the relationship between firm‟s dividend policy and

The Relationship between Firm‟s Dividend

Policy and Expected Earnings Growth

-A Case Study for Listed Firms in the Benelux-

Master Thesis Financial Management

Written by: Kathelijn Peerden

Date: January, 2011

Supervisor: Prof. dr. De Jong

Master Thesis Financial Management 2011

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Master Thesis Financial Management by Kathelijn Peerden*

Supervised by Prof. dr. Frank de Jong, Finance Department

Tilburg University, January 2011

* I am grateful to Prof. dr. Frank the Jong for his good insights and useful comments. His

experience and professionalism have certainly improved my research. Furthermore, I

would like to thank Frederic van Daele from Kempen & Co (Kempen Securities) for his

support and his insights into the real world of dividend policy in the Benelux.


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Table of Content

Abstract ....................................................................................................................... 5

Chapter 1: Introduction ................................................................................................. 6

1.1 The context of the Research ...................................................................................... 6

1.2 Structure of this Paper ............................................................................................... 9

Chapter 2: Dividend Policy .......................................................................................... 10

2.1 Reasons to Pay Dividends ....................................................................................... 10

2.2 Dividend Returns ..................................................................................................... 11

2.3 Financial Policy Irrelevance Theory ....................................................................... 11

2.4 Dividend Life Cycle Theory ................................................................................... 13

2.5 The Dividend Decision Model by Lintner .............................................................. 13

2.6 Types of Dividend Payments .................................................................................. 15

2.7 Stock Repurchases ................................................................................................... 15

2.8 Preference for Dividends ......................................................................................... 16

2.9 Predictors for Dividend Paying Companies ............................................................ 17

Chapter 3: Current State of Literature & Hypotheses Development ..................... 18

3.1 Related Research on Dividend and Profitability ..................................................... 18

3.2 The Payout Ratio predicts Future Earnings Growth ............................................... 20

3.2.1 Gordon‟s Constant-Growth Valuation Model .................................................. 20

3.2.2 Main conclusions by Arnott & Asness ............................................................. 22

3.2.3 Some Explanations for the Positive Relationship by Arnott & Asness ............ 23

3.3 An Extension of Arnott‟s & Asness‟s Research ..................................................... 24

3.4 Hypotheses Development ........................................................................................ 25

3.4.1 Hypothesis 1: The Payout Ratio is positively correlated to the Expected Future

Earnings Growth for Benelux Indices ....................................................................... 25


Earnings Growth for the Individual Listed Firms on the Benelux Indices ................ 26

3.4.3 Hypothesis 3: The dividend yield is positively correlated to expected future

earnings growth of individual listed firms on the Benelux indices ........................... 26

Chapter 4: Data & Methodology ................................................................................. 27

4.1 Time Series Regression Analyses ........................................................................... 27

4.1.1 Sample Construction Indices ............................................................................ 27

4.1.2 Variable Description & Model Building Indices .............................................. 28

4.1.3 Methodology for Time Series Analyses ........................................................... 30

4.2 Cross Sectional Regression Analyses ..................................................................... 31

4.2.1 Assumptions and Sample Construction Individual Firms ................................ 31

4.2.2 Variable Description & Model Building Individual Firms .............................. 33


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4.2.3 Methodology for Cross Sectional Analyses ..................................................... 35

4.2.4 Cross Sections over a Longer Time Period ...................................................... 36

4.2.5 Advantages of Panel Data ................................................................................. 37

Chapter 5: Results ........................................................................................................ 38

5.1 Descriptive Statistics for the Benelux Indices ......................................................... 38

5.2 Indices Time Series Regressions ............................................................................. 40

5.2.1 Robustness Tests ............................................................................................... 43

5.3 Descriptive Statistics for the Individual Firms of the Benelux Indices ................... 46

5.4 Cross Sectional Regressions for the Individual Firms within the Indices ............... 47

5.4.1 Dutch versus Belgian Indices & Large- versus Mid-Cap Firms ...................... 51

5.5 Dividend Yield and Future Profitability .................................................................. 54

5.6 An Expanded Model to Forecast Future Earnings Growth ..................................... 55

Chapter 6: Conclusions & Recommendations ........................................................... 59

6.1 Conclusions with respect to the First Hypothesis ................................................... 59

6.2 Conclusions with respect to the Second Hypothesis ............................................... 59

6.3 Conclusions with respect to the Third Hypothesis .................................................. 60

6.4 Recommendations ................................................................................................... 61

References ..................................................................................................................... 62

Appendices ..................................................................................................................... 65

Appendix I; Variable Description for the Indices ......................................................... 65

Appendix II; Deleted Firms from the Three Samples ................................................... 67

Appendix III A; Example of the Sample Composition 2010 ........................................ 68

Appendix III B; Legend of Industry Codes ................................................................... 71

Appendix IV; Variable Description Individual Listed Firms Benelux Indices ............. 72

Appendix V; Scatter Plots Total Sample ....................................................................... 74

Appendix VI; Scatter Plots of the Subsamples; the Netherlands versus Belgium ........ 76

Appendix VII; Coefficients of the Single Yearly Model Total Sample ........................ 77

Appendix VIII; Coefficients of the Single Yearly Models Dutch vs Belgian Indices .. 78

Appendix IX; Coefficients of the Single Yearly Models Large- vs Medium-Cap ....... 80

Appendix X; Cross Sectional Regressions with Dividend Yield for the Three Indices 82

Appendix XI; Pearson‟s Correlation Matrix ................................................................. 83

Appendix XII; Coefficients of the Single Yearly Expanded Models for the Total

Sample ........................................................................................................................... 84

Appendix XIII; Coefficients of the Single Yearly Limited Expanded Models for the

Total Sample ................................................................................................................. 87


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Abstract

The relationship between the payout ratio and the expected earnings growth was

investigated for listed firms in the Benelux, specifically firms listed on the AEX, AMX or

BEL20 stock exchanges. This research is based on the model developed by Arnott and

Asness (2003). Firstly, there is a positive relation on the indices level between the

dividend payout ratio and future earnings growth for the three Benelux indices. Secondly,

this research is an extension of the research done by Arnott and Asness (2003). In

addition, this study analyzes the same relationship for all individual listed firms within

the AEX, AMX or BEL20. It was shown in this research that the payout ratio is

positively related to the expected future earnings growth for the individual listed firms in

the Benelux. Some robustness checks with subsamples of the Dutch & Belgian firms, and

the large- & mid-cap firms were done to examine how robust the results were.

Afterwards, an expanded model with some other interesting variables was constructed to

forecast the expected earnings growth. As a result, only two variables, payout ratio and

dividend yield, were found to be significant within the expanded model. Overall, this

research is unique because I have focused on Benelux-listed firms.


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Chapter 1: Introduction

Worldwide, the current financial crisis almost directly impacted the dividend policy of

companies given the significant change in market conditions. Some firms on the AEX

exchange changed their dividend policies. For example, Royal Dutch Shell froze

dividends in the first quarter of 2010, as written by Het Financieele Dagblad on February

4th

, 2010. Here, „froze‟ means that they did not increase the dividends. Shell paid

dividends of $0, 42 per share in the first quarter of 2010; this is the same DPS as last

year. Normally, Shell minimally increases DPS with the rate of inflation. As Peter Voser,

CEO of Royal Dutch Shell stated; “During this financial crisis and difficult market

circumstances, it is suitable to freeze dividends (FD, February 4th

, 2010)”. In this manner,

the CEO of Shell was giving a signal to the market by adapting the dividend policy.

Generally, the dividend signaling theory suggests that paying more dividends act as a

signal to the market that a given firm‟s manager is confident about the future prospects of

the firm. Chapter 2 discusses the signaling theory in more detail. First of all, Chapter 1

shortly explains the context of the research. At the end of Chapter 1 the structure of this

research is given.

1.1 The context of the Research

It is useful for investors to understand the influence of a firm‟s dividend policy on future

growth. The more specific question that arises is the degree to which the future earnings

growth for a firm change, if the dividend payout ratio changes. Does a change in the

dividend payout ratio change the outlook for future earnings growth or is it the other way

around? In earlier literature two main ideas were recognized. On the one hand, people

who believe a negative relation between dividends and future earnings growth exists. In

other words, lower dividends result in higher expected earnings growth. On the other

hand, some researchers believe a positive relation between dividends and earnings growth

exists. These researchers think that a high payout ratio demands capital discipline and

results in a more efficiently run company. At first sight, the negative relation seems a

logical relationship. Indeed, if the firm retains high percentage of their earnings (low


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payout ratio) investors expect the managers use the retained earnings to finance profitable

new projects which results in future earnings growth. In the past, many researchers had

found results which were in accordance with a negative relationship between dividends

and expected earnings growth. Most of the researches focused on American firms, like

researches by Grullon et al.. (2002) and Benartzi, Michaely and Thaler (1997).

Nevertheless, the validity of this negative relationship was doubted. Perhaps positive

relationship between payout ratios and expected future earnings growth exists? One

important explanation could be that a high payout ratio encourages managers to use the

limited capital available in the best way and limits the likelihood of empire building and

improves efficiency of the current business. If firms have too many cash within the

company, the so called free cash flow problem arises. One possible solution to this

problem is to change the capital structure of the firm. If the firm decreases the amount of

equity and increases the amount of debt, this problem is mitigated.

It should be pointed out that most of the earlier research about dividend policy focused on

U.S. firms, as mentioned above. For example, Nissim & Ziv (2001) and Arnott & Asness

(2003), who found a positive relationship between dividend payouts and future earnings

growth. Therefore it is less interesting to include the U.S. as investigation region and so

the research focuses on European stock exchanges, in particular Benelux-listed firms.

This region is not yet investigated comprehensively. It would be very interesting to know

whether the findings and conclusions for the U.S companies corresponded with those for

Benelux-listed firms. In other words, I would like to explore whether this research can

come to similar conclusions looking at the Benelux market.

Both the origin of the companies in the sample (Belgian and Dutch firms) and the focus

on a positive relationship make this research unique. Hopefully, the results can fill the

gap in the existing literature. This research provided some additional information to

investors and equity markets in the Benelux.


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The study provided investors and equity markets some extra understanding of the

relationship between dividends and expected earnings growth for Benelux-listed firms. In

particular, for Kempen&Co1, a Dutch merchant bank, this study provides some additional

information. Kempen&Co is specialized in financial services for companies, institutional

investors, and high-net-worth individual clients. Kempen Securities is one of the four

business units of Kempen&Co. At this division experts do considerable amount of

research on equities in the Benelux. This research team monitors about 75 large-, mid-

and small-cap companies in the Benelux. Kempen Securities gives advice to investors

whether they have to buy, hold or sell a particular share. All of these firms have their own

dividend policy. For Kempen Securities it could be relevant to know the impact of the

dividend payout on expected earnings growth for these Benelux companies over a longer

historical period of time. After all, the historical relationship between earnings growth

and payout ratios could be used to forecast the future impact of the dividend payout on

the earnings growth. This analysis provides another approach of looking at the Benelux

companies, their valuation and their earnings growth profile. In summary, the results of

this research could be useful to elaborate the current insights of Kempen Securities.

1 Source: Kempen&Co (www.kempen.nl); department ‘Securities Research’

http://www.kempen.nl/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=38



http://www.kempen.nl/


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1.2 Structure of this Paper

This study begins with a theoretical framework, composes of a theoretical chapter with

some definitions and facts about dividend policy (Chapter 2). Chapter 3 presents a

literature overview of research done in the past. This section compares various papers and

discusses the empirical methods, the main conclusions, and interesting findings of earlier

research. At the end, Chapter 3 presents the hypotheses of this research. It is in Chapter 4

that method of analyzing the sample and the type of database is presented and explained.

To test the developed hypotheses, this research makes use of time-series and cross-

sectional analyses. More detailed information about how the statistics were calculated is

provided in Chapter 4. Chapter 4 also discusses the limitations and assumptions of this

research. Using the empirical method of research, the hypotheses are tested in Chapter 5.

Conclusions and recommendations are drawn in Chapter 6.


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Chapter 2: Dividend Policy

Firstly, it is of essential concern to define some different terms that are important during

this research. The word „dividend‟ derives from the Latin word „dividendum‟ which

literally means „a thing to be divided‟ (Encyclopedia Britannica, 1911). Each year, if the

firm realizes a profit, the Annual General Meeting of Shareholders of the firm votes for

the purposed profit allocation. The dividend policy of the firm can be defined as the

firm‟s position on whether the firm should distribute the free cash flows as dividends or

keep the free cash flows in the company. If the firm decides to allocate the profit as

dividends, the dividend policy clarifies when the dividends are issued and more

importantly how big the dividend payment is. Usually, if the firm has a dividend policy,

the company presents a target pay-out ratio or prefers to have a stable dividend each year.

