The relationship between share repurchase

announcement and share price behaviour

Name: P.G.J. van Erp

Submission date: 18/12/2014

Supervisor: B. Melenberg

Second reader: F. Castiglionesi

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Master Thesis Finance

Tilburg University

Tilburg School of Economics and Management

Department of Finance

Title: The relationship between share repurchase announcement and share price behaviour

Name: P.G.J. van Erp

ANR: 600141

Supervisor: B. Melenberg

Submission date: 18/12/2014

Number of words: 15329

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Table of Contents Chapter 1. Introduction ........................................................................................................................... 6

Chapter 2. Theory section ..................................................................................................................... 12

2.1 Shares repurchase methods ........................................................................................................ 12

2.1.1 Open market repurchases .................................................................................................... 12

2.1.2 Fixed price tender offers ...................................................................................................... 12

2.1.3 Dutch auction repurchases ................................................................................................... 12

2.2 Hypotheses .................................................................................................................................. 13

2.2.1 Mispricing hypothesis .......................................................................................................... 13

2.2.2 Free cash flows hypothesis ................................................................................................... 14

2.2.3 Earnings per share dilution hypothesis ................................................................................ 15

2.2.4 Leverage hypothesis ............................................................................................................. 16

2.2.5 Tax benefits hypothesis ........................................................................................................ 17

Chapter 3. Data analysis ........................................................................................................................ 19

3.1 Sample ......................................................................................................................................... 19

3.2 Variables ...................................................................................................................................... 23

3.2.1 Dependent variable .............................................................................................................. 23

3.2.2. Independent variables ......................................................................................................... 23

3.2.3 Control variables................................................................................................................... 25

3.3 Method ........................................................................................................................................ 26

Chapter 4. Empirical analysis ................................................................................................................. 30

4.1 Empirical results .......................................................................................................................... 30

4.1.1 The effect of the book to market ratio on the firm’s CARs .................................................. 34

4.1.2 The effect of the logarithm of total assets on the firm’s CARs ............................................ 34

4.1.3 The effect of the logarithm of market capitalization on the firm’s CARs ............................. 35

4.1.4 The effect of the total number of employees on the firm’s CARs ....................................... 35

4.1.5 The effect of the free cash flow on the firm’s CARs ............................................................. 35

4.1.6 The effect of the return on assets ratio on the firm’s CARs ................................................. 36

4.1.7 The effect of the options outstanding on the firm’s CARs ................................................... 36

4.1.8 The effect of the options exercised on the firm’s CARs ....................................................... 37

4.1.9 The effect of the debt to asset ratio on the firm’s CARs ...................................................... 37

4.1.10 The effect of the percentage of dividend on the firm’s CARs ............................................ 37

4.1.11 The effect of the dividend dummy on the firm’s CARs ...................................................... 38

4.2 Sensitivity analysis ....................................................................................................................... 45

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Chapter 5. Conclusion and discussion ................................................................................................... 46

Literature ............................................................................................................................................... 51

Appendix ................................................................................................................................................ 54

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Abstract

In this thesis I investigate the relationship between share repurchase announcement and share

price behaviour for the period 2000-2012, by assessing the cumulative abnormal returns

(CARs) of share repurchases. The study includes 749 unique share repurchase

announcements. I find a cumulative average abnormal return of 0.04, by investigating the

window of one day before until one day after the share repurchase announcement. Moreover,

this is statistically significant at the 1% significance level. Additionally, I find a statistically

significant positive relation between the book to market ratio and the firm’s CARs, at the 5%

significance level. I also find a statistically significant negative relation between the logarithm

of total assets and the firm’s CARs, at the 1% significance level. Furthermore, I find a

statistically significant negative relation between the logarithm of market capitalization and

the firm’s CARs, at the 1% significance level. All three of these results are consistent with the

mispricing hypothesis. Additionally, I find a statistically significant negative relation between

dividend paying firms and the firm’s CARs, at the 10% significance level. This is consistent

with the tax benefits hypothesis. Also the hypotheses: free cash flow hypothesis, earnings per

share dilution hypothesis, and the leverage hypothesis, are tested. The free cash flow and the

return on assets are used to test the free cash flow hypothesis. I find mixed results between the

relations of these variables on the firm’s CARs. But none of these results are significant. For

the relation between the options outstanding and options exercised with the firm’s CARs, a

positive insignificant relation is found. So, no evidence is found to support the earnings per

share dilution hypothesis. Finally, I find a positive relation between debt to asset ratio and the

firm’s CARs. This is consistent with the theory for the leverage hypothesis. However this

result is not statistically significant.

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Chapter 1. Introduction

Share repurchase programs have been increasingly popular over the last years and are also

gaining importance. One of the explanations for the increasing popularity of share repurchase

behaviour is the change in governance over the last years. Brav et al. (2005) conclude that

share repurchases are now a more important form of payout compared to the past. This

upward trend of popularity for share repurchases already started in the 1980s. Since then there

are multiple studies conducted about the short- and long-term effects of share repurchases.

Most of the considered ‘relevant papers’, such as Vermaelen (1981), Ikenberry et al. (1995),

Stephens and Weisbach (1998), and Lie (2005), find positive cumulative abnormal return

(CAR) for the short- and long term horizon around the announcement date. But, this is not the

case for all studies, for example Grullon and Michaely (2004) find that repurchase

announcements are not followed by an increase in performance. Additionally, there is a

debate about many of the explanations and consequences of the effect share repurchase

announcements have on share price behaviour. Moreover, mixed results are found about these

explanations in the literature. In this study I try to tackle this problem by investigating the

possible effects share repurchase announcements have on share price behaviour. I do this by

first investigating the relation between share repurchase announcements and share price

behaviour. Next, to investigate the possible reasons for the effect share repurchase

announcements have on share price behaviour, I come up with five hypothesis based on past

literature. These hypotheses are the mispricing hypothesis, free cash flow hypothesis, earnings

per share dilution hypothesis, leverage hypothesis, and tax benefits hypothesis. These

hypotheses have already been tested collectively in the literature, or in some papers only one

or more of these hypothesis are tested. In this study I explain the effects that multiple

variables could have on the firm’s CARs. Furthermore, this study is conducted over a

relatively recent period of 2000 until 2012

The mispricing hypothesis is based upon signalling undervaluation of a company. Assuming

the market is imperfect, because there is asymmetric information between insiders (typically

the managers) and outside investors. The asymmetry could lead to mispricing of companies.

Vermaelen (1981, 1984) concludes that firms offer premia for their shares, mainly in order to

signal positive information. Also Dann (1981) concludes that firm values significantly

increase within one day of a common stock repurchase announcement, principally due to the

signalling of information. Stephens and Weisbach (1998) support the mispricing hypothesis

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by finding a negative relation between share repurchases and prior stock performance,

suggesting that firms increase their purchasing depending on their degree of perceived

undervaluation. In addition, also Dittmar (2000) finds evidence that supports the

undervaluation hypothesis. However, in contrast to the studies discussed before, Chan et al.

(2004) and Jagannathan and Stephens (2003) show only modest evidence for the mispricing

hypothesis. Dittmar and Dittmar (2008) even show evidence against it, by analyzing waves in

corporate finance events including stock repurchases.

The free cash flow hypothesis is based on the separation of ownership and control in

companies, because this separation could possibly lead to agency costs. By repurchasing

shares, the managers could be more constrained, because it reduces the excess free cash flow.

For example, by reducing the free cash flows there is less room for investing in negative net

present value projects. The free cash flow hypothesis is supported by Stephens and Weisbach

(1998). Also, Gangopadhyay et al. (2010), and Grullon and Michaely (2004) support the free

cash flow hypothesis. They find that firms with free cash flows earn significantly higher

abnormal returns than all other firms. Finally, Chan et al. (2004) find limited support for the

free cash flow hypothesis.

The earnings per share (EPS) dilution hypothesis is based on the tendency that managers often

try to manage measurements such as the EPS. Moreover, one of the reasons to repurchase

stocks could be to prevent EPS dilution, because of the simple principle that the reduction of

common shares outstanding leads to an increase in the value per share, since the company’s

value is shared by less investors. Kahle (2002) provides evidence to support the earnings per

share dilution hypothesis. Also Bens et al. (2003) show support for the prevention of the EPS

dilution by firms, by repurchasing shares. Moreover, Brav et al. (2005) conclude that firms

are likely to repurchase shares when their stock’s float is adequate, and when Chief Financial

Officers (CFOs) have the desire to offset option dilution. Additionally, they find that two-

thirds of their survey respondents feel that offsetting dilution is an important or very

important factor affecting their repurchase decisions.

The leverage hypothesis is based on the adjustment of the capital structure. By repurchasing

shares, firms could adjust their capital structure to look for a more beneficial leverage ratio.

Modigliani and Miller (1958, 1963) opened the discussion in scientific research about

adjusting the capital structure in order to find a more optimal leverage ratio. This theory is

mainly based on the effect of tax benefits of interest payments by adjusting the firm’s capital

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structure. Dittmar (2000) and Hovakimian et al. (2001) provide evidence to support the

leverage hypothesis. On the other hand, Chan et al. (2004) do not find enough evidence to

support the leverage hypothesis.

Finally, the tax benefits hypothesis is based on the difference in tax burdens. For example,

share repurchases could be more beneficial to shareholders than dividends. Black (1976)

states that a corporation that pays no dividends will be more attractive to taxable individual

investors than a similar corporation that pays dividends, when dividends are taxed more

heavily than capital gains, and where capital gains are not taxed until realized. Grullon and

Michaely (2002) state that it is more efficient to return capital to shareholders through a stock

repurchase program instead of dividends after the reduction in long-term capital gains tax.

However, Jagannathan et al (2000) do not find evidence to support this conclusion. Moreover,

Brav et al. (2005) conducted a survey about the payout behaviour of managers. They find that

managers at most firms do believe that taxes are not a dominant factor that affects the payout

decision making.

The method used in my master thesis to measure share price behaviour is an event study.

First, the abnormal returns are calculated. This is used to measure the economic impact of

share repurchases, over a relatively short period of time. By subtracting the firm’s predicted

‘normal returns’ from the firm’s actual returns, the abnormal returns are calculated. The

predicted ‘normal returns’ are calculated by using an estimation window that is considered not

to be affected by the announcement of share repurchases. The main event windows used in

this study are: one day before until one day after the announcement of share repurchase [-1,1],

and ten days before until 2 days before the announcement of share repurchase [-10,-2]. The

estimation window is 200 days before until 31 days before the announcement of share

repurchase [-200,-31]. Furthermore, to evaluate the effect of share repurchase announcements

on share price behaviour, more event windows are used. In total nine event windows are used:

[-1,1], [-3,3], [-1,1], [-5,1], [-5,5], [-10,-2], [-10,10], [-15,1], [-15,15], [-30,30], these indicate

days around the announcement day of share repurchase at day 0. To calculate the abnormal

returns, the market model is used, opposed to the related Capital Asset Pricing Model

(CAPM), and the Arbitrage Pricing Theory (APT). This is further explained in chapter 3.3. As

explained by MacKinlay (1997), the market model is a statistical model which relates the

return of any given security to the return of the market portfolio.

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In this study I obtained a sample that consists of companies from the United States. They are

listed on the indices: NASDAQ, NYSE and AMEX. The sample includes 1087 share

repurchase announcements with unique companies, and it only includes open market

repurchases. In order to create a sample where all variables have the same number of

observations, 749 firms from the full sample are selected. Firms from the divisions Finance,

Real Estate, and Insurance are excluded from the study. Also, firms from the division

Communications, Electric, Gas and Sanitary service, and Transportation, are excluded from

the study. The reason for this is that it is more likely that these firms were regulated. To

obtain my data, the Center for Research in Securities Pricing (CRSP) database, the Securities

Data Corporation (SDC) database, and COMPUSTAT are used.

In order to test the mispricing hypothesis, the free cash flow hypothesis, the earnings per share

dilution hypothesis, the leverage hypothesis, and the tax benefits hypothesis, a time series

ordinary least squares (OLS) regression is used. To test the five hypothesis further explained

in chapter 2.2, several variables are used, further explained in chapter 3.2. Each of the

independent variables is used to test for one of the five hypotheses. Most variables are based

on past literature, such as Bens et al (2003), Ikenberry et al. (1995), Grullon and Michaely

(2002, 2004) and Lie (2005). To test for the mispricing hypothesis, I added the number of

employees as a proxy for size.

In my study I investigate the effect share repurchase announcements have on share price

behaviour. This study is conducted for several windows around the announcement day. Based

on the window [-1,1] I find a cumulative average abnormal return (CAAR) of 0.04, this is

statistically significant1 at the 1% significance level. So, share repurchase has a significant

positive effect on share price behaviour. Besides, the window [-10,-2] is used to test the

relation between share repurchase and prior stock performance. The corresponding CAAR is

-0.07 and it is statistically significant, at the 5% significance level. This provides evidence for

the mispricing hypothesis. Furthermore, I find a positive statistically significant relation

between the book to market ratio and the firm’s CARs, at the 5% significance level. I also

find a negative statistically significant relation between the logarithm of market capitalization

and the firm’s CARs, and also for the relation between the logarithm of total assets and the

firm’s CARs, both at the 1% significant level. All these results show support for the 1 The default level for statistical significance throughout the rest of this study is set to a 10% statistical significance level. So, with statistical significance I mean the statistical significance at a 10% level. If the statistical significance level is different, this is mentioned explicitly.

