment is expected to be
positive (see hypothesis 1). Cash flow has a positive impact on the investments, but this
impact increases when the Q ratio goes up. Thus the investment – cash flow sensitivity
increases for higher levels of Q. There is also a positive relation between the Q ratio and
investment (see hypothesis 2). The Q ratio has more impact on investment for higher
levels of cash flow.
Hypothesis 6: The interaction effect of cash flow and the firm size is negative.
There is a negative relationship expected between the size of the firm and the
investment – cash flow sensitivity; larger firms have lower investment – cash flow
sensitivity. Larger firms have better access to external finance. Smaller firms have less
access to external capital markets and should therefore be more affected by the
availability of internal funds. Smaller firms have higher investment – cash flow
sensitivity.
Hypothesis 7: The interaction effect of cash flow and overconfidence is positive.
There is a positive relation expected between the level of overconfidence of the
CEO and the investment – cash flow sensitivity. The sensitivity of investment to cash
flow increases in overconfidence. The more overconfident the manager is, the less likely
he is to finance projects with external funds.
This last hypothesis is the most important one because is answers the research
question mentioned in the introduction of this paper.
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5 Data
5.1 Sample
This paper analyzes a sample of 5 Dutch listed firms from the AEX for the years
2003 to 2009. The companies from this index are the most actively traded securities on
the exchange and are chosen because they are representative for all the companies in the
Netherlands. The data are all found in the financial statements of the firms.
For all data and values in this paper the Dutch standard for figure notation is used.
This holds also for the SPSS outputs and other tables in this paper.
5.2 Variables
In order to test the hypotheses in a regression specification, the data set contains a
dependent variable and a number of independent variables. The explanation and the
abbreviation used in the regression specification are given below for both the dependent
and the independent variables. The dependent variable investment and the variables
cash flow, Q ratio and size are numerical variables because they are real numbers. The
data that are given in dollars are converted into euro using the exchange rate at the end
of the year. The last variable overconfidence is a nominal variable because it indicates a
category.
Investment (y)
Investment is the dependent variable. It is measured as capital expenditures at the end of
the year and can be found in the statement of cash flows. Amounts are in millions of
euros.
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Cash flow (x1)
Cash flow is one of the independent variables. It is measured as earnings plus
depreciation at the end of the year and can be found in the statement of cash flows and
the statement of income. Amounts are in millions of euros.
Q ratio (x2)
The Q ratio is one of the independent variables. It is measured as the ratio of the market
value of assets to book value of assets at the end of the year. The market value of assets
is total assets plus market equity minus book equity. Market equity is defined as
common shares outstanding multiplied by the fiscal-year closing price. Book equity is
calculated as stockholders’ equity (total assets minus total liabilities). The book value of
assets is equal to total assets. The data can be found in the balance sheet and the notes of
the financial statements.
Size (x3)
Size is one of the independent variables. It is measured by the natural logarithm of total
assets at the end of the year. Total assets can be found in the balance sheet.
Overconfidence (I1)
To test the effects of overconfidence of CEOs on investment, this dummy variable is
added. This paper uses the Holder 67 measure. The manager is overconfident when his
option is more than 67% in-the-money. The Holder 67 measure is calculated by adding
67% to the exercise price. More about this overconfident measure is given in chapter 3.
The dummy variable takes the value 1 if the manager shows a signal of overconfidence
and the value 0 if the manager shows no signals of overconfidence.
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6 Test and results
Now that the hypotheses, the data and the variables are defined, the model’s
predictions can be tested and the results can be interpreted. The statistical program
SPSS is used to be able to execute the linear regression. Before analyzing the
coefficients from this regression, the model has to satisfy some requirements.
6.1 Linear model
Before analyzing the results of the linear regression, it is important to check
whether or not a linear model is suitable for the regression. The scatter diagram gives an
indication about a possible linear relation between the dependent and the independent
variables. The scatter diagrams are drawn for the independent variables cash flow, Q
ratio and size. A linear line is fitted through the data with the use of SPSS to show the
relationship. The scatter diagrams can be found in Appendix A.
The first diagram shows a clear linear relationship between investment and cash
flow. The second diagram does not show a clear linear relationship between investment
and Q ratio, because here there are some outliers. When these outliers are excluded, a
better linear relationship can be seen between investment and Q ratio. The last diagram
shows that there is also a linear relationship between investment and size.