It should be noted that, this study calculates the pay-out ratio as dividing dividends by net

earnings. This chapter gives some theories and facts which are related to dividend policy.

It is a simple and general introduction to the topic dividend policy. Afterwards, in

Chapter 3 the main related researches about the relationship between expected earnings

growth and dividend policy are presented.

2.1 Reasons to Pay Dividends

Companies pay dividends for many reasons. Firstly and most plausibly, firms pay

dividends to reward the investors of the firm who put their money in the company.

Investors run some risks by investing their money. In the case of bankruptcy, the

shareholder is the residual claimant that receives (a part of) his invested money back. In

other words, this implies an increased risk for the shareholder. If the firm goes bankrupt,

the investor only receives back the invested money if some money is left after all other

creditors are paid. Secondly, paying dividends also gives, a signal to investors about the

confidence of the manager in the firm‟s future profitability. This is called the dividend

signaling theory. In the dividend signaling theory of Bhattacharya (1979) there is a

positive relation between information asymmetry and dividend policy. The higher the

level of asymmetric information, the higher the sensitivity of the dividend is to future


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expectations of the company. Paying this dividend could attract new investors who will

invest in the company at higher share prices. Notice, firm managers only increase

dividends if they really believe the increase is sustainable. On the other hand, dividend

decreases could signal a worsening of the firm‟s position and future earnings prospects.

Additionally, there is another possible reason for limiting dividends: managers are

confident that more interesting investment opportunities are available. If these

investments increase the value of the firm, the investors gain. In summary, a good

number of reasons can be identified for paying or not paying dividends. Lease et al.

(2000) called this phenomenon „the dividend puzzle with pieces that just do not fit

together‟.

2.2 Dividend Returns

As Lease et al. (2000) have argued, the dividend returns are a significant part of the total

returns to investors. The total return consists of changes in the value of the company

because positions of the company increases or decreases worth. Furthermore, the total

return includes distributed dividends. In this manner, it is possible that the total return is

positive and the dividend return is zero. With the total return one measures the

performance of a company. If one compares total returns with dividend returns, the most

important difference is the volatility of these two types of returns. Total returns to

investors fluctuate considerably (in line with market prices), whereas dividend returns

tend to be very stable over time. The dividend yield is the number expressing how many

dividend is being paid, as a percentage of the share price. Firms can have very different

dividend yields. More important to understand is that theoretically the present value of

the future dividends determines the stock price.

2.3 Financial Policy Irrelevance Theory

Miller and Modigliani (1961) concluded that the financial policy of a firm is irrelevant

for the firm‟s value. In other words, it has no effect on the firm‟s market value. The price

of the firm‟s share and the cost of capital are not affected by the dividend policy of the

firm. This means that investors do not need to worry about the financial decisions that the


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firm makes. The investors can make some adaptations in their individual investment

portfolio, which is called „homemade dividends‟. The three important assumptions made

by Miller and Modigliani were; perfect capital markets, rational behavior, and perfect

certainty. Perfect capital markets imply that all investors have the same costless

information available. It was assumed that no taxes and transaction costs were paid.

Rational behavior means the investor always prefer greater wealth above lesser wealth.

They do not care about how they receive this wealth in cash dividends or as an increase in

market value of their shares. The third assumption is the existence of complete assurance

on the investment programs and their future profits. This is notable in that it is currently

not a realistic assumption. After all, the company and the shareholders have to pay

income taxes and there are significant foundation and transaction costs. Moreover, it is

unusual that the shareholders have the same information available like the managers of

the company have. Nevertheless, the financial policy irrelevance theory by Miller and

Modigliani (1961) is a well-known and frequently used theory to analyze financial

problems and decisions.


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2.4 Dividend Life Cycle Theory

The Dividend Life Cycle Theory explains the policy with respect to dividend payout ratio

changes during different stages of a firm‟s life cycle. For example, the firm generally

pays no dividends during the early stage of the firm‟s life cycle („Introduction‟). Because

of capital requirements for future

growth, no money is left to pay

dividends to the investors. In

addition, there are generally no

agency costs in the early stages of

the firm‟s life cycle because often

the managers (agents) are also the

owners (principals).

Figure 1: Firm’s Life Cycle Stages

If the company progresses to a more mature stage, agency costs evolve if the problem of

separation of ownership and control arise. Moreover, there are fewer positive investment

opportunities available. For these reasons, one expects that the firm pays more dividends

in a more mature stage of life. As Lease et al. (2000) write, if new related products are

developed and the market erosion increased, the operational cash flows are much larger

than the investment requirements. “The firm can begin to self-liquidate through

extremely high dividend payout levels (Lease et al., 2000)”.

2.5 The Dividend Decision Model by Lintner

The first dividend payments, to shareholders of the VOC2, took place 400 years ago in the

year 1610. The shareholders were only compensated with nominal amount of invested

money and an annual interest of 6,25%. This annual interest was equal to the return on

Dutch obligations which were issued at that time. The dividend was paid with money and

2 The ‘Verenigde Oost-Indische Compagnie’ was founded is 1602. The VOC traded spices by ship from Asia. This organization had grown into a large multinational.


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goods. Lintner (1956) presents a basic model of the dividend decision for companies. He

did empirical research by developing a theoretical model about the decision making with

respect to dividends. Equation (1) presents this model.

(1) ΔDit = At + Ci (riEit - Di(t-1)) + Uit

Where;

At = the intercept term for firm i

Ci = the speed of adjustment coefficient for firm i3

ri = the target payout ratio for firm i

Eit = the earnings after taxes per share in period t for firm i

Di (t-1) = the dividends per share paid out last period for firm i

Uit = the error term for firm i in period t

He calculated the change in dividends per share by constructing a model with different

variables. He used time series analyses during his empirical research. Lintner selected the

most important determinants for paying dividends that he observes in his field work.

Resulting from the regression model, he found a R2 of 85%. This implies that the model

explains 85% of the variation in dividend changes (ΔDit). The parameters in this model

are reasonably stable over time involving changes in external conditions and as a result

the model remains valid to this day. In this model Ci is the fraction that express how

quick the dividend can be adapted from the current dividends paid to the target payout

ratio. This gap between current and target payout ratio is expressed by (riEit - Di(t-1)). The

variable Ci is positively related to the change in dividends because if the speed of

adjustment from current to target payout ratio increases, the dividend changes are larger.

One of the main conclusions by Lintner (1956) is that managers try to do what they say.

3 The fraction of the difference between this ‘target’ dividend Dit* and the actual payment made in the preceding year Di (t-1) (Lintner, 1956).


15

Therefore, “Managers avoid dividend cuts if at all possible, they stabilize dividends with

gradual, sustainable increase whenever possible and they establish an appropriate target

payout ratio (Lease et al., 2000)”. In other words, firms only increase dividends if the

managers expect that the earnings are increased permanently. This model by Lintner

(1956) is still often cited by current researchers.

2.6 Types of Dividend Payments

A few different types of dividends can be recognized. First of all, the most common type

is cash dividend. In this case, the investors receive cash and these earnings are taxable.

Secondly, another method of sharing corporate profits with the investors of the firm is

stock dividend. The shareholders receive extra shares of the issuing firm or a subsidiary

firm. Mostly, this is in proportion to the number of shares the investor already holds.

Sometimes, this stock dividend involves a share issue which makes the dividend less

attractive as the dilution could be equal to the dividend paid. Like a stock split, the price

per share decreases but the total value of the shares hold does not change. Most important

to notice, stock dividends distribute no cash to shareholders. Thirdly, property dividend

implies that investors are paid in the form of assets of the issuing firm or a subsidiary

firm. This type of dividends is rare. Furthermore, other types of dividends are warrants

and financial assets which have a known market value. Stock dividends are not taken into

account because stock dividends are no cash flows.

2.7 Stock Repurchases

Alternatively, share repurchases („buy backs‟) can be used to reward shareholders. In the

case of share repurchases the firm buys back some of its own shares. For this reason the

number of shares outstanding decreases, which increases the earnings per share and often

it tends to increase the market value of the remaining outstanding shares. Mostly, the

company uses share repurchases if it believes the shares are undervalued. Moreover,

share repurchases could be used if there are not enough profitable investment

opportunities available or if the shares are more attractively valued than the returns on the

potential projects. By doing a buy back, the capital structure of the firm can change.


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There are several methods of common stock repurchases. Some differences exist between

cash dividend payments and common stock repurchases. Firstly, cash dividends are

treated as normal income, while stock repurchases are treated as capital gains. If taxes on

capital gains are more favorable, investors would prefer common stock repurchases.

Secondly, only the investors who decide to sell back (a fraction of) their shares receive a

cash distribution. As a result, the shareholders‟ holdings can change, which changes the

ownership structure of the firm.

2.8 Preference for Dividends

There are two alternatives for paying dividends to receive cash; share repurchases and

cash financed acquisitions. With these alternatives the firm distributes cash to the

shareholders in exchange for the shares of the shareholder, as explained by Bagwell and

Shoven (1989). Bagwell and Shoven (1989) mentioned a possible explanation for

shareholders‟ preference of dividends above share repurchases and cash acquisitions. If

dividends are paid, the ownership structure remains the same, while in the case of share

repurchases this ownership structure may change, as mentioned above. If the ownership

structure changes, this can result in a change of control with respect to future company

decisions. Besides, the transaction costs and the information supply can change. The

statistics calculated by Bagwell and Shoven (1989) suggest that the majority of the cash

payments are nondividends; respectively cash via acquisitions and share repurchases.

Brennan and Thakor (1990) have also examined the preferences of shareholders. They

focused on shareholders‟ preferences with respect to the different types of cash

distribution. They have made two important assumptions for their method of corporate

cash distribution. Firstly, “the share price is not a perfect aggregator of the private

information of investors about the prospects of the firm (Brennan and Thakor, 1990)”.

Secondly, it is costly to shareholders to collect information. Their most important finding

is that in the case of small distributions the shareholders prefer a dividend payment.

While they prefer open market stock repurchases for intermediate payouts. And in the


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case of large distributions, they like a tender offer repurchase. One explanation for these

preferences by different shareholders is the information costs. Assuming a fixed cost for

collecting information, small shareholders have a smaller incentive to become informed

in a repurchase than the larger shareholders. As a result, despite of the more preferred tax

rates on capital gains for individual investors, the shareholders prefer dividend payments

in small distributions. If the distributed amount increases, it pay more investors to be

informed in the share repurchase. Further, shareholders who have a small part of

ownership and paying low effective personal income tax prefer dividend payments. On

the other hand, large shareholders, who paid high personal taxes, prefer share

repurchases.

2.9 Predictors for Dividend Paying Companies

Bulan, Subramanian and Tanlu (2007) have found a few additional predictors for

dividend paying firms. Fama and French (2001) have already documented some

predictors that determine whether the firm pays dividend. These predictors are

successively firm size, profitability, current growth and growth opportunities for the

future. Bulan et al. (2007) complement these predictors with the variables capital

expenditures, cash balances, dividend premium and risk. Bulan et al. (2007) investigate

the timing and significance of paying dividends during the lifecycle of companies. They

follow a sample of firms from the initial public offer (IPO) onwards.

These researchers find that dividend payers are large firms with low growth rates and

relatively high cash balances and profitability. More mature firms tend to have lower

growth rates but are still profitable and have high cash balances. For this reason their

findings fit well with the dividend life cycle theory as explained in Section 2.3 of this

chapter. Like Bulan et al. (2007) also document: ”In contrast to previous results on

dividend changes, our work shows that systematic risk does not change significantly

around initiations to pay dividends (Bulan et al., 2007)”.


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Chapter 3: Current State of Literature & Hypotheses Development

In history, a lot of research tried to identify the impact of dividend changes on different

variables and documented which economic variables are significant in relation to the

dividend policy of firms. This research specifically focuses on the relationship between

dividends and the expected earnings growth. Earnings represent the amount of profit that

a company produces during a specific period, for example a quarter or a year. Earnings

typically refer to after-tax net income. Notice, the firm‟s earnings are the most important

determinant of its share price, because earnings and the circumstances relating

to them can assess whether the business will be profitable and successful in the long run.

Notice, Chapter 5 empirically tests this relation between dividend decisions and future

earnings growth. To be complete, an overview of earlier research written on this specific

relationship between dividends and future earnings growth is provided.