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mispricing hypothesis. Furthermore, I find a negative relation between the number of

employees and the firm’s CARs. However, this is not statistically significant.

To investigate for the free cash flow hypothesis, I tested the effects of free cash flow and

return on assets on the firm’s CARs. For both variables I find mixed results about the relation

with the firm’s CARs. But none of these results are statistically significant.

To investigate for the earnings per share dilution hypothesis, I tested the effects of options

outstanding and the options exercised on the firm’s CARs. I predicted it to have a negative

relation with the firm’s CARs. However, for both variables I find a positive relation between

the independent variable and the firm’s CARs. But these relations are not statistically

significant.

To investigate for the leverage hypothesis, I tested the effect of debt to assets ratio on the

firm’s CARs. I predicted it to have a positive relation between the debt to assets ratio and the

firm’s CARs. I indeed find a positive relation between these variables, but this relation is not

statistically significant.

Finally, to investigate the tax benefits hypothesis, I tested the effects of dividend dummy and

percentage of dividend on the firm’s CARs. For both of these relations I predicted it to have a

negative relation with firm’s CARs. I also find a negative statistically significant relation

between dividend dummy and the firm’s CARs. On the other hand, I find a positive

statistically significant relation between percentage of dividend and the firm’s CARs.

However, this last result is based on a regression which has some levels of multicollinearity.

This is explained in chapter 4.1. So, this last result is treated with caution.

To conclude, my results show support for the mispricing hypothesis and the tax benefits

hypothesis. Based on the other results, I do not provide enough evidence to support the free

cash flow hypothesis, earnings per share dilution hypothesis, and the leverage hypothesis.

I also provide evidence for different abnormal returns because of share repurchases among

different industries. This might be interesting to investigate as part of future research. Another

recommendation for future research is to try combining other data sources such as Zephyr,

Orbis, or firm’s financial reports, to obtain a full sample without performing a selection

process. Besides, it would be interesting to also test for the long term effect of share

repurchases. Furthermore, the agency costs theory, explained in chapter 2.1, could be

investigated more specifically. I found evidence for the mispricing hypothesis, which is

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related with agency costs, but it is for example interesting how this would relate to manager’s

compensation and ownership.

The structure to explain the sample analysis, method, and empirical results shows similarities

with my previous conducted master thesis, van Erp (2014). Moreover, the description of the

dependent variable and the description of the variable industry dummy are also similar to the

descriptions provided in this master thesis.

The remainder of my master thesis is organized as follows: Chapter 2 presents the theory

section. Chapter 3 presents the data analysis, it describes the sample, variables, and the

method used in this study. Chapter 4 presents the empirical analysis, it consists of the

empirical results and a sensitivity analysis. Chapter 5 presents the conclusion and discussion.

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Chapter 2. Theory section

2.1 Shares repurchase methods Repurchasing Shares of its own company became increasingly popular over the last years and

are also gaining importance When repurchasing its own shares, a company distribute a large

amount of money to its shareholders. There are several methods to repurchase the shares, the

most essential methods are: open-market, fixed price tender offer, and Dutch auction tender

offer. These three methods are explained below.

2.1.1 Open market repurchases

Among the three methods, the open market repurchase is the most popular. Chan et al. (2004)

report a big increase in the number of firms announcing open market stock repurchases in the

1990s. In an open market repurchase, the firm announces to repurchase their stock on the

open market. So, the repurchase price of the stock will be based on the market price, and is

just the same as for any other investor.

2.1.2 Fixed price tender offers

In fixed price tender offers the share price, number of shares, and duration of the offer is

predefined. In contrast to open market repurchases these conditions are fixed. When issuing

fixed price tender offers the firm gives shareholders the option to tender within the predefined

condition. Additionally, the firm can set a target to the minimum number of tendered shares.

If this condition is not met it still has the option to repeal the offers. Moreover, the firm is also

allowed to adjust the duration of the period for repurchase and it could change the number of

shares it want, to repurchase.

2.1.3 Dutch auction repurchases

The Dutch auction was a specification of the fixed price tender method, introduced in 1981.

Dutch auction repurchases are somewhat similar to fixed price tender offers, except for Dutch

auction repurchases the firm offers a range of prices shareholders can sign up to. Shareholders

can tender for the minimum price of a specified number of shares they are willing to accept.

The firm can repeal the offers when there are fewer shares submitted for sale as the

predefined amount, or it can repurchase the shares at the tendered prices. When there are more

shares submitted for sale, the firm repurchases shares at a uniform lowest price possible that

allows them to buy back the predetermined number of shares. So, this price is offered to

shareholders who tendered at or below this price.

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2.2 Hypotheses

In this section I elaborate on the possible explanations why firms repurchase shares.

Furthermore, I come up with different hypotheses about the relation between share

repurchases and firm performance.

2.2.1 Mispricing hypothesis

The mispricing hypothesis is based upon signalling for undervaluation of a company.

Assuming the market is imperfect, because there is asymmetric information between insiders

(typically the managers) and outside investors. The asymmetry could lead to mispricing of

companies. Following the reasoning provided by Erken (2012), In most cases the managers

are better informed about the current and future prospects of the company. For example, about

the company’s expectations, its opportunities, and prospects. When the corporation is

undervalued, so the company is actually worth more than the current market value, managers

could have the incentive to adjust this mispricing. To make the statement of undervaluation

believable, the managers could repurchase their own stock to signal a positive view about the

future of the corporation. Eventually, this could lead to a positive stock price reaction. Often

in the literature a distinction is made between value stocks and growth stocks. Firms with a

high book to market ratio are considered to have value stocks, and firms with a low book to

market ratio are considered to have growth stocks. As concluded by Ikenberry, Lakonishok,

and Vermaelen (1995), value stocks are more likely to have undervaluation as their primary

motivation. Vermaelen (1981, 1984) concludes that firms offer premia for their shares, mainly

in order to signal positive information. It also makes a distinction between small firms and

large firms. Small firms are considered to have higher levels of information asymmetry. Also

Dann (1981) concludes that firm values significantly increase within one day of a common

stock repurchase announcement. These increases principally appear to be due to an

information signal from the repurchasing firm. As stated by Dann (1981), these positive

values are considered to be permanent in a way that the share prices do not return to their pre-

announcement date levels following expiration of the opportunity for stockholder to tender

shares. However, the nature of the new information that results in a positive stock price

change is still unidentified. Stephens and Weisbach (1998) support the mispricing hypothesis

by finding a negative relation between share repurchases and prior stock performance,

suggesting that firms increase their purchasing depending on their degree of perceived

undervaluation. In addition, also Dittmar (2000) finds evidence that supports the

undervaluation hypothesis. However, in contrast to the studies discussed before, Chan et al.

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(2004) show only modest support for the mispricing hypothesis, when testing for the short-

horizon market reaction to the share repurchase announcement. Moreover, over a four-year

window after the announcement, earnings surprises tend to be positive and significant. This

result suggests that the market does not completely incorporate the information in share

repurchase announcements. Also, the study of Jagannathan and Stephens (2003) causes

doubts for the undervaluation hypothesis. They examine differences in firms that repurchase

shares frequently versus firms that repurchase only occasionally or infrequently. They find

that frequent repurchasers may be using it as a substitute for increasing dividends, but

unlikely because of firm undervaluation. On the other hand, they find that infrequent

repurchases may be motivated by undervaluation. Still, they find a positive market reaction

for all repurchase announcements on average, but the infrequent repurchases have a much

stronger positive reaction. Finally, Dittmar and Dittmar (2008) find evidence against the

mispricing hypothesis, by analyzing waves in corporate finance events including stock

repurchases, leading to a conclusion that equity issuance (and repurchases) predicts lower

returns and likely reflects time-varying costs of capital rather than mispricing. So, to

conclude, firms could use share repurchase to adjust for the undervaluation of their company.

2.2.2 Free cash flows hypothesis

A general believe in finance is that the separation of ownership and control in companies can

lead to agency costs. By repurchasing the shares of your own companies, the managers could

be more constrained, because it reduces the excess free cash flow. As argued by Jensen (1986)

followed upon Rozeff (1982) and Easterbrook (1984)

“ Payouts to shareholders reduce the resources under managers' control, thereby reducing managers'

power, and making it more likely they will incur the monitoring of the capital markets which occurs

when the firm must obtain new capital”.

Stephens and Weisbach (1998) conducted a study including the relation between cash flow

and share repurchases. They find that both expected and unexpected cash flows are positively

related to repurchases, therefore, suggesting that firms actively adjust their share repurchases

behaviour to their cash position. Furthermore, Gangopadhyay et al. (2010) test the free cash

flow hypothesis by examining the announcement-period abnormal returns of repurchasing

firms sorted by their available investment opportunities, as measured by Tobin’s q ratio and

cash flows. They find that firms with free cash flows earn significantly higher abnormal

returns than all other firms. By reducing the free cash flows there is less room for investing in

negative net present value projects. By repurchasing stock, shareholders can invest in other

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positive investment opportunities. In general, this could lead to a more optimal capital market

allocation, because capital is moved from negative net present value investments, towards

positive net present value investments, as argued by Grullon and Ikenberry (2000).

Eventually, it would be more beneficial for firms with a high level of free cash flow to

repurchase stock. This is supported by Grullon and Michaely (2004), who find that the market

reaction to share repurchase announcements is more positive among those firms that have

higher levels of free cash flows, and therefore were more likely to overinvest. So, by

repurchasing stock, the corporation reduces the risk of investing disproportionally. This is one

of the possible solutions to reduce empire building, because it prevents managers to focus too

much on increasing the size of the corporation, rather than the size of its profits. Furthermore,

reducing the free cash flow could reduce the fringe benefit consumption. It also could prevent

managers for holding on to underperforming subordinates for too long. However, Chan et al.

(2004) find limited support for the free cash flow hypothesis. They support the hypothesis by

showing that repurchase firms tend to have above-average free cash flow compared to their

industry peers. Moreover, the long-run drift is greater for high free cash flows firms compared

to low free cash flow cases. But, on the other hand, they can’t support the hypothesis that the

gains from high free cash flow firms should be linked to cases where managers actually

disgorge cash. So, to conclude, managers could use share repurchase to reduce agency costs.

2.2.3 Earnings per share dilution hypothesis

Earnings per share (EPS) is an important measure to analyse firms in general. Often managers

try to improve measurements such as the EPS. Moreover, one of the reasons to repurchase

stocks could be to prevent EPS dilution, because of the simple principle that the reduction of

common shares outstanding leads to an increase in the value per share, since the company’s

value is shared by less investors. Stock dilution could emerge from the issue of additional

shares. This could occur from primary market offerings, preferred shares or warrants into

stock, exercised stock options, or by conversed convertible bonds. According to Kahle (2002),

firms announce for share repurchases when executives have large numbers of options

outstanding and when employees have large numbers of options currently exercisable.

Moreover, there is a positive relation between the amount repurchased and the total options

exercisable by all employees, once the repurchase decision is made. However, the amount

repurchased is independent of managerial options according to Kahle (2002). Also Bens et al.

(2003) show support for the prevention of the EPS dilution by firms, because they find an

increase in the level of stock repurchases when the dilutive effect of outstanding employee

16

stock options on diluted EPS increases. This increase also occurs when the firm’s earnings are

lower than required to achieve their EPS growth target. This increase is based on the incentive

to manage EPS dilution, instead of also the incentive to adjust the basic EPS, according to

Bens et al. (2003). Consistent with this theory, Brav et al. (2005) find that Chief Financial

Officers (CFOs) are very conscious of the affect share repurchases have on the earnings per

share. Furthermore, they conclude that firms are likely to repurchase when their stock’s float

is adequate, and when CFOs have the desire to offset option dilution. Additionally, they find

that two-thirds of their survey respondents feel that offsetting dilution is an important or very

important factor affecting their repurchase decisions. So, share repurchase could be used to

adjust for possible share dilution.

2.2.4 Leverage hypothesis

By repurchasing shares, the corporation could adjust their capital structure, especially if the

amount of repurchases is high. This could lead to a more beneficial leverage ratio. Modigliani

and Miller (1958, 1963) opened the discussion in scientific research about adjusting the

capital structure in order to find a more optimal leverage ratio. This theory is mainly based on

the effect of tax benefits of interest payments by adjusting the firm’s capital structure. So, by

repurchasing stocks, firms are able to adjust their leverage ratio. According to Dittmar (2000),

this is one of the reasons why firms repurchase stocks during certain periods. Hovakimian et

al. (2001) continue on the theory that firms adjust their capital structure, to move to a target

debt ratio. They take a closer look at the impediments firms may face when moving toward

their target ratio, and the possible change in the target ratio over time. To conclude, they find

that capital structure considerations are more important for firms when they repurchase stock

rather than raise capital. Moreover, Hovakimian et al. (2001) state

“The tendency of firms to make financial choices that move them toward a target debt ratio appears

to be more important when they choose between equity repurchases and debt retirements than when

they choose between equity and debt issuances.”

