Working Paper Series

n. 122 ■ April 2019

"SPILLOVER EFFECTS WHEN SHAREHOLDERS ARE DISTRACTED: EVIDENCE FROM THE US MARKET”

Luigi Carabelli Winner 10th UniCredit Best Paper Award

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UNIVERSITÀ CATTOLICA DEL SACRO CUORE DI MILANO

Facoltà di Scienze Bancarie, Finanziarie e Assicurative

MSc in Banking and Finance

SPILLOVER EFFECTS

WHEN SHAREHOLDERS ARE DISTRACTED:

EVIDENCE FROM THE US MARKET

Supervisor: Ch.mo Professor Ettore CROCI

Tesi di laurea di:

Luigi CARABELLI

Matr. n. 4606698

Anno Accademico 2017-2018

Acknowledgment

I would like to thank my thesis advisor Professor Ettore Croci for his helpful comments

and guidance. He provided me with important piece of data that was necessary to conduct

this research. Furthermore, when I had a doubt or a question, he was always available

to help me. I also thank him for placing his trust and confidence in me.

Finally, I would also like to thank my family and Giulia for their constant support

throughout my years of study and the process of writing this thesis.

Thank you.

Luigi Carabelli

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Table of Contents

1. Introduction .............................................................................................................. 3

2. Literature Review and Empirical Predictions ........................................................... 7

2.1. The Agency Theory ........................................................................................... 7

2.1.1. Types of Agency Problems ........................................................................ 8

2.1.2. Causes of the Agency Problems ............................................................... 10

2.1.3. Agency Costs ............................................................................................ 11

2.1.4. Answers to the Agency Problem .............................................................. 12

2.2. Shareholders’ Influence, Firm Performance, Corporate Governance .............. 14

2.2.1. Large Shareholders and Monitoring Institutions ...................................... 14

2.2.2. Activist Investors ...................................................................................... 17

2.2.3. Passive Institutional Ownership ............................................................... 20

2.3. Limited Shareholders Attention ....................................................................... 25

2.3.1. Distraction Measure .................................................................................. 28

2.3.2. Distraction Effects on Corporate Governance .......................................... 31

2.4. Hypothesis Development ................................................................................. 33

2.4.1. Empirical Predictions: Direct Effects ....................................................... 34

2.4.2. Empirical Predictions: Spillover Effects .................................................. 35

3. Empirical Analysis ................................................................................................. 40

3.1. Data Sources .................................................................................................... 40

3.2. Direct Effects of Shareholders’ Distraction ..................................................... 41

3.2.1. Merger Frequency and Diversifying Merger ............................................ 41

3.2.2. Payout Policy ............................................................................................ 46

3.2.3. Lucky Option Grants ................................................................................ 49

3.3. Spillover effects ............................................................................................... 54

3.3.1. Cash holdings and peer companies ........................................................... 55

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3.3.2. Dividends and Peer Companies ................................................................ 60

3.3.3. Leverage and peer companies................................................................... 64

4. Conclusions ............................................................................................................ 68

5. Appendix ................................................................................................................ 70

6. References .............................................................................................................. 74

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1. Introduction

The conflict of interest between shareholders and managers is well known in the

economic literature since Jensen and Meckling (1976) underlined how the “separation

and control” issue enhances agency costs. Managers who have little stake in the company

are the agents of the shareholders who hold most of the firm’s equity (called principals).

This separation of ownership and control enhances agency costs. Because who is in

control of the firm has less incentive than shareholders to maximize profits, the agents

may use the firm’s resources to satisfy their own private desires at shareholders’ expenses.

As a result, the separation of ownership and control brings about the so-called agency

costs that affect investors’ wealth. One way to reduce such costs is to supervise the

managers in order to make sure that they act in the owners’ interest. Monitoring, however,

is a time consuming and expensive activity. Not many stakeholders may have the

incentives to engage in such activities because the relevant costs could exceed the

expected benefits. But large shareholders, such as institutional investors, who have

invested enough money in the company have a huge interest in making sure that it is run

efficiently.

Many papers verified the beneficial effects of blockholders in improving firms’

performances through monitoring activities (e.g. Shleifer and Vishny 1986; Fich, Harford

and Tran 2015). However, big investors have not infinite resources to devote in

monitoring the target company. Because they cannot constantly provide the same level of

monitoring to the target company they are said to have limited attention. Kempf, Manconi

and Spalt (2017) proved that managers have greater leeway to maximize private benefits

when institutional investors lack to provide constant levels of monitoring. Especially,

investors are said to be distracted when they temporarily shift attention away from a firm.

Because distraction1 cannot be directly observed, the authors use exogenous shocks to

unrelated industries held by a given institutional investor as proxy of distraction measure.

Investors are assumed to be distracted with reference to a firm if they experience

extremely positive or negative shocks in unrelated components of their portfolio (i.e. if a

company in a different industry suffer a large loss in terms of market capitalization).

Because distracted shareholders provide less monitoring, the agency costs increase. One

way for managers to take advantage of distracted shareholders is to become more active

in the takeover market (i.e. empire building). Moreover, firms with distracted

1 The distraction measure D will be explained in detail in the following chapters of this thesis.

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shareholders may pay less dividends, benefiting mangers who might use the excess free

cash flow for personal benefits.

However, the distraction of institutional investors might have not only direct effects on

the target firm. The loosening in monitoring constraints in the target company could also

spill over into other peer companies. This means that peers may directly or indirectly

observe that the monitoring constraints on competitors are loosening and act accordingly.

This thesis is divided in two parts. In the first, we test the “Distracted Shareholder

Hypotheisis” introduced by Kempf, Manconi and Spalt (2017) by analyzing the takeover

market, the payout policies and the employee stock options during the periods when

institutional investors shift attention away from the target firm. Our results indicate that

firms are more likely to announce a merger and/or a diversifying acquisition when

shareholders are distracted. In details, the probability of announcing at least one merger

in a given year increases by 33% for a one-standard deviation increase in the distraction

measure relative to the mean. The effect of distraction on the probability of announcing

a diversification deal is even stronger. Indeed, according to our analysis, one-standard

deviation rise in distraction increases the chances of a diversifying deal by 150.6%

relative to the mean. In both cases distractions is statistically significant at 5% level after

controlling for time x industry fixed effects. Furthermore, CEOs and independent

directors are more likely to receive a lucky grant when shareholders are distracted. In

details, one-standard deviation rise in distraction increases the probability for the CEO

and directors of receiving at least one lucky grant in a given year by 172% and 118%

respectively. Finally, the results regarding the effect of distraction on payout policies are

ambiguous. We would expect that distraction affects negatively the propensity to pay

dividends. However, we found that the odds of observing a dividend cut is lower when

shareholders are distracted after controlling for time x industry fixed effects and firm

fixed effects.

The second part studies the indirect effects (i.e. “spillover effects) of investors’

distraction on peer companies. Although the literature on spillover effects is growing, the

effect of shareholders’ distraction on peers’ companies’ actions is unexplored. The goal

of this paper is to verify whether there is a relationship between managerial actions and a

lack in monitoring performed by investors of close competitors. The lack of monitoring

constraints for one company is reasonably assumed to have repercussions on its peer

firms’ performances and corporate actions. Specifically, peer companies may observe

directly or indirectly the self-serving behavior of managers and react accordingly. They

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may perceive directly that competitors’ investors are distracted, or they can detect

indirectly the effect of shareholders’ distraction on corporate decisions. Specifically, we

assume that peer companies will take actions to defend themselves from the threat of

being taken over. Since this paper and Kempf, Manconi and Spalt (2017) prove that firms

are more active in the takeover market when their shareholders are distracted, competitors

fearing to lose control would take actions to reduce the probability of being taken over

when they perceive a loosening in monitoring constraints in their opponents. Especially,

companies may hold more cash, pay less dividends and keep low the leverage ratio when

threatened by close competitors with distracted shareholders. This theory agrees with

Hoberg, Phillips and Prabhala (2014) who state “paying lower dividends and

repurchasing fewer shares while retaining cash and liquid assets can provide flexibility to

firms in less stable markets, allowing them to react more aggressively to competitive

threats when they materialize”. Close competitors of companies with distracted

shareholders may adopt more conservative financial policies in order to acquire the

required flexibility and resources to fight against a takeover attempt. The results of linear

probability models and logit models are consistent with this theory. In details, cash

holdings increase, on average, by 16% relative to the mean if shareholders of close

competitors are highly distracted. Moreover, the probability of paying dividends

decreases by 8.93% relative to the mean when close competitors have distracted

shareholders. In both analyses the results are statistically significant at 1% level after

controlling for year and industry fixed effects. Finally, the average long-term debt

normalized by total assets and total debt normalized by assets decrease by 2.5% and 4.5%

respectively when companies fear to be taken over by peers with distracted shareholders.

To sum up, this paper show that shareholders’ distraction has both direct and indirect

effects that are both economically and statistically significant. On one hand, a loosening

in monitoring constraints give the managers of the target company more chances to

maximize private benefits at the expense of the shareholders. On the other hand, peer

companies of distracted shareholders’ corporations change their policies in order to avoid

being taken over.

This paper is related to the work by Shleifer and Vishny (1986), Fich, Harford and Tran

(2015), Brav, et al. (2008), Klein and Zur (2009), Appel, Gormley and Keim (2016) and

Schmidt and Fahlenbrach (2017). These authors study the relationship between

monitoring performed by different types of shareholders and firms efficiency. Moreover,

this paper contributes to the scarce literature on behavioral finance, and especially

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contributes to the work of Kempf, Manconi and Spalt (2017). In the end, this paper

contributes to the corporate finance literature that studies the spillover effects (e.g.

Gantchev, Gredil and Jotikasthira 2014 and Servaes and Tamayo 2014).

This paper proceeds as follows. The literature review and the hypothesis development

are presented in Chapters 2. In Chapter 3, we describe the construction of the sample and

present the empirical results. Chapter 4 contains the conclusions and the Appendix

provides a description of the variables of interest in this study.

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2. Literature Review and Empirical Predictions

Many companies are operated by managers who are not the majority shareholders (or are

not shareholders at all). Their interests are not necessarily the same as the ones of the

shareholders. This can cause conflict of interests. The problem is usually addressed as

“agency problem” (Jensen 1986; Fama and Jensen 1983; Fama 1980; Jensen and

Meckling 1976).

This paper examines the agency problem between shareholders (the principal) and

managers (the agent) in the particular circumstances of attention grabbing events that

diverts principal attentions from the agent’s action. Managers can exploit their superior

information acting in their own interest rather than in the interest of the stockholders.

We will see later that this type of manager behavior not only has direct impact on their

shareholders’ wealth but may also create spillover effects on the financial decisions of

competitors.

In the first paragraph, the agency theory and the agency problem between investors and

managers is discussed in general terms. The second paragraph presents the literature that

explains how the shareholders should allocate resources and incentives to motivate

managers and ensure they behave according to the owners’ interests. The third paragraph

states the causes and the effects of the limited shareholder attention. Specifically, the

Distracted Shareholder Hypothesis will be introduced. In the fourth paragraph, we present

the focus of the research and the empirical predictions.

2.1. The Agency Theory

The agency theory can be considered one of the most important and oldest theory in the

economics literature (Wasserman 2006). The theory is indeed an important tool in

different research fields such as: accounting (Ronen and Balachandran 1995), finance

(Jensen 1986; Fama and Jensen 1983; Fama 1980), economics (Jensen and Meckling

1976; Ross 1973), political science (Hammond and Knott 1996), sociology (Adams

1996), organizational behavior (Kosnik and Bettenhausen 1992) and marketing (Tate, et

al. 2010).

The agency theory describes the relationship between the agent and the principal. The

so-called agency relationship is defined as “a contract under which one or more persons

(the principal(s)) engage another person (the agent) to perform some service on their

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behalf which involves delegating some decision-making authority to the agent” by Jensen

and Meckling (1976). Because there is a delegation of power from the principal to the

agent, the latter could act in her interest at the principal expenses. This occurs because

there are conflicts of interests between the agent and the principal.

2.1.1. Types of Agency Problems

The conflict between the owner who delegates and the designated party to perform the

service on behalf of the owner is called agency problem. There are three types of agency

problem according to the economic and finance literature. The first type agency problem

is the most relevant to this work because it relates to the conflict of interest between the

needs of managers and the needs of stockholders. To provide the reader with a complete

overview, we present a summary of each type of agency dilemma.

Type 1

The agency problem of first type addresses the conflicts between stockowners

(principal) and managers (agent). The agency problem between managers and

shareholders is typical of large corporations with dispersed ownership in which

shareholders delegates the task to run the company to managers. The conflicts could arise

because managers have the objective to maximize their compensation while shareholders

are focused on the value of their equity stake and the dividends. The misalignment of

goals between the principal and the agent is due to the separation of ownership and

control: the managers in control have less incentives to maximize the value of the

company than stockholder-owners (Mishkin, Kent and Giuliodori 2013). Moreover, the

separation of ownership and control involves moral hazard. By having the opportunity,

the agents could act in their own personal interest affecting the shareholders’ wealth. The

agents have this opportunity not only because they have the direct control of the company,

but also because they have more information about the company’s activity than the

stockholders. Many finance papers convey that managers have incentives to maximize

their own ends and their actions may have negative consequences to shareholders (e.g.

Jensen 1986; Murphy 1985; Kostiuk 1990; Shleifer and Vishny 1989). In particular, three

papers have been crucial for the development of the first type agency problem literature:

Jensen and Meckling (1976), Fama (1980) and Fama and Jensen (1983). The first paper

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defined the agency costs and formalized a theory of corporate ownership structure that

describes how “inside equity” held by managers can be fundamental in aligning

management’s interests with those of owners. The second one further investigates the

separation of security ownership and control by using a “set of contracts” perspective.

Fama (1980) affirms how important are both the competition among top managers and

the efficiency in capital markets for limiting the self-serving behavior of top executives

in enterprises with diffused ownership. Finally, Fama and Jensen (1983) develops

methods for controlling agency problems in the decision process in different

organizational forms.

Type 2

The second type of conflict is between majority owners and minority shareholders. By

holding the majority of the shares of the company, thus having higher voting rights

relative to the others, majority shareholders have the control over the company. Majority

shareholders, that are also called blockholders, may take decisions that benefits them at

the expense of the minority (Fama and Jensen 1983). This conflict type is present in

corporations where the ownership is concentrated in the hands of few individuals

(Demsetz and Lehn 1985). Some major Italian companies, that are usually family owned

business, are a typical example of corporations where the conflicts between minor and

major shareholders may be quite severe.

Type 3

The third type of agency problem occurs between owners and creditors: once obtained

the funds, the owner may take much more risk than the creditors would wish.

Shareholders may engage in activities that are undesirable from the lender’s point of view

making the money less likely to be paid back. Shareholders may indeed have the incentive

to invest in risky projects, or even in negative net present value projects affecting the total

value of the firm. Especially in financial distress situations, managers might take actions

that benefit shareholders but harm debtholders. Indeed, when there is the risk that no

equity value will remain after paying off all the contractual obligations, shareholders are

better off by investing in highly risky investments. The owners, being the residual

claimants, have little to lose in the bad case scenario if the project fails to generate the

desired cash flow while they make huge profits in the good case scenario. Even if

shareholders have nothing to lose, debt holders are worse off if the firm invest in projects

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with negative expected value since the probability of being repaid decreases. In addition,

only shareholders enjoy the huge profit while the interest of the creditors is limited as

they get only a fixed rate of interest (Panda and Leepsa 2017; Jensen and Meckling 1976;

Berk and DeMarzo 2014; Damodaran 1997). In other words, debt holders are the ones

who bear the cost if the highly risky project goes wrong while stockowners receive most

of the profit if the investment takes off.

2.1.2. Causes of the Agency Problems

The causes of the agency problems are summarized in the works of Panda and Leepsa

(2017) and Chowdhury (2012).

The main cause of the first type agency problem is the separation of ownership and

control. In modern diffused ownership companies, shareholders have a claim to the profits

of the firm but have little direct control over management decisions. Correspondingly,

managers have control but have only a small (if any) stake in the business (Marks 1998).

Another important cause is the asymmetry of information between the two parties.

Because it is the manager who oversees the day by day operations, it may be difficult for

the owners to realize whether the business is being used for private purposes. Except for

the information that the firm is required to disclose by law, the shareholders depend upon

the managers to share the information about the ongoing business, and such information

may be manipulated too (Panda and Leepsa 2017). The separation between ownership

and control, and the presence of asymmetric information implies moral hazard (Mishkin,

Kent and Giuliodori 2013): managers are incentivized to engage activities that hurts

shareholders (e.g. empire building). Another cause of first type agency problem can be

the limited period of time the managers work for the organization. The agents may try to

maximize their benefits, even at the company expenses, for the short period of their

mandate and then move to another company. Shareholders have difficulties in preventing

value destroying management decisions because of the free-rider problem or coordination

problems (Marks 1998). Even if monitoring the firm’s activity or a proxy solicitation

could mitigate moral hazard problems, that may be very expensive in terms of money and

time for shareholders with only a small stake in the company. Monitoring activities and

collecting information about top managers’ activities benefit every equity owner, so some

shareholders could free ride on the monitoring activities performed by other ones. Free

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riding is a problem because it reduces the amount of information produced and its

effectiveness in mitigating the moral hazard problem (Grossman and Hart 1980; Mishkin,

Kent and Giuliodori 2013).

The cause of the conflicts between minority and majority shareholders is related to the

power to make decisions. As far as decision-making concerns, the major shareholders

have the majority stake of the company shares and of the voting rights. So they practically

have full control over the company, including the choice of the top managers and the

capability to influence their decisions. Essentially, they are the only ones who take

decisions or influence the management policy, while the minor shareholders, having no

power, can only accept the majority will.

The main cause of the third type agency problem is the difference in risk preference

between debt holders and shareholders. The problem arises even because of the creditors’

limited earnings: even if they are important stakeholders of the company, the interest they

earn is fixed and limited (Panda and Leepsa 2017).

2.1.3. Agency Costs

The agency problem caused by the misalignment of the interests between the agent and

the principal implies agency costs. Jensen and Meckling (1976) defined the agency costs

as the sum of:

i. Monitoring costs incurred by the principal,

ii. Bonding costs incurred by the agent,

iii. Residual loss.