3.1 Related Research on Dividend and Profitability

Grullon, Michaely and Swaminathan (2002) documented that the systematic risk of firms

which increase dividends, decline around the announcement of the firm to increase

dividends. Furthermore, “dividend payout ratios of dividend-increasing firms do increase

permanently, which suggest that these firms are able to maintain their higher dividends

(Grullon et al., 2002)”. This result by Grullon et al. is consistent with the dividend

smoothing model of Lintner (1956). Lintner (1956) concludes that managers try to do

what they say. Therefore, “Managers avoid dividend cuts if at all possible, they stabilize

dividends with gradual, sustainable increase whenever possible and they establish an

appropriate target payout ratio (Lease et al., 2000)”. In other words, firms only increase

dividends if the managers expect that the earnings are increased permanently.

As mentioned by Grullon et al. (2002), following the maturity hypotheses, firms pay

more dividends (dividend increase) if they enter a more mature period of their life cycle.

In a mature stage the investment opportunities and systematic risk decline and the

company generates higher free cash flows. Logically, because of the fewer profitable


19

investment opportunities, it is expected that the firm distributes these incremental free

cash flows to shareholders. Unfortunately, in practice this is not always the case.

Sometimes, a dividend increase induces a decline in profitability because managers can

simply overinvest in the mature stage of the firm‟s life cycle, as documented by Grullon

et al. (2002).

Furthermore, recent research by DeAngelo, DeAngelo and Stulz (2006) has documented

a significant relationship between the determination to distribute dividend and the RE/TE

ratio (Retained earnings/ Total equity). They focus on industrial firms which are listed on

the NYSE, Nasdaq or AMEX during the period 1973-2002. One important finding of

DeAngelo et al. (2006) is that firms pay more dividends in the case retained earnings are

a large proportion of total equity. As DeAngelo et al. write; “All our evidence supports a

life-cycle theory of dividends, in which a firm‟s stage in that cycle is well captured by its

mix of internal and external capital, so that dividend payers tend to have high earned

equity relative to contributed capital, and non-payers the reverse (DeAngelo et al.,

2006)”.

When firms change dividend payouts this influences the level of earnings, as Benartzi,

Michaely and Thaler (1997) mentioned. Generally, the market reaction to dividends is;

“dividends are good, and more is better (Benartzi et al., 1997)”. Benartzi et al. (1997)

investigate the period 1979-1991. The sample consists of companies that are traded on

the New York Stock Exchange (NYSE) or the American Stock Exchange (AMEX) for at

least two years. They concluded that if dividends decrease, the earnings in the year prior

to the change (t-1) and in the year of the change (t0) have decreased. However, the year

after the dividend decrease (t+1), the earnings have increased. This finding suggests that

there exists a negative relation between dividend payout and the future earnings. The

other way around, a dividend increase took place, earnings at t-1 and t0 have increased.

Notice, no evidence is found that the earnings at t+1 increase after a dividend increase. It

should be noted that in my research no separation is made between dividend decreases

and increases.


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Nissim and Ziv (2001) investigated the connection between dividend changes and future

profitability. The profitability in the future is measured in terms of future earnings and

future abnormal earnings. The sample period is 1963-1998. The firms in the sample are

listed on the NYSE or AMEX. The sample excluded financial institutions. Changes in the

dividend policy give information with regard to the profitability in the next years. Future

abnormal earnings are calculated by taken the difference between total earnings and

normal earnings. Normal earnings are the required return to shareholders predicated upon

the costs and the level of invested equity capital. One of Nissim‟s and Ziv‟s (2001) main

conclusions is that there exists a positive relationship between dividend changes and

earnings changes, during the first two years after the dividend policy changed. They find

that dividend changes are positively correlated to future profits. For example, if dividends

were increased by the company, this is associated with future profits for the next four

years after dividends increased. However, if dividends decrease this is not related to

future profits.

3.2 The Payout Ratio predicts Future Earnings Growth

Arnott and Asness (2003) have examined whether the payout ratio (dividend policy)

predicts the future earnings growth. They use the basic growth model of Gordon to

analyze the relation between payout ratios and future earnings growth.

3.2.1 Gordon’s Constant-Growth Valuation Model

Gordon (1962) develops the constant-growth valuation model, where he forecasts the

share price by the formula P= (D/(r-g)). Restructuring this formula gives Equation (2).

The dividend yield of a firm is calculated by using Equation (3).

(2) g = r - (D/P)

(3) (D/P) = (D/E)(E/P)


21

Where;

g = the constant-growth term

r = expected return

(D/P) = dividend yield

(D/E) = the payout ratio (dividend to earnings ratio)

(E/P) = the earnings yield

If the dividend yield has declined on the long term, this decline must be offset by an

increase in the growth to retain the expected return at the same level. The model of

Gordon implies that there exists a negative relationship between dividend yield and future

earnings growth.

Like Arnott and Asness (2003) documented, Gordon had assumed in his model that we

live in a world of perfect capital markets. Some typical „perfect capital market‟

assumptions are presented. Firstly, one had assumed the investment policy is unmodified

because all investors dispose of the same information. Furthermore, the amount of

dividend paid is irrelevant and taxes paid are equal for distribution and retention. In the

end, one had assumed that management always acts in the best interest for their investors.

Arnott and Asness (2003) did not believe this perfection. In addition, they said;

“dividends are sticky; they tend not to fall in notional terms, although they can fall during

severe earnings downturns and can fall in real terms during periods of high inflation

(Arnott and Asness, 2003)”. For this reason, they concluded that dividends are more

volatile than earnings. See Figure 2 for a graphical reproduction. This graph shows that

no negative relation had existed between payout ratios and expected earnings growth.

Indeed, the two lines did not move in opposite direction.


22

Figure 24: Payout Ratio and Subsequent 10-year Earnings Growth, 1946-2001

3.2.2 Main conclusions by Arnott & Asness

Arnott and Asness (2003) have investigated the modern period, from 1946 till 2001, for

the U.S. equity market. They used the data provided by Schwert (1990), Shiller (2000)

and Ibbotson Associates (2001). First of all, Arnott and Asness (2003) calculate the EPS

for the S&P500 index for the specified period. Arnott and Asness (2003) developed a

simple regression model;

(4) 10YREG = α + (b)PR

Where,

PR = Preceding payout ratio

10YREG= 10-Year earnings growth

4 Source: Arnott, R.D. and Asness, C.S. (2003), Surprise! Higher dividends = Higher Earnings Growth.

Financial Analysts Journal Jan/Febr 2003, pp. 70-87


23

They found a positive significant coefficient (β=0,25) within this equation. Doing a

robustness check by adding some other variables to the model, the payout ratio remained

positive related to the expected earnings growth. For example, Arnott and Asness (2003)

have added the variables „prior-10-year real earnings growth‟ and „average of real

earnings over the past 20 years‟ to the model. Both variables were negatively, but weakly

related to the dependent variable (expected earnings growth). With the variable „prior-10-

year real earnings growth‟, Arnott and Asness would like to investigate whether there

exists mean reversion in earnings. The main conclusion by Arnott and Asness (2003) is

that the higher the payout ratio, the higher the aggregate earnings growth for next ten

years for that firm. Afterwards, they have done some robustness tests.

3.2.3 Some Explanations for the Positive Relationship by Arnott & Asness

Arnott and Asness (2003) give some possible explanations for the positive relationship

between current payout ratio and future earnings growth. Here, the most important ones

are mentioned. Firstly, as Lintner (1956) documented, a high level of payout indicates the

confidence the managers had in the company. Therefore, managers are not likely to cut

dividends. Secondly, some managers undertake inefficient investment projects which

result in low or no earnings growth in the future. Jensen (1986) called this phenomenon

„empire building‟. On the other hand, high payout ratios lead to more carefully chosen

investment projects. In third place, companies would like to optimize tax treatment for

their investors. Further, it is also possible that the dividends are sticky and the fluctuating

earnings are mean reverted. This combination will also lead to a positive relationship.

Finally, Arnott and Asness (2003) said it is also possible that there is an error in their

research data. As one already read above, managers could represent a very dominant role

in dividend policies. Additionally, another possible incentive for managers not to pay out

dividends are their stock options. Dividend reduces the stock price and so indirectly

reduces the executive stock options.

Notice, a possible mistake of Arnott‟s and Asness‟s research (2003) could be that they do

not take into account the increase in buybacks of the recent years. Nowadays, these stock


24

repurchases are popular in some countries and could be a substitute for dividend

payments. It is possible that the lower payout ratios are caused by a new sensitivity to

shareholders with respect to tax optimization, rather than other more negative forces like

empire building by managers. One or more of these reasons could have changed the

dividend policy of firms.

3.3 An Extension of Arnott’s & Asness’s Research

Gwilym, Seaton, Suddason, and Thomas (2006) have extended the work done by Arnott

and Asness (2003). Two important differences can be recognized compared to the

research of Arnott and Asness. First of all, Gwilym et al. (2006) have done their study for

eleven major international markets5

. They would like to test whether the same

conclusions can be drawn for other countries. Secondly, they have additionally

investigated the relationship between returns and the payout ratio6. Gwilym et al. (2006)

have obtained their data from DataStream. The main variables of interest were the

monthly values of dividend yield, earnings yield, a retail price index, a stock market

index level and the payout ratio. The sample period differs by country because the

availability of data differs by index. Descriptive statistics show that the mean payout ratio

in the U.K. is the highest one with a percentage of 53% (payout ratio is 0.53), during the

period from 1973 till 2004. The Netherlands have a payout ratio of 0.48 for the same

period of time. They have concluded that there exists a positive and mostly significant

relation between future earnings growth and payout ratios, like Arnott & Asness (2003)

documented. In other words, Gwilym et al. (2006) have concluded that “substantial

reinvestment of retained earnings does not lead to faster future earnings growth, although

it does lead to faster real dividend growth (Gwilym et al., 2006)”. For the U.K., the U.S.,

France and Japan the adjusted R2 was reasonably high. Unfortunately for the other

5 The eleven countries that were included in the sample were respectively France, Germany, Greece, Italy,

Japan, the Netherlands, Portugal, Spain, Switzerland, the United Kingdom, and the United States.

6 Gwilym et al. (2006) defined the payout ratio as dividing the 1-year trailing dividends by the 1-year

trailing earnings.


25

countries, including the Netherlands, this R2 was low. In the end, Gwilym et al. (2006)

have not proved a significant positive relationship between the payout ratio and the

returns.

3.4 Hypotheses Development

In summary, some researchers have concluded that a negative relationship exists between

dividend payouts and future earnings (growth). Other researchers found a positive

relationship, for example Nissim & Ziv (2001) and Arnott & Asness (2003). There are

any numbers of possible explanations for these opposite relationships. For example,

differences in sample, sample size, firm cultures, branches and sectors could result in

different relationships. Further research is needed to find and explain some possible

explanations for these opposite relationships. Now, this research focuses on three specific

hypotheses.


Earnings Growth for Benelux Indices

Concluding from the literature review, this research will focus on the influence of payout

ratios on expected future earnings growth. Obviously, there are much more determinants

for the level of future earnings growth. However, during this research I have focused on

the most important variables related to future earnings growth. This research has used the

basis model developed by Arnott and Asness (2003), as represented in equation (4) in this

research. Arnott and Asness (2003) developed first this basic model. Afterwards, they

optimize the model by doing some robustness tests. This research follows the method

used in the research of Arnott and Asness (2003). I am curious whether the positive

relation between dividend distribution and future earnings growth also exists in the

Benelux. Therefore, the main hypothesis during this research is -The payout ratio is

positively correlated to the expected future earnings growth for Benelux indices-.


26


Earnings Growth for the Individual Listed Firms on the Benelux Indices

This research gives an extension to the research done by Arnott and Asness (2003)

because this study also tests the same relationship for the individual listed firms within

the AEX, AMX or BEL20. By developing the yearly compositions of these indices, the

study runs some cross sectional analyses. As a result, another important hypothesis of this

research is -The payout ratio is positively correlated to the expected future earnings

growth for the individual listed firms in the Benelux-. In this manner, one can analyze

individual effects better and it is possible to provide some more detailed results.