However, Chan et al. (2004) do not support the leverage hypothesis, although they find that

repurchasing firms tend to have below average leverage, but these firms do not have any

higher drift when compared to firms with a higher leverage. Also, there was no significant

distinction in returns for firms that had sharp declines in leverage. Also, Brav et al. (2005)

find little support for the leverage hypothesis by surveying financial executives and

conducting in-depth interviews. So, to conclude, firms could repurchase share to adjust their

capital structure to reach for a more optimal capital structure.

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2.2.5 Tax benefits hypothesis

There are several ways of returning profits or an excess of capital to the shareholders. For

example, share repurchases could be more beneficial to shareholders than dividends, because

of tax benefits. So, the difference in tax burden could be a reason for companies to repurchase

shares. Black (1976) states that a corporation that pays no dividends will be more attractive to

taxable individual investors than a similar corporation that pays dividends, when dividends

are taxed more heavily than capital gains, and where capital gains are not taxed until realized.

The tax benefits of share repurchases in the US were especially beneficial before the For Jobs

and Growth Tax Relief Reconciliation Act of 2003, because of the much lower taxation on

capital gains versus dividend payments. After the Jobs and Growth Tax Relief Reconciliation

Act of 2003, the taxes on qualified dividends 2were lowered to the capital gains level. This

was set to expire after 2010. However, in the Tax Relief, Unemployment Insurance

Reauthorization, and Job Creation Act of 2010 the dividends and capital gains rates were

extended for another two years. Finally, in the American Taxpayer Relief Act of 2012, the tax

rate levels on capital gains and dividends for 2013 remained the same as in 2012 for

individuals with taxable income of $400,000 per year or less. For individuals with taxable

income over $400,000, the top marginal tax rate on long-term capital gains was set to the

level of 20% for under expiration of the Jobs and Growth Tax Relief Reconciliation Act of

2003. The top marginal tax rate on dividends remained at the similar level as capital gains, so

also 20%, instead of the much higher 39,6% rate under expiration of the Jobs and Growth Tax

Relief Reconciliation Act of 2003. But after these changes in tax rates, share repurchases are

still considerate to be more beneficial than dividends, because investors have the option to

defer capital gains or losses by holding on to their shares. On the other hand, dividends are

taxed immediately when received. Blouin et al. (2007) tests for policy changes in dividends

and repurchases following the Jobs and Growth Tax Relief Reconciliation Act of 2003. They

find evidence consistent with dividends crowding out repurchases, as a result of reductions in

dividends and capital gains tax rates. However, Grullon and Michaely (2002) state that it is

more efficient to return capital to shareholders through a stock repurchase program instead of

dividends after the reduction in long-term capital gains tax. Moreover, they show that there is

a more positive market reaction to repurchases when the tax gains from repurchases relative

to dividends are larger. On the other hand, Jagannathan et al (2000) do not find evidence to

2 For a description of qualified dividends, see

http://www.irs.gov/instructions/i1099div/ar02.html#d0e154

18

support this conclusion. Their work suggests that besides taxes much more is necessary to

explain differences in motives to use dividends and repurchases. As discussed earlier, Brav et

al. (2005) conducted a survey about the payout behaviour of managers. They find that

managers at most firms do believe that taxes are not a dominant factor that affects the payout

decision making. Moreover, they state that executives believe that repurchases and dividends

are equally attractive to most institutional investors. So in general, share repurchase could be

used to return profits or an excess of capital to shareholders in a more tax beneficial way for

the shareholders.

19

Chapter 3. Data analysis

In order to analyze my data I will first describe the sample. Next, the variables will be

analyzed. Finally, the method I use in this study will be explained. This is the same order to

analyze data as used in van Erp (2014).

3.1 Sample In this study I obtained a sample that consists of companies from the United States. They are

listed on the indices: NASDAQ, NYSE and AMEX. 498 of the companies are listed in the

NYSE index, 552 in the NASDAQ index, and 37 in the AMEX index. The sample includes

share repurchase announcements for the period 2000-2012. It only includes open market

repurchases. Firms from the divisions Finance, Real Estate, and Insurance are excluded from

the study. These are marked by the Standard Industrial Classification (SIC) codes 6000 until

6799. Also, firms from the division Communications, Electric, Gas and Sanitary service,

Transportation, marked by the SIC codes 4000 until 4999, are excluded from the study. The

reason for this is that it is more likely that these firms were regulated. In total 505 firms were

removed from the sample because of this. Eventually, I end up with a sample consisting of

1087 share repurchase announcements with unique companies. The statistics about the

variables used in this study are listed in table 1. To obtain my data, the Center for Research in

Securities Pricing (CRSP) database, the Securities Data Corporation (SDC) database, and

COMPUSTAT are used.

As displayed in table 1 not all the variables have the same number of observations, this is

because of missing data for some firms. In order to conduct my study, I created a sample

where all variables have the same number of observations. This is displayed in table 2.

Because of this selection, a selection bias might occur. Moreover, t-tests are conducted to test

for differences in means among table 1 and 2. In the tables A13 and A14, the dependent

variables CAR[-1,1] and CAR[-10,-2] are tested respectively. In these t-tests the 𝐻0

hypothesis is that the mean CAR of table 1 and the mean CAR of table 2 are equal. So, the

difference between the two variables is zero. The alternative hypothesis is: the difference

between the mean CAR of table 1 and the mean CAR of table 2 is unequal to zero. Based on

both tables the 𝐻0 hypothesis: the mean CAR of table 1 and the mean CAR of table 2 are

equal, cannot be rejected, because the corresponding p-values are 0.3901 and 0.9681 in tables

A13 and A14 respectively. So, there is no evidence found that the two samples are different.

20

Furthermore, for each independent variable a similar t-test is conducted. For all of the

variables except for the options outstanding, the 𝐻0 hypothesis: the mean of the variable in

table 1 and the mean of the variable in table 2 are equal, cannot be rejected. In table A15 the t-

test for the options outstanding is displayed. Based on the p-value of 0.0939, the 𝐻0

hypothesis can be rejected, at the 10% significance level. But, based on all the t-tests I

conclude that there does not seem to be any level of selection bias, and that the sample

displayed in table 2 is still representative of the population that I intend to analyse. Moreover,

by conducting a t-test for all the 17 variables separately, at the 10% significance level, I could

expect that 10% of the test results will be rejected.

21

Table 1

Descriptive statistics of the full sample

This table provides the descriptive statistics of the full sample over the period 2000-2012. It provides the statistics about the number of observations, mean, standard deviation, and the minimum and maximum value of the variables. The currency in the descriptive statistics is the U.S. dollar. The ‘MM’ behind some variables stands for measured in millions. The cumulative abnormal return (CAR) [-1,1] is based on the window 1 day before the announcement of share repurchase until 1 day after the announcement of share repurchase. The window [-10,-2], stands for 10 days before the announcement of share repurchase until 2 days before the announcement. The book to market ratio is calculated by dividing the book value of equity by the market value of equity. The market capitalization is calculated by multiplying the share price with the number of common shares outstanding. The free cash flow is calculated by dividing the variable ‘Cash’ by the book value of equity. The capital expenditure is used as a percentage of the total assets. The return on assets is calculated by dividing net income by total assets. Options outstanding represent the stock options that are exchangeable for common stock of the firm. Options exercised represent the total number of stock options that were exercised for common stock. Debt to asset ratio is calculated by dividing long term debt by total assets. Total dividend ratio is calculated by dividing total dividend by total assets. Dividend dummy is a variable about the distinction between firms that pay dividends and firms that do not pay dividend. Firms that pay dividend are marked by ‘1’ and firms that do not pay dividend are marked by ‘0’. Finally, three industry dummies are displayed. Each of the dummies takes on the value 1 if the firm is from that particular industry.

Variable Observations Mean Std. Dev. Min Max

CAR [-1.1] 1087 0.04 0.34 -5.03 2.45

CAR [-10.-2] 1087 -0.07 0.97 -16.43 5.14

Book to market ratio 1081 0.59 0.51 0.00 7.16

Logaritm of total assets 1084 6.65 1.83 -3.41 13.53

Logaritm of market capitalization 1082 6.76 1.97 -4.89 12.86

Number of employees 1071 15450.39 58385.41 4 1383000

Free cash flow 1074 0.36 1.45 0 35.38

Percentage of capital expenditures 1078 0.05 0.06 0 0.55

Return on Assets 1084 0.04 0.25 -5.78 1.48

Options outstanding (MM) 923 14.97 53.16 0 949

Options exercised (MM) 770 2.00 9.43 0 198

Debt to asset ratio 1076 0.15 0.16 0 0.75

Percentage of dividend 1083 0.01 0.03 0 0.48

Dividend dummy 1087 0.41 0.49 0 1

Manufacturing industry dummy 1087 0.53 0.50 0 1

Service industry dummy 1087 0.27 0.44 0 1

Remaining industry dummy 1087 0.20 0.40 0 1

22

Table 2

Descriptive statistics of the sample with equal amounts of observations for each variable

This table provides the descriptive statistics of the sample with equal amounts of observations for each variable over the period 2000-2012. It provides the statistics about the number of observations, mean, standard deviation, and the minimum and maximum value of the variables. The currency in the descriptive statistics is the U.S. dollar. The ‘MM’ behind some variables stands for measured in millions . The cumulative abnormal return (CAR) [-1,1] is based on the window 1 day before the announcement of share repurchase until 1 day after the announcement of share repurchase. The window [-10,-2], stands for 10 days before the announcement of share repurchase until 2 days before the announcement. The book to market ratio is calculated by dividing the book value of equity by the market value of equity. The market capitalization is calculated by multiplying the share price with the number of common shares outstanding. The free cash flow is calculated by dividing the variable ‘Cash’ by the book value of equity. The capital expenditure is used as a percentage of the total assets. The return on assets is calculated by dividing net income by total assets. Options outstanding represent the stock options that are exchangeable for common stock of the firm. Options exercised represent the total number of stock options that were exercised for common stock. Debt to asset ratio is calculated by dividing long term debt by total assets. Total dividend ratio is calculated by dividing total dividend by total assets. Dividend dummy is a variable about the distinction between firms that pay dividends and firms that do not pay dividend. Firms that pay dividend are marked by ‘1’ and firms that do not pay dividend are marked by ‘0’. Finally, three industry dummies are displayed. Each of the dummies takes on the value 1 if the firm is from that particular industry.

Variable Observations Mean Std. Dev. Min Max

CAR [-1.1] 749 0.03 0.31 -5.03 1.98

CAR [-10.-2] 749 -0.07 0.92 -16.43 3.06

Book to market ratio 749 0.59 0.52 0.00 7.16

Logaritm of total assets 749 6.70 1.77 2.15 13.53

Logaritm of market capitalization 749 6.80 1.86 1.32 12.86

Number of employees 749 12467.03 37857.83 4 760000

Free cash flow 749 0.41 1.72 0 35.38

Percentage of capital expenditures 749 0.05 0.05 0 0.52

Return on Assets 749 0.06 0.14 -2.00 0.70

Options outstanding (MM) 749 10.95 44.68 0 949

Options exercised (MM) 749 2.00 9.53 0 198

Debt to asset ratio 749 0.14 0.16 0 0.75

Percentage of dividend 749 0.01 0.03 0 0.48

Dividend dummy 749 0.42 0.49 0 1

Manufacturing industry dummy 749 0.56 0.50 0 1

Service industry dummy 749 0.26 0.44 0 1

Remaining industry dummy 749 0.18 0.38 0 1

23

3.2 Variables

3.2.1 Dependent variable

In this master thesis I test for the relation between share repurchase announcement and share

price behaviour. To explain share price behaviour I use the cumulative abnormal return

(CAR), based on the share prices of firms around the announcement of share repurchase. As

described in van Erp (2014), to calculate the abnormal returns the differences between the

stock prices and the expected returns are taken. In chapter 3.3 about the method, this is further

explained. In this study multiple event windows are used, as displayed in table 4. To test for

the relation of different variables with the firm’s CARs for share repurchase, I focus on the

event window of one day before the announcement of share repurchase until one day after the

announcement of share repurchase, resulting in the window [-1,+1]. Furthermore, to test for

the mispricing hypothesis a window of 10 days before the announcement until 2 days before

the announcement [-10,-2] is used. This window is used to test the relation between share

repurchases and prior stock performance. As argued by Stephens and Weisbach (1998), firms

adjust their share repurchase behaviour based on the perceived undervaluation. Finally, how

the CARs are obtained is described in chapter 3.3

3.2.2. Independent variables

In order to explain the possible relation between share repurchase announcement and share

price behaviour, I test for five hypotheses, as explained in chapter 2.2. To test these

hypotheses I use several different variables. In this chapter these are further explained.

Book to market ratio is calculated by dividing the book value of equity by the market

value of equity. This variable is a proxy for firm size. It is used to test for the mispricing

hypothesis. Firms with a high book to market ratio are considered to have value stocks, and

firms with a low book to market ratio are considered to have growth stocks. As concluded by

Ikenberry, Lakonishok and Vermaelen (1995), value stocks are more likely to have

undervaluation as their primary motivation.

The natural logarithm of total Assets is another proxy for firm size. After calculating

the natural logarithm of total assets, it is also used to test for the mispricing hypothesis.

According to Vermaelen (1981), small firms have higher levels of information asymmetry.

This affects the share repurchase behavior of managers.