From the scatter diagrams it can be concluded that a linear model is suitable for
the regression. But these diagrams only give a first impression. Further on in this paper,
it will be tested with an F-test and a T-test whether or not the independent variables
really have a linear relation with the dependent variable.
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6.2 Descriptive statistics
Table 1 gives the descriptive statistics for the dependent and independent
variables of the regression specification.
Table 1 Descriptive statistics
6.3 Model summary
Table 2 gives the model summary for the base model without interaction terms.
These numbers are needed for the residual analysis and for testing the validity of the
model.
Table 2 Model summary base model
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6.4 Regression specification
The next step in the analysis is determining the regression specification to be able
to test the specified hypotheses. The model is a first order model with interaction and
consists of three variables, one dummy variable and three interaction terms. The
regression specification is:
y = β0 + β1x1 + β2x2 + β3x3 + β4I1 + β5x1x2 + β6x1x3 + β7x1I1 + ε
This regression specification will be used to test the hypotheses mentioned earlier
in this paper. Before interpreting the coefficients, a residual analysis is executed. This
test is executed only for the first part of the regression specification, without the
interaction terms. The reason for this is that the basis of the model has to be good and
the error variable has to satisfy the required conditions. The regression specification for
the base model without the interaction terms is:
y = β0 + β1x1 + β2x2 + β3x3 + β4I1 + ε
6.5 Residual Analysis
When the error variable (ε) from the regression specification is large, the errors
will be large and the model will be less good. The first method to check whether or not
the error variable is large is comparing the standard error of estimate of 2197 from table
2 with the mean of the dependent variable of 3948 from table 1. But with this method it
is difficult to estimate whether or not the model is good. Therefore the error variable (ε)
has to satisfy three requirements. With the residual analysis it will be tested whether or
not the error variable satisfies these three requirements. To check whether or not the
error variable departs from the required conditions, the standardized residuals are
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examined. The graphs and calculations from the residual analysis can be found in
Appendix B.
The first requirement is that for every independent variable, the error variable is
normally distributed with mean equal to zero. The error variable satisfies the first
requirement. The second requirement is that the variance of the error variable is
constant for every independent variable. The error variable satisfies the second
requirement; there is homoscedasticity. The third requirement is that the values of the
error variable are independent of each other. Here there is positive first order
autocorrelation. So the last requirement is violated and this makes the model less good.
6.6 Interpreting coefficients base regression
The coefficients are first interpreted for the base regression without the
interaction terms. The SPSS outputs for the base regression can be found in Appendix
F. The regression specification:
y = β0 + β1x1 + β2x2 + β3x3 + β4I1 + ε
6.6.1 Coefficients
Table 3 Coefficients base model
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Table 3 shows the coefficients for the base model without interaction terms. The
first column gives the independent variables that are used in the regression. The column
Unstandardized Coefficients consists of the column B and the column Std. Error. The
value for the constant in the column B is the value of the dependent variable when all
the variables are equal to zero. This value will not be interpreted. The other values in the
column B give the coefficients from the regression specification (β). The beta
coefficients in the column Standardized Coefficients tell how strongly the independent
variables are associated with the dependent variable. It is equal to the correlation
coefficients between the two variables. These values will not be interpreted. With the
values in the column t it can be tested whether the independent variable has a linear
relation with the dependent variable. It is the B coefficient divided by the Std. Error.
The t-value for the constant will not be interpreted. The last column Sig. gives the p-
values. These p-values will be compared to α.
6.6.2 Testing the validity
A way of testing the validity of the model is using the determination coefficient
R2. Table 2 gives the model summary of the base model. When there are many
independent variables in comparison with the number of values, the R2 will be large,
and the model erroneously appears to be good. Therefore, it is better to look at the
adjusted R2, which corrects for the degrees of freedom. The adjusted R2 = 0,883. This
means that 88,3% of the variance in the dependent variable is justified by the model.
A second way of testing the validity of the model is using the F-test. The F-test
is used to examine whether or not at least one of the independent variables has a linear
relationship with the dependent variable. From the F-test for the base regression in
Appendix C it can be concluded that the data support the claim that the model fits. A
larger F means that more of the variance in the dependent variable is justified by the
model, meaning a better model.