3.4.3 Hypothesis 3: The dividend yield is positively correlated to expected future

earnings growth of individual listed firms on the Benelux indices

In addition to the second hypothesis, this research studies the following hypothesis -The

dividend yield is positively correlated to the expected future earnings growth of listed

firms in the Benelux-. The dividend yield expresses the dividend per share as a

percentage of the share price. It is expected that dividend yield is a substitute variable

instead of the payout ratio to measure dividend distribution. These two variables have the

same numerator but a different denominator. Additionally, the payout ratio is scaled with

earnings and the dividend yield is scaled with the share price. In this research, I expect a

positive relation between payout ratio and expected earnings growth. For this reason I

also expect a positive relation between dividend yield and expected earnings growth.


27

Chapter 4: Data & Methodology

This chapter discusses the data and methodology for this research. The assumptions and

limitations of this research were highlighted. Important to understand, the research is

divided in two different parts. Firstly, a time series regression analysis to investigate the

first hypothesis -The payout ratio is positively correlated to the expected future earnings

growth for Benelux indices-. Afterwards to analyze the second hypothesis this research

uses cross sectional regressions. This second hypothesis is -The payout ratio is positively

correlated to the expected future earnings growth for the individual listed firms in the

Benelux-.

4.1 Time Series Regression Analyses

To analyze hypothesis 1 -The payout ratio is positively related to the expected future

earnings growth for Benelux indices-; this research makes use of time series regression

analyses. This implies the research firstly focuses on the Benelux indices. Important to

understand is that the research does not focus yet on the individual firms within the

Benelux indices.

4.1.1 Sample Construction Indices

This research focuses on three indices, in particular the AEX, AMX and BEL20. First of

all, this section mentions some facts about these indices. The Amsterdam Exchange Index

(AEX) compounds of the 25 largest shares on the Amsterdam stock exchange. The AEX

was found in March 1983 under the name EOE-index (European Option Exchange). The

composition of the AEX is calculated by the market volume. The market value of the

shares of all firms is calculated by using the formula below (Equation 5).

(5) Market Value = Last share price * Number of Shares * Free Float7 * Capping

factor

7 The free float represents the percentage of shares that is freely negotiable on the exchange.


28

After all individual market values are calculated, these values are summed and as a result

the index value is generated. In the end, this index value is divided by the divisor (for

example; the divisor was 825.684.986,93 in October 2010). The weight is of firm‟s share

in the AEX and AMX is capped at 15%, which is transformed in the capping factor. In

the AEX, Unilever and Royal Dutch Shell have a maximum weight of 15%, at this

moment. The Amsterdam Midkap Index (AMX) was founded in October 1995 under the

name Midcap Index. First, the 25 largest firms (number 1-25) are selected for the AEX

index. Afterwards, the second 25 largest firms (number 26-50) are selected. The

selection of these 25 firms occurs in the same way as the AEX selection is made. The

composition of the AEX and AMX do change each year in March.

The BEL20 was founded in March 1991. This index is located in Brussels. Market

authorities of Euronext select minimal 10 firms and maximal 20 firms for the BEL20.

Euronext makes use of some selection criteria. First of all, the firms must have a high

enough market capital. Furthermore, the liquidity and marketability are important criteria.

The weighting of the individual shares in the BEL20 are limited to 10%, compared to a

maximum weight of 15% on the AEX and AMX. Notice, the BEL20, AMX, and AEX

index include some shares with a (very) high weighting. For example, about 50% of the

total shares of the BEL20 index are present by GDF Suez, KBC, Dexia, Ageas and Inbev

in 2009. Because of this, a share or a group of shares could have a large impact on the

index.

4.1.2 Variable Description & Model Building Indices

DataStream 5.0 generates for the indices the variables price index (P), dividend yield

(DY) and price/earnings (P/E) ratio on a monthly basis. The database DataStream 5.0

provides a lot of financial information about European firms, for example information

about bonds and equities. Notice, DataStream makes use of Worldscope. DataStream

generates the financial information from institutions like the IMF, CBS, OECD, Eurostat

and some national statistical offices. This database uses information provided by the

international stock exchanges like the AEX.


29

In Appendix I the variables and their description are presented for the three Benelux

indices. Like Appendix I shows, the price index (P) is an aggregated number which is

weighted by market value. The price index is calculated using a representative list of

shares in an index for that specific time period. The dividend yield represents an average

number of the individual yields of the firms within the indices. Finally, the P/E ratio is an

earnings-weighted average of all individual P/E ratios in the index. See Appendix I for

more details. The variables EPS8, DPS

9 and Payout Ratio (PR)

10 derives from the

variables; P, DY and P/E ratio. In the first set of regressions, the PR is the independent

variable and the expected earnings growth (EEG) is the dependent variable.

This study focuses on different EEG levels, successively the 1, 3, and 5 years annualized

growth (EEG1YR, EEG3YR and EEG5YR11

). Arnott and Asness (2003) uses the same

independent and dependent variable in their first models. The first simple model is

presented in Equation (6).

(6) EEGt years = α + β(PR)+ ut

Where, t = 1, 3 or 5 years

The testing periods differ per index because the historical availability of the indices

differs a lot. For the AEX, information is available from November 1983 on. For the

AMX the information begins on January 1983. The BEL20 presents information from

February 1990.

8 EPS = EY * P , where Earnings yield (EY) =( 1/ (P/E))

9 DPS = DY * P

10 PR = DPS/EPS

11 EEG5YR = (Current EPS/ 6 years ago EPS -1)/5


30

4.1.3 Methodology for Time Series Analyses

This research makes use of simple linear regression analyses. This method makes it

possible to estimate the relationship between a dependent variable (EEG) and a

independent variable (PR). This research develops a time series model.

This time series model explains the relationship between the monthly aggregate payout

ratio and the monthly aggregate future earnings growth for the Benelux indices. In

summary, in these models one focuses not on the firm specific information but focuses on

the average values for the index. A general form of a simple linear model can be

expressed as follows:

(7) Yt+k = α + βXt+ ut+k

Where, t = 1, 2, ….., N and k = 1, 3, or 5 years

The ordinary least squares method (OLS) is the used point estimator. This study works

with the OLS estimator because this estimator is theoretically unbiased. There are a few

assumptions of the OLS regression. Firstly, error terms are statistically independent of

other independent variables. In addition, the expected value of the errors is always zero.

The independent variables are not too strongly collinear. Secondly, all error terms

together have an expected value of zero. Furthermore, an assumption related to the error

term is its constant variance. The error terms are normally distributed. The model is linear

in parameters. In general, the data is a random sample of the population but in this

research the data is not because of the serial correlation.

The least squares criterion can be described as:

(8) Minimize

2

21

22)ˆˆ()ˆ(ˆ iiiii XYYYu

, with respect to 1̂ and 2̂

This study uses Excel and SPSS to run the simple regression models.


31

4.2 Cross Sectional Regression Analyses

Firstly, this research analyses the aggregate indices by time series regressions, as

explained in Section 4.1. In other words, hypothesis 1: „The payout ratio is positively

correlated to the expected future earnings growth for Benelux indices‟. In addition, some

extra cross sectional regressions are run to create a more detailed view. In this manner,

one can analyze the firm specific effects in a clear way. Furthermore, the differences

between Belgian & Dutch firms and Mid-cap & Large-cap firms are analyzed in more

detail. The next section describes the sample construction and explains some assumptions

for these cross sectional regressions.

4.2.1 Assumptions and Sample Construction Individual Firms

This second sample includes individual firms of the indices AEX, AMX or BEL20. The

historical index composition is generated from Euronext12

. For the AEX and AMX a

complete overview of the yearly composition is presented. Unfortunately, information

given by Euronext about the historical BEL20 composition is much poorer. The definitive

sample consists only of the companies for which enough information on all variables is

available.

Some assumptions are made to develop a useful dataset. First of all, the exact

composition of the BEL20 index is not available for the first years of existence of the

index. For the period 1991- 2002 the constant BEL20 composition of the year 2003 is

used. Firms which were not yet active in one or more of these earlier years were deleted

from the sample for that specific year. From 2003 on this research uses the correct

composition of the BEL20. In this manner, it was hard to analyze the exact BEL20 index

from to moment of the foundation. For further research it will be useful to generate the

BEL20 composition from 1991 on.

12 Source: www.euronext.com

http://www.euronext.com/


32

Furthermore, the construction of the three indices differs each year. For example, to

calculate the annualized 5 year expected earnings growth, the EPS value in t+5 is needed.

Notice, the firms which are in the indices do not have to be the same at t and t+5. Indeed,

each year some firms enter the indices and some firms leave the indices. For simplicity,

this research uses the index composition in year t to generate the EPS in further years

(t+1, t+3, and t+5).

Thirdly, another important limitation to this research is the absence of negative numbers

of earnings per share in DataStream 5.0. Earnings per share are for all firms in all indices

during all years zero or higher than zero. The net income13

is used to conclude about the

profitability of the specific firm. In general, firms with a negative net income have a EPS

of zero in that year. This EPS cap on zero is a major limitation of DataSteam 5.0. One

has to take this into account when the dataset is used for further research. In addition, in

the case the EPS in year t is (nearly) zero and the EPS in year t+1, t+3, or t+5 is not

(nearly) equal to zero, it is not possible to calculate the earnings growth in a good way.

For this reason, the research deleted the firms which have both an EPSt of (nearly) zero

and an EPSt+1, t+3, and t+5 above zero. In Appendix II a summary of the deleted number of

firms is presented.

Fourthly, if no data is available in DataStream 5.0 for the main variables; EPS and/ or

DPS, the firm is deleted from the sample because it is not possible to run the regressions

for these firms. For example, if a firm within one of the indices goes bankrupt or is

acquired then this firm is deleted from the sample. Additionally, the outliers are excluded

from the dataset. For example, the Belgian firm D‟ieteren has a 3 years annualized

expected earnings growth of +1371,74% in 1995. This number heavily influences the

results and therefore such outliers are deleted from the sample. This research assumes a

maximum earnings growth of 250% for all companies in all indices during all years. This

13 The ‘Net income’ in this research is the net income after preferred dividends that the company uses to

calculate its basic earnings per share (source: DataStream 5.0).


33

implies that the expected earnings growth is capped on 250% if the value of the earnings

growth is above the maximum percentage of 250%. Also a few payout ratio outliers are

capped on 250%.

Further, the regressions for 2006 and further on do not include the 5 years annualized

expected earnings growth because EPS for 2011 and further years is not realized yet. In

addition, for the same reason the 3 years annualized expected earnings growth cannot be

calculated for the year 2008 and further. In general, the last 5 years of my sample period

cannot be used to run the regressions. For this reason, in Appendix II only the

information is given till 2005. The sample size is about 50 - 60 firms for each year.

Important to understand is that the sample composition differs each year for each index.

Appendix III A and Appendix III B present the sample composition of the year 2010. The

company name, index name and sector are given in Appendix III.

Notice, the variable stock repurchases is not taken into account during this empirical part

of this research. From the database which was used during this research, no „share

repurchases‟ variable is available. With the information available in DataStream 5.0 it

was also not possible to create a good and reliable alternative measurement for the yearly

level of stock repurchases. Therefore, it will be useful to focus more on „buybacks‟ in

further research.

4.2.2 Variable Description & Model Building Individual Firms

Afterwards, to test hypothesis 2 -The payout ratio is positively related to the expected

future earnings growth for the individual listed firms in the Benelux-, Equation (9) is

used. Importantly, these cross sectional regressions analyze the firms separately rather

that the first aggregate index regressions which are based on times series. Equation (9)

presents the model developed for the cross sectional regressions.

(9) EEG0,t years = α0+β1PR + β2DY+β3EY+β4SA+β5EBIT+β6ND+β7MT+ β8TA+

β9CX+ ut


34

Where,

EEG0,t years = Earnings growth (t = 1, 3 or 5 years)

PR= Payout ratio (DPS/EPS)

DY = Dividend Yield (Total Dividends/(Share price* Number of shares)

EY = Earnings Yield (calculated as (1/(P/E ratio))

SA = Sales

EBIT = Earnings before interest and taxes, depreciation and amortization

ND = Net Debt

MT= Market to Book Ratio

TA = Total Assets

CX= Capital expenditures

ut = Error term

The dependent variable of interest in Equation (9) is future net profit at t+1, t+3 or t+5,

measured by the Expected earnings growth (EEG). Also other researchers have used the

future earnings growth as the dependent variable, like Arnott and Asness (2003), Gwilym

et al. (2006), and Zhou and Ruland (2006).