The monitoring costs are the costs incurred for observing the behavior of the agent and

limiting her abnormal activities, the ones that may damage the principal.

The bonding costs are the resources needed by the agent to guarantee the principal that

she will not take any harmful action from the principal point of view. Bonding costs

include the compensation paid to the principal if she suffered a loss caused by the agent.

Finally, the residual loss is the amount of the reduction in the principal wealth due to

the divergences between the agent’s decisions and the ones that would maximize the

principal’s gain. In other words, even if the agents and the principal incur in monitoring

and bonding costs, the agent can still not act in the principal’s best interests.

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2.1.4. Answers to the Agency Problem

Many solutions have been proposed by the literature to the agency problems. We list the

remedies suggested by the literature in this paragraph.

According to Jensen and Meckling (1976), only by eliminating the separation of

ownership and control it is possible to mitigate the conflicts between owners and agent.

This can be done in different ways such as granting the agents with stocks or stock

options. Equity stake and stock options may facilitate the alignment of managers and

shareholders’ interests since managers participate in the company’s profit. However, it is

necessary to remark that stock options may have negative results from the principal’s

point of view because the option holder makes gains if the company’s stock price goes

up but does not suffer a loss if the company performs badly. So, options can create

incentives for managers to take risks they would not otherwise take (Hull 2018). As a

result, shareowners may be worse off by this managerial risk behavior.

Core, Holthausen and Larcker (1999) found a correlation between agency problems and

CEOs compensations suggesting that inadequate compensation package may encourage

managers to pursue private goals. So, compensation is an important tool to motivate

managers to work for the best interests of the company.

Frierman and Viswanath (1994) showed how leverage can prevent managers from

wasting firm’s resources and force them to run the company efficiently. Indeed, leverage

mitigates the free cash flow problem theorized by (1986) by reducing excess cash on

hands. Free cash flow is defined as the liquid resources in excess of what is needed to

fund all positive net present value investments. In presence of high level of free cash flow,

the agency problems between shareholders and mangers may be severe. It may be difficult

for shareholders to prevent managers from using the cash for their benefits (e.g. by empire

building). But, high levels of debt force the firm to make periodic interest payments to

creditors, thus obligating managers to be cautious on wasting resources.

For a similar reason, an increase in dividends payout decreases the firm’s free cash flow

limiting the manager self-serving behavior (Jensen 1986; Park 2009).

Many papers state that independent directors are more likely to act in the interest of

shareholders since they watch better the managers’ actions than dependent directors and

help in making the alignment of the interest among owners and managers (Wyatt and

Rosenstein 1990).

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Takeover threats usually reduce agency costs because the managers, fearing of being

replaced, tend to put more effort in running the business efficiently. Otherwise, the

company would be underpriced and become an easy target for a takeover (Kini, Kracaw

and Mian 2005).

Monitoring is fundamental for facilitating the alignment of managers and shareholders’

interests. This paper focuses mostly on this shareholders’ activity. The literature shows

the incentives of blockholders to supervise the company activities and managers’

behavior (e.g. Shleifer and Vishny 1986; Fich, Harford and Tran 2015; Panda and Leepsa

2017; Burkart, Gromb and Panunzi 1997). Large shareholders are more willing than

smaller shareholders to closely watch the actions of managers. They have enough stake

in the company to justify the monitoring costs or the efforts to promote a change in the

management. They will be engaged in value-increasing activities as far as the costs are

smaller than the profit they would earn from the price appreciation. In other terms, based

on the tradeoff between benefits and costs, blockholders supply the optimal amount of

monitoring. The large shareholders’ actions benefit every stockowner since they enjoy

gains on their own shares while only the monitors bear the costs. This may result in an

amount of performed monitoring that is lower than the one that would be ideally required

to fully maximize the firm’s value. This theory is confirmed by Shleifer and Vishny

(1986) who point out that, although the extremely positive effects, there is too little

monitoring, and this is due to the small gain of monitors to engage in such expensive

activity. Indeed, their gain is limited to the price appreciation.

The incentive of large shareholders to make sure the company is run efficiently is one

of the solutions2 to the free rider problem mentioned above in paragraph 2.1.2. According

to Shleifer and Vishny (1986), in a company with only atomistic shareholders, there will

be no one willing to absorb the costs to improve the company. Shleifer and Vishny (1986)

based their reasoning on the work of Grossman and Hart (1980) who claim that the

takeover bid mechanism is not the solution to the free rider problem in a widely dispersed

2 Grossman and Hart (1980) provide another solution to the free rider problem. Because the rider bears all

social costs but obtain only a small part of social benefits as small shareholders have no incentives to tender

their shares, there may be less takeover then should take place. Therefore, in absence of the takeover, the

management will be not replaced, and the company will keep on being run inefficiently. However, one way

to promote takeover activities and thus mitigate the free rider problem is to permit raiders to exclude

shareholders who do not tender their shares from sharing all the profits brought about by the raider. One

solution, proposed by the authors, is to allow the raider to sell assets or engage in commercial activities

with one company owned by her at advantage conditions from the bidder’s point of view. In this way, the

raider receives more than the sole asset appreciation while remaining shareholders suffer a “voluntary

dilution of their property rights”. In their article, Grossman and Hart (1980) found the optimal amount of

dilution for the society and for shareholders.

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ownership company because no raider would have interest in making the takeover bid.

This occurs because each shareholder may have so few shares that she may think that she

does not compromise the raid by not tendering the shares. Furthermore, if the shareholders

think that the raider will succeed in taking over the company and will be capable of

improving its performance, they will be reluctant to tender the shares since they would

earn more from the price appreciation. As a result, no one will be willing to tender their

shares at a price lower than that they would obtain if the company was run efficiently.

The tender bid results unprofitable from the bidder’s point of view since the takeover

costs exceed the benefits even if there is room for improvement and the current

management is not acting in the shareholder’s interest. Shleifer and Vishny (1986) argue

that for similar reasons, atomistic shareholders do not have enough stake in the company

to justify the monitoring costs or the costs to replace the managers as large shareholders

do.

2.2. Shareholders’ Influence, Firm Performance, Corporate Governance

In the previous paragraph it was explained that if a shareholder has large enough stake in

the company, she will do some monitoring of the incumbent management or engage in

value increasing activities if the expected benefits exceed the relevant costs.

In the following subparagraphs, the different types of shareholders, their motives and

their monitoring effects on corporate decisions will be discussed. In the subparagraph

2.2.1, the beneficial monitoring effects of both large shareholders (Shleifer and Vishny

1986) and monitoring institutions (Fich, Harford and Tran 2015) will be described.

Section 2.2.2 explains how hedge fund activism may reduce agency problems of the target

company providing evidence of two of the most important works in the literature: Brav,

et al. (2008) and Klein and Zur (2009). Finally, section 2.2.3 presents a debate about the

influence of passive investors on corporate governance and firm value based on the

studies of Appel, Gormley and Keim (2016) and Schmidt and Fahlenbrach (2017).

2.2.1. Large Shareholders and Monitoring Institutions

In this section, the beneficial effects of blockholders and monitoring institutions will be

discussed in detail. Two articles are particularly interesting: Shleifer and Vishny (1986)

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and Fich, Harford and Tran (2015). The first paper uses a mathematical approach while

the second uses a statistical approach to corroborate the beneficial effect of institutional

ownership.

Shleifer and Vishny (1986) mathematically demonstrated the positive relationship

between proportion of shares held by large shareholders and performance of the

corporation. They found that the higher the number of shares held by large shareholders,

the more likely a takeover becomes and the greater is the increase in the market value of

the firm. According to their model, only large shareholders increase the value of the

company through takeovers. The higher profits, brought about by large shareholders

through their monitoring activities, give also an explanation of the popularity of dividend

paying firms. Different tax regimes apply to small shareholders (generally retail

investors) and large shareholders (usually institutional investors). The former prefers

capital gains to avoid dividend tax, while the latter prefer dividends. The advantages

accomplished by the large shareholder could be so high that small shareholders may

permit the payments of dividends even if not convenient for them, just to make sure that

the blockholder remains in the firm (Shleifer and Vishny 1986).

Fich, Harford and Tran (2015) studied institutional investors with multiple holdings in

different companies. They argue that institutional investors are better off by paying closer

attention to firms whose performance can have a greater impact on their portfolio value.

An institutional investor, therefore, is more likely to focus its attention on the largest

holdings of its portfolio. In contrast with the study of Shleifer and Vishny (1986), most

recent literature indicates that investors who hold an important block of the company’s

equity might not be willing to allocate resources in monitoring it. Indeed, the specific

firm’s shares may be a small part of the assets of the investor’s portfolio.

Therefore, unlike many papers that study the relationship between the fraction of shares

held by the largest investors in the company3 and the companies efficiency (e.g. Shleifer

3 Traditional ownership proxies are (Fich, Harford and Tran 2015):

- Number of blockholders – number of institutions whose ownership in the target is greater than 5%

of the shares outstanding.

- Proportion of blockholders – ratio between the number of blockholders and all institutions holding

the firm’s share.

- Ownership of blockholders – fraction of the firm’s stock owned by blockholders.

- Ownership of the five largest institutions – fraction of the firm’s stock owned by the five largest

institutional investors in the target.

- Ownership of the largest institution – fraction of the firm’s stock owned by the largest institutional

investor in the target.

- 16 -

and Vishny 1986), Fich, Harford and Tran (2015) introduce new proxies that investigates

the effects of institutional investors on corporate decisions: institutional ownership is

measured relative to the institution’s entire portfolio4. Then, their baseline assumption is

that institutions’ monitoring effort is proportional to the relative importance of the

specific firm’s stocks in their portfolio. Following this reasoning, they define “monitoring

institutions” those investors for which the value they hold in the firm is in the top 10% of

their portfolio. They proved that monitoring institutions are likely to engage in monitoring

activities that enhance gains for each shareowner by analyzing the merger and

acquisitions market. Monitoring institutions seem to play a role in the completion of the

deal. Moreover, their study shows that their proxies of institutional ownership (e.g.

ownership of monitoring institutions) better explains the effects of monitoring activities

rather than traditional ones (e.g. total ownership of blockholders).

According to Fich, Harford and Tran (2015) findings, a one standard deviation increase

in ownership by monitoring institutions increases the chances of deal completion by 6%.

This seem to demonstrate that monitoring institutions are interested to step in for

facilitating the completion of the acquisition bid. They may vote in favor of the operation,

force the existing managers not to take defensive measures and/or tender their own shares

to the bidder. The last hypothesis agrees with Shleifer and Vishny (1986) who claim that

even if important investors have no influence over the incumbent management, they still

can facilitate takeovers by sharing the huge gains with the bidder.

Not only institutional investors’ holdings affect positively the odds of deal competition,

but also the probability of upward bid revision. Fich, Harford and Tran (2015) logit

regressions show that the odds of upward bid revision increase by more than 5% if the

fraction of shares held by monitoring institutions increase by one standard deviation. The

higher premiums suggest that the activity of these investors benefits every shareholders

of the targets.

If monitoring institutions are able to influence the premiums favoring target

shareholders, the effect on bidder shareholders will be negative. It has to be expected that

4 The proxies of institutional ownership, that takes into account the relative importance of the firm’s stock

in the investor portfolio, introduced by Harford and Tran (2015) are:

- Number of monitoring institutions – number of institutions whose holding value in the target is in

the top 10% of their portfolio.

- Proportion of monitoring institutions – ratio between monitoring institutions and all institutions

holding the firm’s share

- Ownership of monitoring institutions – fraction of the firm’s stock owned by monitoring

institutions.

- 17 -

the synergies that will be created after the merge are uncorrelated to the monitoring

institutions bargaining for higher premiums. Indeed, the synergies depend on operational

fit between the bidder and the target, and are not affected by the monitoring institution

action. As a result, the bidder will pay more for the acquisition than she would otherwise

do in absence of monitoring institutions, independently on what are the expected gains

from synergies. The authors demonstrate this theory by showing that acquirer returns

around the announcement date are lower when investors have incentives to monitor the

target firm. In other terms, a one standard deviation increase in monitoring institutions’

holdings leads to 0.62% decrease in bidder’s returns.

But how blockholders or monitoring institutions exert their influence over managers

and directors? They influence managerial decisions through the use of two powerful tools:

“voice” and “exit” (Edmans 2014; Hirschman 1970). Voice refers to the direct

involvement of blockholders in firms’ activities (e.g. proxy voting, phone calls or private

letters to managers and directors). Exit refers to the threat of selling shares if managers

underperform (i.e. the “Wall Street walk”). Managers exposed to the potential loss of

compensation, in case a massive sale of their company shares pushes down their price,

will be incentivized to act in accordance with blockholders interests and maximize the

firm value.

2.2.2. Activist Investors

This paragraph discusses whether activists are beneficial for the other shareholders.

An activist stockholder can be defined as an investor who buys a large stake in a

publicly traded firm for the purpose of influencing corporate boards, senior executives

and other investors by engaging the company directly, taking legal proceedings or via

proxy fights and public campaigns (Lin 2015; Webber 2012; Klein and Zur 2009). The

intention of the activist is, therefore, to bring about a substantial change in the

management of the target company to maximize the return on the investment. Of

particular interest to the activism literature are the works of Brav, et al. (2008) and Klein

and Zur (2009).

In contrast with previous studies showing that institutional investors (particularly

pension funds and mutual funds) who pursue an activist campaign do not bring significant

benefits to shareholders (e.g. Gillan and Starks 2007), the work of Brav, et al. (2008)

- 18 -

demonstrated that hedge fund activism has a positive impact on target shareholders’

wealth. According to the authors, the difference in activism effects among different types

of institutional investors is primarily due to the regulation that governs mutual funds,

pension funds and hedge funds. Unlike mutual funds and pension funds, hedge funds are

not subject to strict regulations forcing them to highly diversify their portfolio. The less

stringent regulation allows hedge funds to invest in a smaller number of companies

compared to traditional funds, and to use leverage or derivatives to extend their influence.

By concentrating their investments in few firms, hedge funds are more aware of how

companies they invested in are run than mutual funds and pension funds. As a result,

hedge funds are more likely to engage in monitoring activities and to influence managerial

decisions in the interest of each shareholder rather than other institutional investors. A

proof of what just said is the tendency of hedge funds to target small-cap firms: it is

cheaper to obtain a significant stake in smaller firms.

Brav, et al. (2008) as other papers (e.g. Klein and Zur 2009) define the beginning of an

activist campaign as the filing of the Securities and Exchange Commission (SEC)

Schedule 13D. Investors are required to submit the Schedule 13D within ten days if they

acquire, directly or indirectly, more than 5% of the ownership of any publicly held

corporation with the intent of influencing the firm’s policies. The authors’ research

indicates that the markets react positively to announcement of activism performed by

hedge funds. In details, targeted firms experience economically significant positive

average abnormal returns of more than 7% around the announcement date of an activist

investment that requires the filing of the Schedule 13D. In addition, these abnormal

returns do not reverse over time since there is no a significant decrease in price in the year

the follows the announcement of the activist campaign. These results imply that investors

perceive activism to be value-increasing.

Moreover, they have shown that the market expectations for an improvement in the

performance of the target company after the activist intervention are correct. After the

announcement, target firms’ payout and book leverage tend to increase, thus, reducing

the free cash flow under the control of the management. This is consistent with a reduction

of agency problems: managers have less opportunities to pursue private interests. Other

performance indicators such as EBITDA/Assets and EBITDA/Sales of target firms

significantly increase after two years, and the probability to be downgraded by rating

agency decreases after the announcement. Another proof of a reduction in agency

problems provided by the study of Brav, et al. (2008) is the increased CEOs turnover rate

- 19 -

the year after the intervention is announced. Only the fear to be replaced could be enough

for aligning the interests of managers and shareholders.

The study of Klein and Zur (2009) further explores the research on hedge fund activism

by focusing on those campaigns that can be characterized as aggressive or

confrontational. For this purpose, the authors exclude from their analysis all the activist

campaign in which the investor is willing to collaborate or communicate with the target’s

managers regularly. As the study of Brav, et al. (2008), Klein and Zur (2009) associate

the announcement of hedge fund activism with the filing of an initial Schedule 13D that

requires the investor to state their intentions to affect the target’s policies.

Klein and Zur (2009) found that hedge fund activism is followed by an average

abnormal stock return of more than 10% in the target. This return is 46% higher than

those found in the study of Brav, et al. (2008). In line with Klein and Zur (2009) findings,

not only hedge fund activism, but also campaigns led by other institutional investors seem

to have a positive impact on shareholders’ wealth. However, campaigns led by hedge

funds seem to create more value to investors. Targets of non-hedge-funds activist, indeed,

exhibit a positive abnormal return of more than 5% on average around the announcement

date. Moreover, the authors demonstrate that the hedge fund targets’ abnormal returns do

not reverse but persist in the subsequent years after the activists’ intervention. The

average abnormal return is more than 11% for hedge fund’s targets and almost 18% for

targets of other institutional investors’ intervention the year after the Schedule 13D filing.

These findings are consistent with those of Brav, et al. (2008) cited above.

Activism seems to be a powerful tool for aligning management’ interests with those of

owners since more than 60% of the times incumbent management meets the demands that

the activist listed in the Schedule 13D in the sample used by Klein and Zur (2009).

The authors provide further evidence of the willingness of hedge funds in addressing

agency problems and in particular the free cash flow problem (Jensen 1986). In contrast

with other activists, hedge funds are usually to target “profitable and financially healthy

firms”. This is a peculiar characteristic of hedge funds since other activists tend to invest

in companies with poor performances (Becht, et al. 2009). Instead of demanding changes

in the investment and operating strategies as other entrepreneurial activists do, hedge

funds demand changes in CEO’s salary, dividend payout policies and capital structure

policies because they are interested in reducing the cash at management’s disposal and

thus the agency costs. The results of Klein and Zur (2009) are consistent with this

hypothesis since the targets of hedge fund’s activism are likely to substantially raise

- 20 -

dividends, greatly decrease the cash and short-term investments, and significantly

increase leverage (e.g. debt to assets ratio) in the year following SEC Schedule 13D filing.