The natural logarithm of Market capitalization is also a proxy for firm size. Therefore,

it is also used to test for the mispricing hypothesis. The market capitalization is the total value

24

of shares outstanding. It is calculated by multiplying the share price with the total common

shares outstanding.

The total number of employees is the last proxy for firm size used in this study. Also

this variable is used to test for the mispricing hypothesis.

Free cash flow is calculated by dividing the variable ‘Cash’ by the book value of

equity. The free cash flow is used to test for the free cash flow hypothesis. A general believe

in finance is that the separation of ownership and control in companies can lead to agency

costs. By repurchasing shares the free cash flow will decrease. This could reduce agency costs

because managers are more constraint, because there is for example less room for investing in

negative net present value project, or using cash flows for empire building or fringe benefit

consumption. It also could prevent managers for holding on to underperforming subordinates

for too long.

Return on assets is calculated by dividing net income by total assets. It may also

provide information about the free cash flow hypothesis, because the return on assets is

related to the free cash flow. Therefore, having higher returns on assets could lead to higher

agency costs. On the other hand, having higher levels of return on assets could signal financial

strength when financially healthy firms repurchase shares.

Options outstanding represent the stock options that are exchangeable for common

stock of the firm, that have not been exercised or cancelled. It includes, shares outstanding at

year-end, including employee plans and non-employee plans (i.e. director plans), Options

exchangeable for all classes of common stock, and "Stand alone" Stock Appreciation Rights

(SARs) that are not associated with options or "Additive" Stock Appreciation Rights (SARs)

that pay a cash amount when the option is exercised. This variable is used to test the earnings

per share dilution hypothesis. As stated by Kahle (2002), Firms announce repurchases when

executives have large numbers of options outstanding. A reason for this is to prevent share

dilution.

Options exercised represent the total number of stock options that were exercised for

common stock. Also this variable is used to test for the earnings per share dilution hypothesis.

According to Kahle (2002), there is also a positive relation between options exercised and the

announcement of share repurchase.

25

Debt to asset ratio is calculated by dividing long term debt by total assets. This ratio is

used to test the leverage hypothesis, because by repurchasing shares the capital structure

could be adjusted. As stated by Dittmar (2000), this is one of the reasons why firms

repurchase stocks during certain periods. Moreover the debt to asset ratio could be a driver for

agency cost, because having higher levels of debt could make manager more constraint.

Total dividend ratio is calculated by dividing total dividend by total assets. This

variable is used to test for the tax benefits hypothesis. There are several ways of returning

profits or an excess of capital to the shareholders. The difference in tax burden between

paying dividend versus repurchasing shares could be a reason for companies to repurchase

shares. This trade-off is addressed by Black (1976).

Dividend dummy is created to make a distinction between firms that pay dividends and

firms that do not pay dividend. Firms that pay dividend are marked by ‘1’ and firms that do

not pay dividend are marked by ‘0’. Again, this is also used to test for the tax benefits

hypothesis.

3.2.3 Control variables

In order to improve the model control variables are added to the test.

Capital expenditure represents the funds used for additions to property, plant, and

equipment. The capital expenditure is used as a percentage of the total assets.

Industry dummy is a dummy variable to test for differences between industries. This

variable is also used in van Erp (2014). To make a distinction between industries, the

Standard Industrial Classification (SIC) codes are used. Moreover, it makes a distinction

between the service, manufacturing, and remaining industries. The firms from the

manufacturing industry are indicated by the SIC codes 2000-3999. The firms from the Service

industry are indicated by the SIC codes 7000-8999. The industries and their corresponding

SIC codes for the group of the remaining industries are division Agriculture, Forestry and

Fishing (0100-0999), division Mining (1000-1499), division Construction (1500-1799),

division Wholesale Trade (5000-5199), division Retail Trade (5200-5999), and finally

division Public Administration (9100-9729).3

3 The website of United States Department of Labor (https://www.osha.gov/pls/imis/sic_manual.html)

provides an overview of the divisions using the SIC codes

26

3.3 Method The method used in this master thesis to measure share price behaviour is an event study. A

graphical representation of my event study is displayed in figure 1. First, the abnormal returns

are calculated. This is used to measure the economic impact of share repurchases, over a

relatively short period of time. Different windows are used to calculate the cumulative

abnormal returns (CARs), displayed in table 4. This study focuses on the event window of one

day before the share repurchase announcement until one day after the announcement. By

subtracting the firm’s predicted ‘normal returns’ from the firm’s actual returns, the abnormal

returns are calculated. The predicted ‘normal returns’ are calculated by using an estimation

window that is considered not to be affected by the announcement of share repurchases. The

estimation window used in this study is 200 days before the announcement of share

repurchase until 31 days before the announcement of share repurchase. As described in van

Erp (2014), to calculate normal performance returns are compared to market returns. These

market returns are based on the S&P500 index. To calculate the abnormal return a number of

approaches are available, for example the market model, The Capital Asset Pricing Model

(CAPM), and the Arbitrage Pricing Theory (APT). In this study the market model is used. As

explained by MacKinlay (1997), the market model is a statistical model which relates the

return of any given security to the return of the market portfolio. One of the assumptions for

the market model is that asset returns are jointly multivariate normal and independently and

identically distributed through time. According to MacKinlay (1997), this assumption is

empirically reasonable and inferences using the normal return models tend to be robust to

deviations from the assumption, therefore generally not leading to problems. The CAPM and

the APT are both economic models that cast some restrictions on statistical models, such as

the market model, to provide more constrained normal return models. But, according to Fama

and French (1996), the validity of the restrictions imposed by the CAPM on the market model

is questionable. Additionally, according to MacKinlay (1997), the gains from using an APT

model compared to the market model are small. Moreover, as discussed by Stephen Brown

and Mark Weinstein (1985), a general finding is that with the APT the most important factor

behaves like a market factor and additional factors add relatively little explanatory power.

These are one of the reasons why I chose to use the market model. Additionally, the market

model is used in many event studies in the literature, for example by Ikenberry et al. (1995)

and Stephens and Weisbach (1998). To explain the market model, I will use similar equations

and descriptions, as I did in my previous conducted master thesis, van Erp (2014). The

27

equations plus descriptions for the abnormal return (AR) and the cumulative abnormal return

(CAR) are obtained from my study van Erp (2014).

Based on the market model the abnormal return is:

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡- (𝛼𝑖 + 𝛽𝑖 𝑅𝑚𝑡),

hereby 𝐴𝑅𝑖𝑡 = abnormal return on stock i for day t; 𝑅𝑖𝑡 = return on stock i for day t; 𝛼𝑖 =

constant; 𝛽𝑖 =beta of stock i; and 𝑅𝑚𝑡 = return on the market portfolio for day t

The sum of the abnormal returns results in the cumulative abnormal return.

𝐶𝐴𝑅𝑖(𝑡1,𝑡2) = ∑ 𝐴𝑅𝑖𝑡𝑡2𝑡1 ,

where t1= the beginning day of the event window; t2= the last day of the event window; 𝐴𝑅𝑖𝑡

= abnormal return on stock i for day t. So 𝐶𝐴𝑅𝑖,(𝑡1,𝑡2) stands for the cumulative abnormal

return within the event window for each firm.

Finally to calculate the cumulative average abnormal return (CAAR), the average of all the

firm’s CARs is taken.

𝐶𝐴𝐴𝑅(𝑡1,𝑡2) =1

𝑁 ∑ 𝐶𝐴𝑅𝑖(𝑡1,𝑡2)

𝑡2𝑡1 ,

where t1= the beginning day of the event window; t2= the last day of the event window; N=

the total number of share repurchase announcements, which corresponds with the total

number of firms; and 𝐶𝐴𝑅𝑖 = cumulative abnormal return on stock i for day t. So 𝐶𝐴𝐴𝑅(𝑡1,𝑡2)

stands for the cumulative average abnormal return within the event window for the entire

sample.

By running an ordinary least squares (OLS) regression on the market model the estimates of

𝛼𝑖 and 𝛽𝑖 are obtained.

28

To test for possible explanations for the relation between share repurchase announcement and

share price behaviour, a time series OLS regressions is used. To test the five hypothesis

explained in chapter 2.2, several variables are used, explained in chapter 3.2. An important

assumption of OLS regressions is that the errors in the independent variable are negligible. In

order for the OLS estimators to be consistent, the independent variables in the OLS regression

are considered to be exogenous and there is no perfect multicollinearity. So, the explanatory

variables are contemporaneously exogenous of the error process. Furthermore, the variance of

the residuals is homogenous, and the errors are uncorrelated between observations. Also, the

OLS estimators are asymptotically normally distributed.

Exogeneity: E(ε𝑖 |𝑥𝑖) = 0 for all i = 1, …, n,

Where, ε𝑖 = the error term; and 𝑥𝑖 = the vector of explanatory variables.

And the variance of the error term is constant for each observation, resulting in:

Homoscedasticity: Var(ε𝑖 |𝑥𝑖) = 𝜎2

Figure 1

Event study

This figure displays the estimation window, event window, and the share repurchase announcement date. The estimation window is 200 before the announcement of share repurchase until 31 days before the announcement date. The event window is one day before until one day after the announcement of share repurchase. Finally, the share repurchase announcement is on day 0. The graphical representation is based on the design provided in van Erp (2014).

29

where, ε𝑖 = the error term; 𝑥𝑖 = the vector of explanatory variables; and 𝜎2 = variance of the

error term

Furthermore, the errors are uncorrelated between observations:

No autocorrelation: E(ε𝑖ε𝑗 |𝑥𝑖𝑇) = 0 for i ≠ j,

where, ε𝑖 and ε𝑗 are the error terms for different observations; and 𝑥𝑖𝑇 = the transposed vector

of explanatory variables.

By performing an ordinary least squares (OLS) regression, the regression equation is:

𝐶𝐴𝑅𝑖(𝑡1,𝑡2)= 𝛼 + 𝛽1 Book to market ratio𝑖 + 𝛽2 Logarithm of market capitalization𝑖 + 𝛽3

Logarithm of total assets𝑖 + 𝛽4 Number of employees𝑖 + 𝛽5 Return on assets𝑖 + 𝛽6

Free cash flow𝑖 + 𝛽7 Debt to asset ratio𝑖 + 𝛽8 Total dividends𝑖 + 𝛽9 Dividend dummy𝑖 +

𝛽10 Options exercised𝑖 + 𝛽11 Options outstanding𝑖 + 𝛽12 control variables𝑖 + ε𝑖,

where 𝐶𝐴𝑅𝑖 = cumulative abnormal return on stock i for day t; 𝛼 = constant; and 𝛽𝑗 =beta of

variable j, it is the intercept corresponding to each specific regressor; and ε𝑖 = the error term

To calculate the unknown 𝛽 the equation is: �̂� = (𝑋𝑇 𝑋)−1𝑋𝑇y,

where X= the matrix of explanatory variables; y = the dependent variable; and 𝑋𝑇 = the

transpose of matrix X.

30

Chapter 4. Empirical analysis

4.1 Empirical results To find possible abnormal returns for the days around the announcement of share repurchase,

an event study is conducted. This event study is conducted for several windows around the

announcement day. Table 4 displays the cumulative average abnormal returns (CAARs) for

nine different windows. This is the average of all the cumulative abnormal returns (CARs) for

all the firms for each of the displayed windows. Moreover, in table A12 in the appendix this is

also displayed for the three different indexes, namely: NYSE, NASDAQ, and AMEX. Most

of the CAARs are positive, but not all of them are statistically significant. The CAAR for the

window [-1,1] is 0.04, this is statistically significant 4 at the 1% significance level. The

window [-1,1] is often used in the literature to test for short term abnormal performance. This

window will also mainly be used in this study. The positive CAAR provides evidence for the

hypothesis that share repurchases have a positive effect on firm performance. Besides, the

window [-10,-2] is used to test the relation between share repurchases and prior stock

performance. The selected window is based on past literature, such as Ikenberry et al. (1995).

The corresponding CAAR is -0.07 and it is statistically significant, at the 5% significance

level. This provides evidence for the mispricing hypothesis. As argued by Stephens and

Weisbach (1998), firms adjust their share repurchase behaviour based on the perceived

undervaluation. It supports the hypothesis that managers tend to repurchase shares when the

share price is in a period of decline.

There might be several explanations why share repurchase affects share price behaviour. This

is tested by investigating the relation between independent- and control variables, explained in

chapter 3.2, on the CARs of share repurchase. It is tested by using an ordinary least squares

(OLS) regression. These results are discussed in this section.

4 The default level for statistical significance throughout the rest of this study is set to a 10% statistical significance level. So, with statistical significance I mean the statistical significance at a 10% level. If the statistical significance level is different, this is mentioned explicitly.