The most important independent variable is the payout ratio of the firm. The payout ratio

is calculated by dividing dividends per share by earnings per share. Furthermore, the

dividend yield is included. Gordon (1962) already developed the constant-growth

valuation model. He has used the dividend yield and the expected return to forecast

growth. Furthermore, the earnings yield was added. Like Gwilym et al. (2006) have

documented the earnings yield is most of the time negatively and significantly related to

the earnings growth. „Sales‟ is the last independent variable that was plugged in the

model. Also the control variables EBITDA, Net debt, Market to Book Ratio, Total assets,

and Capital expenditures were inserted in the model. Total assets are used to control for

the firm size.


35

Notice, the main independent variable is the payout ratio. The basic model is the bold

part; EG0,t = α0+β1PR. The model is elaborated with some variables during this research.

The variables which fit best with the model, based on their significance, and result in a

higher R2 are added to the basic model. Not all control variables are added because it is

possible that two or more variables correlate strongly within the model (the

„multicollinearity‟ problem). To check for this, a univariate analysis is made first using

the Pearson correlation matrix. Thereafter, an analysis with a multivariate regression is

used to test the relationship between future earnings growth and the payout ratio for firms

in the Benelux indices. Appendix IV provides a more detailed description of all the

variables of Equation (9).

4.2.3 Methodology for Cross Sectional Analyses

Cross sectional analyses are done by linear regression models. The cross sectional

analyses for each year are done, based on the yearly composition of the three indices.

Equation 10 presents the basic model for these cross sectional analyses.

(10) Yi,t+k = βt Xit + u i,t+k

Where, i is the firm of interest, t is the year of interest, and k = 1, 3, or 5 years

Within each regression per year, all firms of the specific indices are added. In this way

the sample size per year is about 50 à 60 companies because some firms are removed

from the sample based on the assumptions mentioned above. The model can be run for

the total sample of the three indices (AEX, AMX and BEL20). However, with these

simple linear regressions one can also do some robustness checks. The differences

between the AEX and AMX, in other words the differences between large-cap and mid-

cap firms, were analyzed. Furthermore, the main differences between the Dutch indices

and the Belgian index can be recognized. Notice, only for the period 1995-2005 all data is

available for all indices. The period 2006-2010 is not taken into account because the EEG

on a 3 and 5 years basis is not yet available for that period.


36

4.2.4 Cross Sections over a Longer Time Period

After the cross sectional regression analyses are made, the research developed an average

model to forecast the future earnings growth in more detail. The average model measured

over time arises by taking the average coefficients of the yearly model. The independent

variable is the average coefficient of earnings growth for all specific firms in the index

during the period 1995-2005. Some different average models are built. Firstly, an average

model for the three indices together is built (period 1995-2005). Furthermore, Dutch and

Belgian indices are compared over all years. For the exact average models see Chapter 5.

Firstly, only the independent variable „payout ratio‟ is inserted in the model. Afterwards,

the model is elaborated by some other independent variables. This is an alternative way to

develop a kind of a panel data analysis. The panel data analysis is a form of longitudinal

data analyses and is increasingly popular. A panel is a cross-section or group of firms

which are surveyed periodically over a given time span. In other words; “panel data

analyses are repeated measures of one or more variables on one or more persons repeated

cross-sectional time-series (Brüderl, 2005)”. Panel data analyses take both into account

the space and time dimension of the data. The standard equation14

in panel data analyses

is presented below (Equation 11).

(11) Yi,t+k = βXi,t + ui,t+k

Where Y is the dependent variable and X the independent variable. The intercept (u) is

added to the model to capture the constant factors that might affect the dependent

variable Y. Two subscripts for each variable are added. The subscript i rends the

particular cross section unit and the second subscript t rends the particular time period.

14 Source: Baddeley, M.C. and Barrowclough, D.V. (2009), Running regressions ‘A practical Guide to

Quantitative Research in Economics, Finance and Development studies’. Cambridge University Press, pp.

249-263


37

Unfortunately, this research cannot use panel data in this standard way because of a

changing sample composition each year. Therefore, this research uses the average

coefficients of the yearly cross sectional models. Using this average model, the research

tried to analyze the influence of the payout ratio on the expected earnings growth over all

eleven years (1995-2005). Pesaran and Shin (1999) did research to this type of average

models. They mentioned that in practice, people run all separate regressions and calculate

the coefficient means. Pesaran and Shin (1999) called this the „mean group (MG)

estimator‟.

4.2.5 Advantages of Panel Data

The average model, to analyze the influence of the payout ratio on the earnings growth

over all years, could be compared with panel data analyses. In this manner, some of the

advantages of panel data analyses are also valid for my average model over time. Brüderl

(2005) mentions a few advantages of using panel data. Below, the advantages that applied

to my average models are presented. Firstly, panel data is more informative because the

data is more variable and there is less collinearity. Secondly, with panel data one can

study individual dynamics of the different firms in more detail. Furthermore, with panel

data one can control for individual unobserved heterogeneity. This last reason is a very

important one. “Heterogeneity bias occurs because the time invariant fixed effects

influencing individual cross-sectional units have been left out of the deterministic part of

the model so in essence heterogeneity bias is a form of omitted variable bias (Baddeley

and Barrowclough, 2009)”.


38

Chapter 5: Results

The results are presented in this chapter. The first part of this chapter presents the

regression results of the time series analyses. The second part of the chapter describes the

yearly cross sectional analyses. In the end, the cross sectional regressions for all years are

presented. These last regressions are calculated by the average model as explained in

Subsection 4.2.4.

5.1 Descriptive Statistics for the Benelux Indices

Table 5.1 presents the descriptive statistics of the indices. This table includes the mean,

median, standard deviation, minimum and maximum for the independent and dependent

variables of the model. The AEX index presents the highest average payout ratio of the

three Benelux indices; an average payout ratio of 46,73% with a corresponding average 5

years annualized earnings growth of 7,09%. The AMX has the lowest average payout

ratio of 40,67% with a corresponding average 5 years annualized earnings growth of

9,67%. Comparing large- and medium-caps, one observes that the average PR of the

mid-cap index (AMX) is smaller than the average of the AEX. One possible explanation

is that the AMX includes smaller firms, which are growing faster compared to the firms

of the AEX. For this reason, firms in the AMX have a higher retention rate and pay out

less dividends. These midcap firms need their money to finance future growth. Notice,

this reasoning supports the life cycle theory as mentioned in Chapter 2 of this research. If

the firm ends up in a more mature stage of the life cycle, the firm pays more dividends.

Generally, the AEX firms are in a more mature stage of their life cycle compared to the

AMX firms. The highest average payout ratio paid by these three Benelux indices in this

sample is a payout ratio of 69,36%. Focusing on the dependent variable, expected

earnings growth, one sees that the EEG increases over time for all indices. In other

words, for all indices the EEG5YR is a larger percentage compared to the EEG1YR.

Therefore, the AEX, AMX and BEL20 increases their future profitability, which is

expressed in expected earnings growth.


39

Table 5.1

Descriptive Statistics In this table the three Benelux indices are presented. The testing periods differ per index, because

the existence of the indices differs a lot. For the AEX, information is available since November

1983 on. For the AMX information is provided since January 1983. And the BEL20 presents

information since February 1990. For all indices the monthly data is generated till November

2010. The sample size ranges from N = 182 to N = 325. The dependent variable; the payout ratio

(PR) is calculated by dividing DPS by EPS. The different levels of „annualized‟ expected

earnings growth are calculated by the change in EPS for t years, dividing by t years. For example,

EEG5YRt= (EPSt+5/EPSt -1)/5. The same method is used to calculate EEG1YR and EEG3YR.

PR EEG1YR EEG3YR EEG5YR

Panel A; AEX

Mean 46,73% 5,44% 6,12% 7,09%

Median 46,01% 10,18% 6,94% 6,29%

St. Deviation 11,56% 24,24% 12,77% 8,91%

Minimum 27,71% -70,67% -23,90% -11,23%

Maximum 60,08% 66,35% 35,05% 30,46%

N 313 313 289 265

Panel B; AMX

Mean 40,67% 8,32% 9,21% 9,67%

Median 38,04% 8,55% 7,73% 8,40%

St. Deviation 11,75% 28,43% 12,70% 9,48%

Minimum 23,80% -60,79% -15,53% -9,06%

Maximum 69,33% 127,20% 56,46% 36,82%

N 315 315 291 267

Panel C; BEL20

Mean 41,81% 5,10% 8,29% 9,97%

Median 39,21% 6,10% 10,14% 9,07%

St. Deviation 12,30% 24,67% 12,07% 7,97%

Minimum 24,67% -61,30% -17,44% -7,97%

Maximum 69,36% 59,90% 39,13% 25,01%

N 230 230 206 182

For all indices the average annualized expected earnings growth increases with the

number of prior years that are taken into account. So, the expected earnings growth for 1

year (EEG1YR) on the AEX, AMX and BEL20 is smaller than the 5 years annualized

expected earnings growth (EEG5YR). Logically, the standard deviation for the

annualized 1 year growth is much higher than the standard deviation on the annualized 5


40

years growth. For instance 28,43% versus 9,48% on the AMX. Important to understand,

the sample size (N) differs per index because the inception date of the three indices

differs. Furthermore, the sample size differs with the level of annualized earnings growth

(1, 3 and 5 years) because in the case of an annualized 5 years earnings growth, a data

point of the next 5 years is needed to calculate the percentage of growth. For this reason,

the sample size decreases if the level of earnings growth increases.

5.2 Indices Time Series Regressions

This research run some simple linear regression models, based on Equation (7);

EEGt+k = α + β(PR)t+ ut+k. The three different levels of annualized earnings growth are

used in the models. For this reason, Table 5.2 presents three horizon (k= 1, 3, or 5)

estimations per index, displayed in Panel A, B and C. This table shows that in all models

the payout ratio is significant and positively related to the expected annualized earnings

growth. Arnott and Asness (2003) found the same positive relationship. Notice, Arnott

and Asness (2003) investigate an older and much longer time period, namely from 1871

till 2001. In these models, the independent variables are all significant at a 0,001 level.

Figure 3 plots the relation between the average payout ratio of all three indices and the

annualized 5 years expected earnings growth of the three indices. As a result, one

observes an increasing line which implies the variables are positively related. For

example, the maximum EEG5YR for the average AMX index is 36,82%. This number

can be obtained from Table 5.1 and from Figure 3. The bullets in Figure 3 represent the

real monthly average observations for relationship between expected earnings growth and

payout ratios for the AMX. In addition, the line in Figure 3 is a regression line of these

observations. The values are measured in the time period March 1983 – March 2005.

This is an increasing line which means there is a positive relationship between PR and

EEG5YR. The R2

of this regression model is 0,159. This R2

implies that this model

explains 15,9% of the variance in the future profitability. In practice, it is very difficult to

forecast the determinants of the future profitability of the firm. Therefore, a R2

of 0,159 is

meaningful in this case.


41

Figure 3: Scatter Plot of the Average Payout Ratio (x-axis) versus the Average 5 years

Expected Annualized Earnings Growth (y-axis) for the AMX Index

A notable result is the difference between the Belgian index and the Dutch indices. In

Belgium, the payout ratio influences the expected annualized earnings growth less. In

other words, the coefficients for the BEL20 models (Panel C) are lower. Except for the

EEG3YR because Model V (AMX) presents a lower coefficient compared to Model VIII

(BEL20).


42

Table 5.2

Indices Time Series Regressions The data is generated on a monthly basis. The sample period differs per model because the

existence of the indices differs a lot. The dependent variable, payout ratio (PR) is calculated by

dividing DPSt by EPSt. The different levels of „annualized‟ expected earnings growth are

calculated by the change in EPS for t years, dividing by t years. For example, EEG5YRt=

(EPSt+5/EPSt -1)/5. The same method is used to calculate EEG1YR and EEG3YR. Model I

presents the influence of the AEX payout ratio on the 1 year expected earnings growth. Model II

and III present the relationship between the AEX payout ratio and respectively the 3 years EEG

and the 5 years EEG. These three models (Model I, II and III) are presented in Panel A (AEX).

Panel B includes the Model IV, V and VI for the AMX. Finally, Model VII until IX shows the

results of the regressions on the BEL20 (Panel C).