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6.6.3 T-test
The independent variables can be tested with the t-test to see whether or not
there is a linear relationship with the dependent variable and whether or not the relation
is significant at the 1% (***), 5% (**) and 10% (*) level. The t-values at the three
significance levels can be found in Appendix D. Another way to see whether or not the
relation is significant is to look at the column Sig. value. When this Sig. value is lower
than α, it is significant. Table 4 gives the independent variables and whether or not they
are significant. The stars show the level of significance.
Independent variable t- value Cash flow t = 4,692 (***) Q ratio t = - 2,549 (**) Size t = 0,273 Overconfidence t = - 0,619
Table 4 T-values base regression
6.6.4 Testing hypotheses
Now the hypotheses can be tested with the information from table 4. Given the
fact that the regression is only executed for the base regression, only the hypotheses that
refer to the variables in this base regression can be tested. These are hypothesis 1, 2, 3
and 4.
The first hypothesis is that the relation between cash flow and investment is
positive. A larger amount of cash flow results in higher investments. This hypothesis is
confirmed by the linear regression. The coefficient for cash flow (β1) is positive and
statistically significant at the 1% level. The second hypothesis is that the relation
between the Q ratio and investment is positive. A larger Q ratio results in higher
investments. This hypothesis is not confirmed by the linear regression. The coefficient
for Q ratio (β2) is negative and statistically significant at the 5% level. This result differs
from the expected relation. The third hypothesis is that the relation between the size of
the firm and investment is positive. Larger firms make more investments. The linear
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regression confirms this, because the coefficient for size (β3) is positive. But this result
is not statistically significant. The fourth hypothesis is that the relation between
overconfidence and investment is positive. Firms with more overconfident CEOs make
more investments. The linear regression does not confirm this relation. The coefficient
for overconfidence (β4) is negative although this result is not statistically significant.
Now the coefficients from the base regression are interpreted and tested. But it is
important to look at the coefficients from the total regression including the interaction
terms to be able to answer the research question. Conclusions will be based on these
coefficients and not on the coefficients from the base regression.
6.7 Interpreting coefficients within-case
This paper uses the method of a multiple case study in which will be looked at
five companies separately without trying to bring in the findings or lessons from another
case. After the within-case analysis, all the companies will be analyzed simultaneously,
also known as cross-case analysis, to look for common patterns or significant variations
across the cases. The coefficients for the cases separately can be interpreted for both the
base regression and the total regression. The aim of this paper is to look at
overconfidence and the influences on the investment – cash flow sensitivity. Therefore
the interaction terms have to be added to the regression specification to be able to
analyze this.
6.7.1 Testing the validity
A linear regression without the interaction terms and a linear regression including
the interaction terms are executed for the five companies separately. The SPSS outputs
from these five regressions can be found in Appendix G. A stepwise regression is done
here with the first regression without the interaction terms and the second regression
including the interaction terms. The R2 values are large, but the F-values are very low.
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When adding the interaction terms, the F-values become even lower. This is due to the
fact that the number of independent variables is large in comparison with the number of
values.
Some of the interaction variables are excluded from the regression because of
collinearity. The independent variables are correlated with each other when looking at
the companies separately. It is therefore difficult to draw conclusions on the coefficients
of the cases separately and test the hypotheses. It is better to analyze all the cases
simultaneously, because then there are more values to draw conclusions on.
6.7.2 Degree of managerial overconfidence
It is difficult to draw conclusions on the coefficients from the regression of the
cases separately. But the degree of managerial overconfidence can be shown. The table
5 below shows for the five companies whether or not the CEO was overconfident in the
years 2003 to 2009. This is measured using the Holder 67 measure.
Year Royal Dutch Shell KPN Philips Ahold Akzo Nobel2003200420052006 Overconfident Overconfident Overconfident2007 Overconfident Overconfident Overconfident Overconfident2008 Overconfident Overconfident Overconfident Overconfident2009 Overconfident Overconfident
Table 5 Overconfidence of CEOs
From this table it can be concluded that many CEOs are overconfident in the
Netherlands. It does not say anything about all the managers in the Netherlands but
gives a first impression of the degree of overconfidence.