In contrast with previous evidence on activism (e.g. Bebchuk 2005, 2007), Klein and

Zur (2009) found that hedge fund activists pursue their goals and reduce agency problems

by strengthening their representatives in the board of directors through the use of proxy

fights. Many times, threatening a proxy fights is more than enough for obtaining one or

more board seats that allows the activist to gain sufficient power over the management

for reaching their goals.

2.2.3. Passive Institutional Ownership

So far, we have presented evidence that activists are able to influence firm’s governance

by accumulating a high number of shares. By doing this, the investors gain enough power

to address the agency problem and correct eventual inefficiencies when managers

perform poorly. In this section, we will discuss whether passive institutional investors,

that hold a growing ownership of U.S. stocks, influence firms’ governance and

performances.

In contrast with activists (especially hedge funds) who invest in a small number of

companies based on current information and forecasting techniques with the intention of

obtaining higher returns relative to the market, passive investors hold a more diversified

portfolio that mimics the performance of some benchmark. The underlying assumption

of a passive strategy is the efficient market hypothesis (Fama 1970) implying that stock

prices reflect all available information. If the markets are efficient, it is impossible for the

investor to earn above-average returns without accepting above-average risks after

adjusting for management fees and transaction costs (Fabozzi and Pachamanova 2016;

Malkiel 2003). Passive portfolio strategies are also called indexing because they are

benchmarked to some market index (e.g. Standard and Poors (S&P) 500, Russell 3000

Index or the Barclays Capital U.S. Aggregate Bond Index).

Because the passive investor tries to obtain returns the most similar to those of the

benchmark, the trade universe – the set of assets that are under scrutiny for investment –

tend to be the same securities comprised in the benchmark (Fabozzi and Pachamanova

2016).

- 21 -

Based on what said above (e.g. Shleifer and Vishny 1986; Fich, Harford and Tran 2015;

Brav, et al. 2008; Klein and Zur 2009), someone could argue that passive investors lack

the incentives and the resources to engage in management monitoring activities. The lack

of incentives and resources is due to the high diversification of the portfolios they manage.

They have less incentives to oversee companies’ activities both because the number of

shares they own in a single company could be so low that the benefits from monitoring

activities do not justify the corresponding costs, and because, in contrast with activists,

passive portfolio managers do not attempt to outperform the market but to match the index

performance. Thus, passive portfolio managers will be not blamed if the fund yields poor

performances as far as the returns will not deviate from those of the benchmark. The

market will be blamed in their place. In other terms, passive investors are responsible to

eliminate (or reduce) the idiosyncratic (or unsystematic) risk but not the systematic (or

market) risk that cannot be diversified away.

Finally, passive investors are believed to lack resources because they have no time to

supervise most of the companies they invest in given the high diversification.

The Economist (2015)5 confirms this view. In contrast with activists that bring

improvements more often than not (e.g. increase in profits, capital investment and R&D),

passive investors are described as “lazy investors” looking for “lazy money” and having

“little interest in how firms are run”. According to this opinion, the presence of passively

managed funds, by not getting involved in firms’ management, weakens companies’

governance and performance.

However, Booraem (2013)6, the controller of the Vanguard Group’s funds explicitly

expresses a different opinion in an article published in the company website. According

to him, even if it is a passive investor, Vanguard does not passively own the companies

it acquires. The controller claims that, as major and long term (if not permanent) holder

of companies comprised in the index chosen as benchmark, Vanguard has a huge interest

in engaging in value increasing practices from the investors’ point of view, focusing

especially on poor performing companies. This is done by making a good use of proxy

voting at shareholder meetings, and by directly engaging with managers and directors.

According to Booraem (2013), the difference between activists and passive investors is

the approach to the agency problem, not whether they address the issue. Instead of

5 Link to the article: https://www.economist.com/leaders/2015/02/05/capitalisms-unlikely-heroes 6 Link to the article: https://global.vanguard.com/portal/site/institutional/ch/en/articles/research-and-

commentary/topical-insights/passive-investors-passive-owners-tlor

- 22 -

activism that is “noisy”, passive investors approach managers and directors quietly

through the use of diplomacy and not of court litigation. This may explain why The

Economist (2015) perceives passive investors as “lazy”. Unfortunately, it may difficult to

verify whether passive investors engage in this activity when they claim to do it so

silently, careful not to draw attention.

Carleton, Nelson and Weisbach (1998) agrees with Booraem (2013) stating that,

because passive investors buy and sell shares less frequently, they may hold shares in

inefficient companies for a long period of time, performing some monitoring and forcing

them to improve their value.

Guercio and Hawkins (1999) provide another reason in favor of Booraem’s theory

“passive investors, not passive owners”. Passive investors are interested in successfully

mitigating agency problems by monitoring because this would increase the value of the

assets under management, thus benefitting both the prestige and compensation of the fund

managers.

Important pieces of literature that deal with the effects of passive ownership on

corporate performance and governance are Appel, Gormley and Keim (2016) and

Schmidt and Fahlenbrach (2017).

Appel, Gormley and Keim (2016) study the impact of passive investors on firms’

governance and performance by exploiting the variation in ownership by passive mutual

funds that occurs around the Russel 1000 and Russel 2000 cutoff. In this way the authors

obtain a causal relation. In details, the authors have verified whether passive investors

funds are able to (1) guarantee the independence of the board of directors, (2) remove

antitakeover measures and (3) reduce unequal voting rights (e.g. dual class share

structure). The authors choose to focus on these aspects since they are considered the

common objectives of passive investors’ policies.

The results of Appel, Gormley and Keim (2016) show that passive institutional

investors’ ownership has an economically important impact on these aspects of corporate

governance. First, the increase in ownership by passive funds leads to a greater board

independence. Second, the authors demonstrate that passive investors are likely to remove

takeover defenses: poison pills are more likely to be removed and shareholders have more

possibilities to call for a special meeting when passive ownership becomes larger. Third,

dual class share structure becomes less likely as passive ownership rises. These results

suggest that shareholders benefit from passive investors’ activities because independent

directors are more likely to be effective monitors (Fama and Jensen 1983), takeover

- 23 -

defenses such as poison pills make managers removal more difficult when the company

is run badly, and unequal rights represented by dual class share structures is considered a

powerful takeover defense that have a negative impact on the firm value (Klausner 2013;

Gompers, Ishii and Metrick 2010).

Active investors obtain the cited above results via the power of their large voting rights

(i.e. voice, paragraph 2.2.1). Because passive investors cannot always sell the shares of

underperforming companies given their objective to match the benchmark performance,

they cannot discipline managers by threatening to trade the shares in the secondary market

(i.e. exit, paragraph 2.2.1), so their influence is limited to voice. The authors proved that

passive investors exert influence via voice by verifying that larger passive ownership is

associated with less support for management proposals and higher support for governance

proposals. The tendency of voting against management proposals and for governance

proposals shows the willingness of passive investors to address agency problems by

exercising voice.

Finally, Appel, Gormley and Keim (2016) found that active investors have a positive

influence over the overall performance of the firm. Longer-term passive ownership is,

indeed, associated with significant improvement in firms’ return on assets (ROA) and

Tobins’s Q (the ratio between total market value and total book value). The authors’

results indicate passive investors are effective monitors, capable of enhancing firms’

value by focusing on corporate governance issues and through their ability to exercise

voice.

In contrast with the work of Appel, Gormley and Keim (2016), the most recent literature

proved that passive ownership is not beneficial to shareholders because, it intensifies the

agency problem instead of mitigating it. The paper of Schmidt and Fahlenbrach (2017)

provides evidence that an increase in passive ownership increases agency costs.

As opposed to the 2016 paper, Schmidt and Fahlenbrach (2017) claim that passive

investors cannot exercise the only two mechanisms through which large institutional

investors can affect corporate governance decisions: voice and exit. Schmidt and

Fahlenbrach (2017) agree with Appel, Gormley and Keim (2016) in relation to the exit

option, that is impracticable for index-tracking institutions whose compensation and

evaluation depends on a tracking error7 that penalizes all deviations of the funds returns

7 The tracking error is the parameter used to evaluate the risk of index funds. The tracking error is the

standard deviation (σ) of the portfolio deviations from the benchmark. It goes to zero as the manager holds

- 24 -

from those of the benchmark. Managers of passive funds, then, cannot threaten lazy

managers to sell their stake in the company because they are more worried about matching

the returns of the index. The authors, however, disagree with regard to the second

mechanism. According to Schmidt and Fahlenbrach (2017), the voice channel, indeed, is

not likely to be exercised by index-tracking institutions because it is too expensive to

actively interact with the managers. This would especially happen for the passively

managed funds that keep commission costs very low. The evidence provided by the

authors suggest that an increase in passive ownership is associated with fewer

shareholders’ proposals, indicating that the passive investors are less likely to exercise

voice through the initiation of shareholders’ proposals.

By using exogenous changes in passive ownership driven by the annual reconstitution

of the Russel 1000 and Russel 2000 indexes to establish causality as in Appel, Gormley

and Keim (2016), Schmidt and Fahlenbrach (2017) show that not only passive investors

seem to approach managers less often, but can be responsible for value-decreasing

changes in corporate governance. Indeed, the authors’ findings suggest that a CEO is

likely to gain power when passive investors acquires more shares since the likelihood for

the CEO to became president increases significantly as passive institutional ownership

rises. Then, they tested whether the fraction of independent directors varies after changes

in the stake of the company held by passive investors, and whether the appointment of

new independent directors is more likely when passive ownership gets larger. The results

show than even if changes in passive ownership have no statistically significant impact

in the independence of the board, an increase in passive ownership leads to fewer

independent directors. This may indicate that the CEO consolidates its power, thanks to

a loosening monitoring constraint of passive owners, by influencing the selection process

of board members. As a result, fewer independent directors get appointed (Shivdasani

and Yermack 1999).

more and more of the assets in the benchmark. Higher values correspond to worse manager performances

in replicating the index returns. Given Δt the difference between the return of the portfolio and the return

of the benchmark at time t and Δ̅ the arithmetic average of Δt, the tracking error (TE) is computed as follows

(Fabozzi and Pachamanova 2016):

TE = σ∆ = √1

T∑(Δt − Δ̅)2

T

t=1

- 25 -

Moreover, the findings of Schmidt and Fahlenbrach (2017) suggest that shareholders

are worse off by the changes in corporate governance brought on by an increase in passive

ownership. The markets, indeed, react more negatively when CEO gains power and when

new directors are appointed in firms with more passive owners. These results imply that

the corporate governance changes achieved by passive investors are value-destroying.

Finally, the authors show that managers are likely to extract private benefits by

undertaking more value-decreasing mergers and acquisitions (M&A) when passive

ownership increases. Following the prediction of Jensen (1986), that claims that managers

are more likely to affect shareholders by engaging in empire building when agency

problems intensify, Schmidt and Fahlenbrach (2017) found evidence that cumulative

announcement returns to mergers and acquisitions drop after exogenous increases in

passive ownership.

Schmidt and Fahlenbrach (2017) claim that their results are not inconsistent with those

of Appel, Gormley and Keim (2016) since they focus on different aspects. The evidence

provided by the former authors show that the passive institutions might have not the

resources to monitor complex value-reducing actions performed by managers, while the

evidence provided by the latter implies the ability of passive investors to positively impact

simple aspects of corporate governance. The monitoring costs to supervise board

appointments and prevent value-destroying mergers and acquisitions may be much higher

than the monitoring costs of guaranteeing the independence of the board of directors,

removing antitakeover measures or reducing unequal voting rights. Therefore, passive

investors might be effective monitors for supervising low-surveillance-cost governance

activities, but they are inadequate when it comes to high-surveillance-cost governance

activities.

However, the miscellaneous effects of passive ownership on corporate governance and

firms’ performances raise questions on the effective capability of index tracking funds to

reduce agency problems.

2.3. Limited Shareholders Attention

So far, we have examined the literature that explains the role of attentive shareholders in

reducing agency costs, thus enhancing firms value and shareholders’ wealth. Hereafter

we will discuss the implications of limited shareholder attention on corporate finance

- 26 -

following the work of Kempf, Manconi and Spalt (2017). The purpose of their paper is

to find an answer to the open question highlighted by Bakera and Wurglerb (2012) in

their update survey on behavioral corporate finance: “What is the impact of investor

inertia and limited attention on corporate finance?”.

The paper of Kempf, Manconi and Spalt (2017) is not the first work that addresses the

question. Hirshleifer and Teoh (2003) examine the consequences of limited investor

attention for information disclosure and financial reporting, providing evidence of the

managers interest to manipulate the investors’ perception by taking advantage of their

distraction. The papers of Teoh, Welch and Wong (1998a) and Teoh, Welch and Wong

(1998b) found similar results.

Kempf, Manconi and Spalt (2017) base their study on the principal agent problem

discussed in the first paragraphs of this chapter (Jensen 1986). Monitoring activities by

shareholders limit the managers’ self-serving behavior that may cause reductions in firm

value. The purpose of the research of Kempf, Manconi and Spalt (2017) is slightly

different from the previously cited works (e.g. Fich, Harford and Tran 2015). Instead of

identifying the group of shareholders willing to supervise managers and verifying

whether their monitoring activities has positive effects on firm performances, the last

authors study the same issue the way around. They wonder whether the absence of

shareholder monitoring encourage managers to maximize private benefits even at

shareholders’ expenses.

In the paper of Kempf, Manconi and Spalt (2017), monitoring decreases when

“institutional shareholders shift their attention away from the firm”. Institutional investors

draw the focus of their monitoring away from a firm because of their limited resources:

they cannot spend the optimal monitoring capacity with every stock in their portfolio. In

other terms, monitors face an optimization problem: they need to maximize the benefits

of their limited monitoring resources by allocating the optimal amount of attention to each

firm in their portfolio. This implies a shift of shareholders attention: away from firm 1

and towards firm 2 when there is a positive marginal benefit in monitoring firm 2 instead

of firm 1. In line with Barber and Odean (2008), the attention-grabbing events that make

them evaluate to move attention from one company to another are industry shocks, like

extremely positive or very negative industry returns. For example, if firm 2 experienced

a shock (either positive or negative), shareholders will give more attention to it. Because

the overall attention is limited, at the same time they will provide less monitoring to firm

1. As a result, the reduction of supervision in firm 1 gives managers the opportunity to

- 27 -

maximize private benefits. In other terms, the severity of the agency problem intensifies

in firm 1.

This reasoning can be summarized in the Distracted Shareholder Hypothesis that states

as follows: “If institutional shareholders shift attention away from a firm, this loosens

monitoring constraints and managers have greater leeway to maximize private benefits”

(Kempf, Manconi and Spalt 2017). This thesis has one underlying assumption. When one

shareholder diverts attention from a firm, her reduction in supervision is not costlessly

and instantaneously substituted by other monitors such as other institutional investors or

the board of directors.

There are two reasons why the lack of investors’ attention might lead to value-

decreasing managerial actions. First, the distraction of one institutional investor could be

observable by managers who takes advantage of the lack of monitoring by initiating

private benefit maximizing projects. This assumption seems reasonable considering that

managers could use the expertise of investor relations (IR) departments to be aware of

investors’ distraction. Moreover, managers could detect shareholder distraction either

from fewer investors direct interventions (“voice”, such as fewer conference calls, less

stakeholder’s requests for direct communications and meetings), or from a minor use of

the threat of “exiting” the company. Managers can sense the loosening of investors’

attention not only by noticing a minor use of said two control mechanisms (voice and

exit), but also by observing that investors are paying particular attention on industries that

are either booming or facing difficulties (e.g. tech industry during the Dotcom Tech

Bubble in the late 1990s and financial institutions during the financial crisis in

2007/2008).

Second, agency problems could worsen even if managers do not directly observe the

shareholder distraction. This could occur because distracted shareholders may not notice

the self-serving managers’ behavior, and hence may not even try to prevent value-

destroying deals. As a result, distraction has negative consequences on shareholders’

wealth regardless of managers’ awareness.

Overall, the authors associate shareholder’s distraction with attention-grabbing events

(i.e. shocks) that force to shift their attention away from a firm. In the next subparagraph

we will explain in detail how the authors measure the shareholders’ distraction level.

- 28 -

2.3.1. Distraction Measure

Distraction, denoted by D, is the main variable in most analysis carried out in this paper.

It is a firm-level proxy of the level of distraction of all institutional investors of a firm f

in a given period q. The variable D is constructed in a way that higher values imply

shareholders are more distracted and therefore they are less likely to monitor firm f.

As mentioned above, a given investor i is assumed to be distracted in relation to the

activities of the firm f, operating in a given industry INDf , if there is an attention-grabbing

event in another industry (IND ≠ INDf), and if companies belonging to that other industry

IND are a big portion of investor i portfolio. The distracting event is assumed to be an

industry shock. Once computed the level of distraction of the investor i and the relative

impact that her distraction has on firm f, the distraction D is aggregated across all

investors of the firm to obtain the overall distraction of all institutional investors of firm

f.

In detail, the proxy distraction D for a given firm f and a quarter q is defined as (Kempf,

Manconi and Spalt 2017):

𝐷𝑓,𝑞 = ∑ ∑ 𝐼𝑆𝑞𝐼𝑁𝐷 × 𝑤𝑖,𝑞−1

𝐼𝑁𝐷 × 𝑤𝑖𝑓,𝑞−1

𝐼𝑁𝐷≠𝐼𝑁𝐷𝑓𝑖𝜖𝐹𝑞−1

[1]

where 𝐹𝑞−1 represents the set of all institutional investors of firm f at end of the previous

quarter q-1. IND is one of the twelve industry assigned by Fama-French; 𝐼𝑁𝐷𝑓 denotes

the Fama-French 12 industry in which firm f operates. 𝐼𝑆𝑞𝐼𝑁𝐷 captures the attention-

grabbing events (i.e. shocks) that occur in an industry different from 𝐼𝑁𝐷𝑓. Specifically,

it is a dummy variable equal to one if the industry IND (other than 𝐼𝑁𝐷𝑓) has the highest

or lowest return across all 12 Fama-French industries in given quarter q. Otherwise, 𝐼𝑆𝑞𝐼𝑁𝐷

is zero when firm f operates in the industry 𝐼𝑁𝐷𝑓 that experiences the shock at quarter q.