31

Figure 2

Theoretical framework

This figure displays the theoretical framework including all the dependent and independent variables. The dependent variable is the cumulative abnormal return for share repurchase, based on the window 1 day before until one day after the announcement of share repurchase. In my analysis I test if the independent variable affects the dependent variable. The plus or minus sign stands for the predicted positive or negative relation the independent variable has with the dependent variable, based on the theory. The design of the framework is based on the theoretical framework provided in van Erp (2014)

32

To test for the mispricing hypothesis, the free cash flow hypothesis, the earnings per share

dilution hypothesis, the leverage hypothesis, and the tax benefits hypothesis, several

independent variables are used. For each of the independent variables I came up with a

hypothesis. The theoretical framework is displayed in figure 2. The design of the framework

is based on the theoretical framework provided in van Erp (2014). For each regression

coefficient, the tested null hypothesis is that the coefficient is equal to zero. The 𝐻1hypothesis

is that the coefficient is not equal to zero. The 𝐻0 hypothesis for each of the statements below

is:

The independent variable does not affect the firm’s CARs. The associated 𝐻1 hypotheses for

this are the following:

1. The book to market ratio affects the firm’s CARs.

2. The natural logarithm of total assets affects the firm’s CARs.

3. The natural logarithm of market capitalization affects the firm’s CARs.

4. The total number of employees affects the firm’s CARs.

5. The free cash flow affects the firm’s CARs.

6. The return on assets ratio affects the firm’s CARs.

7. The options outstanding affects the firm’s CARs.

8. The options exercised affects the firm’s CARs.

9. The debt to asset ratio affects the firm’s CARs.

10. The percentage of dividend affects the firm’s CARs.

11. The dividend dummy affects the firm’s CARs.

The results of this study are displayed in table 3.

Additionally, the sample is checked for multicollinearity, by analyzing the variance inflation

factor (VIF) scores. Furthermore, the correlations between the variables are analyzed. The

outcomes of the VIF analysis are displayed in table 7 and 8. To analyze possible

multicollinearity the magnitude of the VIF scores are analyzed. If the number is relatively

high it might signal multicollinearity. If this is the case the variable should be treated with

caution. In general, VIF scores higher than 10 are considered relatively high and might be up

33

for a closer examination. In my study the VIF scores of logarithm of market capitalization and

logarithm of total assets are greater than 10, displayed in table 7. Additionally, the variables

options outstanding and options exercised are close to 7 and considerably higher compared to

the rest. These variables are related, the higher VIF values indicate that one of these variables

is possibly redundant.

Moreover, to further analyze the relations between the variables, a correlation table is

analyzed, displayed in the tables 9a and 9b. The proxies for firm size all have relatively high

correlations among them, and measure the same thing. For example, the correlation between

logarithm of market capitalization and logarithm of total assets is 0.91, and between logarithm

of market capitalization and number of employees 0.38. Furthermore the correlation between

options outstanding and options exercised is 0.91. The correlation between percentage of

dividend and dividend dummy is 0.42. So, these variables show relatively high correlations.

To test differences between the variances of these variables, a variance-comparison test is

performed. In these tests the 𝐻0 hypothesis is: the difference between the variables’ variance

is equal to zero. The corresponding 𝐻1 hypothesis is: the difference between the variables’

variance is unequal to zero. For all of the tests, except for the test between the variance of the

logarithm of market capitalization and the variance of the logarithm of total assets, the 𝐻0

hypothesis can be rejected, at the 1% significance level. So, most of these variables’ variances

are considered different from each other. Furthermore, I performed a t-test to test the 𝐻0

hypothesis: the difference between the mean logarithm of market capitalization and the mean

logarithm of total assets is equal to zero. The corresponding 𝐻1 hypothesis is: the difference

between the mean logarithm of market capitalization and the mean logarithm of total assets is

unequal to zero. For this test, the 𝐻0 hypothesis can be rejected, at the 1% significance level.

In my regression tables with dependent variable CAR for share repurchase, all of the columns

4 consist of an OLS-regression with all the independent- and control variables together.

However, because of the possibility of multicollinearity, I included only one proxy variable

for size in each of the columns 1, 2, and 3, namely: logarithm of market capitalization,

logarithm of total assets, and number of employees, respectively. Furthermore, I also included

only one variable related to options in the columns 1 to 3. So, either options outstanding or

options exercised. Finally, I only included one variable related to dividends in the columns 1

to 3. So, either dividend dummy or percentage of dividend. The VIF scores for these

regressions are displayed in table 8. All of these VIF scores are much lower than 10.

34

Furthermore, to test for heteroskedasticity of residuals, the White’s test and the Breusch-

Pagan test are conducted, displayed in table A18 and A19. Hereby, the 𝐻0 hypothesis is: the

variance of residuals is homogenous. The corresponding 𝐻1 hypothesis is: the variance of

residuals is not homogenous. These results are based on the regression displayed in table 5

column 4. For both tests, the 𝐻0 hypothesis cannot be rejected.

4.1.1 The effect of the book to market ratio on the firm’s CARs

For the book to market ratio I predicted it to have a positive effect on the firm’s CARs,

because firms with a high book to market ratio are considered to have value stocks, and firms

with a low book to market ratio are considered to have growth stocks. If the book to market

ratio is larger than 1, the share is considered to be undervalued. And, as concluded by

Ikenberry, Lakonishok and Vermaelen (1995), value stocks are more likely to have

undervaluation as their primary motivation. So, the announcement of repurchasing stock

would have a greater positive impact on the CARs compared to growth stocks. As predicted, I

find a positive relation between the book to market ratio and the firm’s CARs, displayed in

table 5. Moreover, the value in column 2 is 0.040. This is statistically significant different

from zero, at my default significance level of 10%5. As displayed in column 3, when the

number of employees is used as a proxy for size, instead of market capitalization and the

logarithm of total assets, the value is 0.049 and statistically significant, at the significance

level of 5%. Therefore, the 𝐻0 hypothesis: book to market ratio does not affect the firm’s

CARs, can be rejected, at the 5% significance level. So these results provide evidence for the

mispricing hypothesis.

4.1.2 The effect of the logarithm of total assets on the firm’s CARs

The natural logarithm of total assets is a proxy for firm size. For the natural logarithm of total

assets I predicted it to have a negative effect on the firm’s CARs, because smaller firms are

considered to have higher levels of information asymmetry. The higher levels of information

asymmetry lead to higher abnormal returns because managers are most likely to be better

informed about the financial and operational conditions of the firm. As predicted, I find a

negative relation between the natural logarithm of total assets and the firm’s CARs, displayed

in table 5 column 2. The value in column 2 is -0.020, it is statistically significant, at the 1%

significance level. Therefore, the 𝐻0 hypothesis: natural logarithm of total assets does not

5 The default level for statistical significance throughout the rest of this study is set to a 10% statistical significance level. So, with statistical significance I mean the statistical significance at a 10% level. If the statistical significance level is different, this is mentioned explicitly.

35

affect the firm’s CARs, can be rejected, at the 1% significance level. These results show

support for the mispricing hypothesis.

4.1.3 The effect of the natural logarithm of market capitalization on the firm’s CARs

Furthermore, the logarithm of market capitalization is another proxy for firm size. I predicted

it to have a negative effect on the firm’s CARs. Again, this is based on the difference in

information asymmetry. So, smaller firms are considered to have higher levels of information

asymmetry. The higher levels of information asymmetry could lead to higher abnormal

returns because managers are most likely to be better informed about the financial and

operational conditions of the firm. As predicted, I find a negative relation between the natural

logarithm of market capitalization and the firm’s CARs, displayed in table 5 columns 1 and 4.

Only the value of -0,021 in column 1 is statistically significant, at the 1% significance level.

So, based on this result, the 𝐻0 hypothesis: market capitalization does not affect the firm’s

CARs, can be rejected, at the 1% significance level. Moreover, it provides evidence to support

the mispricing hypothesis.

4.1.4 The effect of the total number of employees on the firm’s CARs

Also, the number of employees is used as a proxy of firm size. I predicted it to have a

negative effect on the firm’s CARs. Because this is also proxy of firm size, the prediction is

the same as for the logarithm of total assets and market capitalization. So, smaller firms are

considered to have higher levels of information asymmetry. The higher levels of information

asymmetry could lead to higher abnormal returns because managers are most likely to be

better informed about the financial and operational conditions of the firm. As predicted, I find

a negative relation between the total number of employees and the firm’s CARs, displayed in

table 5 columns 3 and 4. However, these variables are not statistically significant.

Furthermore, the economic significance is low, because both values are smaller than 0.001.

So, the 𝐻0 hypothesis: the number of employees does not affect the firm’s CARs, cannot be

rejected. Moreover, it does not provide evidence to support the mispricing hypothesis.

4.1.5 The effect of the free cash flow on the firm’s CARs

For the free cash flow I predicted it to have a positive effect on the firm’s CARs, because a

high amount of free cash flow could be the cause of agency problems. So, firms with high

levels of free cash flow could benefit the most from repurchasing shares. Because, otherwise

managers could use the excess cash flow to invest in negative net present value projects, or

managers could engage in empire building or fringe benefit consumption. Additionally,

Managers could hold on to underperforming subordinates for too long, because of the excess

36

of cash flow. Repurchasing shares reduces the excess free cash flow, and therefore reducing

the managers’ power. As argued by Jensen (1986), managers are more constrained after

reducing the excess free cash flow. In contrast of what I predicted, I find mixed results. As

displayed in table 5, the values for free cash flow are positive in column 1 and 3, and negative

in column 2 and 4. But none of these values are statistically significant. Additionally, the

economic impact is considered low, since all the values are 0.001 or smaller. So, the 𝐻0

hypothesis: free cash flow does not affect the firm’s CARs, cannot be rejected. Moreover, it

does not provide enough evidence to support the free cash flow hypothesis.

4.1.6 The effect of the return on assets ratio on the firm’s CARs

For the return on assets I predicted it to have a positive effect on the firm’s CARs, because a

higher return on assets could be a reason for higher agency costs, because the return on assets

is related to free cash flow. So, firms with a high level of return on asset could benefit the

most from repurchasing shares. As argued before, managers could use the excess cash flow to

invest in negative net present value projects, or managers could engage in empire building or

fringe benefit consumption. Managers could also hold on to underperforming subordinates for

too long, because of the excess of cash flow. In contrast of what I predicted, I find mixed

results among the different regressions I used. For example, in table 5 the value for the return

on assets is negative in column 2 and 3, but positive in column 1 and 4. Namely, the exact

values are: 0.004, -0.006, -0.021 and 0.010 respectively. Additionally in table 6 all the values

for the returns on assets in all the columns are positive. A possible explanation for the positive

relation between the return on assets and the firm’s CARs is that having higher levels of

return on assets could signal financial strength when financially healthy firms repurchase

shares. Furthermore, firms with high levels of return on assets benefit the most from share

repurchases because of the possible reduction in agency cost. However, for all of the values

mentioned in the tables 5 and 6 the results are not statistically significant. So, the 𝐻0

hypothesis: return on assets does not affect the firm’s CARs, cannot be rejected. Moreover, it

does not provide enough evidence to support the free cash flow hypothesis.

4.1.7 The effect of the options outstanding on the firm’s CARs

For the options exercised I predicted it to have a negative effect on the firm’s CARs, because

earnings per share dilution is negatively valued by managers. For example, Bens et al. (2003)

find an increase in the level of stock repurchases when the dilutive effect of outstanding

employee stock options on diluted EPS increases. Additionally, Brav et al. (2005) find that

two-thirds of their survey respondents feel that offsetting dilution is an important or very

37

important factor affecting their repurchase decisions. In contrast of the predicted negative

effect, I find a positive relation between the options exercised and the firm’s CARs. Displayed

in table 5, the value for options exercised is positive in all columns. I do not know the reason

why this relation might be positive. However the values are not statistically significant.

Moreover, the impact is considered to be small, because the values are 0.001 or smaller. So,

the 𝐻0 hypothesis: options exercised does not affect the firm’s CARs, cannot be rejected.

Moreover, it does not provide evidence to support the earnings per share dilution hypothesis.

4.1.8 The effect of the options exercised on the firm’s CARs

For the options outstanding I predicted it to have a negative effect on the firm’s CARs,

because earnings per share dilution is negatively valued by managers. This follows the same

reasoning as explained above for the options exercised variable. In contrast of what I

predicted, I find a positive relation between the options outstanding and the firm’s CARs. As

displayed in table 6, the values for options outstanding are positive in all the columns. Again,

I do not know the reason why this relation might be positive. But, also these values are not

statistically significant. Moreover, the economic impact is considered to be small, because the

values are all smaller than 0.001. So, the 𝐻0 hypothesis: options outstanding does not affect

the firm’s CARs, cannot be rejected. Moreover, it does not provide evidence to support the

earnings per share dilution hypothesis.

4.1.9 The effect of the debt to asset ratio on the firm’s CARs

For the debt to asset ratio I predicted it to have a positive effect on the firm’s CARs, because

an increase in the debt to asset ratio could lead to a more beneficial leverage ratio. This could

be beneficial for firms, because of the effects of tax benefits of interest payments associated

with adjusting the firm’s capital structure. Modigliani and Miller (1958, 1963) opened the

discussion in scientific research about adjusting the capital structure in order to find a more

optimal leverage ratio. In line with the prediction, I find a positive relation between the debt

to asset ratio and the firm’s CARs. Displayed in table 5, the values for the debt to asset ratio

for all the columns are positive. For example the value in column 1 is 0.077. However these

results are not statistically significant. So, despite the positive relation, the 𝐻0 hypothesis:

debt to asset ratio does not affect the firm’s CARs, cannot be rejected. Moreover, it does not

provide enough evidence to support the leverage hypothesis.