Panel A: AEX Model I

(EEG1YR)

Model II

(EEG3YR)

Model III

(EEG5YR)

Intercept (α) -0,316 -0,464 -0,229

β (PR) 0,816*** 1,163*** 0,651***

T-test 5,055 14,465 9,328

R² 0,076 0,422 0,249

N 312 289 265

Panel B: AMX Model IV

(EEG1YR)

Model V

(EEG3YR)

Model VI

(EEG5YR)

Intercept (α) -0,208 -0,190 -0,082

β (PR) 0,745** 0,723*** 0,450***

T-test 3,915 9,229 7,079

R² 0,047 0,228 0,159

N 315 291 267

Panel C: BEL20 Model VII

(EEG1YR)

Model VIII

(EEG3YR)

Model IX

(EEG5YR)

Intercept (α) -0,210 -0,265 -0,043

β (PR) 0,659** 0,900*** 0,374***

T-test 2,806 7,281 4,197

R² 0,033 0,206 0,089

N 230 206 182

***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)


43

5.2.1 Robustness Tests

One has to be skeptical about the first models presented in Table 5.2. In the models, there

is a high level of autocorrelation. This research tests for the presence of autocorrelation

by doing a Durbin-Watson Test in SPSS. For example, Model VIII (BEL20) has a

Durbin-Watson statistic of 0,109. This number provides the evidence that this model

deals with positive serial correlation. To correct for the high autocorrelation, this research

has done a Newey-West correction on the standard errors. This Newey-West correction is

done with Eviews 615

. Also Arnott and Asness (2003) did a robustness check because

their model forecasts overlapping 10-year earnings growth over a 55-year span. In other

words, they have only a few independent observation. Therefore, Arnott and Asness

(2003) developed a model with shorter earnings-growth periods to analyze more

nonoverlapping periods. Arnott and Asness (2003) concluded that the same relationship

between future earnings growth and dividend policy holds for these shorter earnings-

growth periods. In my research I use a Newey-West correction to solve this problem. In

Table 5.3 the corrected regression results are presented (Model I – IX). The relationship

remains positive and the coefficients do not change a lot. The payout ratio variable

becomes less significant within the models. The F-test, T-test, P-values, and the Standard

errors decrease for most of the models. The most important change after doing this

Newey-West correction are shortly discussed. The payout ratio coefficient is more often

not significant within the model as a result of this correction for autocorrelation. In other

words, in most of the corrected models of Table 5.3 the t-statistic decreases.

15 Eviews 6 measures autocorrelation of a series Y at lag k by the formula below;

Where; is the sample mean of


44

Focusing on Table 5.3, one sees that these corrected models explain a part of the variance

of the different levels of expected earnings growth. The R2‟s of Model I- Model IX are in

the range [0,080; 0,387]. All coefficients of the variable „payout ratio‟ remain positive.

This supports the first hypothesis -The payout ratio is positively correlated to the

expected future earnings growth for Benelux indices-. The results of this research remain

the same as the results found by Arnott and Asness (2003). Specifically, a positive

relation between future earnings growth and the dividend policy of firms.

Figure 4: Graph of the Residuals of the AEX based on the EEG3YR

In Appendix V the scatter plots of the payout ratio versus the earnings growth are given.

Three scatter plots for the years 1995, 2000 and 2005 are given. Comparing this

scatterplots, one sees that the observations in the years 1995 and 2000 (Figure A and B)

are much more concentrated compared to the observations is 2005 (Figure C). It is

possible that the current financial crisis has already impacts the 5 years annualized

expected earnings growth in the year 2005. In other words, the observations of 2005

fluctuate most probably more because of the current crisis.


45

Table 5.3

Corrected Indices Regressions based on a Newey-West Correction The data is generated on a monthly basis. The sample period differs per model because the

existence of the indices differs a lot. The dependent variable, payout ratio (PR) is calculated by

dividing DPSt by EPSt. The different levels of „annualized‟ expected earnings growth are


(EPSt+5/EPSt -1)/5. The same method is used to calculate EEG1YR and EEG3YR. Model I

presents the influence of the AEX payout ratio on the 1 year expected earnings growth. Model II

and III present the relationship between the AEX payout ratio and respectively the 3 years EEG

and the 5 years EEG. These three models (Model I, II and III) are presented in Panel A (AEX).

Panel B includes the Model IV, V and VI for the AMX. Finally, Model VII until IX shows the

results of the regressions on the BEL20 (Panel C).

Panel A: AEX Model I

(EEG1YR)

Model II

(EEG3YR)

Model III

(EEG5YR)

Intercept (α) -0,376 -0,353 -0,229

β (PR) 0,995* 0,939*** 0,651***

T-test 2,154 7,050 4,896

R² 0,109 0,329 0,249

N 265 265 265

Panel B: AMX Model IV

(EEG1YR)

Model V

(EEG3YR)

Model VI

(EEG5YR)

Intercept (α) -0,238 -0,175 -0,082

β (PR) 0,888 0,703*** 0,450**

T-test 1,659 4,555 3,042

R² 0,080 0,234 0,159

N 276 276 276

Panel C: BEL20 Model VII

(EEG1YR)

Model VIII

(EEG3YR)

Model IX

(EEG5YR)

Intercept (α) -0,772 -0,295 -0,043

β (PR) 2,239*** 1,031*** 0,373

T-test 10,280 5,313 1,910

R² 0,387 0,348 0,089

N 182 182 182

***, **,* Significant at a 0,001, 0,01, and 0,05 level (two-tailed)


46

5.3 Descriptive Statistics for the Individual Firms of the Benelux Indices

Table 5.4 presents the descriptive statistics of individual firms within the three Benelux

indices. This table includes the mean, median, standard deviation, minimum and

maximum for the independent and dependent variables of the model. From 1995 till 2005

all variables for all indices are available. To get a good view of the descriptive statistics

for the individual firms in the three indices, the statistics for the years 1995, 2000 and

2005 are calculated. For these three different years the descriptive statistics are presented

in Panel A, B, and C of Table 5.4.

Important to understand, the variables EEG and PR have a maximum value of 250,00%,

as showed in Table 5.4. This research assumes EEG and PR are capped on a value of

250,00%. As mentioned in Chapter 4, DataStream documented a negative EPS as an EPS

of zero. Of course, the minimum payout ratio is 0,00%. The average payout ratio has

increased during the years 1995, 2000, and 2005, respectively 37,42%, 40,25%, and

43,24%. This implies that the Benelux-listed firms have changed their dividend policy.

On average, the firms of the sample pay more dividends as a percentage of earnings in

2005 compared to the years 1995 and 2000. For example, the annualized 3 years expected

earnings growth in 2000 was -2,45%. This means that the earnings growth from 2000 till

2003 was on average negative. In other words, on average the EPS decreases from 2000

to 2003.


47

Table 5.4

Descriptive Statistics The sample consists of all firms in the three Benelux indices. The sample size (N) differs per year

because each year some firms are deleted from and added to the sample. The dependent variable;

the payout ratio (PR) is calculated by dividing DPSt by EPSt. The different levels of „annualized‟

expected earnings growth are calculated by the change in EPS for t years, dividing by t years. For

example, EEG5YRt= (EPSt+5/EPSt -1)/5. The same method is used to calculate EEG1YR and

EEG3YR.

PR EEG1YR EEG3YR EEG5YR

Panel A; 1995

Mean 37,42% 10,84% 23,77% 21,99%

Median 35,98% 8,66% 18,92% 15,93%

St. Deviation 22,12% 43,10% 38,78% 39,02%

Minimum 0,00% -100,00% -28,85% -20,00%

Maximum 160,87% 250,00% 250,00% 250,00%

N 62 62 62 62

Panel B; 2000

Mean 40,25% 12,67% -2,45% 8,15%

Median 36,81% 10,33% -10,08% 0,00%

St. Deviation 34,92% 66,86% 50,99% 47,14%

Minimum 0,00% -100,00% -33,33% -20,00%

Maximum 250,00% 250,00% 250,00% 250,00%

N 60 60 60 60

Panel C; 2005

Mean 43,24% 37,24% 14,59% 6,79%

Median 32,03% 28,73% 9,39% -2,65%

St. Deviation 43,21% 72,93% 43,06% 33,91%

Minimum 0,00% -100,00% -33,33% -20,00%

Maximum 250,00% 250,00% 233,33% 185,00%

N 60 60 60 60

5.4 Cross Sectional Regressions for the Individual Firms within the Indices

First of all, some regression models of the total sample are presented in Table 5.5. The

total sample consists of all listed firms of the AEX, AMX or BEL20 index. Notice, the

models are presented on an individual single year basis. This implies that the firms which

are active on the index in a particular year are included in the sample of that year. For this

reason, the sample composition changes each year. In the year 1995 62 firms were

included in the sample. One of the firms in the sample of 1995 is Ahold N.V. that is listed


48

on the AEX stock-exchange. This company has a payout ratio of 35,56% and an

EEG1YR, EEG3YR and EEG5YR of respectively 15,56%, 22,96% and 29,33%. For

example, Model I‟ in Table 5.5 explains the influence of the payout ratio of individual

listed Benelux firms on the 1 year expected earnings growth during the year 1995. Model

II‟ and III‟ explains the relationship between the payout ratio and respectively the 3 years

annualized EEG and the 5 years annualized EEG for the listed firms in the AEX, AMX or

BEL20 during the year 1995.

These models create a more detailed insight in the relationship between the two main

variables of interest. For example, the coefficient of 1,015 in Model III‟ implies that if the

payout ratio in 1995 increases by 10%, the annualized 5 years expected earnings growth

is 10,15%. In most of the models the β (PR) is positively and significantly related to the

future earnings growth. This implies that this models support the second hypothesis -The

payout ratio is positively correlated to the expected future earnings growth for the

individual listed firms in the Benelux-. In the cases the payout ratio coefficient is negative

this coefficient is not significant within the model. Focusing on the 1 year expected

earnings growth (Model I‟, IV‟, and VII‟), the coefficient of the payout ratio variable is

decreased from 1995 to 2005 to 0,610. This implies that the impact of the dividend policy

on the future expected earnings growth weakened during these eleven years. The R2

also

decreased from 1995 to 2005 for all models in Table 5.5. This means that less of the

variance in the earnings growth is explained by the basic model (Equation (10)) in 2005

compared to 1995.

To get a more complete view of the relationship between dividend policy and future

earnings growth, all years within the sample period 1995-2005 were analyzed. This

research estimates the cross section for every year and I present the average coefficients

of the yearly cross sectional models. Using this approach, I have analyzed the influence

of the payout ratio on the expected earnings growth in more detail. All eleven years

(1995-2005) are taken into account. This provides a better and more accurate view. Table

5.6 shows the three average models (Models I‟‟, II‟‟, and III‟‟) for the total sample. The


49

statistics of Table 5.6 are calculated by taking the average of the period 1995-2005

(eleven years). For a complete overview of all single yearly statistics and the resulting

average models see Appendix VII.

Table 5.5

Cross Sectional Regressions The data is generated on a yearly basis for all individual firms in the three Benelux indices for the

specific year. The sample period is 1995-2005. For these models the regressions are run for the

individual years 1995, 2000 and 2005. These different years are presented in respectively Panel

A, B, and C. The dependent variable; the payout ratio (PR) is calculated by dividing DPSt by

EPSt. The different levels of „annualized‟ expected earnings growth are calculated by the change

in EPS for t years, dividing by t years. For example, EEG5YRt= (EPSt+5/EPSt -1)/5. The same

method is used to calculate EEG1YR and EEG3YR. The sample size is 62 in the year 1995. In

2000 and 2005 the sample consists of 60 firms.

Panel A: 1995 Model I‟

(EEG1YR)

Model II‟

(EEG3YR)

Model III‟

(EEG5YR)

Intercept (α) -0,271 -0,142 -0,160

β (PR) 1,014*** 1,015*** 1,015***

T-test 4,720 5,500 5,448

R2

0,271 0,335 0,331

N 62 62 62

Panel B: 2000 Model IV‟

(EEG1YR)

Model V‟

(EEG3YR)

Model VI‟

(EEG5YR)

Intercept (α) -0,157 -0,352 0,130

β (PR) 0,705** 0,814*** -0,121

T-test 3,013 5,111 -0,686

R2

0,135 0,310 0,008

N 60 60 60

Panel C: 2005 Model VII‟

(EEG1YR)

ModelVIII‟

(EEG3YR)

Model IX‟

(EEG5YR)

Intercept (α) 0,109 0,176 0,011

β (PR) 0,610** -0,069 0,132

T-test 2,953 -0,532 1,296

R2

0,131 0,005 0,028

N 60 60 60



50

Table 5.6

Average Cross Sectional Regressions over all years The data is generated on a yearly basis for all individual firms in the three Benelux indices for the

period 1995-2005. The different statistics are calculated by taking the average of the statistics

during the period 1995-2005 (eleven years). The dependent variable; the payout ratio (PR) is

calculated by dividing DPSt by EPSt. The different levels of „annualized‟ expected earnings

growth are calculated by the change in EPS for t years, dividing by t years. For example,


The N represents the average number of firms per year for the three Benelux indices.

