Thesis Managerial Overconfidence - Tilburg University

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

Master Thesis Finance

Managerial Overconfidence 2

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.



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.



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



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



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.



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.



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.



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.



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.



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.



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.



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



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



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



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



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.



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.



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.



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.



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



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.



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.



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



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.



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.



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.



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.



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.



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.



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.



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.



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.



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



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



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



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.



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



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.


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.



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.



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



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.


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



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.



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.



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.


Managerial Overconfidence I

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


Managerial Overconfidence II

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


Managerial Overconfidence III

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


Managerial Overconfidence IV

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.


Managerial Overconfidence V

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


Managerial Overconfidence VI

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.


Test statistic: t


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 =


rejected.


Test statistic: t


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 =


rejected.


Managerial Overconfidence VII

E T-test total regression


Test statistic: t


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 =


rejected.


Test statistic: t


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 =


rejected.


Test statistic: t


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 =


rejected.


Managerial Overconfidence VIII

F Regression results base regression


Managerial Overconfidence IX

G Regression results within-case

Company 1: Royal Dutch Shell


Managerial Overconfidence X

Company 2: KPN


Managerial Overconfidence XI

Company 3: Philips


Managerial Overconfidence XII

Company 4: Ahold


Managerial Overconfidence XIII

Company 5: Akzo Nobel


Managerial Overconfidence XIV

H Regression results cross-case


Managerial Overconfidence XV

References

Books

Blumberg, B., D.R. Cooper and P.S. Schindler, (2008), Business research methods,

London: McGraw-Hill Higher Education

Maylor, H. and K. Blackmon, (2005), Researching business and management,

Basingstoke: Palgrave Macmillan

Papers

Alicke, M.D., M.L. Klotz, D.L. Breitenbecher, T.J. Yurak, et al, (1995), Personal

contact, individuation, and the better-than-average effect, Journal of Personality and

Social Psychology, 86, 804 - 825

Ben-David, I., J.R. Graham and C.R. Harvey, (2007), Managerial overconfidence and

corporate policies, Chicago meetings paper

Chuang, W. and B. Lee, (2006), An empirical evaluation of the overconfidence

hypothesis, Journal of Banking & Finance, 30, 2489 – 2515

Cordeiro, L., (2009), Managerial Overconfidence and Dividend Policy, Working paper

London Business School

Daniel, K. and S. Titman, (1999), Market efficiency in an irrational world, Financial

Analysts Journal, 55, 28 – 40

DeBondt, W.F. and R. Thaler, (1985), Does the Stock Market Overreact?, The Journal

of Finance, 3, 739 – 805


Managerial Overconfidence XVI

DeBondt, W.F. and R. Thaler, (1995), Financial decision-making in markets and firms:

a behavioral perspective, Handbooks in Operations research & Management scriences

9, 385 – 410

Deshmukh, S., A.M. Goel and K.M. Howe, (2009), CEO overconfidence and dividend

policy, Working paper

Dittrich, D.A., W. Guth and B. Maciejovsky, (2005), Overconfidence in investment

decisions: An experimental approach, The European Journal of Finance, 6, 471 – 491

Doekas J.A., and D. Petmezas, (2007), Acquisitions, overconfident managers and self-

attribution bias, European Financial Management, 13, 531 – 577

Fairchild, R.J., (2006), The Effect of Managerial Overconfidence, Asymmetric

Information, and Moral Hazard on Capital Structure Decisions, The ICFAI Journal of

Behavioral Finance

Fama E.F., (1998), Market efficiency, long-term returns and behavioral finance, Journal

of financial economics, 49, 283 – 306

Fazzari, S. R., G. Hubbard and B. Peterson, (1988), Financing constraints and corporate

investment, Brookings Papers on Economic Activity, 141-195.