4.1.10 The effect of the percentage of dividend on the firm’s CARs

For the dividend dummy I predicted it to have a negative effect on the firm’s CARs, because

share repurchases could be more beneficial to shareholders than dividends. Share repurchases

38

might be more beneficial for shareholders, because of tax benefits. As stated by Black (1976),

a corporation that pays no dividends will be more attractive to taxable individual investors

than a similar corporation that pays dividends, when dividends are taxed more heavily than

capital gains, and where capital gains are not taxed until realized. In line with the prediction, I

also find a negative relation between the dividend dummy and the firm’s CARs. Displayed in

table 6, the value for dividend dummy is negative for all of the columns. Moreover, the value

-0.042 in column 3 is statistically significant. Also, in column 4 the value is statistically

significant. So these results are statistically significant different from zero. Therefore, the 𝐻0

hypothesis: dividend dummy does not affect the firm’s CARs, can be rejected, at the 10%

significance level. These results provide evidence for the tax benefits hypothesis.

4.1.11 The effect of the dividend dummy on the firm’s CARs

For the percentage of dividend I predicted it to have a negative effect on the firm’s CARs,

because share repurchases could be more beneficial to shareholders than dividends. So having

a higher ratio of dividends negatively influences the firm’s abnormal return. This is because

share repurchase might be more beneficial for shareholders, because of tax benefits. In

contrast of what I predicted, I find a positive relation between the percentage of dividend and

the firm’s CARs. Displayed in table 5, the value for percentage of dividend is positive in all

the columns. The value 0.8222 is statistically significant. However, in the columns 1, 2, and 3

the value isn’t statistically significant. Also, as I explain in chapter 4.2, column 4 is

considered to have less explanatory power, because there might be some level of

multicollinearity. Therefore, the 𝐻0 hypothesis: percentage of dividend does not affect the

firm’s CARs, cannot be completely rejected. Additionally the predicted negative relation

between the percentage of dividend and the firm’s CARs is not found. So, it does not provide

evidence to support the tax benefits hypothesis.

Finally, the industry dummies also seem to affect the firm’s CARs. Displayed in table 5, the

values for the manufacturing industry dummy and the service industry dummy are all negative

in all the columns. By executing the regression in STATA the dummy variable for the group

of remaining industries is automatically omitted. Hereby, the manufacturing industry dummy

and the service industry dummy are compared on the dummy variable for the group of

remaining industries. The value for service industry dummy in column 1, 2, and 3 is

negatively statistically significant. This is also the case in all the other regression tables. For

example, the value in table 5 column 1 is -0.064. This suggests that firms from the service

industry experience lower abnormal returns from share repurchases, compared to other

39

industries. The reason for this might be up for further investigation. On the other hand, the

values for the manufacturing industry dummy are not statistically significant.

Table 4

Overview of the Cumulative average abnormal returns (CAARs)

This table displays the CAARs of the full sample for different windows. The windows are measured in days around the announcement day of share repurchases; hereby the share repurchase announcement day is 0. Furthermore it displays the standard error, t-statistic, and the probability.

Table 3

Overview prediction and results for all the hypotheses

This table displays the prediction and results for all the hypotheses. To test for the mispricing hypothesis, four independent variables are used. To test for the free cash flow hypothesis two variables are used. To test for the earnings per share dilution hypothesis 2 variables are used. To test for the leverage hypothesis one variable is used. Finally, to test for the tax benefits hypothesis two variables are used. The column ‘Prediction’ displays whether the relation on the cumulative abnormal return (CAR), with a window of 1 day before until 1 day after the share repurchase announcement, is expected to be positive or negative. After conducting multiple OLS-regressions, the conclusive results are displayed in the last three columns.

window CAAR Std. Error t-stat. prob.

[-1,1] 0.04 0.01 3.90 0.00

[-3,3] 0.03 0.02 1.35 0.18

[-5,1] -0.29 0.32 -0.91 0.37

[-5,5] 0.01 0.03 0.42 0.68

[-10,-2] -0.07 0.03 -2.31 0.02

[-10,10] -0.03 0.06 -0.52 0.60

[-15,1] 0.10 0.12 0.80 0.43

[-15,15] 0.10 0.14 0.71 0.48

[-30,30] -0.13 0.19 -0.69 0.49

Hypothesis Variable Prediction Result Statistical significance of result Supports hypothesis

Mispricing hypothesis Book to market ratio + + Significant** Yes

Logarithm of total assets - - Significant*** Yes

Logarithm of market capitalization - - Significant*** Yes

Number of employees - - Not significant No

Free cash flow hypothesis Free cash flow + +/- Not significant No

Return on Assets + +/- Not significant No

Earnings per share dilution hypothesis Options exercised - + Not significant No

Options outstanding - + Not significant No

Leverage hypothesis Debt to asset ratio + + Not significant No

Tax benefits hypothesis Dividend dummy - - Significant* Yes

Percentage of dividend - + Significant* No

*** p<0.01, ** p<0.05, * p<0.1

40

Table 5

Regression results with dependent variable Cumulative abnormal return (CAR)

This table displays the regression results for every selected independent variable. The dependent variable is CAR for share repurchase. This is based on the window, one day before until one day after the announcement of share repurchase. The results are obtained by using an ordinary least squares (OLS) regression. The first displayed value of every independent variable is the regression coefficient, the value between parentheses is the standard error. Column 4 reports the regression results where all independent- and control variables are included. In columns 1, 2, and 3, only one measure of firm size is included in the regression. These are logarithm of market capitalization, logarithm of total assets, and the number of employees respectively. Additionally, in column 1, 2, and 3, to test for the earnings per share dilution hypothesis, only options exercised is used. In column 4, options outstanding is added to the test. Furthermore, to test for the tax benefits hypothesis, only the percentage of dividend is used in column 1, 2, and 3. In column 4, dividend dummy is also added to the test.

(1) (2) (3) (4)

VARIABLES CAR CAR CAR CAR

Book to market ratio 0.018 0.040* 0.049** 0.022

(0.026) (0.024) (0.024) (0.037)

Logarithm of total assets -0.020*** 0.001

(0.008) (0.027)

Logarithm of market capitalization -0.021*** -0.017

(0.008) (0.027)

Number of employees -0.000 -0.000

(0.000) (0.000)

Free cash flow 0.000 -0.000 0.001 -0.000

(0.007) (0.007) (0.007) (0.007)

Return on Assets 0.004 -0.006 -0.021 0.010

(0.087) (0.087) (0.087) (0.088)

Options exercised 0.001 0.001 0.000 0.001

(0.001) (0.001) (0.001) (0.003)

Options outstanding 0.000

(0.001)

Debt to asset ratio 0.077 0.115 0.044 0.091

(0.075) (0.079) (0.074) (0.091)

Dividend dummy -0.052*

(0.028)

Percentage of dividend 0.466 0.447 0.469 0.822*

(0.399) (0.399) (0.401) (0.447)

Percentage of capital expenditures -0.058 -0.064 -0.063 -0.054

(0.226) (0.226) (0.227) (0.226)

Manufacturing industry dummy -0.035 -0.037 -0.034 -0.037

(0.032) (0.032) (0.033) (0.033)

Service industry dummy -0.064* -0.064* -0.048 -0.070*

(0.037) (0.037) (0.036) (0.037)

Constant 0.179** 0.157** 0.026 0.163**

(0.071) (0.065) (0.040) (0.075)

Observations 749 749 749 749

R-squared 0.020 0.020 0.012 0.025

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

41

(1) (2) (3) (4)

VARIABLES CAR CAR CAR CAR

Book to market ratio 0.019 0.039 0.046* 0.022

(0.026) (0.024) (0.024) (0.037)

Logarithm of total assets -0.018** 0.001

(0.008) (0.027)

Logarithm of market capitalization -0.018** -0.017

(0.008) (0.027)

Number of employees -0.000 -0.000

(0.000) (0.000)

Free cash flow 0.000 -0.000 0.000 -0.000

(0.007) (0.007) (0.007) (0.007)

Return on Assets 0.033 0.023 0.015 0.010

(0.087) (0.086) (0.086) (0.088)

Options exercised 0.001

(0.003)

Options outstanding 0.000 0.000 0.000 0.000

(0.000) (0.000) (0.000) (0.001)

Debt to asset ratio 0.085 0.118 0.060 0.091

(0.075) (0.079) (0.074) (0.091)

Dividend dummy -0.030 -0.028 -0.042* -0.052*

(0.025) (0.025) (0.024) (0.028)

Percentage of dividend 0.822*

(0.447)

Percentage of capital expenditures -0.060 -0.065 -0.064 -0.054

(0.226) (0.226) (0.226) (0.226)

Manufacturing industry dummy -0.035 -0.036 -0.032 -0.037

(0.032) (0.032) (0.033) (0.033)

Service industry dummy -0.066* -0.066* -0.053 -0.070*

(0.037) (0.037) (0.036) (0.037)

Constant 0.176** 0.155** 0.045 0.163**

(0.072) (0.065) (0.041) (0.075)

Observations 749 749 749 749

R-squared 0.021 0.020 0.015 0.025

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 6

Regression results with dependent variable Cumulative abnormal return (CAR)

This table displays the regression results for every selected independent variable. The dependent variable is CAR for share repurchase. This is based on the window, one day before until one day after the announcement of share repurchase. The results are obtained by using an ordinary least squares (OLS) regression. The first displayed value of every independent variable is the regression coefficient, the value between parentheses is the standard error. Column 4 reports the regression results where all independent- and control variables are included. In columns 1, 2, and 3, only one measure of firm size is included in the regression. These are logarithm of market capitalization, logarithm of total assets, and the number of employees respectively. Additionally, in column 1, 2, and 3, to test for the earnings per share dilution hypothesis, only options outstanding is used. In column 4, options exercised is added to the test. Furthermore, to test for the tax benefits hypothesis, only the dividend dummy is used in column 1, 2, and 3. In column 4, the percentage of dividend is also added to the test.

42

Table 7

The variance inflation factor (VIF) scores for all the independent- and control variables

This table displays the VIF scores. All the independent- and control variables are included in this table. It

is a test for multicollinearity in an ordinary least squares regression analysis. Generally speaking a

variable whose VIF values are greater than 10 may need to be investigated further.

Variable VIF 1/VIF

Logarithm of market capitalization 19.20 0.052092

Logaritm of total assets 17.23 0.058043

Options outstanding 6.31 0.158518

Options exercised 6.17 0.161998

Book to market ratio 2.87 0.348960

Manufacturing industry dummy 2.05 0.488148

Service industry dummy 2.05 0.488973

Debt to asset ratio 1.65 0.605481

Dividend dummy 1.51 0.663286

Percentage of dividend 1.31 0.765767

Return on Assets 1.23 0.811819

Number of employees 1.23 0.815324

Percentage of capital expenditures 1.13 0.888316

Free cash flow 1.04 0.960984

Mean VIF 4.64

Table 8

The variance inflation factor (VIF) scores

This table displays the VIF scores. Several independent- and control variables are included in this table.

Only one variable for size is used, which is logarithm of market capitalization. Also, only one variable for

dividend is used. Furthermore, only one variable for options is used. The VIF scores test for

multicollinearity in an ordinary least squares regression analysis. Generally speaking a variable whose

VIF values are greater than 10 may need to be investigated further. These VIF scores are based on the

regression in table 5 column 1, but these scores are also representative for column 2 and 3. Moreover,

these scores are also representative for all the other regressions in the columns 1, 2, and 3 in all the

regression tables, because the scores are very similar and all below 10.

Variable VIF 1/VIF

Service industry dummy 2.02 0.494280

Manufacturing industry dummy 2.01 0.498436

Logarithm of market capitalization 1.61 0.620937

Book to market ratio 1.44 0.696703

Return on Assets 1.21 0.829004

Options exercised 1.18 0.850846

Percentage of capital expenditures 1.12 0.889497

Debt to asset ratio 1.10 0.907766

Percentage of dividend 1.04 0.960761

Free cash flow 1.03 0.966324

Mean VIF 1.38

43

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44

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45

4.2 Sensitivity analysis I tested for differences of the cumulative average abnormal returns (CAARs) between

different indexes. These indexes are: the NYSE, NASDAQ, and AMEX index. The CAARs

are displayed in table A12. Based on table 4, I concluded that the CAAR including all indexes

for the window one day before until one day after the announcement of share repurchase [-

1,1] is 0.04, this is statistically significant at the 1% significance level. Moreover, the values

for the CAARs for the window [-1,1] for all the different industries separately are all positive.

However, only the CAAR for the firms in the NASDAQ index is statistically significant, at

the 1% significance level. Moreover, to test for differences across the indexes I performed

three t-tests. In these tests I test if the differences between the mean CARs for the sample

including all indexes and the mean CARs for one particular index are equal to zero. Hereby,

the 𝐻0 hypothesis is: the difference between the mean CARs for the sample including all

indexes and the mean CAR for the particular index is equal to zero. The corresponding

alternative hypothesis 𝐻𝑎 is: the difference between the mean CAR for the sample including

all indexes and the mean CAR for the particular index is unequal to zero. Based on the p-

values of the two sided test, only the 𝐻0 hypothesis: the difference between the mean CAR for

the sample including all indexes and the mean CAR for the NYSE index is equal to zero, can

be rejected, at the 10% significance level. For the other two indexes, the 𝐻0 hypothesis is: the

difference between the mean CAR for the sample including all indexes and the mean CAR for

the particular index is equal to zero, cannot be rejected. Finally, also a control variable ‘Sales’

is added to the regressions. However, compared to table 5 the results are very similar, and it

doesn’t change anything about the final conclusions.