As previously mentioned, Barber and Odean (2008) justify the use of the indicator 𝐼𝑆𝑞𝐼𝑁𝐷

as variable capturing the event in which investor i employs her attention and energy on

industry IND. Both extremely positive and extremely negative returns can deviate the

investors’ attention from “quite” industries to more “turbulent” industries. The

explanation is the following. Release of important news about a company may result in

large fluctuations in the stock price. Release of vital news that affects stock price, then,

are likely to attract investors’ attention because their wealth depends on it. However, even

when a price movement is due to private and not public information, significant returns

- 29 -

will always catch the investors’ attention. Indeed, investors are likely to investigate the

causes of large changes in price. Examples of industry shocks that are likely to have

drawn the attention of many investors in the past are the financial crisis that affected the

banking sector in 2007-08, and the dot.com bubble (1998-2002) that impacted the tech

industry. Figure 1 shows that the computer industry reported a positive return of 13.3%

in only three months in the first quarter in 1999. The same graph highlights the dramatic

drop in the banking industry at the beginning of the financial crisis (e.g. negative returns

of 13.5% in the last quarter in 2007)8. These events are too important to go unnoticed. As

a result, it is reasonable to assume that they grabbed the attention of investors for a long

period of time. Because attention is a scarce resource, then, the monitoring in firms not

affected by such dramatic shocks would have been relaxed.

Finally, 𝑤𝑖,𝑞−1𝐼𝑁𝐷 measures the relative importance of the industry other than 𝐼𝑁𝐷𝑓 in

investor’s i portfolio. Specifically, it is the weight of industry IND in the holdings of

investor i. 𝑤𝑖𝑓,𝑞−1 estimates the relative importance of firm f in the portfolio of investor i

at time q-1. In line with the large shareholder and monitoring institutions literature

illustrated in paragraph 2.2.1 (Shleifer and Vishny (1986); Fich, Harford and Tran 2015),

the distraction measure D takes into account the differences between institutional

investors, their holdings and their motivation to monitor. On one hand, it is reasonable to

assume that the institutional investor i is more motivated to oversee the firm’s f operations

the higher the relative stake of firm f in her portfolio. On the other hand, firm f is more

likely to please investor’s i demands the higher the ownership of investor i in firm f. As a

result, Kempf, Manconi and Spalt (2017) “give more weight to investor i if firm f has

more weight in i’s portfolio, and if i owns a larger fraction of firm f’s shares”.

Therefore, the weight 𝑤𝑖𝑓,𝑞−1 is computed as follows:

𝑤𝑖𝑓,𝑞−1 =𝑄𝑃𝐹𝑤𝑒𝑖𝑔𝑡ℎ𝑖𝑓,𝑞−1 + 𝑄𝑃𝑒𝑟𝑐𝑂𝑤𝑛𝑖𝑓,𝑞−1

∑ (𝑄𝑃𝐹𝑤𝑒𝑖𝑔𝑡ℎ𝑖𝑓,𝑞−1 + 𝑄𝑃𝑒𝑟𝑐𝑂𝑤𝑛𝑖𝑓,𝑞−1)𝑖𝜖𝐹𝑞−1

[2]

In this case 𝑄𝑃𝐹𝑤𝑒𝑖𝑔𝑡ℎ𝑖𝑓,𝑞−1 is the quantile of the ratio between the market value of firm

f and the investor i’s portfolio value. It responds to the question “How much important is

firm f to investor i ?”. Similarly, 𝑄𝑃𝑒𝑟𝑐𝑂𝑤𝑛𝑖𝑓,𝑞−1 is the fraction of firm f’s stock owned

by the institutional investor i. It answers the question “How much important is investor i

8 This example is similar to those presented in Kempf, Manconi e Spalt 2017.

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to firm f ?”. The term in the denominator in [2] scales the weight 𝑤𝑖𝑓,𝑞−1 so that it adds

up to one.

Figure 1 – Performance of the Tech and Bank Industries, 1998-2011

This figure plots the daily adjusted closing price of the NASDAQ Computer Index (Symbol:

IXCO) and the NASDAQ Bank Index (Symbol: BANK). The former includes stocks of

NASDAQ-listed companies classified according to the Industry Classification Benchmark (ICB)

as Technology excluding Telecommunications Equipment. The latter includes securities of

NASDAQ-listed companies classified according to ICB as Banks. They should represent the

financial health of the overall tech industry and of the banking industry respectively.

Data source: Yahoo! Finance - http://finance.yahoo.com/

To sum up, the distraction measure D in [1] depends on the occurrence of shocks (in

terms of extremely large or low returns) in other industries, how much investors care

about the other industries and the likelihood that investors are relevant monitors.

Kempf, Manconi and Spalt (2017) performed various tests to access the ability of the

distraction measure D in measuring the actual institutional investors’ distraction. First,

they provide evidence that institutional investors are less likely to attend a conference call

when they are distracted. In other terms, there is a strong negative relationship between

conference call participation and the measure D. Second, there are fewer shareholder

500

1000

1500

2000

2500

3000

3500

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

NASDAQ Computer and bank index performance

IXCO Adj Close BANK Adj Close

- 31 -

proposals if investors are distracted, implying a reduction in firm’s involvement when D

is high.

2.3.2. Distraction Effects on Corporate Governance

The work of Kempf, Manconi and Spalt (2017) provides lots of evidence of the value-

destruction activities performed by managers while institutional investors are distracted,

showing the importance of monitoring activities for reducing agency problems.

First, they provide evidence that managers engage in empire building to maximize their

private benefits when they perceive a lack of monitoring from institutional investors.

Indeed, not only the likelihood to announce a merger increases but also the probability of

a diversifying deal significantly rises when institutional investors are distracted. This

offers a solid proof of the incentives for managers to act in their own interests when there

is a loosening of monitoring constraints since the literature suggests that diversifying

acquisitions are likely to increase managerial private benefits at shareholders’ expenses.

Managerial benefits from diversifying deals are, for example, gains in terms of prestige

and salary and a reduction of the risk of being replaced (Morck, Shleifer and Vishny

1990).

Second, the M&A deals are more likely to be value-destroying when institutional

investors are distracted. The results show that the announcement returns are much lower

when the shareholder distraction is high. Bidders announcement returns are 33% lower,

on average, if shareholders shift their attention away from the firm. Moreover, the

weighted average by market capitalization of bidder and target announcement return –

that indicates the synergies created from the deal – decreases when shareholders gets more

distracted. This is consistent with the view that the market recognizes the bad quality of

the deal immediately at the announcement date. Even in the long run, the cumulative

returns of the bidders are much lower if institutional investors lack their monitoring

activities prior to the announcement of the deal.

Institutional investors seem to influence less the corporate decision when they are

distracted through the exercise of the “exit” channel. Indeed, institutional investors are

less likely to sell their stake in the company when they are distracted if a bad M&A deal

is announced. This implies that the exit channel is not used to prevent value-decreasing

activities. Finally, the authors provide evidence that managers prevent shareholders from

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getting notice of the deal by showing that firms with distracted shareholders are more

likely to issue less (especially less than 20% of) new shares for the competition of the

deal. Managers are willing to issue less than 20% of new shares when they want to take

advantage of distracted monitors to maximize private benefits because most exchanges

require shareholders of the bidding firm to vote if the deal involves issuing more than

20% of new shares. As a result, shareholders may not get notice of the deal if less than

20% of new shares are issued.

As far as top executive’s benefits concerns, the CEO is more likely to have a higher pay

and longer tenure if shareholders are distracted. CEO gets more powerful and have more

opportunities to pursue private benefits even because the board of directors is weaker

when institutional investors’ monitoring is absent. Indeed, the presence of a dependent

board is more likely if shareholders are distracted.

Other evidence beyond the takeover market in favor of the Distracted Shareholder

Hypothesis relates to the likelihood to reduce dividends, the probability for top managers

and directors to receive lucky grants and the odds for CEOs to be fired.

As far as payout policies concern, consistent with the Free Cash Flow Theory cited

above (Jensen 1986), managers are more willing to pay less dividends in periods of

loosening in monitoring constraints. Managers take advantage of distracted shareholders

by not paying the excess cash to shareholders and, thus, gaining the opportunity to use

the funds for private benefits.

In reference to the stock options granted to top managers and directors, the likelihood

of receiving a lucky grant increases when shareholders are distracted. “Lucky grant” is

the stock option grants given to managers and directors when the stock price is the lowest

of the month (Bebchuk, Grinstein and Peyer 2010). Lucky grants benefit managers while

they unlikely benefit shareowners. Since distraction has a meaningful impact on the

probability of receiving a lucky grant, managers may have the opportunity and the

incentive to act immorally for increasing their wealth without the knowledge to

institutional shareholders.

Finally, Kempf, Manconi and Spalt (2017) show that bad performance has a weaker

impact on the probability of forced CEO turnover when shareholders are distracted. This

means that institutional investors are less likely to put pressure on directors for firing

managers after bad performance when they are willing to focus their attention to other

components of their portfolios.

- 33 -

Overall, firms with distracted shareholders underperform in the short and in the long

run in relation to peer companies with attentive stockowners. This indicates that managers

engage in private maximization activities that destroy value for shareholders. In order to

capture the effects of value-destroying actives other than M&A, the authors verified that

shareholder distraction has a meaningful negative impact on the short term and long-term

stock returns.

2.4. Hypothesis Development

In this paragraph we explicit the focus of this thesis and our empirical predictions. In this

paper, the empirical analysis is divided in two parts.

The first part studies the direct effects of investors’ distraction on corporate decisions.

We want to verify whether managers take advantage of distracted monitors by

maximizing private benefits at shareholders’ expenses. Therefore, the Distracted

Shareholder Hypothesis introduced by Kempf, Manconi and Spalt (2017) will be tested

by analyzing the takeover market, the payout policies and employee stock options in

situations in which institutional investors shift attention away from the target firm.

The second part studies the indirect effects of investors’ distraction on peer companies.

We hypothesize that the lack of monitoring constraints for one firm will have

repercussions for its peer firms. The latter firms are not usually the ones that made

shareholders distracted. Indeed, as we will see, we consider only close competitors of the

companies with most distracted shareholders. Therefore, peers are likely to operate in the

same industry of the competitor and unlikely to operate in an industry in which an

attention-grabbing event (i.e. a shock) occurred.

In other terms, the distraction of investors for one firm creates spillover effects on close

competitors. Specifically, peer companies may observe directly or indirectly the self-

serving behavior of managers and react accordingly. Because Kempf, Manconi and Spalt

(2017) proved that firms are more active in the takeover market and the deal is more likely

to destroy value for shareholders, we hypothesize that peer firms fear to be acquired even

if they are run efficiently. This theory will be tested looking at the cash holdings, payout

policies and leverage levels.

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2.4.1. Empirical Predictions: Direct Effects

In this part we want to provide additional evidence to the Distracted Shareholder

Hypothesis of Kempf, Manconi and Spalt (2017). Indeed, such Hypothesis is a pre

requisite to prove the Spillover effects that will be discussed later in details.

Monitoring and especially the monitoring activity performed by large shareholders (e.g.

institutional investors) is fundamental in reducing agency costs as shown largely in this

chapter. In contrast to retail investor, only large shareholders have the expertise and the

funds to do some monitoring. However, institutional investor may not have the required

resources to always monitor the target firms with the same intensity. Indeed, the Investor

Responsibility Research Center Institute (Goldstein 2011) documents that investors do

not have the time to monitor every stock in their portfolio as they wish by surveying 335

issuers of stock and 161 investors. For such reasons, shareholders are assumed to have

limited attention in supervising their holdings. Following the reasoning of Kempf,

Manconi and Spalt (2017), there some periods in which the monitoring intensity of

institutional investors may decrease because they are required to allocate attention in

other stocks of their portfolios. The attention-grabbing event that diverts institutional

investors’ attention is, then, assumed to be exogenous shocks in other industries in which

the investors have invested hugely. When investors divert their attention away from the

firm are said to be “distracted” in relation to the target firm. We expect that the lack of

monitoring constraints will have some negative effects on the policy of the firm from the

shareholders’ point of view. For example, it is likely that managers of firms with

distracted shareholders hold more cash and pay less dividends in order to use the funds

for private maximation benefits in line with the Free Cash Flow theory (Jensen 1986). In

addition, the conflict of interests between manager and shareholders worsened by a

loosening of monitoring constraints could give additional incentives for managers to

“cause their firms to grow beyond the optimal size” (Jensen 1986). These two

consequences of investors’ distraction on firm policies can be tested in the following

ways.

First, the propensity to cut dividends can be shown by relating a dummy variable

indicating a reduction in dividends with the distraction measure D. We expect the chances

of observing a dividend cut to increase when institutional investors are distracted.

Second, empire building motives can be proved by looking at the relationships between

takeover activity and the shareholders’ distraction. We expect to find similar results to

- 35 -

those of obtained by Kempf, Manconi and Spalt (2017): an increase in the propensity to

announce a merger when institutional investors are distracted. In addition, we want to

verify whether managers engage in more diversifying deals when they perceive a lack of

monitoring constraints. Diversifying deals are more likely to benefits managers than

shareholders. Managers benefit from job security since the business gets less risky while

shareholders are worse off because diversifying deals are likely to bring lower returns.

Shareholders can achieve the benefits of diversification themselves by holding a well-

diversified portfolio. In addition, shareholders may be hurt by diversifying deals since

managerial costs are likely to rise with the size of the company (Morck, Shleifer and

Vishny 1990; Berk and DeMarzo 2014).

Finally, we want to test whether the chances for managers and independent directors to

receive a lucky grant9 is affected by the level of investors’ distraction. Managers have

incentives to maximize the gains from the option by manipulating the information

disclosed to the market prior the option grant date (Chauvin and Shenoy 2001). We

expect managers are more likely to act immorally in order to increase their wealth when

they are less monitored. As a result, we should find an increase in the chances of receiving

a lucky grant when shareholders are distracted.

2.4.2. Empirical Predictions: Spillover Effects

In this section we focus on the role of distraction in influencing real policy changes at

peer companies. The goal of this thesis is to provide evidence of spillover effects10 caused

by distracted shareholders and study the reaction of competitors.

Based on the evidence provided by Kempf, Manconi and Spalt (2017), firms with

distracted shareholders are more likely to make acquisitions. The increased frequency of

takeovers within an industry may represent a threat to peer firms. Competitors of firms

with distracted shareholders may fear to be acquired and act accordingly. Competitors

may perceive the takeover threat both directly and indirectly. Directly by observing the

higher number of deal competition of peer companies, by participating at industry

association meetings, by interacting with common shareholders or by reading the news.

9 A lucky grant is the stock option grants given to managers and directors when the stock price is the lowest

of the month (Bebchuk, Grinstein and Peyer 2010). 10 The business dictionary defines a spillover effect as “A secondary effect that follows from a primary

effect, and may be far removed in time or place from the event that caused the primary effect”.

Link: http://www.businessdictionary.com/definition/spillover-effect.html

- 36 -

Moreover, peer firms may perceive the takeover threat indirectly even if the competitor

has not yet incurred in the takeover activity by verifying whether their institutional

shareholders are distracted. Managers of big companies are likely to be informed

regarding the corporate finance literature and they are presumed to know that a loosening

in monitoring constraints increases the opportunities for managers to maximize private

benefits through acquisitions. Therefore, even if they do not observe directly an increased

number of acquisitions, they may expect that competitors engage in empire building when

there is an attention-grabbing event (i.e. shocks in other industries) that shifts the

shareholder’s attention. As a result, we expect that peer companies fear of being taking

over when shareholders of close competitors are distracted. Then, high levels of

shareholder’s distraction have not only a direct effect on their own company, but also on

peer companies that fear to become the possible target for a takeover.

The perception of a takeover as a threat is plausible since managers are likely to be

replaced or to experience a reduction in their compensation if the takeover is successful.

Even before the completion of the deal, managers may fear that proxy fights end up with

the replacement of top executives in order to facilitate the acquisition. Indeed, it is

documented a positive relationship between the frequency of takeovers and directors and

CEOs turnover (e.g. Lel and Miller 2015; Harford 2003; Bebchuk, Fried and Walker

2002). Not only managers, but also employee may suffer from employment or wage

reduction. In addition, any takeover brings uncertainty in terms of work relationships,

business plans and rules within a corporation (Pendleton 2016). Therefore, the managers

of not yet target firms may rationally expect a rise in the probability that their firm will

be targeted, and that they may be fired when competitors are seen (or expected) to be

more active in the takeover market. It is important to remind that the takeover threat is

always caused by shareholders’ distraction of peer firms in this paper. Servaes and

Tamayo (2014) provide evidence that hostile takeovers have a strong impact on industry

peers such that the latter change their investment and financing policies after the control

threat. As a result, in line with Servaes and Tamayo (2014) we expect that the managers

of not yet target firms will respond to a takeover threat by bringing about real changes.

Gantchev, Gredil and Jotikasthira (2014) applied a similar reasoning in explaining the

incentives of managers to respond against activism threat.

Substantial literature has studied the spillover effects in the contexts of proxy

solicitations and takeovers threats. Fos (2017) shows that threat of proxy contests is likely

to affects firm policies that benefits shareholders. As far as the takeover market concerns,

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Song and Walkling (2000) proved that rivals of M&A targets experience abnormal returns

because of the increased probability of becoming a target. Servaes and Tamayo (2014)

showed that a hostile takeover is likely to be disciplinary to targets’ peer companies.

Industry peers respond to a takeover threat by reducing agency costs and making

themselves less attractive to the bidder. Another important piece of literature has studied

the effects of competitive threats on firm policies. Hoberg, Phillips and Prabhala (2014)

found that firms tend to cut dividends and hold higher cash balances when they fear strong

product market threats by rival firms. According to the authors, paying less dividends and

retaining liquid assets provide firms with the flexibility required to face competitive

threats when they actually occur.

However, there is no literature that links a weakening in monitoring constraints due to

investors’ distraction and changes in corporate policies in peer companies. Hence, the

goal of this paper is to fill this gap in the literature.