Gervais, S., J.B. Heaton, and T. Odean, (2002), The Positive Role of Overconfidence

and Optimism in Investment Policy, Working paper

Gervais, S., J.B. Heaton, and T. Odean, (2003), Overconfidence, Investment Policy, and

Executive Stock Options, Working Paper

Goel, A.M. and A.V. Thakor, (2008), Overconfidence, CEO selection, and corporate

governance, Journal of Finance forthcoming


Managerial Overconfidence XVII

Graham, J.R. and C.R. Harvey, (2001), The theory and practice of corporate finance:

evidence from the field, Journal of Financial Economics, 60, 187 - 243

Hackbart, D., (2007), Managerial Traits and Capital Structure Decisions, Working paper

Hackbart, D., (2009), Determinants of corporate borrowing: A behavioural perspective,

Journal of Corporate Finance, 15, 389 – 411

Hall, B.J. and K.J. Murphy, (2002), Stock options for undiversified executives, Journal

of Accounting and Economics, 33, 3 - 42

Heaton, J.B., (2002), Managerial Optimism and Corporate Finance, Financial

Management, 31, 2

Hribar, P. and H. Yang, (2006), CEO Overconfidence, Management Earnings Forecasts,

and Earnings Management, Working paper

Jegadeesh, D. And S. Titman, (1993), Returns to buying winners and selling losers:

Implications for stock market efficiency, Journal of Finance, 48, 65 – 91

Jensen, M. C., (1986), Agency costs of free cash flow, corporate finance and takeovers,

The American Economic Review, 76, 323 - 329

Jensen, M. C. and W. H. Meckling, (1976), The theory of the firm: Managerial

behavior, agency costs, and ownership structure, Journal of Financial Economics, 3,

305 – 360

Kadapakkam, P., P.C. Kumar and L.A. Riddick, (1998), The impact of cash flows and

firm size on investment: the international evidence, Journal of Banking & Finance, 22,

293 - 320


Managerial Overconfidence XVIII

Kahneman, D. and A. Tversky, (1986), Rational choice and the framing of decisions,

Journal of business, 59

Kahneman, D. and A. Tversky, (1979), Prospect Theory: An Analysis of Decision under

Risk, Econometrica, 47, 263 – 292

Kahneman, D. and D. Lovallo, (1993), Timid Choices and Bold Forecasts: A Cognitive

Perspective on Risk Taking, Management Science, 39, 17 – 31

Kaplan, S.N. and L. Zingales, (1997), Do investment – cash flow sensitivities provide

useful measures of financing constraints? Quarterly Journal of Economics, 112, 169 -

215

Kaplan, S.N. and L. Zingales, (2000), Investment – cash flow sensitivities are not valid

measures of financing constraints, Quarterly Journal of Economics

Ko, K. J. and Z.J. Huang, (2007), Arrogance can be a virtue, Journal of Financial

Economics, 84, 529 – 560

Land, L.H.P and R.H. Litzenberger, (1989), Cash flow signaling vs. free cash flow

hypothesis, Journal of Financial Economics, 24, 181 – 191

Leary, M.T. and M.R. Roberts, (2005), Financial slack and tests of the pecking order’s

financing hierarchy, Working Paper Duke University

Lin, Y., S. Hu and M. Chen, (2004), Managerial optimism and corporate investment:

some empirical evidence from Taiwan, Pacific-Basin Finance Journal, 13, 523 - 546

Malmendier, U. and G. Tate, (2005a), CEO overconfidence and corporate investment,

The Journal of Finance, 6


Managerial Overconfidence XIX

Malmendier, U. and G. Tate, (2005b), Does overconfidence affect corporate

investment? CEO overconfidence measures revisited, European Financial

Management, 11, 649 – 659

Malmendier, U. and G. Tate, (2007), How makes acquisitions? CEO confidence and the

market’s reaction, Journal of Financial Economics

Malmendier, U. and G. Tate, (2008), How makes acquisitions? CEO confidence and the

market’s reaction, Journal of Financial Economics, 89, 20 – 43

Malmendier, U, G.A. Tate and J. Yan, (2009), Managerial beliefs and corporate

financial policies, NBER Working Paper

Miller, D.T. and M. Ross, (1975), Self-serving biases in the attribution of causality: Fact

or fiction? Psychological Bulletin, 82, 213 – 225

Modigliani, F. and M.H. Miller, (1958), The cost of capital, corporate finance and the

theory of investment, The American Economic Review, 3, 261 – 297

Myers, S.C. and N.S. Majluf, (1984), Corporate financing and investment decisions

when firms have information that investors do not have, Journal of Financial

Economics, 13, 187 – 221

Oliver, B.R., (2005), The impact of management confidence on capital structure,

Working paper

Roll, R., (1986), The Hubris Hypothesis of Corporate Takeovers, Journal of Business,

59, 197 – 216

Samuelson, W. and R. Zeckhauser, (1988), Status Quo Bias in Decision Making,

Journal of Risk and Uncertainty, 1, 7 – 59

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