46

Chapter 5. Conclusion and discussion

In this study I provide evidence that share repurchase announcement positively affect share

price behaviour, because I found higher abnormal returns after share repurchase

announcements. This result is based on a window of 1 day before until 1 day after the

announcement of share repurchase, and statistically significant at the 1% significance level. In

table 4 this result is displayed.

Furthermore, I investigated five hypotheses, based on past literature. These hypotheses are:

the mispricing hypothesis, the free cash flow hypothesis, the earnings per share dilution

hypothesis, the leverage hypothesis, and the tax benefits hypothesis. The results are displayed

in table 3. First of all, I provide evidence for the mispricing hypothesis, because displayed in

table 4, the cumulative average abnormal return (CAAR) for the window [-10,-2] is -0.07, at

the 1% significance level. The window is 10 days before until 2 days before the

announcement of share repurchase. It supports the hypothesis that managers tend to

repurchase shares when the share price is in a period of decline. This provides support for

Stephens and Weisbach (1998), who state that firms adjust their share repurchase behaviour

based on the perceived undervaluation.

Moreover, I provide evidence for the mispricing hypothesis based on the book to market ratio.

I predicted it to have a positive effect on the firm’s CARs, because firms with a high book to

market ratio are considered to have value stocks, and firms with a low book to market ratio

are considered to have growth stocks. And if the book to market ratio is larger than 1, the

share is considered to be undervalued. As predicted, I found a positive relation between the

book to market ratio and the firm’s CARs, displayed in table 5. Moreover, the value in

column 2 is 0.040, at my default significance level of 10%6. This provides evidence that

supports the mispricing hypothesis.

Also, I provide evidence for the mispricing hypothesis based on the natural logarithm of total

assets as a proxy for firm size. I predicted it to have a negative effect on the firm’s CARs,

because smaller firms are considered to have higher levels of information asymmetry. As

predicted, I found a negative relation between the natural logarithm of total assets and the

6 The default level for statistical significance throughout the rest of this study is set to a 10% statistical significance level. So, with statistical significance I mean the statistical significance at a 10% level. If the statistical significance level is different, this is mentioned explicitly.

47

firm’s CARs, displayed in table 5 column 2. This relation is statistically significantly

negative, at the 1% significance level. The value for the natural logarithm of total assets in

table 5 column 2 is -0,020. The results show support for the mispricing hypothesis.

Next, I provide evidence for the mispricing hypothesis based on the natural logarithm of

market capitalization. I predicted it to have a negative effect on the firm’s CARs, because

smaller firms are considered to have higher levels of information asymmetry. As predicted, I

found a statistically significant negative relation between the natural logarithm of market

capitalization and the firm’s CARs, at the 1% significance level, displayed in table 5 column

1. The value for the natural logarithm of market capitalization in table 5 column 1 is -0,021.

These results show support for the mispricing hypothesis.

Additionally, I also tested the effect of the number of employees on the firm’s CARs. I

predicted it to have a negative relation, because smaller firms are considered to have higher

levels of information asymmetry. The values for the number of employees are negative as

predicted, displayed in table 5 column 3 and 4. However, these results are not statistically

significant. So, these results do not provide enough evidence to support the mispricing

hypothesis.

Next, I do not find enough evidence to support the free cash flow hypothesis based on the free

cash flow. I predicted the free cash flow to have a positive effect on the firm’s CARs, because

high amount of free cash flow could be the cause of agency problems. So, firms with high

levels of free cash flow could benefit the most from repurchasing shares, because of the

possible reduction in agency costs. I found mixed results for the relation between the free cash

flow and the firm’s CARs. As displayed in table 5, the value free cash flow is positive in

column 1 and 3, and negative in column 2 and 4. But none of these values are statistically

significant. So, it does not provide enough evidence to support the free cash flow hypothesis.

Also, based on the return on assets, the results do not provide evidence to support the free

cash flow hypothesis. For the return on assets I predicted it to have a positive effect on the

firm’s CARs, because firms with a high level of return on asset could benefit the most from

repurchasing shares, because of the possible reduction in agency cost. In contrast of what I

predicted, I found mixed results among the different regressions I used. For example, in table

5 the value for the return on assets is negative in column 2 and 3, but positive in column 1 and

4. But, none of these are statistically significant. So, it does not provide enough evidence to

support the free cash flow hypothesis.

48

Additionally, the results do not provide evidence to support the earnings per share dilution

hypothesis. For both the options outstanding and options exercised I predicted it to have a

negative effect on the firm’s CARs. However, in contrast of the predicted negative effect, I

found a positive relation with the firm’s CARs, displayed in table 5 and 6. But none of these

values are statistically significant. So it does not provide evidence to support the earnings per

share dilution hypothesis.

Based on the debt to asset ratio, the results do not provide enough evidence to support the

leverage hypothesis. For the debt to asset ratio I predicted it to have a positive effect on the

firm’s CARs. The reason for this is the tax benefits of interest payments associated with

adjusting one’s capital structure. As predicted, I found a positive relation between the debt to

asset ratio and the firm’s CARs. However, the results displayed in table 5 are not statistically

significant. So, it does not provide enough evidence to support the leverage hypothesis.

Finally, when testing for the tax benefits hypothesis, I found evidence to support the

hypothesis. This hypothesis is tested by the two variables, dividend dummy and percentage of

dividend. For both the variables I predicted it to have a negative effect on the firm’s CARs,

because share repurchases could be more beneficial to shareholders than dividends. This is

because of tax benefits. As predicted, I found a negative relation between the dividend

dummy and the firm’s CARs, displayed in table 6. In column 3 and 4, I found a statistically

significant negative value for dividend dummy. Namely, in column 3 the statistical significant

value is -0.042. These results support the tax benefits hypothesis. On the other hand, for the

percentage of dividend a positive relation on the firm’s CARs is found. In table 5 column 4

the value 0.8222 is statistically significant. So this might provide evidence against the tax

benefits hypothesis. But, as already explained in chapter 4.1, column 4 is considered to have

less explanatory power, because there might be some level of multicollinearity. To be more

specific, both the variables dividend dummy and percentage of dividend are very much

related. So the values in column 4 have to be treated with caution.

To conclude, I provide evidence to support the mispricing hypothesis and the tax benefit

hypothesis. On the other hand, I do not provide enough evidence to support the free cash flow

hypothesis, earnings per share dilution hypothesis, and the leverage hypothesis.

This study contributes to the Finance literature, because I explain the effects that multiple

variables could have on the firm’s CARs. The corresponding hypotheses about the effects of

multiple variables on the firm’s CARs are based on past literature. Furthermore, this study is

49

conducted over a relatively recent period of 2000 until 2012. Additionally, evidence is found

to support the mispricing hypothesis and the tax benefit hypothesis. I also provide evidence

for different abnormal returns because of share repurchases among different industries. This

might be interesting to investigate as part of future research. Besides, it would be interesting

to also test for the long term effect of share repurchase. Furthermore, the agency costs theory

could be investigated more specifically. I found evidence for the mispricing hypothesis, which

is related with agency costs. But it is for example interesting how this would relate to

manager’s compensation and ownership.

A limitation of this study is that the derived conclusions might not be generalizable for firms

in other countries, because the sample only includes firms listed on the NYSE, NASDAQ, and

AMEX. So, the effects might be different for other countries, for example due to regulation or

cultural differences. Another limitation of the study is that COMPUSTAT has some missing

data among firms. So, finally a sample is selected where all the firms have the same number

of observations for every variable. A recommendation for future research is to try combining

other data sources such as Zephyr, Orbis, or firm’s financial reports. However, this might be

very time consuming. Also, this sample only includes open market repurchases. For example,

fixed price tender offers and dutch auction repurchases could be included.

Finally, there might be a problem of simultaneous causation among some variables, resulting

in endogeneity. This might be the case between dividend dummy and CAR for share

repurchases. For example, instead of the relation that firms experience lower abnormal returns

when firms pay dividends, it might be the case that because of lower/higher abnormal returns,

the firms adjust their dividend payout behavior. So, there is a problem of simultaneity among

the variables. This might also be the case for the variable ‘percentage of dividends’. Also, this

might be the case between options exercised and CAR for share repurchases. So, instead of

the relation that options exercised affect the CAR for share repurchases, the CAR for share

repurchases affect the options exercised. For example, if firms experience higher abnormal

returns, option holders could decide to exercise their options, which would increase the level

of options exercised. There also might be simultaneous causation between options outstanding

and the CAR for share repurchases. So, instead of the relation that options outstanding affect

the CAR for share repurchases, the CAR for share repurchases affect the options outstanding.

For example, if firms experience higher abnormal returns, option holders could decide to

exercise their options, which would decrease the level of options outstanding.

50

To illustrate this, I provide the example of possible simultaneous causation between dividend

dummy and CAR for share repurchases. The equation of CAR is based on my study van Erp

(2014)

The equation could be: 𝐶𝐴𝑅𝑖(𝑡1,𝑡2)= 𝛼 + 𝛽1 Dividend dummy𝑖 + 𝛽2

Other independent variables𝑖 + ε𝑖,

where as described in chapter 3.3, 𝐶𝐴𝑅𝑖(𝑡1,𝑡2) = cumulative abnormal return on stock i for day

t; t1= the beginning day of the event window; t2= the last day of the event window; 𝛼 =

constant; and 𝛽𝑗 =beta of variable j, it is the intercept corresponding to each specific

regressor; and ε𝑖 = the error term

If there is simultaneous causation, dividend dummy is not randomly assigned and it will

correlate with ε𝑖. In that case 𝛽1is inconsistent.

Moreover, CAR could influence dividend dummy, leading to the equation:

Dividend dummy = 𝑦1𝐶𝐴𝑅𝑖(𝑡1,𝑡2) + 𝑢,

where 𝐶𝐴𝑅𝑖(𝑡1,𝑡2) = cumulative abnormal return on stock i for day t; t1= the beginning day of

the event window; t2= the last day of the event window; and 𝑦𝑗 = is the intercept of the

variable j; and 𝑢 = the error term

In this case 𝐶𝐴𝑅𝑖(𝑡1,𝑡2) also varies as a function of ε𝑖.

In order to test for the problem of simultaneity a two-stage least squares (2SLS) regression

could be used. This is used to estimate simultaneous equations where one or more predictors

are endogenous.

51

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54

Appendix

Table A10

Regression results with dependent variable Cumulative abnormal return (CAR)

This table displays the regression results for every selected independent variable. The dependent variable is CAR for share repurchase. This is based on the window, one day before until one day after the announcement of share repurchase. The results are obtained by using an ordinary least squares (OLS) regression. The first displayed value of every independent variable is the regression coefficient, the value between parentheses is the standard error. Column 4 reports the regression results where all independent- and control variables are included. In columns 1, 2, and 3, only one measure of firm size is included in the regression. These are logarithm of market capitalization, logarithm of total assets, and the number of employees respectively. Additionally, in column 1, 2, and 3, to test for the earnings per share dilution hypothesis, only options exercised is used. In column 4, options outstanding is added to the test. Furthermore, to test for the tax benefits hypothesis, only the dividend dummy is used in column 1, 2, and 3. In column 4, the percentage of dividend is also added to the test.

(1) (2) (3) (4)

VARIABLES CAR CAR CAR CAR

Book to market ratio 0.020 0.039 0.046* 0.022

(0.026) (0.024) (0.024) (0.037)

Logarithm of total assets -0.018** 0.001

(0.008) (0.027)

Logarithm of market capitalization -0.018** -0.017

(0.008) (0.027)

Number of employees -0.000 -0.000

(0.000) (0.000)

Free cash flow 0.000 -0.000 0.000 -0.000

(0.007) (0.007) (0.007) (0.007)

Return on Assets 0.031 0.021 0.015 0.010

(0.087) (0.086) (0.087) (0.088)

Options exercised 0.001 0.001 0.000 0.001

(0.001) (0.001) (0.001) (0.003)

Options outstanding 0.000

(0.001)

Debt to asset ratio 0.084 0.117 0.060 0.091

(0.075) (0.079) (0.074) (0.091)

Dividend dummy -0.030 -0.029 -0.042* -0.052*

(0.025) (0.025) (0.024) (0.028)

Percentage of dividend 0.822*

(0.447)

Percentage of capital expenditures -0.060 -0.065 -0.063 -0.054

(0.226) (0.226) (0.226) (0.226)

Manufacturing industry dummy -0.034 -0.035 -0.032 -0.037

(0.032) (0.032) (0.033) (0.033)

Service industry dummy -0.065* -0.066* -0.052 -0.070*

(0.037) (0.037) (0.036) (0.037)

Constant 0.174** 0.153** 0.044 0.163**

(0.071) (0.065) (0.041) (0.075)

Observations 749 749 749 749

R-squared 0.021 0.020 0.015 0.025

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

55

Table A11

Regression results with dependent variable Cumulative abnormal return (CAR)

This table displays the regression results for every selected independent variable. The dependent variable is CAR for share repurchase. This is based on the window, one day before until one day after the announcement of share repurchase. The results are obtained by using an ordinary least squares (OLS) regression. The first displayed value of every independent variable is the regression coefficient, the value between parentheses is the standard error. Column 4 reports the regression results where all independent- and control variables are included. In columns 1, 2, and 3, only one measure of firm size is included in the regression. These are logarithm of market capitalization, logarithm of total assets, and the number of employees respectively. Additionally, in column 1, 2, and 3, to test for the earnings per share dilution hypothesis, only options outstanding is used. In column 4, options exercised is added to the test. Furthermore, to test for the tax benefits hypothesis, only the percentage of dividend is used in column 1, 2, and 3. In column 4, dividend dummy is also added to the test.