We define peer firms the companies that have high similarities in terms of goods and

services they provide. Following Hoberg and Phillips (2016), the closer the competitors,

the higher the number of similarities in the product description in the 10-K annual filings.

We expect to see real changes in corporate policies of close competitors of firms with

high levels of distraction (i.e. in the top quartile). We will prove this theory by looking

whether peer companies respond to the control threat by managing cash holdings,

dividend policies and leverage policies. We summarize our hypothesis as follows:

Spillover effects of shareholders’ distraction: “peer companies of distracted

shareholders’ corporations change their policy in order to avoid being taken over”.

In the following text we list our expectations about changes in cash holdings, payout

policies and leverage policies of firms threatened to be taken over by companies with

distracted shareholders. We will see that in each case the effects of takeover threat on

those policies can be ambiguous.

As far as the cash holdings concern, cash rich firms might be desirable takeover

candidates for several reasons. Jensen (1986), Shleifer and Vishny (1993) Palepu (1986)

provide motivations why firms with excess cash are more likely to be taken over. First of

all, large cash holdings of the target company make the leveraged buyout success more

likely. Second, cash rich firms can provide the acquirer with the resources to fund their

own projects. Finally, according to the Free Cash Flow Theory, high levels of cash are

- 38 -

likely to intensify the agency costs that decrease the overall firm’s value. Riders are likely

to target such firms because they are usually underpriced by respect to their potential

value. As a result, a raider can buy the stocks of a poorly performing corporation cheap,

replace the management and sell the stocks dear once the company has become more

efficient. However, there is a piece of literature that is in contrast with the scenario just

mentioned. Faleye (2004), Pinkowitz (2002), Bagwell (1991), Rege (1984) point out that

holding excess cash reduces the chances of becoming a takeover target because large

cashpiles signals low investment opportunities and provide the firm with anti-takeover

defenses. In details, excess cash allows companies to repurchase a big amount of their

own shares, thus increasing the stock price and making any acquisition more expensive.

Moreover, cash-rich firms can defend themselves against a takeover threat by acquiring

a competitor of the bidder and then filing an antitrust litigation. In this way, the bidder

has no longer the legal right to acquire the target. In addition, it is difficult to evaluate the

value of companies with high amounts of cash because they can pay out dividends,

repurchase shares or make investments that reduce such value. The uncertainty about the

value of a company increases the risk that a raider has to bear, thus reducing the potential

attractiveness of the acquisition. Finally, as mentioned before, Hoberg, Phillips and

Prabhala (2014) proved that firms are likely to hold more cash when they face competitive

threats because they need the flexibility to react more aggressively when the threat

materializes. According to the authors, an unstable environment requires low levels of

debt and high levels of cash. We can apply the same reasoning to the takeover threat: a

large amount of cash could be the necessary condition for implementing takeover

defenses. To sum up, targets may keep large amount of cash and be ready to use it for

implementing takeover defenses. They will use the cash as soon as the takeover threat

materializes.

However, the takeover threat represented by companies with distracted shareholders

can be assumed to be different from other takeover threats. Indeed, when investors are

distracted, the manager engages in empire building because she is seeking to maximize

her private benefits (e.g. job security) and not the firm value in the interest of

shareholders. As a result, bad acquisitions are likely to occur as documented by Kempf,

Manconi and Spalt (2017). Therefore, we can assume that managers are not necessarily

looking for taking over poorly performing companies. Any acquisition that can benefit

managers is likely to occur. Because managers are not looking for badly run companies

to take over when shareholders are distracted, target companies are not specifically

- 39 -

motivated to increase debt and reduce cash holdings to defend against the takeover threat

according to the Free Cash Flow Theory. This because low levels of cash may not be a

deterrence for takeovers. Managers will encourage acquisitions whether the target is run

efficiently or not. As a result, reducing cash could not be the solution to avoid of being

acquired by companies with distracted shareholders. By contrast, firms with large

stockpile of cash have the resources to implement the above-mentioned strategies that can

mitigate the chances of being taking over. For such reasons, we expect that that peer

companies should respond by holding more cash when they fear a takeover threat led by

companies with distracted shareholders.

Similar reasoning can be applied to leverage and dividend policies. In line with the Free

Cash Flow hypothesis, firms reduce agency costs and thus the probability of a takeover

by increasing the leverage and paying more dividends because that policy commits

managers to run the company proficiently, reducing the efficiency gains from a takeover

(Servaes and Tamayo 2014; Safieddine e Titman 1999; Berger, Ofek e Yermack 1997;

Hendershott 1996; Denis and Denis 1993). However, according to Novaes (2002),

companies that lever up after a takeover threat signals bad managers performance. In

addition, in line with Hoberg, Phillips and Prabhala (2014), unlevered companies that pay

less dividends are more flexible and have more resources to face threats. Finally, as

already mentioned, the probability of bad deals increases when shareholders are

distracted. Then, we can suppose, that making a company more efficient by levering up

and paying more dividends does not decrease the probability of acquisitions. As a result,

we assume that it is more likely that peer companies will respond to takeover threat by

reducing leverage and paying less dividends in line with Novaes (2002) and Hoberg,

Phillips and Prabhala (2014).

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3. Empirical Analysis

The first paragraph of this chapter describes the data sources. In the second paragraph,

some of the direct effects of institutional investors’ distraction will be tested. Finally, in

the third paragraph we are going to analyze the spillover effects of investors’ distraction

on the closest competitors’ financial policies.

3.1. Data Sources

The sample used for the analysis in the second paragraph of this chapter was constructed

following Kempf, Manconi and Spalt (2017). We start with the CRSP-Compustat sample

that contains 180,411 yearly firm observations from 1990 to 2017 relating to 19,290

individual firms. After eliminating duplicates, we merge the CRSP-Compustat dataset

with the Kempf et. al. (2017) distraction dataset found in the Professor Manconi’s

university website11. Because the latter dataset reports quarterly firm observations while

the CRSP-Compustat reports only yearly firm observation, we take the average of the

distraction measure over the four quarters of the year obtaining a yearly firm measure.

The distraction dataset contains 73,874 yearly-firm observations from 1980 to 2010

relating to 9,750 firms. After the merger our dataset reduces to 54,029 yearly observations

that relate to 8,087 firms from 1990 to 2010 (after dropping missing values). The

institutional ownership data was obtained from Thomson Reuters institutional holding

database.

The final dataset used for the payout policy analysis comprises 15,240 yearly

observations related to 3668 firms over the period 1999-2010.

Merger announcement data is obtained from the Thomson One database. We considered

only M&A operations with deal value greater than $1 million in which both the acquirer

and the target are American listed companies in the period 1990-2010 since the

Distraction dataset contains data only for such period. In this period 24,717 M&A

operations have been reported in the Thomson One database and the firms engaging in

such activities are 8,693. We obtain the Fama-French-12 industry classification Table in

the Professor Kenneth R. French data library. This information is required to determine

11 Link to website:

http://mypage.unibocconi.eu/albertomanconi/index.php?IdUte=196687&idr=28703&lingua=eng

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the diversifying mergers. The resulting merger and diversifying merger dataset comprise

18,083 yearly observations related to 3855 firms over the period 1999-2010.

The data about CEO and independent director “lucky” stock option grants is obtained

from Professor Lucian Bebchuk’s website12 (Bebchuk, Grinstein and Peyer, Lucky CEOs

and Lucky Directors 2010). The date on which the first price appears on CRSP is used in

the calculation of firm age. The resulting lucky grant dataset comprises 2569 individual

firms over the period 1999-2005.

As far the analysis in the third paragraph regards, the rival score is obtained from the

TNIC-3 database in the Professor Gerald Hoberg’ website13 (Hoberg and Phillips 2016;

Hoberg and Phillips 2010). This dataset is merged to the CRSP-Compustat-distraction

dataset. The resulting sample consists of 39,106 yearly observations related to 6,303

individual firms in the period 1990-2010.

3.2. Direct Effects of Shareholders’ Distraction

First, we will focus on the direct effects of investors’ distraction on corporate decisions

that may benefit firm’s managers at the expense of shareholders’ wealth, such as: the

propensity to announce an acquisition and especially a diversifying one, the likelihood to

cut dividends, and the probability that CEOs or directors will receive a “lucky grant”. We

will test, therefore, the Distracted Shareholder Hypothesis (Kempf, Manconi and Spalt

2017).

3.2.1. Merger Frequency and Diversifying Merger

There are several reasons why managers have incentives to grow their firms even at the

expense of shareholders’ wealth.

According to the “free cash flow” theory, cash-rich firms are more likely to grow

beyond their optimal size when there are low investment opportunities (Jensen 1986).

Managers have motives to grow their companies beyond the optimal size because

compensation is positively correlated with sales growth (Murphy 1985) and firm size

(Kostiuk 1990). As the firm grows, the manager has more resources under control,

12 Link to website: http://www.law.harvard.edu/faculty/bebchuk/data.shtml 13 Link to website: http://hobergphillips.usc.edu/industryclass.htm

- 42 -

increasing her power and prestige. Moreover, Shleifer and Vishny (1989) proved that

managers tend to increase their value to shareholders reducing the chance of being

replaced by making suboptimal investments in assets whose value is higher under them

than under the best alternative manager.

Table 1 – Summary statistics of the merger and diversifying merger sample

This Table presents the summary statistics for the merger and diversifying merger sample. It

consists of 3855 firms with non-missing annually distraction measure over the period 1999-2010.

Observations are required to be in CRSP, Compustat, Thomson Reuter and in the Kempf et al.

average distraction dataset (2017). The description of the variables is in the Appendix.

N mean sd p25 median p75

Dependent Variables

Merger 19,258 0.217 0.412 0 0 0

Diversifying merger 19,258 0.0149 0.121 0 0 0

Key independent variable

Distraction 19,258 0.142 0.0568 0.0997 0.147 0.180

Control variables

Size ($m) 19,254 11,623 74,702 627.2 1,670 4,855

Tobin’s Q 19,258 1.948 5.190 0.522 1.048 2.068

Cash flow 18,455 0.0931 0.526 0.0347 0.0883 0.146

Cash holdings 18,810 0.324 0.292 0.134 0.249 0.436

Top 5 ownership 19,258 0.264 0.112 0.191 0.259 0.327

Institutional Ownership (IO) 19,258 0.652 0.254 0.479 0.688 0.841

Since managers are likely to maximize their private benefits at the expense of the

owners when they are less accountable to shareholders (Jiraporn, et al. 2006), it is likely

that they engage in empire building when institutional investors are distracted.

In this section we will study whether the level of shareholders’ distraction may impact

the probability for a company to make suboptimal investments such as acquisitions and

diversifying ones14. If the prospect of announcing acquisitions and especially diversifying

ones increases when investors are assumed to be distracted, there will be additional

support to the Distracted Shareholder Hypothesis (Kempf, Manconi and Spalt 2017).

14 Diversifying acquisitions be suboptimal investment since investors can achieve the benefits of

diversification themselves by holding a well-diversified portfolio. In addition, managing costs increase with

the size and the diversification of the company making more convenient for investors to achieve

diversification themselves rather than to have the corporation do it through acquisition (Berk and DeMarzo

2014).

- 43 -

We will relate merger announcements and diversifying merger announcements of firms

to distraction and other control variables using models and controls variables suggested

by the literature (e.g. Hartzell and Starks 2003; Malmendier and Tate 2008; Kempf,

Manconi and Spalt 2017). The description of the variables is in the Appendix.

First, we present the summary statistics for the sample used to verify the relationship

between distraction and acquisition frequency in Table 1. It reports the number of

observations, the mean, the standard deviation, the first quartile, the median and the third

quartile of the variables of interest. It shows that the odds to announce a merger and a

diversifying acquisition are 21.7% and 1.49% respectively in our sample. Therefore, the

conditional probability of observing a diversifying merger conditional on observing a

merger announcement is 6.87% (=1.49/21.7) in our sample over the years 1999-2010.

Then, we regress an acquisition announcement indicator on shareholders distraction and

controlling variables such as institutional ownership (IO), institutional ownership of the

five largest institutional investors (Top-5 share), the logarithm of total book assets,

Tobin’s Q, Cash Flow and Cash holdings following the literature (e.g. Kempf, Manconi,

& Spalt, 2017) using both linear probability model and logit model. The results of the

regressions are presented in Table 2. All specifications include industry x year fixed

effects and robust standard errors adjusted for clustering by firm while only specifications

(3) and (6) include firm fixed effects.

The specifications (1), (2) and (3) show that a firm is more likely to announce a merger

when institutional investors are distracted. In column (1) the results show that the

probability of announcing at least one merger increases by 33% (=0.819*0.0568/0.142)

for a one-standard deviation rise in the distraction measure relative to the mean and

holding the other variables at a fixed value. In column (2) the results show that a unit

increase in distraction increases the log odds by 0.286. In other terms, keeping the other

variables at their mean levels, the probability of observing a merger increases by 6.58%

when distraction increases by one unit from 1 to 2 (figure 1).

The main predictor variable is statistically significant at 5% level or 10% level in all

specifications except for the linear probability model that includes firm fixed effects in

column (3). Overall, it seems that the measure distraction has an impact on the takeover

activity confirming the results15 obtained by Kempf et. al., 2017.

15 In our analysis an observation is one firm in one year while Kempf et. al. (2017) used quarterly data in

their study.

- 44 -

Table 2 – Merger frequency and Diversifying merger

The Table reports the results of linear probability model in columns (1), (3), (4) and (6) and logit

model in columns (2) and (5) on our sample of 18,083 observations based on annual firm

observations from 1999 to 2010. The dependent variable “Merger” is a dummy variable equal to

one if the company announces at least one acquisition in one year with a minimum deal value of

$1 million and zero otherwise. The dependent variable “Diversifying merger” is a dummy

variable equal to one if the firm announces at least one diversifying merger in one year. A merger

is diversifying if the acquirer operates in a different Fama-French-12 industry than the target.

Observations are required to be in CRSP, Compustat, Thomson Reuter and in the Kempf et al.

average distraction dataset (2017). The description of the variables is in the Appendix. All

independent variables are standardized prior to fitting regressions (2) and (5) to permit more

intuitive comparison across variables. Standard errors shown in parenthesis are robust and

clustered by firm. Numbers marked with *, **, *** are significant at the 10%, 5% and 1% levels

respectively. For the logit models, the pseudo R-squared is reported.

Merger Diversifying merger

(1) (2) (3) (4) (5) (6)

VARIABLES OLS Logit OLS OLS Logit OLS

Distraction t 0.819** 0.286* 0.274 0.395** 1.883*** -0.049

(0.417) (0.168) (0.540) (0.183) (0.696) (0.202)

IO 0.229*** 0.386*** 0.144*** 0.008 0.173 0.002

(0.022) (0.038) (0.047) (0.006) (0.121) (0.014)

Top-5 share -0.393*** -0.288*** -0.274*** -0.022* -0.159 0.018

(0.043) (0.036) (0.075) (0.013) (0.119) (0.022)

Log Size 0.046*** 0.448*** 0.030** 0.009*** 0.783*** 0.003

(0.003) (0.030) (0.012) (0.001) (0.077) (0.004)

Tobin’s Q 0.004*** 0.078 0.003* 0.002*** 0.203*** 0.001

(0.001) (0.048) (0.002) (0.001) (0.049) (0.001)

Cash flow 0.076*** 0.460*** 0.044 -0.007 -0.143* 0.002

(0.021) (0.145) (0.029) (0.007) (0.074) (0.006)

Cash holdings 0.025 0.047 -0.004 -0.004 -0.038 0.003

(0.016) (0.030) (0.019) (0.004) (0.077) (0.007)

Constant -0.233** -1.006*** 0.151 -0.163*** -3.998*** 0.028

(0.119) (0.388) (0.184) (0.047) (0.611) (0.065)

Industry x year FE Yes Yes Yes Yes Yes Yes

Firm FE No No Yes No No Yes

Observations 18,083 18,083 18,083 18,083 13,981 18,083

R-squared 0.085 0.085 0.045 0.025 0.101 0.016

Number of firms 3,666 3,666 3,666 3,666 3,503 3,666

- 45 -

Figure 2 – Logistic probability model for observing a merger and a diversifying

merger

The specifications (4), (5) and (6) investigate the relationship between diversifying deals

and investors supervision. It was shown that managers have incentives to diversify

through acquisitions. For example, diversifying acquisitions are likely to improve the

manager’s job security. Bad and overpaid acquisitions should be more likely to occur

when managers have high private benefits (Morck, Shleifer and Vishny 1990). Therefore,

as shareholders are distracted, the managers have greater leeway to pursue private benefits

and the likelihood of diversifying deals should rise. The figures reported in Table 2

provide evidence to this theory.

The results in column (4) and (5) show that the likelihood of announcing a diversifying

merger rise when shareholders are distracted. Only the results of the linear probability

model that includes firm fixed effect in column (6) are in contrast with the above-

mentioned theory, but the coefficient of distraction is not statistically significant in this

case.

Looking at the estimates in column (4), a one-standard deviation rise in distraction

increases the chances of a diversifying deal by 150.6% (=0.395*0.0568/0.0149) relative

to the mean. Looking at the logit model in column (5), a unit increase in the level of

distraction yields a change in the log odds of 1.883. In other terms, keeping everything

else at their mean level (which is 0 since the variables have being standardized), a unit

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4

Pro

bab

ilit

y

Standardized distraction (annual average)

Merger probability

Merger Diversifying merger

- 46 -

increase in the level of distraction from 1 to 2 rises the odds of observing a diversifying

merger by 33.46% from 12.71% to 44.23%.

Overall, the results illustrated in column (1), (2), (4) and (5) of Table 2 show that

shareholders’ limited attention may give managers the opportunity to pursue

private/personal benefits in the takeover activity when they perceive a weakening of

monitoring constraints and confirm the Distracted Shareholder Hypothesis (Kempf,

Manconi and Spalt 2017).

3.2.2. Payout Policy

Excessive free cash flow under management control may intensify agency costs (Hamdan

2018). The resources, indeed, could be used in enterprises that benefit management and

not shareholders. According to Jensen (1986), dividends may be used to reduce free cash

flow, making it more difficult for the management to make suboptimal investments at the

shareholders expenses.