Striking is the fact that for the years 2006 to 2009 the CEOs were more
overconfident than in the years before. Stock options have become an increasingly large
portion of total compensation recently so there is more opportunity to be overconfident
according to the Holder 67 measure.
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Another explanation for this has to do with the moments on which the stock
options are granted. There is a vesting period for 3 – 5 years in which the CEO is not
allowed to exercise his options. Stock options granted in 2003 are just exercisable in
2006. When the CEO accomplishes his function since 2003, there are no stock options
to exercise during the first 3 years of his career and therefore the CEO cannot be
overconfident during the first 3 years using the Holder 67 measure.
6.8 Interpreting coefficients cross-case
After the within-case analysis, all the companies will be analyzed simultaneously,
also known as cross-case analysis. The SPSS outputs for the total regression can be
found in Appendix H. The regression specification:
y = β0 + β1x1 + β2x2 + β3x3 + β4I1 + β5x1x2 + β6x1x3 + β7x1I1 + ε
6.8.1 Coefficients
Table 6 Coefficients cross-case
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Table 6 shows the coefficients for the total model. The values in this table are
explained earlier in this paper where the coefficients for the base regression are
interpreted.
6.8.2 Testing the validity
The interaction terms are added to the base model one by one to see whether or
not they make the model better. The interaction term x1*I1 did not improve the model,
but still this interaction term is added to the regression to be able to answer the research
question.
The total model including the interaction terms becomes better with an adjusted
R2 of 98,4% and an F-value of 294,695. From the F-test for the total regression in
Appendix C it can be concluded that the model including the interaction terms fits.
6.8.3 T-test
The independent variables can be tested with the t-test to see whether or not there
is a linear relationship with the dependent variable and whether or not the relation is
significant at 1% (***), 5% (**) and 10% (*). The t-values at the three significance
levels can be found in Appendix E. Table 7 gives the independent variables and whether
or not they are significant. The stars show the level of significance.
Independent variable t- value Cash flow t = - 3,744 (***) Q ratio t = 3,278 (***) Size t = 1,111 Overconfidence t = 0,478 CFQratio t = - 10,746 (***) CFSize t = 6,116 (***) CFOvercondidence t = - 2,222 (**)
Table 7 T-values total regression
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6.8.4 Testing hypotheses
Now all the hypotheses can be tested with the information from table 7. The
results differ from that of the base regression because now the interaction terms are
added. Conclusions are based on this total regression, because this model is better and
more complete.
The first hypothesis is that the relation between cash flow and investment is
positive. This hypothesis is not confirmed by the coefficient from the linear regression.
The coefficient for cash flow (β1) is negative and statistically significant at the 1% level.
The second hypothesis is that the relation between the Q ratio and investment is
positive. This hypothesis is confirmed by the linear regression. The coefficient for Q
ratio (β2) is positive and statistically significant at the 1% level. A higher Q ratio results
in higher investments. The Q ratio shows what the market thinks about you. When the Q
ratio is high, the market likes you and then more investments are made. The third
hypothesis is that the relation between the size of the firm and investment is positive.
The coefficient for size (β3) is positive although not statistically significant. The fourth
hypothesis is that the relation between overconfidence and investment is positive. Also
for this relation, the sign of the coefficient for overconfidence (β4) is as expected but the
result is not statistically significant.
The fifth hypothesis is that the interaction effect of cash flow and the Q ratio is
positive. The hypothesis for this interaction term is not confirmed by the linear
regression. The coefficient (β5) is negative and statistically significant at the 1% level.
The sixth hypothesis is that the interaction effect of cash flow and the firm size is
negative. This hypothesis is not confirmed by the linear regression. Actually, the sign of
the coefficient (β6) is positive and statistically significant at the 1% level. This implies
that larger firms have more investment – cash flow sensitivity. An explanation for this
opposite result could be that larger firms have greater flexibility in timing investments
and may defer investments until internal funds are available. The last hypothesis is that
the interaction effect of cash flow and overconfidence is positive. This hypothesis
answers the research question of this paper. However, the coefficient (β7) for this
interaction term is negative and statistically significant at the 5% level and therefore the
last hypothesis is not confirmed by the linear regression.
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From this it can be concluded that only the second hypothesis is confirmed by the
results of the regression. The third and fourth hypotheses are also true but the results for
these coefficients are not statistically significant. For the other hypotheses the expected
relations are not found and the coefficients do have a sign that is the opposite sign as
expected.