(1) (2) (3) (4)

VARIABLES CAR CAR CAR CAR

Book to market ratio 0.018 0.040* 0.049** 0.022

(0.026) (0.024) (0.024) (0.037)

Logarithm of total assets -0.021*** 0.001

(0.008) (0.027)

Logarithm of market capitalization -0.021*** -0.017

(0.008) (0.027)

Number of employees -0.000 -0.000

(0.000) (0.000)

Free cash flow 0.000 -0.000 0.001 -0.000

(0.007) (0.007) (0.007) (0.007)

Return on Assets 0.006 -0.004 -0.021 0.010

(0.087) (0.087) (0.087) (0.088)

Options exercised 0.001

(0.003)

Options outstanding 0.000 0.000 0.000 0.000

(0.000) (0.000) (0.000) (0.001)

Debt to asset ratio 0.077 0.116 0.044 0.091

(0.075) (0.079) (0.074) (0.091)

Dividend dummy -0.052*

(0.028)

Percentage of dividend 0.457 0.439 0.467 0.822*

(0.399) (0.399) (0.401) (0.447)

Percentage of capital expenditures -0.059 -0.065 -0.064 -0.054

(0.226) (0.226) (0.227) (0.226)

Manufacturing industry dummy -0.036 -0.038 -0.034 -0.037

(0.032) (0.032) (0.033) (0.033)

Service industry dummy -0.064* -0.065* -0.048 -0.070*

(0.037) (0.037) (0.036) (0.037)

Constant 0.181** 0.158** 0.026 0.163**

(0.072) (0.065) (0.040) (0.075)

Observations 749 749 749 749

R-squared 0.020 0.020 0.012 0.025

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

56

Tab

le A

12

Ove

rvie

w o

f th

e C

um

ula

tive

ave

rag

e a

bn

orm

al r

etu

rns

(CA

AR

s) f

or

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isp

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th

e C

AA

Rs

of

the

full

sam

ple

fo

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ach

ind

ex s

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ren

t w

ind

ow

s. It

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bet

we

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hre

e in

dex

es, n

amel

y: N

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ork

Sto

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(N

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, NA

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Q, a

nd

Am

eric

an

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(A

MEX

). T

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win

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57

Pr(T < t) = 0.8050 Pr(|T| > |t|) = 0.3901 Pr(T > t) = 0.1950

Ha: diff < 0 Ha: diff != 0 Ha: diff > 0

Ho: diff = 0 Satterthwaite's degrees of freedom = 1701.79

diff = mean(CARtable1) - mean(CARtable2) t = 0.8597

diff .01325 .0154132 -.0169807 .0434808

combined 1836 .0351202 .0077094 .3303386 .02 .0502404

CARtab~2 749 .0272756 .0113768 .3113599 .0049412 .0496099

CARtab~1 1087 .0405256 .0103987 .3428419 .0201218 .0609294

Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

Two-sample t test with unequal variances

Pr(T < t) = 0.4840 Pr(|T| > |t|) = 0.9681 Pr(T > t) = 0.5160

Ha: diff < 0 Ha: diff != 0 Ha: diff > 0

Ho: diff = 0 Satterthwaite's degrees of freedom = 1663.94

diff = mean(CARtable1) - mean(CARtable2) t = -0.0401

diff -.0017835 .0445231 -.0891108 .0855437

combined 1836 -.0670186 .0220999 .9469498 -.1103622 -.0236749

CARtab~2 749 -.0659626 .0334653 .9158729 -.1316596 -.0002656

CARtab~1 1087 -.0677462 .0293664 .9682002 -.1253674 -.0101249

Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

Two-sample t test with unequal variances

Table A13

Two sample t-test with CAR[-1,1]

This table displays the two sample t-test with unequal variances between the cumulative abnormal

return (CAR) of table 1 and the CAR of table 2. The window for the CARs is one day before until one day

after the share repurchase announcement. In this t-test the 𝐻0 hypothesis is that the mean CAR of

table 1 and the mean CAR of table 2 are equal. So, the difference between the two variables is

zero. The 𝐻𝑎:diff !=0, is the alternative hypothesis. The alternative hypothesis is: the difference

between the mean CAR of table 1 and the mean CAR of table 2 is unequal to zero. The

corresponding probability is displayed below it. In my analysis I focus on this two tailed t-test.

Table A14

Two sample t-test with CAR[-10,-2]

This table displays the two sample t-test with unequal variances between the cumulative abnormal

return (CAR) of table 1 and the CAR of table 2. The window for the CARs is 10 days before until 2 days

before the share repurchase announcement. In this t-test the 𝐻0 hypothesis is that the mean CAR of

table 1 and the mean CAR of table 2 are equal. So, the difference between the two variables is

zero. The 𝐻𝑎:diff !=0, is the alternative hypothesis. The alternative hypothesis is: the difference

between the mean CAR of table 1 and the mean CAR of table 2 is unequal to zero. The

corresponding probability is displayed below it. In my analysis I focus on this two tailed t-test.

58

Pr(T < t) = 0.9531 Pr(|T| > |t|) = 0.0939 Pr(T > t) = 0.0469

Ha: diff < 0 Ha: diff != 0 Ha: diff > 0

Ho: diff = 0 Satterthwaite's degrees of freedom = 1667.95

diff = mean(optionsout1) - mean(optionsout2) t = 1.6763

diff 4.011525 2.393044 -.6821602 8.70521

combined 1672 13.16802 1.212172 49.56584 10.79048 15.54555

option~2 749 10.95352 1.632428 44.67606 7.748835 14.15821

option~1 923 14.96505 1.749811 53.16085 11.53097 18.39912

Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]

Two-sample t test with unequal variances

Table A15

Two sample t-test with options related variables

This table displays the two sample t-test with unequal variances between the options outstanding of

table 1 and the options outstanding of table 2. The window for the CARs is one day before until one day

after the share repurchase announcement. In this t-test the 𝐻0 hypothesis is that the mean options

outstanding of table 1 and the mean options outstanding of table 2 are equal. So, the difference

between the two variables is zero. The 𝐻𝑎:diff !=0, is the alternative hypothesis. The alternative

hypothesis is: the difference between the mean options outstanding of table 1 and the mean

options outstanding of table 2 is unequal to zero. The corresponding probability is displayed

below it. In my analysis I focus on this two tailed t-test.

59

Table A16

Regression results with dependent variable Cumulative abnormal return (CAR)

This table displays the regression results for every selected independent variable. The dependent variable is CAR for share repurchase. This is based on the window, one day before until one day after the announcement of share repurchase. The results are obtained by using an ordinary least squares (OLS) regression. The first displayed value of every independent variable is the regression coefficient, the value between parentheses is the standard error. Column 4 reports the regression results where all independent- and control variables are included. In columns 1, 2, and 3, only one measure of firm size is included in the regression. These are logarithm of market capitalization, logarithm of total assets, and the number of employees respectively. Additionally, in column 1, 2, and 3, to test for the earnings per share dilution hypothesis, only options exercised is used. In column 4, options outstanding is added to the test. Furthermore, to test for the tax benefits hypothesis, only the percentage of dividend is used in column 1, 2, and 3. In column 4, dividend dummy is also added to the test. In this table a control variable ‘Sales’ is added. However, compared to table 5 the results are very similar, and it doesn’t change anything about the final conclusions.

(1) (2) (3) (4)

VARIABLES CAR CAR CAR CAR

Book to market ratio 0.018 0.038 0.047* 0.027

(0.029) (0.026) (0.026) (0.041)

Logarithm of total assets -0.021** -0.004

(0.009) (0.032)

Logarithm of market capitalization -0.021** -0.011

(0.009) (0.031)

Number of employees -0.000 -0.000

(0.000) (0.000)

Free cash flow 0.001 0.001 0.001 0.001

(0.008) (0.008) (0.008) (0.008)

Return on Assets 0.002 -0.007 -0.021 0.007

(0.097) (0.097) (0.097) (0.099)

Options exercised 0.001 0.001 0.001 0.001

(0.001) (0.001) (0.001) (0.003)

Options outstanding 0.000

(0.001)

Debt to asset ratio 0.065 0.106 0.036 0.092

(0.086) (0.090) (0.085) (0.105)

Dividend dummy -0.062*

(0.032)

Percentage of dividend 0.485 0.461 0.518 0.870*

(0.429) (0.430) (0.431) (0.479)

Percentage of capital expenditures -0.004 -0.010 -0.002 0.005

(0.265) (0.265) (0.266) (0.265)

Manufacturing industry dummy 0.037 0.036 0.022 0.042

(0.032) (0.032) (0.031) (0.032)

Other industry dummy 0.079* 0.080* 0.064 0.084**

(0.042) (0.042) (0.042) (0.042)

Sales 0.000 0.000 -0.000 0.000

(0.000) (0.000) (0.000) (0.000)

Constant 0.101 0.085 -0.034 0.073

(0.072) (0.065) (0.036) (0.076)

Observations 620 620 620 620

R-squared 0.021 0.021 0.014 0.027

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

60

Table A17

Regression results with dependent variable Cumulative abnormal return (CAR)

This table displays the regression results for every selected independent variable. The dependent variable is CAR for share repurchase. This is based on the window, one day before until one day after the announcement of share repurchase. The results are obtained by using an ordinary least squares (OLS) regression. The first displayed value of every independent variable is the regression coefficient, the value between parentheses is the standard error. Column 4 reports the regression results where all independent- and control variables are included. In columns 1, 2, and 3, only one measure of firm size is included in the regression. These are logarithm of market capitalization, logarithm of total assets, and the number of employees respectively. Additionally, in column 1, 2, and 3, to test for the earnings per share dilution hypothesis, only options outstanding is used. In column 4, options exercised is added to the test. Furthermore, to test for the tax benefits hypothesis, only the dividend dummy is used in column 1, 2, and 3. In column 4, the percentage of dividend is also added to the test. In this table a control variable ‘Sales’ is added. However, compared to table 6 the results are very similar, and it doesn’t change anything about the final conclusions.

(1) (2) (3) (4)

VARIABLES CAR CAR CAR CAR

Book to market ratio 0.019 0.037 0.044* 0.027

(0.029) (0.026) (0.026) (0.041)

Logarithm of total assets -0.019* -0.004

(0.010) (0.032)

Logarithm of market capitalization -0.018* -0.011

(0.010) (0.031)

Number of employees -0.000 -0.000

(0.000) (0.000)

Free cash flow 0.001 0.001 0.001 0.001

(0.008) (0.008) (0.008) (0.008)

Return on Assets 0.035 0.026 0.020 0.007

(0.097) (0.096) (0.096) (0.099)

Options exercised 0.001

(0.003)

Options outstanding 0.000 0.000 0.000 0.000

(0.000) (0.000) (0.000) (0.001)

Debt to asset ratio 0.075 0.110 0.052 0.092

(0.086) (0.090) (0.085) (0.105)

Dividend dummy -0.039 -0.037 -0.049* -0.062*

(0.028) (0.029) (0.028) (0.032)

Percentage of dividend 0.870*

(0.479)

Percentage of capital expenditures 0.001 -0.005 0.004 0.005

(0.265) (0.265) (0.265) (0.265)

Manufacturing industry dummy 0.039 0.039 0.029 0.042

(0.032) (0.032) (0.032) (0.032)

Other industry dummy 0.079* 0.080* 0.066 0.084**

(0.042) (0.042) (0.042) (0.042)

Sales 0.000 0.000 -0.000 0.000

(0.000) (0.000) (0.000) (0.000)

Constant 0.100 0.085 -0.017 0.073

(0.073) (0.065) (0.036) (0.076)

Observations 620 620 620 620

R-squared 0.022 0.022 0.017 0.027

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

61

Prob > chi2 = 0.6980

chi2(1) = 0.15

Variables: fitted values of car

Ho: Constant variance

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Source chi2 degrees of freedom probability

Heteroskedasticity 87.42 114 0.9695

Skewness 9.29 14 0.8123

Kurtosis 1.41 1 0.2357

Total 98.12 129 0.9803

Table A18

Cameron & Trivedi's decomposition of IM-test

This table displays the White’s test, to test for heteroscedasticity of residuals. Hereby, the 𝐻0 hypothesis is: the variance of residuals is homogenous. The corresponding 𝐻1 hypothesis is: the variance of residuals is not homogenous. These results are based on the regression displayed in table 5 column 4.

Table A19

The Breusch-Pagan test

This table displays the Breusch-Pagan test, to test for heteroscedasticity of residuals. Hereby, the 𝐻0 hypothesis is: the variance of residuals is homogenous. The corresponding 𝐻1 hypothesis is: the variance of residuals is not homogenous. These results are based on the regression displayed in table 5 column 4.