Since monitoring is considered one of the main tools for reducing agency costs, one

may expect to see an increase in dividend payments when monitoring constraints are high.

In other words, firms may be more willing to cut dividends when investors are distracted

because the management perceives a loosening up in monitoring constraints. Kempf,

Manconi and Spalt (2017) suggested another reason why managers may be more willing

to pay less dividends when shareholders are distracted: the lack of accountability caused

by a softening in the monitoring constraints could give the manager the incentive to issue

more bad news.

To test this hypothesis, we relate a dividend cuts indicator to distraction and other

control variables suggested by the literature (e.g. Grullon, Michaely and Swaminathan

2002; Kempf, Manconi and Spalt 2017). The variable “Dividend Cut” is equal to one if

yearly dividends are smaller than the dividends paid in the previous year.

The summary statistics of the payout policy sample are presented in Table 3. The Table

reports that the probability of observing a dividend cut is 11.2%.

- 47 -

Table 3 – Summary statistics of the payout policy sample

This Table presents the summary statistics for the dividend cuts sample. It consists of 3668 firms

with non-missing annually distraction measure over the period 1999-2010. Observations are

required to be in CRSP, Compustat, Thomson Reuters and in the Kempf et al. average distraction

dataset (2017). The description of the variables is in the Appendix.

N mean sd p25 median p75

Dependent variable

Dividend cuts 18,092 0.112 0.315 0 0 0

Main independent variable

Distraction

Control variables

18,092 0.142 0.0570 0.0998 0.148 0.180

Size ($m) 18,092 11,178 68,961 606.8 1,621 4,707

Tobin’s Q 18,092 1.876 3.274 0.552 1.104 2.131

Cash flow 18,092 0.0775 0.214 0.0344 0.0885 0.146

Cash holdings 18,092 0.327 0.290 0.142 0.253 0.436

Top 5 ownership 18,092 0.265 0.111 0.193 0.260 0.328

Institutional Ownership (IO) 18,092 0.656 0.253 0.485 0.693 0.844

Table 4 reports the results of estimating linear probability model and logistic regression

in which the dependent variable is the dummy “Dividend cuts”, the main independent

variable is the annual average of the distraction measure, and the control variables are

Institutional Ownership, Top-5 share of institutional ownership, log of firm size, Tobin’s

Q, cash flow and cash holdings. Columns (1), (3) and (5) use linear probability model

while columns (2) and (5) use logistic model. See the Appendix for a detailed description

of the variables.

Looking at the results reported in column (1), a greater propensity to cut dividends is

associated with high levels of distraction. The distraction coefficient is both positive and

statistically significant at 5% level confirming the Distracted Shareholder Hypothesis

(Kempf, Manconi and Spalt 2017). For example, a one-standard deviation increase in

distraction measurement increases the likelihood of observing a cut dividend by 6.46%

(=0.127*0.057/0.112) relative to the mean. Even the results of the logit model in column

(2) confirms the hypothesis that managers are likely to decrease dividends when

institutional investors are distracted.

- 48 -

Table 4 – Payout policy

The Table reports the results of linear probability models in columns (1), (3) and (5) and logistic

models in column (2) and (4) on our sample of 15,240 observations based on annual firm

observations from 1999 to 2010. The dependent variable “dividend cuts” is a dummy variable

equal to one if dividends in one year are smaller than the dividend paid in the previous period.

Observations are required to be in CRSP, Compustat, Thomson Reuter and in the Kempf et al.

average distraction dataset (2017). The description of the variables is in the Appendix. All

independent variables are standardized prior to fitting regressions (2) and (4) to permit more

intuitive comparison across variables. Standard errors shown in parenthesis are robust and

clustered by firm. Numbers marked with *, **, *** are significant at the 10%, 5% and 1% levels

respectively. For the logit models, the pseudo R-squared is reported.

However, the results that include industry x year fixed effect in column (3), (4) and (5)

show opposite and quite bizarre results. The coefficients relative to the distraction

measure are both negative and statistically significant at 1% level, suggesting that the

likelihood to cut dividends is lower when shareholders are distracted. By controlling

solely for industry fixed effects, distraction increases the odds of dividend cuts.

Dividend cuts

(1) (2) (3) (4) (5)

VARIABLES OLS Logit OLS Logit OLS

Distraction t 0.127** 0.0717** -2.743*** -1.203*** -1.744***

(0.061) (0.031) (0.459) (0.277) (0.477)

IO -0.074*** -0.198*** -0.0475** -0.141** -0.0453

(0.021) (0.076) (0.0231) (0.068) (0.0426)

Top-5 share 0.143*** 0.179** 0.0934* 0.094 0.0850

(0.050) (0.079) (0.0506) (0.059) (0.0692)

Log Size 0.015*** 0.227*** 0.0165*** 0.253*** -0.00151

(0.003) (0.048) (0.00279) (0.048) (0.0127)

Tobin’s Q -0.005*** -0.860*** -0.00345** -0.494*** 0.000954

(0.001) (0.236) (0.00137) (0.165) (0.00150)

Cash flow -0.028* -0.025 -0.0184 0.047 -0.0991**

(0.016) (0.132) (0.0157) (0.085) (0.0397)

Cash holdings -0.046*** -0.205*** -0.0362** -0.177*** -0.0642***

(0.015) (0.056) (0.0148) (0.063) (0.0194)

Constant 0.057 -1.883*** 0.268 0.506***

(0.035) (0.164) (0.424) (0.180)

Industry FE Yes Yes No No No

Industry x year FE No No Yes Yes Yes

Firm FE No No No No Yes

Observations 15,240 15,240 15,240 15,240 15,240

R-squared 0.033 0.053 0.084 0.106 0.072

Number of firms 2,882 2,882 2,882 2,882 2,882

- 49 -

Overall, our results may be biased by the presence of extraordinary dividend payments

that are not expected by shareholders in our dataset. Indeed, Kempf, Manconi and Spalt

(2017) showed how distraction is economically meaningful in explaining the rise in the

chances of dividend cuts by using quarterly dividend announcenemnts data and by

controlling for time fixed effects.

3.2.3. Lucky Option Grants

Options granted to the senior executives of a company has the goal to align the interests

of shareholders and managers reducing agency costs. However, this form of

compensation could induce managers to take on much more risk they would not otherwise

take since option holders do not bear the downside risk as shareholders do. Indeed, if the

firm performance is poor, only shareholders’ wealth is affected while executives lose only

the opportunity to make an additional gain.

In addition, managers may be tempted to release good information to increase the price

of the firm’s stocks prior the option are exercised even if it is both immoral and illegal.

On the other hand, if executives were granted at-the-money options, they could be

tempted to take actions for reducing the stock price just before the grant day to decrease

the exercise price of the option (Hull, Options, Futures, and other Derivatives 2014).

Yermack (1997) proved that CEOs receive stock options awards just before favorable

corporate news that increase the stock price. Therefore, the days before the date in which

the option is granted are characterized by abnormal negative stock returns while the days

afterwards are characterized by abnormal positive returns. Yermack (1997) theorized that

this occurs due to the pressure of managers to their compensation committee to “award

more performance-based pay” when they expect performances to improve in the short

run. Lie (2005) believes that the stock price patterns around option grants date can be

explained with backdating: the official grant date may be set retroactively. According to

Chauvin and Shenoy (2001), by knowing when options will be granted, managers have

the incentive and opportunity to manipulate the information disclosed to the market prior

to the option grant date, thus reducing the strike price.

Bebchuk, Grinstein and Peyer (2010) found that the probability for both CEOs and

independent directors to receive a “lucky grant” is extraordinarily high (e.g. about 12%

of CEO grant were given at the lowest price of the month while only 4% of grants were

- 50 -

awarded at the highest price of the month). The authors define “lucky grant” the stock

option grants given to managers and directors when the stock price is the lowest of the

month. According to their study, CEO compensation, a low number of independent

directors on the board and no independent compensation committee impact the chances

of CEOs and directors of being awarded with lucky grants.

In this section we want to test whether a weakening of monitoring constraints caused

by a temporary investors’ distraction can influences the chances of the management to

maximize their own private benefits by receiving a lucky grant.

The data on lucky grants was obtained from Professor Lucian Bebchuk’s website. Only

unscheduled16 at-the-money option grants awarded to CEOs and directors of public

companies in the 1996-2005 period were considered in this dataset. In addition, grants

awarded on the same month of the ex-dividend date were not considered, since the grant

price is affected by the dividend payment. Director grants awarded within one day from

the annual meeting were removed as well.

We relate CEO and independent director lucky grants to distraction and other control

variables using models and control variables suggested by the literature (e.g. Kempf,

Manconi and Spalt 2017; Bebchuk, Grinstein and Peyer 2010). The variable "CEO luck"

is a dummy equal to one if at least one grant was given to the CEO on a day which had

the lowest price of the month during the year. The value is zero otherwise.

The variable “Director luck” is defined in the same fashion but refers to options granted

to independent directors (Bebchuk, Grinstein and Peyer, Lucky CEOs and Lucky

Directors 2010). Further description of the variables is in the Appendix.

First, we present the summary statistics in Table 5. The Table reports the number of

observations, the mean, the standard deviation, the first quartile, the median and the third

quartile of the variables of interest. The Table shows that the likelihood for a CEO and a

director to receive at least one option grant with strike price equals to the lowest stock

price of the month is 13.2% and 10.2% respectively in our sample over the period 1999-

2005.

16 An option grant is defined as scheduled if another grant was awarded around the same date in the previous

year (Heron and Lie 2007).

- 51 -

Table 5 – Summary statistics of the lucky grants sample

This Table presents the summary statistics for the lucky grants sample. It consists of 2,882 firms

with non-missing annually distraction measure and non-missing control variables over the period

1999-2005. Observations are required to be in CRSP, Compustat, Thomson Reuters, the Kempf

et al. average distraction dataset (2017) and in the Professor Lucian Bebchuk’s dataset. The

description of the variables is in the Appendix.

VARIABLES N mean sd p25 median p75

Dependent variables

CEO luck 2,707 0.132 0.339 0 0 0

Director luck 3,329 0.102 0.302 0 0 0

Key independent variable

Distraction 6,980 0.139 0.0661 0.0757 0.147 0.200

Control variables

Size ($m) 6,980 4,244 21,789 368.3 900.0 2,379

Tobin’s Q 6,980 2.604 4.494 0.743 1.428 2.902

Relative Size 6,980 1.966 1.290 1.008 1.674 2.573

Leverage 6,980 0.184 0.212 0.00189 0.127 0.298

Tangibility 6,980 0.525 0.239 0.357 0.520 0.676

Top 5-shareof IO 6,980 0.261 0.109 0.188 0.255 0.323

Institutional Ownership (IO) 6,980 0.615 0.237 0.455 0.648 0.794

Firm age 6,980 2.413 0.943 1.792 2.398 3.135

Then, we regress the CEO and director lucky grants indicators on yearly average

shareholders distraction and controlling variables such as institutional ownership (IO),

institutional ownership of the five largest institutional investors (Top-5 share), the

logarithm of total book assets, Tobin’s Q, Relative Size, leverage, tangibility and firm

age following the literature (e.g. Bebchuk, Grinstein and Peyer 2010; Kempf, Manconi

and Spalt 2017) using both linear probability models and logit models. The results of the

regressions are presented in Table 6. Industry x year fixed effects are included in all

specifications to compare the effect of distraction on the probability of receiving a lucky

grant within the same industry and year whereas firms specific fixed effects are included

in specifications (3) and (6). Columns (1), (3), (4) and (6) report the results of linear

probability model that estimates the probability of receiving a lucky grant, while the

remaining columns report the results of logit model. Standard errors are robust and

clustered by firm in every specification.

Column (1), (2) and (3) of Table 6 show that the chances for CEOs to receive a lucky

grant rise when shareholders are distracted. In details, the results reported in column (1)

imply that one-standard deviation rise in distraction increases the probability of receiving

- 52 -

a lucky grant by 172% (=3.432*0.0661/0.132) relative to the mean. Looking at the logit

model in column (2), a unit increase in the level of distraction yields a change in the log

odds of 1.532. In other terms, keeping everything else at their mean level (which is 0

since the variables have being standardized before fitting the logit model), a unit increase

in the level of distraction from 1 to 2 rises the probability of CEO lucky grant by 35.32%

from 23.83% to 59.15%.

Overall, the results of specification (1) and (2) show that distraction have an

economically large impact on the odds of receiving a CEO lucky grant (the coefficient is

statistically significant at the 1% level). However, by controlling for firm fixed effects as

in column (3), the relationship is still positive but no longer statistically significant.

Column (4), (5) and (6) of Table 6 investigates the relationship between the odds for

independent directors to receive at least one lucky grant and investors distraction. The

analysis is at firm level: the dependent variable “Director luck” equals one if the firm

granted options to their directors between 1996-2005 and whether the grants were

“lucky”. The results are in line with previous findings: distraction rises the chances of

director lucky grant. Contrarily to previous results, in columns (4) and (5) the distraction

coefficient is statistically significant only at 10% level. In details, the results reported in

column (4) imply that one-standard deviation rise in distraction increases the probability

of receiving a lucky grant by 118% (=1.823*0.0661/0.102) relative to the mean. The logit

model in column (5) suggests that a unit increase in distraction rises the log odds of

director lucky grant by 1.051. It implies that the probability of director lucky grants

increases by 4.62% (from 2.68% to 7.3%) if the standardized distraction measures goes

from 1 to 2 (figure 2).

To sum up, this section provides further evidence of the ability of institutional investors

in monitoring and preventing managers from maximizing their own private benefits at the

shareholders expenses.

- 53 -

Table 6 – CEO and director lucky grants

The Table reports the results of linear probability models in columns (1), (3), (4) and (6) and

logistic models in column (2) and (5) on our samples of 2,278 and 2,775 observations based on

annual firm observations from 1999 to 2005. In specifications (1), (2) and (3) the dependent

variable “CEO luck” is a dummy equal to one if at least one grant was given to the CEO on a day

which had the lowest price of the month during the year and zero otherwise. In specifications (4),

(5) and (6) the dependent variable “Director luck” is defined in the same fashion as “CEO luck”

but refers to options granted to independent directors. Observations are required to be in CRSP,

Compustat, Thomson Reuter, Kempf et al. average distraction dataset (2017) and in the Professor

Lucian Bebchuk’s dataset. The description of the variables is in the Appendix. All independent

variables are standardized prior to fitting regressions (2) and (5) to permit more intuitive

comparison across variables. Standard errors shown in parenthesis are robust and clustered by

firm. Numbers marked with *, **, *** are significant at the 10%, 5% and 1% levels respectively.

As far as the logit models concern, the pseudo R-squared is reported.

CEO luck Director luck

(1) (2) (3) (4) (5) (6)

VARIABLES OLS Logit OLS OLS Logit OLS

Distraction t 3.432*** 1.532*** 2.774 1.823* 1.051* 0.418

(1.121) (0.487) (1.996) (1.067) (0.599) (1.825)

IO 0.051 0.184 0.069 -0.007 -0.017 0.036

(0.053) (0.175) (0.129) (0.045) (0.182) (0.136)

Top-5 share -0.018 -0.037 -0.004 0.086 0.174 -0.133

(0.108) (0.180) (0.246) (0.097) (0.187) (0.213)

Log Size 0.027* 0.429* 0.055 0.032** 0.681*** 0.046

(0.016) (0.226) (0.050) (0.016) (0.262) (0.036)

Tobin’s Q 0.008 0.246 0.022** 0.006 0.321 0.011

(0.006) (0.178) (0.009) (0.006) (0.215) (0.008)

Relative size -0.027 -0.295* -0.072* -0.038** -0.599*** -0.051

(0.017) (0.170) (0.040) (0.016) (0.196) (0.035)

Leverage 0.049 0.101 0.168 0.019 0.053 -0.058

(0.051) (0.099) (0.114) (0.043) (0.106) (0.108)

Tangibility 0.054 0.131 -0.079 0.002 0.004 0.009

(0.055) (0.112) (0.127) (0.046) (0.124) (0.105)

Firm age -0.013 -0.120 -0.031 -0.004 -0.057 -0.075

(0.010) (0.097) (0.091) (0.008) (0.093) (0.075)

Constant -1.042*** -2.694*** -0.583 -0.536* -4.643*** 0.065

(0.298) (0.582) (0.634) (0.292) (1.671) (0.579)

Industry x year FE Yes Yes Yes Yes Yes Yes

Firm FE No No Yes No No Yes

Observations 2,278 2,151 2,278 2,775 2,560 2,775

R-squared 0.055 0.057 0.081 0.032 0.038 0.038

Number of firms 939 915 939 1,051 1,012 1,051

- 54 -

Figure 3 -Logistic probability model for CEO and director to receive a lucky grant

3.3. Spillover effects

In this section, we present the results about the indirect effects of distraction on peer

companies’ liquidity, financing and payout policies.

Since it was proved that managers are more likely to engage in empire building when

investors are distracted, close competitors may react to this by holding more cash, paying

less dividends and keeping low level of leverage in order to have the required resources

and flexibility to face the takeover threat. In other words, there may be important spillover

effects that influence the peers’ decisions of companies with lack of monitoring

constraints by investors.

Our thesis is the following: shareholder distraction and its effects such as the high

likelihood of acquisitions, may be observed by closest competitors that respond to control

threat by managing cash holdings, debt and payout policies. We expect that companies

that fear the takeover threat of close competitors with distracted shareholders will adopt

more conservative financial policies by holding more cash and less leverage and paying

out less dividends. This assures the company with the required liquidity and flexibility to

face the threat17.