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7 Conclusion
This paper focuses on managerial overconfidence in the Netherlands. The
research is based on the research of Malmendier and Tate (2005a). First, the
overconfidence measure Holder 67 is constructed. A manager is classified as
overconfident if he does not exercise stock options in his own firm that are more than
67% in-the-money. From these calculations it can be assumed that many CEOs in the
Netherlands are overconfident. The degree of overconfidence is high in the Netherlands.
Second, a linear regression is done with investment as dependent variable and
cash flow, Q ratio, firm size and overconfidence as independent variables. Interaction
terms for cash flow with the independent variables are added to the regression. The
regression findings do not support all the hypotheses. The coefficient for the interaction
term of cash flow and overconfidence is negative and statistically significant. This
implicates that the hypothesis that investment – cash flow sensitivity increases in
overconfidence is not confirmed by the regression results. The data do not support the
hypothesis and as a consequence the null hypothesis can not be rejected.
Managerial overconfidence can have both positive and negative consequences for
the shareholders of the firms. Given that the degree of overconfidence is considered to
be high in the Netherlands, it is important for organizations to motivate their managers
to make decisions that are in the interest of the shareholders. Misalignment of
managerial and shareholders’ interest can result in a destruction of shareholder value.
Alignment of interests is especially important concerning investment decisions, in
which the overinvestment and underinvestment problem can occur.
This research uses a case study as research method and the results are therefore
not sufficient to reject or support the theory. It does not say anything about all the
managers in the Netherlands. Future research can investigate whether the theory is true
using a larger sample of Dutch firms.
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Appendices A SCATTER DIAGRAMS ............................................................................................................II B RESIDUAL ANALYSIS........................................................................................................... III C F-TEST.......................................................................................................................................V D T-TEST BASE REGRESSION ................................................................................................ VI E T-TEST TOTAL REGRESSION............................................................................................ VII F REGRESSION RESULTS BASE REGRESSION.................................................................VIII G REGRESSION RESULTS WITHIN-CASE............................................................................ IX H REGRESSION RESULTS CROSS-CASE ............................................................................XIV
REFERENCES................................................................................................................................. XV
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A Scatter diagrams
The scatter diagram gives an indication about a possible linear relation between
the dependent and the independent variables. The scatter diagrams are drawn for the
independent variables cash flow, Q ratio and size. A linear line is fitted through the data
with the use of SPSS to show the relationship. The scatter diagrams can be found in the
figure A.1 up to figure A.3.
The values are all standardized. This means that for each value the mean of the
concerning variable is subtracted and the result is divided by the standard deviation. The
result is that all variables have a mean of zero and a standard deviation of one. This
enables comparison of variables of differing magnitudes and dispersions.
Figure A.1 Scatter diagram investment with cash flow Figure A.2.b Scatter diagram investment with Q
ratio (without outliers)
Figure A.2.a Scatter diagram investment with Q ratio Figure A.3 Scatter diagram investment with size
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B Residual Analysis
First requirement: normality
The first requirement is that for every independent variable, the error variable is
normally distributed with mean equal to zero. To check for normality, the histogram of
the residuals is drawn. The histogram is bell shaped and therefore the error variable is
normally distributed. The error variable satisfies the first requirement.
Figure B.1. Histogram of the residuals
Second requirement: constant variance
The second requirement is that the variance of the error variable is constant for
every independent variable, named homoscedasticity. When this requirement is
violated, the condition is called heteroscedasticity. To check for this condition, the
unstandardized predicted values are plotted against the unstandardized residuals. The
error variable satisfies the second requirement.
Figure B.2.a Homoscedasticity Figure B.2.b Homoscedasticity without outliers
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Third requirement: independence of error variables
The third requirement is that the values of the error variable are independent of
each other. Errors terms that are correlated over time are said to be auto-correlated. The
Durbin-Watson test allows determining whether there is evidence of first-order
autocorrelation.
The Durbin-Watson is demonstrated with the d-value. The value d takes a value
between 0 and 4. When the value d takes the value of 2, there is no autocorrelation.
When the value d takes a small value (smaller than 2), there could be positive first order
autocorrelation. When the value d takes a large value (larger than 2), there could be
negative first order autocorrelation. De d-value is 1,018 so there could be positive first
order autocorrelation.