Model I‟‟

(EEG1YR)

Model II‟‟

(EEG3YR)

Model III‟‟

(EEG5YR)

Intercept (α) -0,074 -0,024 -0,015

β (PR) 0,492* 0,408** 0,296**

F-statistics 11,102 17,445 18,432

T-test 2,560 3,113 2,949

R2

0,139 0,189 0,187

N 60 60 60


The N represents the average number of firms per year for the three Benelux indices. In

these models the average number of firms is 60. The constant term (α) is negative in all

average models. All coefficients of the payout ratio (β (PR)) are positively related to the

expected earnings growth. Model I‟‟ has the highest β (PR), namely 0,492. If the payout

ratio increases by 10%, the 1 year expected earnings growth increases by 4,92%.

Remarkably, the β (PR) decreases if the level of EEG increases. For EEG3YR and

EEG5YR the β (PR) is lower compared to the β (PR) of EEG1YR. For instance, the

average β (PR) of Model III‟‟ (EEG5YR) is 0,296. One possible explanation for this

lower coefficient could be that EEG5YR focuses on a longer time period and

predictability is stronger for the first years. The 5 year expected earnings growth is

calculated by EEG5YRt= (EPSt+5/EPSt -1)/5. The research takes an average annualized

earnings growth over a period of 5 years. The EPS varies a lot over time and among

firms. The EEG5YR is a more normalized number than the EEG1YR because this

number is divided by 5 instead of dividing by 1.


51

This research focuses on the average t-test to conclude whether the payout ratio variable

is significant within these average models. The average t-statistics, measured over a

period of eleven years, are above the minimum threshold of 1,9616

. For this reason Model

I‟‟ is significant at a 0,05 level and Model II‟‟ and Model III‟‟ are significant at a 0,01

level. For all the average models it is assumed that the average t-statistic can be used to

analyze the significance of the variable within the model. Therefore, this assumption is

applied to all average models in this research, for example Table 5.6, Table 5.7, and

Table 5.8.

5.4.1 Dutch versus Belgian Indices & Large- versus Mid-Cap Firms

In this subsection the research focuses first on the differences between the listed firms

within the Dutch indices (the AEX and AMX) and the listed firms within the Belgian

index (the BEL20). Furthermore, I analyze the differences between mid-and large-cap

firms in the indices. This subsample regression is used as a robustness check. In Table 5.7

Panel A presents the firms which are active in the Dutch indices. The Belgian firms that

are listed on the BEL20 are presented in Panel B. The average sample size of the two

Dutch indices is 42. The Belgian index consists on average of 18 firms.

The average β (PR) is positively related to the future earnings growth for all models

(Model I^ - Model VI^). The positive relationship as predicted in the second hypothesis

holds for both the Dutch and Belgian indices. This hypothesis says that the payout ratio is

positively related to the expected earnings growth for the individual listed firms in the

Benelux. This positive relationship is consistent with the earlier research done by Arnott

and Asness (2003). In more detail, both the Belgian listed firms and the Dutch listed

firms present this positive relationship between EEG and β (PR).

Firstly, comparing Panel A with Panel B, one important difference is the sample size; 42

versus 18 firms. Another difference between the Dutch and Belgian listed firms is the R2.

16 Source: T-distribution table; 95% confidence interval and 60 degrees of freedom (two-sided)


52

In all models, the R2 for the Belgian firms is higher compared to the R

2 of the Dutch

listed firms. This could mean that the impact of dividend policy on future earnings

growth is larger for listed firms on the BEL20 compared to listed firms on the AEX or

AMX. Notice, very reliable conclusions cannot be drawn because the sample size of the

Belgian index (18 firms) is very small. In Appendix VIII all single year models of the

Dutch indices (Panel A) and the Belgian index (Panel B) for the period 1995-2005 are

presented. In addition, Appendix VI provides two scatter plots of the payout ratio versus

the annualized 1 year expected earnings growth for all individual firms. One scatter plot

of the Dutch firms (Figure A) and one scatter plot of the Belgian firms (Figure B).

Furthermore, in Table 5.7 also the differences between Dutch Large-Cap firms (the AEX)

and Mid-cap firms (the AMX) were presented. Panel C and Panel D are compared to

analyze these differences. Notable, all coefficients are positive in these models (Model

VII^ - Model XII^). Therefore, the size of the firm does not matter for the relationship

between the dividend policy and the future earnings growth for listed firms in the

Benelux. Splitting up the Dutch indices in large-cap (AEX) and mid-cap (AMX) the PR

coefficients for both subsamples remains positive. All coefficients of these models are

within the range [0,103; 0,255]. Appendix IX gives all coefficients of the single yearly

models of the large-cap firms (AEX) and mid-cap firms (AMX).

Finally, comparing the individual firms in the AEX and AMX with the firms in the

BEL20, one sees that the PR coefficients of the BEL20 models; Model IV^ (EEG1YR)

and Model V^ (EEG3YR), are much higher than the same models in the AEX and AMX.

In other words, the influence of the payout ratio is higher for Belgian-listed firms,

compared to listed firms on the AEX or AMX.


53

Table 5.7

Average Cross Sectional Regressions Subsamples The total sample is divided in four subsamples, respectively the Dutch indices (the AEX and

AMX), the Belgian index (BEL20), the large-cap firms and the mid-cap firms. The sample period

is 1995-2005. The different statistics are calculated by taking the average of the statistics during

the period 1995-2005 (eleven years) for both subsamples. The dependent variable; the payout

ratio (PR) is calculated by dividing DPSt by EPSt. The different levels of „annualized‟ expected



The N represents the average number of firms per year. The Dutch indices have an average N of

42 and the BEL20 has an average N of 18. The Models I^ – III^ represents the average Dutch

indices regressions (Panel A), and the Models IV^ – VI^ shows the average Belgian index

regressions (Panel B). Panel C and D represent respectively the AEX and AMX average models.

Panel A:

Dutch indices

Model I^

(EEG1YR)

Model II^

(EEG3YR)

Model III^

(EEG5YR)

Intercept (α) -0,056 0,022 -0,011

β (PR) 0,244 0,231 0,186*

T-test 1,079 1,719 2,027

R2

0,078 0,100 0,161

N 42 42 42

Panel B:

BEL20

Model IV^

(EEG1YR)

Model V^

(EEG3YR)

Model VI^

(EEG5YR)

Intercept (α) -0,032 0,053 0,108

β (PR) 0,619* 0,453 0,184

T-test 2,621 1,988 1,937

R2

0,304 0,254 0,268

N 18 18 18

Panel C:

AEX

Model VII^

(EEG1YR)

Model VIII^

(EEG3YR)

Model IX^

(EEG5YR)

Intercept (α) -0,041 -0,023 0,006

β (PR) 0,255 0,223 0,103

T-test 1,284 1,781 1,445

R2

0,163 0,170 0,199

N 21 21 21

Panel D:

AMX

Model X^

(EEG1YR)

Model XI^

(EEG3YR)

Model XII^

(EEG5YR)

Intercept (α) 0,034 0,057 -0,010

β (PR) 0,105 0,130 0,215

T-test -0,177 0,601 1,645

R2

0,166 0,121 0,185

N 22 22 22



54

5.5 Dividend Yield and Future Profitability

The third hypothesis focuses on dividend yield. Is the dividend yield17

positively related

to the expected future earnings growth of listed firms in the Benelux? In Table 5.8 the

average models are presented. In this model only the dividend yield is the independent

variable and the future earnings growth is the dependent variable. Later on, in Section 5.6

the payout ratio, the dividend yield, and some other variables are inserted in the model to

forecast expected earnings growth. The average models on a 1, 3, and 5 years expected

earnings basis are calculated by using the eleven yearly regression models. In Table 5.8

the results of these average regression models are presented. In Appendix X the

regression results for all single years (1995-2005) are presented.

In all models, the coefficients show that future profitability is negatively related to

dividend yield. None of these dividend yield variables (Model I* - III*) are significant

within the model. This negative relationship between dividend yield and future

profitability is not consistent with the results found for the relationship between the

expected earnings growth and the payout ratio. Therefore, the third hypothesis is not true

because a negative relationship was found. This negative relation means that a lower

dividend yield results in higher expected earnings growth in the future years. What is the

difference between the payout ratio and the dividend yield? These two variables have the

same numerator but a different denominator. Indeed, the payout ratio is scaled with

earnings and the dividend yield is scaled with the share price. For example, a company

with a high dividend yield has a negative earnings growth in the future. This could imply

that investors are skeptical about the firm‟s future. Could the firm continue to pay the

current dividend payout ratio? It is possible that a negative relation exists because of this

skepticism by investors. It should be noted that further research is needed to find the best

explanation for this negative relationship.

17 The dividend yield expresses the dividend per share as a percentage of the share price.


55

Table 5.8

Average Cross Sectional Regressions Dividend Yield for the Three Indices The total sample consists of the AEX, AMX and BEL20. The sample period is 1995-2005. The

different statistics are calculated by taking the average of the statistics during the period 1995-

2005 (eleven years). The independent variable; the dividend yield (DY) expresses the dividend

per share as a percentage of the share price. The different levels of „annualized‟ expected earnings

growth are calculated by the change in EPS for t years, dividing by t years. For example,


The N represents the average number of firms which are listed in the Benelux indices. The

average Models I* - III* are presented below.

Model I*

(EEG1YR)

Model II*

(EEG3YR)

Model III*

(EEG5YR)

Intercept (α) 0,234 0,257 0,225

β (DY) -3,116 -2,985 -3,730

T-test -1,053 -1,152 -1,339

R2

0,046 0,038 0,043

N 60 60 60


5.6 An Expanded Model to Forecast Future Earnings Growth

In this section the research elaborates the simple model with some other variables to

check the robustness of the earlier developed models. Is the payout ratio still significant

within the model if some other variables are inserted in the model? First of all, a Pearson

correlation matrix of all variables is made to analyze the correlation between the

independent variables within the model. The whole correlation matrix is presented in

Appendix XI. As shown in the Pearson correlation matrix, this expanded model makes

use of the total sample which includes the firms listed on the AEX, AMX or BEL20. All

independent variables are included in the matrix, respectively PR, DY, EY, SA, EB, ND,

TA, MT, and CX. In Appendix XI one sees for example, SA, EB, and TA are mutual

highly correlated. All these variables can be used to measure the firm size of the

companies. Furthermore, TA and ND are highly correlated (0,898). For this reason, these

two variables cannot be combined in one model.

Equation (9) in Subsection 4.2.2 is used to develop the expanded model. As a result the

variables PR, DY, SA, ND, and MT are used in the expanded models. CX is excluded


56

from the model because this variable highly correlates with SA (0.943). Sales (SA) rather

than Total Assets (TA) is used to measure firm size because TA is mutually correlated

with Net Debt (ND). In Table 5.9 the average models are showed for the expected

earnings growth on an annualized 1, 3, and 5 years basis. Appendix XII represents all

yearly regressions of this expanded model. One sees in Table 5.9 that only the PR and

DY are most of the time significant variables within the models. For that reason, this

research additionally tests the expected earnings growth as a function of only payout ratio

and dividend yield. In Table 5.10 the regression results for these limited expanded models

are presented.


57

Table 5.9

An Average Expanded Model for the Total Sample The total sample consists of the firms in the AEX, AMX or BEL20. The sample period is 1995-

2005. The different statistics are calculated by taking the average of the statistics during the

period 1995-2005 (eleven years). This model has five independent variables. The payout ratio

which is calculated by dividing DPSt by EPSt. Dividend yield expresses the dividend per share as

a percentage of the share price. Further, sales represents the gross sales and other operating

revenues less discounts, returns and allowances. The net debt variable is added, this variable is

calculated by subtracting cash from the amount of total debt. The fifth variable that is included in

the expanded model is the market to book value. MTBV is calculated by dividing the market

value of common equity by the balance sheet value of common equity. The expected earnings

growth is the dependent variable in these models. The different levels of „annualized‟ expected



The N represents the average number of firms which are listed in the Benelux indices. The values

of the t-tests are in parenthesis. The average Models I‟‟ – III‟‟ are presented below.