17 This argument is similar to the thesis of Hoberg, Phillips e Prabhala (2014) but applied to takeover threat

and not to competitive markets.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4

Pro

bab

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Standardized distraction (annual average)

Lucky grant probability

CEO lucky grant Director lucky grant

- 55 -

The threat is plausible for the following reasoning. The managers of distracted investors

companies are likely to make acquisitions to enhance its private benefits (e.g. job security,

prestige and compensation) without any concern for what their actions would result in

from a shareholder’s perspective. Peer firms are likely to be informed of the propensity

of close competitors’ managers to engage in empire building when their institutional

investors are distracted. They are likely to be informed by reading the news, by analyzing

public available information regarding companies whose shares are quoted in the stock

exchange, by participating to industry association meetings or speaking with common

shareholders and customers. Close competitors of distracted investors companies may

also consider that such companies may be preparing for performing acquisitions also if

they have not actually performed one yet. This is plausible because peer firms may notice

that an attention-grabbing event (e.g. price shocks) occurring in one industry is likely to

shift the attention of relevant blockholders away from one competitor. Then, peer firms

can imagine that managers might take advantage of investors’ distraction when such

events occur.

As a result, the managers of all direct competitors of a company that is either actually

performing acquisitions or may be simply be suspected of preparing an acquisition may

fear that their own firm may be the possible target of a takeover.

3.3.1. Cash holdings and peer companies

In this section we study whether peer companies are more likely to hold more cash in

response to the takeover threat due to distracted investors.

According to Jensen (1986), firms with substantial free cash flow – the cash in excess

of that required to fund positive net present value projects – are desirable leveraged

buyout takeover candidates. Indeed, since leverage buyout transaction are financed with

high level of debt, substantial cash holding makes the success of the operation more

likely. For example, large stockpile of cash can prevent liquidation of asset at less than

their full value to repay the huge debt (Shleifer and Vishny 1993). In addition, according

to Palepu (1986) cash rich firms are likely to be target since they can provide the acquirer

with financial resources to fund their own projects.

Even if Jensen (1986) suggests that the takeover threat is responsible for reducing the

agency problem of free cash flow, recent literature confirms the contrary. Pinkowitz

- 56 -

(2002) found that the probability of becoming a takeover target is negatively related to

the holding of excess cash. Rege (1984) suggested that high liquidity makes a firm

unattractive to predators since it signals low investment opportunities. Faleye (2004)

proposed that large stockpiles of cash provide a firm with valuable anti-takeover defenses.

For example, the target company may reduce the probability of takeover success by

investing huge funds in repurchasing their own shares. Because the supply curve for

stocks is upward sloping (Bagwell 1992), the first shareholders willing to tender their

shares in a repurchase are those who would sell their shares at the lowest price (Bagwell

1991). This makes the acquisition more expensive since the acquirer must buy the shares

from the remaining shareholders who have higher reservation values. Even Denis (1990)

obtained results confirming that repurchases are correlated with high probability in

maintaining independence.

Dann and DeAngelo (1988) showed that cash-rich targets of a hostile bid can defend

themselves by acquiring a competitor of the bidder. They could, then, file a private

antitrust litigation depriving the bidder of the legal right of the acquisition.

Moreover, if the bidder is interested in the target only because of the high levels of cash

holdings, she must be aware that the target could quickly pay out dividends to

shareholders eliminating the reason for the takeover attempt (Hendershott 1996;

Pinkowitz 2002).

Finally, for the above-mentioned reasons, cash rich companies with low level of debt

are more difficult to evaluate: they could pay out dividends, repurchase shares or use cash

to make suboptimal investments that can decrease the value of the company. This could

be a deterrence for takeovers.

Hoberg, Phillips and Prabhala (2014) showed that firms facing market competition are

more likely to hold higher cash balances. Overall, an uncertain and dynamic environment

leads companies to be more cautious. Firms with excess cash holdings may have more

resources for implementing defensive maneuvers to prevail over takeover attempts or to

survive in competitive markets.

In this section we study whether firms hold more cash when they are threatened by close

competitors with distracted shareholders by relating cash balances held by firms and the

variable “Threat”. The variable “Threat” indicates all the companies with shareholders’

distraction levels lower than the first quartile that are also competitors of companies with

shareholders’ distraction levels higher than the third quartile in a given year. In other

words, it is a dummy variable equal to one if the company is among the ones with least

- 57 -

distracted shareholders and at the same time competitor of at least one company that is

among the ones with most distracted investors in one year. It is zero otherwise. In

addition, we require that Threat equals one only if the rank of the Hoberg’s score is less

than 10 for each group of peer companies. The data on Hoberg’s score is obtained from

TNIC-3 database that is online in the Professor Gerald Hoberg’ website (Hoberg and

Phillips 2016; Hoberg and Phillips 2010). The Professor Gerald Hoberg’s TNIC-3

database relates two companies based on their rival level. Companies with high

similarities in the 10-K annual filings are associated high scores. In other words, a higher

score indicates more similarity and consequently companies with higher score are nearer

rivals (Hoberg and Phillips 2016).

Table 7 – Summary statistics of the cash holdings sample

This Table presents the summary statistics for the cash holdings sample. It consists of 2178 firms

with non-missing threat measure and non-missing control variables over the period 1991-2010.

Observations are required to be in CRSP, Compustat, Thomson Reuters, the Kempf et al. average

distraction dataset (2017) and in the Professor Gerald Hoberg’ database (2016). The description

of the variables is in the Appendix.

N mean sd p25 median p75

Dependent variable

Cash holdings 12,513 0.175 0.191 0.0335 0.101 0.250

Cash holdings |

Threat=0

11318 0.162 0.180 0.032 0.094 0.229

Cash holdings |

Threat=1

1195 0.290 0.238 0.074 0.241 0.467

Key independent

variable

Threat 12,513 0.0955 0.294 0 0 0

Control variables

Firm age 12,513 2.712 0.880 2.079 2.708 3.401

Market-to-book 12,513 3.913 61.69 1.631 2.551 4.103

Assets ($m) 12,513 7,981 31,366 459.6 1,249 3,984

Earnings/assets 12,513 0.169 0.138 0.122 0.181 0.239

Cash flow 12,513 0.0988 0.165 0.0621 0.109 0.161

Capx/assets 12,513 0.0575 0.0493 0.0256 0.0450 0.0750

Neg. earnings dummy 12,513 0.0706 0.256 0 0 0

R&D/assets 12,513 0.0553 0.0853 0.00455 0.0260 0.0759

- 58 -

First, we present the summary statistics in Table 7 of the sample. The Table reports the

number of observations, the mean, the standard deviation, the first quartile, the median

and the third quartile of the variables of interest. The Table shows that the 9.55% of

observations of our sample is represented by “threatened” companies. The variable of

interest is cash holdings that is defined as cash normalized by firm assets. To verify

whether the cash holdings of the threatened companies is statistically different from those

of the other companies, we perform the following two-sample t test with equal variances:

H0: E[CH|Threat = 0] − E[CH|Threat = 1] = 0

H1: E[CH|Threat = 0] − E[CH|Threat = 1] ≠ 0

where E is the expected value of cash holdings (CH) whether the company is threatened

(Threat=1) or not (Threat=0). The corresponding t-statistics is -22.5213, the degrees of

freedom are 12511 and the probability to observe more extreme values is less than 0.00%

(Pr(T>|t|) = 0.0000). The null hypothesis is rejected in favor of the alternative at 1%

significance level. However, this is just an approximative analysis, we need to look at

more sophisticated models.

We regress cash holdings on threat and controlling variables such as firm age, market-

to-book, log assets, profitability (earnings/assets), cash flow, Investment ratio

(Capx/asset), negative earnings dummy and R&D/assets following the cash literature

(e.g. Hoberg, Phillips and Prabhala 2014; Bates, Kahle and Stulz 2009). The results of

the regressions are presented in Table 8. Year fixed effects are included in all regressions

whereas FF12 industry fixed effects are included in specification (2) and firm fixed effects

in specification (3). Standard errors are robust and clustered by firm in every

specification. All independent variables except for the dummy variables “Threat” and

“Negative earnings dummy” are standardized prior fitting the linear regression models.

The results illustrated in Table 9 confirms Hoberg et. al. (2014), Faleye 2004 and

Pinkowitz 2002 findings: if the company is threatened by a takeover attempt (Threat=1),

cash holdings increases by 39.4% (=0.069/0.175) and by 16% (=0.028/0.175) relative to

the mean in specification (1) and (2) respectively, holding all other variables unchanged.

The coefficients relative to the key independent variable are positive and statistically

significant at 1% confidence level in all specifications.

- 59 -

Table 8 – Cash Holdings and Threat

The Table reports the results of ordinary least squared regressions on our sample of 12,513

observations based on annual firm observations from 1991 to 2010. The dependent variable

“Cash holding” is equal to firm cash normalized by total assets. The main independent variable

is “Threat” that indicates whether the company is one of the closest competitors of firms with

most distracted shareholders. Observations are required to be in CRSP, Compustat, Thomson

Reuters, the Kempf et al. average distraction dataset (2017) and in the Professor Gerald Hoberg’

database (2016). The description of the variables is in the Appendix. All independent variables

except for dummy variables are standardized prior to fitting regressions to permit more intuitive

comparison across variables. Column (1) includes year fixed effects, column (2) includes both

year and FF12 industry fixed effects and column (3) includes year and firm fixed effects. Standard

errors shown in parenthesis are robust and clustered by firm. Numbers marked with *, **, *** are

significant at the 10%, 5% and 1% levels respectively.

Cash holdings

VARIABLES (1) (2) (3)

Threat 0.069*** 0.028*** 0.023***

(0.009) (0.008) (0.005)

Firm age -0.027*** -0.019*** -0.029***

(0.003) (0.003) (0.008)

Market-to-book 0.003 0.003 0.003

(0.008) (0.007) (0.005)

Log assets -0.030*** -0.031*** -0.057***

(0.003) (0.003) (0.010)

Earnings/assets -0.154* -0.152** -0.150***

(0.085) (0.071) (0.036)

Cash flow 0.113*** 0.095*** 0.047***

(0.035) (0.029) (0.009)

Capx/assets -0.490*** -0.442*** -0.435***

(0.055) (0.052) (0.049)

Neg. earnings

dummy

0.156*** 0.154*** 0.014

(0.020) (0.018) (0.011)

R&D/assets 0.080*** 0.058*** -0.022***

(0.008) (0.007) (0.005)

Constant 0.157*** 0.121*** 0.159***

(0.008) (0.012) (0.008)

Observations 12,513 12,513 12,513

R-squared 0.424 0.466 0.098

Year FE Yes Yes Yes

Industry FE No Yes No

Firm FE No No Yes

Number of firms 2,178 2,178 2,178

- 60 -

This shows that firms facing competitive or takeover threats of companies with

distracted shareholders adopt more conservative financial policies since they hold more

cash. Our results agree Hoberg, Phillips and Prabhala (2014) who demonstrated that firms

facing competitive threats in their product market hold higher amount of liquid assets.

3.3.2. Dividends and Peer Companies

In this section, we study the relationship between payout policy and threatened

companies.

According to the free cash flow theory there should be a negative correlation between

dividends and the probability of takeover since managers have less opportunities to invest

in negative net present value projects by paying out excess cash (Jensen 1988; Jensen

1986). By paying dividends, managers signal they are running the company efficiently to

the market. Therefore, since the acquirer may perceive lower efficiency gain from the

takeover when the target pays dividends, the probability of takeover is lower for dividend

payer firms (Berk and DeMarzo 2014). Dickerson, Gibson and Tsakalotos (1998) proved

this theory by showing that a 10% increase in dividends reduces the probability of

takevorer by 2.3% in a study of UK public firms.

However, this result may contrast with the study of Hoberg. Et. al. (2014). According

to their theory, stability is the necessary condition for dividend payouts. Companies pay

less dividends and retain more cash when they fear competitive markets because liquidity

may give them the tools for facing threats when they occur (Hoberg, Phillips and Prabhala

2014). Even if takeover does not represent a product market threat, the same reasoning of

Hoberg et. al. (2014) can be applied to our case: competitors of firms with distracted

shareholders may reduce dividends since liquid assets allow them to have more flexibility

in applying defensive strategies against a takeover threat.

Table 9 presents the summary statistics of the sample used in the analysis that studies

whether companies threatened by the takeover activity of competitors with distracted

shareholders are dividend paying firms. The Table reports the number of observations,

the mean, the standard deviation, the first quartile, the median and the third quartile of the

variables used in the analysis. The variable “Dividend payer” is a dummy variable equal

to one if the company pays dividends in a given year, zero otherwise. The Table 9 shows

that 10.3% of observations of our sample is represented by “threatened” companies.

- 61 -

53.1% is the probability that a not threatened company pays dividends while only 26.6%

are the chances that a threatened company pays dividends in a given year. To verify

whether the frequency of dividend payers’ companies differs whether the company is

threatened or not, we perform the following two-sample t test with equal variances:

H0: E[DP|Threat = 0] − E[DP|Threat = 1] = 0

H1: E[DP|Threat = 0] − E[DP|Threat = 1] ≠ 0

where E is the expected value of “Dividend payers” (DP) given the company is threatened

(Threat=1) or not (Threat=0). The corresponding t-statistics is 19.6974, the degrees of

freedom are 14541 and the probability to observe more extreme values is less than 0.00%

(Pr(T > |t|) = 0.0000). The null hypothesis is rejected in favor of the alternative at 1%

significance level.

Table 9 – Summary statistics, dividend policy sample

This Table presents the summary statistics for the dividend policy sample. It consists of 2301

firms with non-missing threat measure and non-missing control variables over the period 1991-

2010. Observations are required to be in CRSP, Compustat, Thomson Reuters, the Kempf et al.

average distraction dataset (2017) and in the Professor Gerald Hoberg’ database (2016). The

description of the variables is in the Appendix.

N mean sd p25 median p75

Dependent variable

Dividend Payer 14,543 0.504 0.500 0 1 1

Dividend Payer | Threat=0 13041 0.531 0.499 0 1 1

Dividend Payer | Threat=1 1502 0.266 0.442 0 0 1

Key independent variable

Threat 14,543 0.103 0.304 0 0 0

Control variables

Firm age 14,543 2.694 0.875 2.079 2.708 3.367

Market-to-book 14,543 3.925 57.51 1.653 2.591 4.158

Asset growth 14,543 0.161 0.635 -0.00490 0.0821 0.201

Cash flow 14,543 0.0979 0.170 0.0617 0.110 0.163

R&D/assets 14,543 0.0579 0.0860 0.00510 0.0278 0.0817

Neg. earnings dummy 14,543 0.0742 0.262 0 0 0

Ret. Earnings/Assets 14,543 0.142 0.806 0.0687 0.254 0.442

- 62 -

Table 10 presents the results of the multivariate analysis. We regress the propensity to

pay dividends onto threat and other control variables suggested by the literature such as

firms age, market to book ratio, asset growth, cash flow, the ratio between R&D and

assets, negative earnings dummy variable and the ratio between retained earnings and

assets (Hoberg, Phillips and Prabhala 2014; DeAngelo, DeAngelo and Stulz 2006;

DeAngelo, DeAngelo and Douglas 1992). Columns (1) and (3) present the results of

fitting a linear probability model while columns (2) and (4) show the logistic model

estimates. All specifications include year fixed effects. Specifications (3) and (4) include

also FF12 industry fixed effects while column (5) includes firm fixed effects. Standard

errors are robust and clustered by firm in every specification. All independent variables

except for the dummy variables “Threat” and “Negative earnings dummy” are

standardized prior fitting the models.

All specifications of Table 10 expect for specification (5) agree that threatened

companies are less likely to pay dividends. The coefficient threat is statistically

significant at 1% level in those specifications. Looking at the column (1), if the company

is threatened (threat=1), the probability of paying dividends decreases by 27.2% (=

0.137/0.504) relative to the mean. However, in columns (5), the coefficient of the variable

Threat is not statistically significant. This implies that firm specific characteristics might

influence more the choices of firms to pay dividends rather than our main independent

variable.

Overall, except for column (5), our results agree that there is a negative and strong

relationship between the variable Threat and the indicator variable “Dividend Payer”.

These results seem to agree with Hoberg, Phillips and Prabhala (2014): firms facing a

threat are less likely to pay dividends. The justification could be the same given in

preceding paragraph: companies adopt more conservative financial policies by paying

less dividends and thus holding more liquid assets in order to have the necessary funds to

face the threats represented by competitors with distracted shareholders.

- 63 -

Table 10 – Dividend policy and Threat

The Table reports the linear probability model estimates in column (1) and (3) and logit estimates

in column (2) and (4) for our sample of 14,543 observations based on annual firm observations

from 1991 to 2010. The dependent variable “Dividend payer” is a dummy equal to one if the

firms pays dividends in a given year and zero otherwise. The main independent variable is

“Threat” that indicates whether the company is one of the closest competitors of firms with most

distracted shareholders. The description of the variables is in the Appendix. Observations are

required to be in CRSP, Compustat, Thomson Reuters, the Kempf et al. average distraction dataset

(2017) and in the Professor Gerald Hoberg’ database (2016). All independent variables except for

dummy variables such as threat and negative earnings dummy are standardized prior to fitting

regressions to permit more intuitive comparison across variables. Columns (1) and (2) includes

year fixed effects while columns (3) and (4) includes both year and FF12 industry fixed effects.

Column (5) includes firm and year FE. Standard errors shown in parenthesis are robust and

clustered by firm. Numbers marked with *, **, *** are significant at the 10%, 5% and 1% levels

respectively. As far as the logit models concern, the pseudo R-squared is reported.

Dividend payer

(1) (2) (3) (4) (5)

VARIABLES OLS Logit OLS Logit OLS

Threat -0.137*** -0.629*** -0.045*** -0.405*** 0.006

(0.019) (0.124) (0.016) (0.125) (0.010)

Firm age 0.202*** 0.947*** 0.172*** 0.907*** 0.097***

(0.010) (0.065) (0.010) (0.073) (0.020)

Market-to-book -0.008 -0.021 -0.008 -0.030 -0.001

(0.007) (0.050) (0.010) (0.050) (0.001)

Asset growth -0.024* -0.545*** -0.018* -0.449*** -0.003**

(0.013) (0.090) (0.010) (0.083) (0.001)

Cash flow -0.025 0.316 0.014 0.364 -0.002

(0.017) (0.245) (0.018) (0.243) (0.008)

R&D/assets -0.102*** -1.254*** -0.040*** -0.842*** 0.019***

(0.012) (0.130) (0.011) (0.161) (0.006)

Neg. earnings dummy -0.113*** -1.295*** -0.118*** -1.274*** 0.009

(0.024) (0.253) (0.024) (0.274) (0.014)

Ret. Earnings/Assets 0.035*** 1.122*** 0.039*** 1.293*** 0.031***

(0.008) (0.149) (0.009) (0.163) (0.011)

Constant 0.599*** 0.172 0.799*** 1.406*** 0.511***

(0.018) (0.122) (0.037) (0.332) (0.016)

Observations 14,543 14,543 14,543 14,543 14,543

R-squared 0.289 0.303 0.356 0.355 0.041

Year FE Yes Yes Yes Yes Yes

Industry FE No No Yes Yes No

Firm FE No No No No Yes

Number of firms 2,301 2,301 2,301 2,301 2,301

- 64 -

3.3.3. Leverage and peer companies

In this section we study whether companies are willing to hold more leverage on average

in response to a threat represented by an active takeover activity of competitors with

distracted shareholders.