Durbin-Watson test
Hypothesis: H0: there is no first order autocorrelation
H1: there is positive first order autocorrelation
α = 0,05
Test statistic: d
Rejection region: d < dL: reject H0
dL < d < dU: the test is inconclusive
d> dU: do not reject H0
Durbin-Watson: d = 1,018
Conclusion: d < 1,222 reject the null hypothesis.
There is enough evidence that positive first order autocorrelation exists. This means
that the error variables are not independent. The consecutive residuals tend to be
similar.
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C F-test
F-test base regression
Hypothesis: H0: β1 = β2 = β3 = β4 = 0
H1: at least one of the βi ≠ 0
α = 0,05
Test statistic: F
Rejection region: F > Fα, k, n-k-1
F > F0,05, 4, 30 = 2,690
F-value: F = 65,070
Conclusion: The F-value is in the rejection region so reject the null hypothesis.
F-test total regression
Hypothesis: H0: β1 = β2 = β3 = β4 = β5 = β6 = β7 = 0
H1: at least one of the βi ≠ 0
α = 0,05
Test statistic: F
Rejection region: F > Fα, k, n-k-1
F > F0,05, 7, 27 = 2,373
F-value: F = 294,695
Conclusion: The F-value is in the rejection region so reject the null hypothesis
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D T-test base regression
T-test (significance at 1%) Hypothesis: H0: βi = 0 vs. H1: βi ≠ 0 α = 0,01
Test statistic: t
Rejection region: t > t α/2, n-k-1
t < - t α/2, n-k-1
t > t0,005, 35-4-1 = 2,750
t < - t0,005, 35-4-1 = - 2,750
T-value: t =
Conclusion: When the t-value is in the rejection region the null hypothesis has to be
rejected.
T-test (significance at 5%) Hypothesis: H0: βi = 0 vs. H1: βi ≠ 0 α = 0,05
Test statistic: t
Rejection region: t > t α/2, n-k-1
t < - t α/2, n-k-1
t > t0,025, 35-4-1 = 2,042
t < - t0,025, 35-4-1 = - 2,042
T-value: t =
Conclusion: When the t-value is in the rejection region the null hypothesis has to be
rejected.
T-test (significance at 10%) Hypothesis: H0: βi = 0 vs. H1: βi ≠ 0 α = 0,10
Test statistic: t
Rejection region: t > t α/2, n-k-1
t < - t α/2, n-k-1
t > t0,05, 35-4-1 = 1,697
t < - t0,05, 35-4-1 = - 1,697
T-value: t =
Conclusion: When the t-value is in the rejection region the null hypothesis has to be
rejected.
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E T-test total regression
T-test (significance at 1%) Hypothesis: H0: βi = 0 vs. H1: βi ≠ 0 α = 0,01
Test statistic: t
Rejection region: t > t α/2, n-k-1
t < - t α/2, n-k-1
t > t0,005, 35-7-1 = 2,771
t < - t0,005, 35-7-1 = - 2,771
T-value: t =
Conclusion: When the t-value is in the rejection region the null hypothesis has to be
rejected.
T-test (significance at 5%) Hypothesis: H0: βi = 0 vs. H1: βi ≠ 0 α = 0,05
Test statistic: t
Rejection region: t > t α/2, n-k-1
t < - t α/2, n-k-1
t > t0,025, 35-7-1 = 2,052
t < - t0,025, 35-7-1 = - 2,052
T-value: t =
Conclusion: When the t-value is in the rejection region the null hypothesis has to be
rejected.
T-test (significance at 10%) Hypothesis: H0: βi = 0 vs. H1: βi ≠ 0 α = 0,10
Test statistic: t
Rejection region: t > t α/2, n-k-1
t < - t α/2, n-k-1
t > t0,05, 35-7-1 = 1,703
t < - t0,05, 35-7-1 = - 1,703
T-value: t =
Conclusion: When the t-value is in the rejection region the null hypothesis has to be
rejected.
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F Regression results base regression
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G Regression results within-case
Company 1: Royal Dutch Shell
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Company 2: KPN
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Company 3: Philips
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Company 4: Ahold
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Company 5: Akzo Nobel
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H Regression results cross-case
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