The threat of takeover is considered the solution to many agency problems. Managers

may be forced not to waste resources for growing the company beyond its optimal size

because they would be replaced in case of successful takeover. It was documented,

indeed, that firms that cut capital spending and commit to such policy by increasing debt

have more chances to defeat a takeover attempt (Servaes and Tamayo 2014; Safieddine e

Titman 1999; Berger, Ofek e Yermack 1997; Hendershott 1996; Denis and Denis 1993).

Leverage commits managers to invest more efficientely (Morellec 2004, Servaes e

Tamayo 2014) reducing the advantages of takeover such as the efficiency gain for the

bidder firm.

However, Novaes (2002) showed that even if “leverage conveys good news if it reflects

management’s ability to enhance value, it conveys bad news if inefficient managers feel

more pressured to lever up than the efficient ones”. This translates to a higher probability

for managers of being replaced by their shareholders when leverage is used to end a

takeover threat. The higher leverage before a takeover attempt, then, signals bad

performance of the managers. As a result, managers will be cautious in increasing

leverage if they fear a takeover.

There could be a negative relationship between leverage and threat not only because of

the results of Novaes (2002), but also because an unlevered company is more flexible and

can adopt onerous strategies to end possible takeover attempts. Unlevered companies,

indeed, have more options to adopt when they face a threat than levered companies

because they do not need to worry about recurring interest payments.

We, therefore, relate two different forms of leverage – long-term debt to assets and total

debt to assets – to threat in order to study whether companies that fears the active takeover

activity of competitors tend to hold a smaller amount of debt.

First, we present the summary statistics in Table 11. The Table reports the number of

observations, the mean, the standard deviation, the first quartile, the median and the third

quartile of the variables of interest. The Table shows that the 11.1% of observations of

our sample is represented by “threatened” companies. The unconditional mean of long-

term debt to assets is 15.7%. Table 11 shows that long-term debt is 46.85% higher, on

- 65 -

average, if the company is not threatened by distracted shareholders of competitors.

Indeed, the expected value of the ratio conditioned to absence of threat is 16.3% while

the conditional mean given the presence of threat is just 11.1%. The difference between

the conditional means of total debt is even larger: 18.6% in absence of threat versus 11.6%

in presence of threat, a difference of 700 basis points.

To verify whether leverage of the threatened companies is statistically different from

those of not threatened companies, we perform the following two-sample t test with equal

variances:

H0: E[LG|Threat = 0] − E[LG|Threat = 1] = 0

H1: E[LG|Threat = 0] − E[LG|Threat = 1] ≠ 0

where E is the expected value of “Leverage” (LG) given the company is threatened

(Threat=1) or not (Threat=0). The t-statistics of the test for long term debt to assets and

total debt to assets are 9.4010 and 10.1872 respectively. The degrees of freedom are 9820

for the long-term debt ratio test and 6939 for total debt ratio test. The probability to

observe more extreme values than the t-statistics are less than 0.00% in both cases. There

is enough evidence to reject the null hypothesis in favor of the alternative: companies

hold different leverage when they are threatened.

Table 12 reports the results of the multivariate analysis. We regress leverage onto threat

and other control variables such as EBIT normalized by assets, investment tax credits

normalized by assets, net property, plant and equipment (PP&E) normalized by assets,

the logarithm of assets, research and development expenses (R&D) normalized by assets,

selling, general and administrative expenses (SG&A) normalized by assets following the

leverage literature (e.g. Servaes and Tamayo 2014; Hovakimian, Opler and Titman 2001;

Berger, Ofek and Yermack 1997; Titman and Wessels 1988). In columns (1), (2), and (3)

the dependent variable is long term debt normalized by assets, while in columns (4) , (5)

and (6) the dependent variable is the ratio between total debt and assets. In every

specification all independent variables except for threat are normalized before fitting the

model and standard errors are robust and adjusted for clustering by firm. Moreover, all

specifications include year fixed effects. Specification (2) and (5) include FF12 industry

fixed effects and specification (3) and (6) include firm fixed effects.

- 66 -

Table 11 – Summary statistics, leverage policy sample

This Table presents the summary statistics for the leverage policy sample. It consists of 2089

firms with non-missing threat measure and non-missing control variables over the period 1990-

2010. Observations are required to be in CRSP, Compustat, Thomson Reuters, the Kempf et al.

average distraction dataset (2017) and in the Professor Gerald Hoberg’ database (2016). The

description of the variables is in the Appendix.

N mean sd p25 median p75

Dependent variable

Long-term debt to assets 9,822 0.157 0.173 0.00295 0.120 0.251

Long-term debt to assets |

Threat=0 8733 0.163 0.172 0.00671 0.12969 0.256

Long-term debt to assets |

Threat=1 1089 0.111 0.172 0 0.01059 0.181

Total Debt to assets 6,941 0.177 0.189 0.00406 0.142 0.278

Total Debt to assets | Threat=0 6077 0.186 0.189 0.00945 0.15661 0.289

Total Debt to assets | Threat=1 864 0.116 0.179 0 0.01656 0.189

Key independent variable

Threat 9,822 0.111 0.314 0 0 0

Control variables

Assets ($m) 9,822 6,145 27,331 359.3 948.3 2,953

R&D/assets 9,822 0.0513 0.0765 0.00364 0.0251 0.0776

SG&A expenses / assets 9,822 0.290 0.216 0.142 0.247 0.391

EBIT/assets 9,822 0.107 0.330 0.0568 0.104 0.156

PP&E / assets 9,822 0.256 0.187 0.112 0.213 0.352

Investment tax credits / assets 9,822 0.00102 0.00915 0 0 0.00054

All specifications in Table 12 suggest that threatened firms tend to hold less debt. Even

if every specification agrees with the sign of the relationship between leverage and threat,

the coefficient threat is statistically significant only in specification (1) and (4).

Looking at the specification (1), the average ratio between long term debt and assets

decreases by 19.1% (=0.03/0.157) relative to the mean if the company is threatened. In

column (4), the effect of threat on total debt is even stronger: 23.1% (=0.041/0.177) is the

decrease in the ratio if threat equals one.

Overall, our results agree with Novaes’s study (2002) as opposed to Servaes and

Tamayo (2014). Close competitors of companies with distracted shareholders tend to

keep a low level of leverage. They could, indeed, adopt more prudent financial policies

as they keep resources to be ready to face eventual future threats.

- 67 -

Table 12 – Leverage policy and threat

The Table reports the results of ordinary least squared regressions on our sample of 9,822 and

6,941 observations based on annual firm observations from 1990 to 2010 in columns (1), (2), (3)

and (4), (5), (6) respectively. The dependent variable “Long term debt to assets” is equal to firm

total long-term debt normalized by total assets. The dependent variable “Total debt to assets”

includes current debt. The main independent variable is “Threat” that indicates whether the

company is one of the closest competitors of firms with most distracted shareholders.

Observations are required to be in CRSP, Compustat, Thomson Reuters, the Kempf et al. average

distraction dataset (2017) and in the Professor Gerald Hoberg’ database (2016). The description

of the variables is in the Appendix. All independent variables except for the threat dummy

variable are standardized prior to fitting regressions to permit more intuitive comparison across

variables. All specifications include year fixed effects. Columns (2) and (5) include FF12 industry

fixed effects while columns (3) and (6) include firm fixed effects. Standard errors shown in

parenthesis are robust and clustered by firm. Numbers marked with *, **, *** are significant at

the 10%, 5% and 1% levels respectively.

Long-term debt to assets Total debt to assets

VARIABLES (1) (2) (3) (4) (5) (6)

Threat -0.030*** -0.004 -0.007 -0.041*** -0.008 -0.009

(0.009) (0.008) (0.005) (0.011) (0.009) (0.006)

EBIT/assets -0.045 -0.056** -0.041 -0.034 -0.048** -0.031

(0.029) (0.028) (0.026) (0.022) (0.024) (0.023)

Investment tax

credits / assets

-0.002 -0.002 0.000 -0.002 -0.001 0.000

(0.002) (0.001) (0.000) (0.002) (0.001) (0.000)

PP&E / assets 0.035*** 0.027*** -0.009 0.038*** 0.030*** -0.005

(0.006) (0.006) (0.012) (0.008) (0.008) (0.017)

Log assets 0.023*** 0.023*** 0.037** 0.029*** 0.030*** 0.035

(0.004) (0.004) (0.015) (0.006) (0.005) (0.023)

R&D/assets -0.016** -0.006 0.034*** -0.005 0.002 0.044**

(0.008) (0.007) (0.011) (0.012) (0.011) (0.019)

SG&A

expenses /

assets

-0.020*** -0.019*** -0.021** -0.018*** -0.013** -0.009

(0.005) (0.005) (0.010) (0.006) (0.007) (0.020)

Constant 0.169*** 0.215*** 0.186*** 0.149*** 0.257*** 0.022*

(0.009) (0.018) (0.016) (0.004) (0.025) (0.012)

Observations 9,822 9,822 9,822 6,941 6,941 6,941

R-squared 0.116 0.165 0.046 0.095 0.163 0.032

Year FE Yes Yes Yes Yes Yes Yes

Industry FE No Yes No No Yes No

Firm FE No No Yes No No Yes

Number of

firms

2,089 2,089 2,089 1,673 1,673 1,673

- 68 -

4. Conclusions

The agency problem is a well-known dilemma addressed by the financial literature. Many

have studied the beneficial effects of monitoring activities in mitigating such problem.

Others have studied the negative impacts of a lack of monitoring activities on firm’s

performance. In particular, Kempf, Manconi and Spalt (2017) provide evidence showing

that a loosening in monitoring constraints has adverse consequences from the

shareholders’ point of view. When investors shift attention away from the company, the

managers take advantage of the loosening in monitoring constraints and maximize private

benefits even if their actions damage the shareholders’ wealth. The authors call

“distracted” shareholders those investors that temporarily reduce their monitoring

activities.

In this paper we provided evidence of both direct and indirect effects of distracted

shareholders on corporate finance.

First, following the study of Kempf, Manconi and Spalt (2017), we performed

additional tests to the Distracted Shareholder Hypothesis. We proved that managers take

actions that benefit themselves but damages firm’s owners by looking at the takeover

market, the number of options granted to the senior executives and the firms’ payout

policies. Specifically, our logistic and linear probability models shown that firms are more

likely to announce a merger when the institutional investors are distracted. This indicates

that managers tend to engage in empire building when investors provide less monitoring.

Moreover, the acquisitions performed by firms with distracted shareholders are more

likely to be diversifying deals. Diversification benefits managers in terms of job security

and prestige while might not be in the best interest of shareholders since they could

achieve the benefits of diversification themselves by holding shares in more companies.

Moreover, our results shown that managers may be tempted to engage in immoral

activities to boost the payoff from employee stock options when shareholder are

distracted. Indeed, investors’ distraction increases the chances for managers and

independent directors to receive a “lucky” grant. Finally, as far as payout policy concerns,

we found surprising results. Some of our tests suggested that the distraction decreases the

probability to observe a dividend cut. This is in contradiction with the we would have

expected. Overall our results are consistent with the hypothesis that managers exploit

shareholder distraction by maximizing their benefits.

- 69 -

Second, we analyzed the spillover effects of companies with distracted shareholders on

peers’ firm policies. We focused on the consequences that a greater propensity for firms

with distracted shareholders to engage in acquisitions may have on close competitors. In

details, we provided evidence that peer firms react to the intensifying takeover activity of

companies with distracted shareholders. We found that peer companies are likely to hold

more cash, pay less dividends and keep lower levels of leverage than the other firms in

our statistic sample. This is consistent with our hypothesis that companies threatened of

being taken over by companies without monitoring constraints are likely to adopt more

conservative financial policies in line with the competitive threats argument of (Hoberg

and Phillips 2010). Large cash holdings and low leverage levels may provide the company

with the flexibility required to implement the takeover defenses.

Further research can be done to verify whether the companies that adopt more

conservative financial policies (i.e. high cash holdings and low leverage and payout rate)

have actually less probability of being taken over. In addition, it might be interesting to

study how peer companies react to the firms with distracted shareholders in the product

markets. Peers might take advantage of poorly performing competitors by undertaking

aggressive policies with the aim to increase their market share (e.g. cut prices, invest in

R&D). In this regard, our research highlights the need for additional work related to the

spillover effects caused by limited shareholders’ attention.

- 70 -

5. Appendix

Table A.1 – Variable descriptions

Variables Description

Dependent variables

Merger Dummy variable equal to one if the company announces at least

one M&A transaction with deal value greater than $1 million in

a given year and zero otherwise as in Kempf, Manconi and Spalt

(2017). All transactions between two quoted American

companies in the period 1990-2017 reported in the Thomson

Reuter database are considered

Diversifying merger Dummy variable equal to one if the firm announces at least one

diversifying merger with minimum deal value of $1 million in

a given year, zero otherwise. A merger is defined diversifying

if the acquirer operates in a different Fama-French-12 (FF12)

industry than the target company as in Kempf, Manconi and

Spalt (2017).

Dividend cut Dummy variable equal to one if dividends in one year are

smaller than the previous year, zero otherwise as in Kempf,

Manconi and Spalt (2017).

CEO (director) luck Dummy variable equal to one if at least one grant was given to

the CEO (independent director) on a day which had the lowest

price of the month during the year and zero otherwise as in

Bebchuk, Grinstein and Peyer (2010).

Cash holdings Firm cash normalized by total assets as in Hoberg, Phillips and

Prabhala (2014)

Dividend payer Dummy variable equal to one if the company pays dividends in

a given year and zero otherwise as in Hoberg, Phillips and

Prabhala (2014)

Long-term debt to assets Ratio between total long-term debt and total assets

Total debt to assets Sum of total current and long-term debt normalized by total

assets

(continued)

- 71 -

Table A.1 – continued

Variables Description

Key independent variables

Distraction Annualized Kempf, Manconi and Spalt (2017) distraction

measure.

Threat Dummy variable equals to one if the firm is associated with a

level of investors’ distraction smaller than the first quartile, it

has at least one competitor with shareholders’ distraction greater

than the third quartile calculated in a given year, and zero

otherwise. The competitiveness between two companies is

based on rival score calculated by Hoberg and Philips (2016).

We require that the rank of rival score is less than 10. In other

words, we keep only the ten closest rivals to the most distracted

company in a given year.

Control variables

Log Size Logarithm of previous year total assets as in Kempf, Manconi

and Spalt (2017).

Tobin’s Q Lagged ratio of the market to book value as in Kempf, Manconi

and Spalt (2017).

Cash flow Income before extraordinary items plus depreciation and

amortization divided by lagged total asset as in Kempf, Manconi

and Spalt (2017).

Cash holdings Cash plus receivables divided by lagged total assets as in

Kempf, Manconi and Spalt (2017).

Institutional Ownership (IO) Lagged annual average of the fraction of the firm’s stock owned

by institutional investors as reported in the Thomson Reuters

database as in Kempf, Manconi and Spalt (2017).

Top 5 ownership Lagged annual average of the fraction of the firm’s stock owned

by the five largest institutional investors as reported in the

Thomson Reuters database as in Kempf, Manconi and Spalt

(2017).

Relative size Logarithm of the ratio between the lagged market capitalization

of the firm and the median market capitalization of all the firms

in that year as in Kempf, Manconi and Spalt (2017).

(continued)

- 72 -

Table A.1 – continued

Variables Description

Leverage Ratio between long term debt and total assets as in Kempf,

Manconi and Spalt (2017).

Tangibility (0.715×receivables+0.547×

inventory+0.535×capital+cash)/total assets (Kempf, Manconi

and Spalt 2017; Berger, Ofek and Swary 1996)

Firm age Logarithm of one plus the number of years since the firm

appears on CRSP as in Kempf, Manconi and Spalt (2017).

Market-to-book Ratio between market and book value as in Hoberg, Phillips and

Prabhala (2014).

Log assets Logarithm of firm’s total assets as in Hoberg, Phillips and

Prabhala (2014).

Earnings/assets

(profitability)

Ebitda plus balance sheet deferred taxes and interest expenses

lagged by total assets as in Hoberg, Phillips and Prabhala

(2014).

Asset growth Yearly percentage change in the total assets from year t-1 to year

t as in Hoberg, Phillips and Prabhala (2014).

Capx/assets Ratio between capital expenditure and total assets as in Hoberg,

Phillips and Prabhala (2014).

Neg. earnings dummy Dummy variable equal to one if the company registered

negative earnings in a given year and zero otherwise as in

Hoberg, Phillips and Prabhala (2014).

R&D/assets Ratio between research and development expenses and total

assets as in Hoberg, Phillips and Prabhala (2014) and Servaes

and Tamayo (2014).

Ret. Earnings/Assets Ratio between retained earnings and total assets (Hoberg,

Phillips and Prabhala 2014; DeAngelo, DeAngelo and Stulz

2006).

SG&A expenses / assets Selling, general and administrative expenses normalized by

assets as in Servaes and Tamayo (2014).

EBIT/assets Ratio between earnings before interest and taxes and assets as

in Servaes and Tamayo (2014).

PP&E / assets Net (total) property plan and equipment normalized by assets as

in Servaes and Tamayo (2014).

(continued)

- 73 -

Table A.1 – continued

Variables Description

Investment tax credits /

assets

Ratio between investment tax credits and assets as in as in

Servaes and Tamayo (2014).

- 74 -

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