Model I‟‟

(EEG1YR)

Model II‟‟

(EEG3YR)

Model III‟‟

(EEG5YR)

Intercept (α) 0,049 0,121 0,152

β1 (PR) 0,699***

(3,281)

0,581***

(3,877)

0,462***

(4,061)

β 2 (DY) -8,739

(-1,895)

-7,974**

(-2,332)

-5,727***

(-2,714)

β 3 (SA) 0,000

(-0,462)

0,000

(-0,059)

0,000

(-0,419)

β 4 (ND) 0,000

(0,396)

0,000

(0,082)

0,000

(0,040)

β 5 (MT) 0,012

(0,463)

0,006

(0,073)

-0,001

(-0,197)

Adjusted R2

0,239 0,282 0,276

N 56 56 56



58

Table 5.10

An Average Limited Expanded Model for the Total Sample The total sample consists of the AEX, AMX and BEL20. The sample period is again 1995-2005.

The first independent variable; the payout ratio (PR) is calculated by dividing DPSt by EPSt. The

second independent variable; dividend yield (DY) expresses the dividend per share as a

percentage of the share price. The different levels of „annualized‟ expected earnings growth are


(EPSt+5/EPSt -1)/5. The same method is used to calculate EEG1YR and EEG3YR. The N

represents the number of firms which are listed in the Benelux indices in 2004. N was 56 in this

year. The values of the t-tests are in parenthesis. The Models I‟‟-III‟‟ are presented below.

Model I‟‟

(EEG1YR)

Model II‟‟

(EEG3YR)

Model III‟‟

(EEG5YR)

Intercept (α) 0,086 0,123 0,123

β1 (PR) 0,700***

(2,678)

0,595***

(2,841)

0,455***

(2,742)

β2 (DY) -8,752**

(-2,343)

-7,928***

(-2,711)

-7,219***

(-2,883)

Adjusted R2

0,201 0,261 0,269

N 60 60 60


To analyze the differences of adding DY to the models, compare Table 5.10 with Table

5.6. By adding the independent variable dividend yield, the variable payout ratio remains

positive and significant within all models. Notice, the beta‟s of the payout ratio increases

a little bit by adding this variable. Furthermore, the adjusted R2

increases in Model I‟‟,

II‟‟, and III‟‟. This means the variable DY helps to explain more of the variance in the

dependent variable „future profitability‟. For a detailed overview of the single yearly

limited expanded models see Appendix XIII.

The dividend yield is negatively related to the future earnings growth in all models

(Model I‟‟-III‟‟). This result is not in accordance with hypothesis 3 -The dividend yield is

positive correlated to the expected future earnings growth of listed firms in the Benelux-.

As mentioned in Section 5.5, further research is needed to find possible explanations for

this negative relationship.


59

Chapter 6: Conclusions & Recommendations

This research has focused on the influence of the payout ratio on expected future earnings

growth for listed firms in the Benelux. The model developed by Arnott and Asness

(2003) was used during this research. Some new interesting results about the influence of

dividend policy on the future profitability of individual listed firms in the Benelux are

documented.

6.1 Conclusions with respect to the First Hypothesis

The first part of this research focuses on the indices level. There is a positive relation on

the indices level between dividend distribution and future earnings growth in the

Benelux; especially for the AEX, AMX and BEL20. This relationship on the indices

level is in accordance with the results found by Arnott and Asness (2003) for the United

States. Therefore, the first hypothesis -The payout ratio is positively correlated to the

expected future earnings growth for Benelux indices- is confirmed.

Another important result of the time series analyses is that the mid-cap index (AMX)

exhibited smaller PR coefficients compared to the coefficients of the large-cap index

(AEX). One possible explanation is that the AMX includes smaller firms which tend to

grow faster. For this reason, firms in the AMX have a higher retention rate and pay less

dividend. These midcap firms need their money to finance future growth. It should be

noted that this supports the life cycle theory. If the firm ends up in a more mature stage of

the life cycle, the firm pays more dividend. Generally, the AEX firms are in a more

mature stage of their life cycle compared to the AMX firms.

6.2 Conclusions with respect to the Second Hypothesis

The second part of this research focuses on the individual firms within the indices. This

research was an extension on the research done by Arnott and Asness (2003) because my

research also analyzes the same relationship for the individual listed firms within the

AEX, AMX or BEL20. Firstly, the yearly compositions of these indices were determined.

Secondly, the cross sectional analyses per year for the period 1995-2005 were made. To


60

get a more complete view of the relationship between the dividend policy and the future

earnings growth over all years (1995-2005), the average coefficients of all yearly cross

sectional models are calculated. These models show that the relationship between the

dividend policy and the future profitability remains positive and significant on an

individual firm basis. The dividend policy is measured with the payout ratio and the

future profitability is measured with expected earnings growth. As a result, another

important hypothesis of this research -The payout ratio is positively correlated to the

expected future earnings growth for the individual listed firms in the Benelux- was

confirmed. There are any numbers of possible explanations for this positive relationship.

To clarify these possible explanations, further research is needed.

Some robustness checks were done with these results. The large- and mid-cap firms and

the Dutch and the Belgian firms within the different indices were compared. The payout

ratio remains positively related to the expected earnings growth but is no longer

significant in all models.

6.3 Conclusions with respect to the Third Hypothesis

In addition to the second hypothesis, this research studied the following hypothesis -The

dividend yield is positive correlated to the expected future earnings growth of listed firms

in the Benelux-. However, in all yearly regressions which were run for the period 1995-

2005, DY was negatively related to the future profitability. This negative relationship

between dividend yield and expected earnings growth was not consistent with the

relationship between payout ratio and expected earnings growth. Therefore, the payout

ratio and the dividend yield were not a substitute for each other. Further research is

needed to be able to draw any meaningful conclusions about the explanations for this

negative relationship.

In the end, an average expanded model with the variables payout ratio, dividend yield,

sales, net debt, and market to book value was used to conduct an analysis. With this

expanded model one can analyze whether the payout ratio is still significant after adding


61

some other variables. In these average expanded models, as a result, only the payout ratio

and the dividend yield were significant variables. The variables which were not

significant within the model are deleted of the expanded model. Therefore, a limited

expanded model with only two significant independent variables (payout ratio and

dividend yield) was constructed.

6.4 Recommendations

During this research the positive relationship between earnings growth and dividend

policy was proved for Benelux-listed firms, during the period 1995-2005. It would be

interesting for further research to analyze the possible explanations for this positive

relationship. Further research could likewise analyze which theories apply best to the

relationship between the payout ratio and future profitability. This could extent on my

research and provides better understanding.

In addition, an interesting topic for further research could be a sector specific empirical

study. In other words, the impact of dividend policy on the future profitability for

different industries in the Benelux or Europe could be investigated. It is expected that, for

example, the variable „capital expenditures‟ is more important for capital-intensive firms

than for labor-intensive firms. Such further detailed research could provide additional

insights.


62

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65

Appendices

Appendix I; Variable Description for the Indices18

Mnemonic: Name

variable:

Description:

PI Price Index Sector and market aggregations are weighted by market value

and are calculated using a representative list of shares. The

index is calculated as follows:

= index value at base date = 100

Where:

= index value at day t

= index value on previous working day (of t

= unadjusted share price on day t

= unadjusted share price on previous working day (of t)

= number of shares in issue on day t

f = adjustment factor for a capital action occurring on day t

n = number of constituents in index

The summations are performed on the constituents as they exist

on day t.

DSDY Dividend

Yield

For sectors, dividend yield is derived by calculating the total

dividend amount for a sector and expressing it as a percentage of

the total market value for the constituents of that sector. This

provides an average of the individual yields of the constituents

18 For the description of the variables this research has used descriptions given by DataStream 5.0.


66

Mnemonic: Name

variable:

Description:

weighted by market value. It is calculated as follows:

Where:

= aggregate dividend yield on day t

= dividend per share on day t




DSPE Price/Earnin

gs Ratio

For DataStream sectors, the PER is derived by dividing total

market value by the total earnings, thus providing an earnings-

weighted average of the PERs of the constituents. It is given by:

Where:

= price earnings ratio at day t



= earnings per share on day t. (Negative earnings per share

are treated as zero)



67

Appendix II; Deleted Firms from the Three Samples Per index and per year the number of firms which are deleted from the sample are showed.

They are deleted because no correct expected earnings growth can be calculated. The last column

shows the total amount of deleted firms per year for the three indices together. Note, in the period

1983-1990 only the AEX exists. Additionally, during the period 1991-1994 the AEX and BEL

were found. Only from 1995 all indices are active. The average and maximum per index are

given. In this table, NA means Not Available yet.

Year AEX AMX BEL20 Total

1983 1 NA NA 1

1984 1 NA NA 1

1985 0 NA NA 0

1986 0 NA NA 0

1987 1 NA NA 1

1988 1 NA NA 1

1989 0 NA NA 0

1990 0 NA NA 0

1991 3 NA 2 5

1992 4 NA 4 8

1993 3 NA 4 7

1994 0 NA 5 5

1995 0 0 4 4

1996 0 1 4 5

1997 0 0 3 3

1998 0 0 2 2

1999 2 1 3 6

2000 1 2 1 4

2001 5 0 1 6

2002 6 4 4 14

2003 7 5 1 13

2004 5 3 1 9

2005 3 2 0 5

Average 1.87 1.64 2.60 4.35

Maximum 7 5 5 14


68

Appendix III A; Example of the Sample Composition 2010 Three Amsterdam and Brussels indices were used to run the cross sectional regression models.

The sample size for 2010 was 69, all firms were listed on the Bel20, AEX or AMX index in

October 2010. Only NPM Capital was excluded from the sample. NPM Capital was a holding

company and DataStream did not provide enough information for this company. As a result, 69

firms remained.

Firm

Code: Firm name: Index: Sector:

Sector

Code:

1 AB Inbev BEL-20 Drinks 5

2 Ackermans&van Haaren BEL-20 Investment company 11

3 Ageas (ex Fortis) BEL-20 Banks & Insurers 1

4 Befimmo BEL-20 Real Estate 20

5 Bekaert BEL-20 Industry 10

6 Belgacom BEL-20 Telecommunication 17

7 Cofinimmo BEL-20 Real Estate 20

8 Colruyt BEL-20 Retail Food & Medicines 15

9 Delhaize Groep BEL-20 Retail Food & Medicines 15

10 Dexia BEL-20 Banks & Insurers 1

11 GBL BEL-20 Investment company 11

12 GDF-Suez BEL-20 Energy & Environment 7

13 KBC BEL-20 Banks & Insurers 1

14 Mobistar BEL-20 Telecommunication 17

15 Omega Pharma BEL-20 Pharmaceutics & Biotechnology 9

16 Solvay BEL-20 Chemically 4

17 Telenet BEL-20 Telecommunication 17

18 UCB BEL-20 Pharmaceutics & Biotechnology 9

19 Umicore BEL-20 Chemically 4

20 Aegon AEX Banks & Insurers 1

21 Ahold AEX Retail Food & Medicines 15

22 Air France KLM AEX Transport & Storage 18

23 Akzonobel AEX Chemically 4

24 Arcelor Mittal AEX Steel & Metal 16


69

Firm


Sector

Code:

25 ASML AEX IT Hardware 12

26 BAM AEX Building 2

27 Boskalis AEX Building 2

28 Corio AEX Real Estate 20

29 DSM AEX Chemically 4

30 Fugro AEX Energy support 8

31 Heineken AEX Drinks 5

32 ING Groep AEX Banks & Insurers 1

33 KPN AEX Telecommunication 17

34 Philips AEX Electronics 6

35 Randstad AEX Employment agency & Second 19

36 Royal Dutch Shell AEX Energy & Environment 7

37 Reed Elsevier AEX Media 14

38 SBM Offshore AEX Energy support 8

39 TNT AEX Transport & Storage 18

40 TomTom AEX IT Hardware 12

41 Unibail Rodamco AEX Real Estate 20

42 Unilever AEX Food 21

43 Wereldhave AEX Real Estate 20

44 Wolters Kluwer AEX Media 14

45 Aalberts AMX Industry 10

46 AMG AMX Steel & Metal 16

47 Arcadis AMX Building support 3

48 ASMI AMX IT Hardware 12

49 Binckbank AMX Banks & Insurers 1

50 Brunel AMX Employment agency & Second 19

51 Crucell AMX Pharmaceutics & Biotechnology 9

52 CSM AMX Food 21


70

Firm


Sector

Code:

53 Delta Lloyd AMX Banks & Insurers 1

54 Draka AMX Electronics 6

55 Eurocom Prop AMX Real Estate 20

56 Heijmans AMX Building 2

57 Imtech AMX Building support 3

58 Logica AMX IT Software & Services 13

59 Mediq AMX Retail Food & Medicines 15

60 Nutreco AMX Food 21

61 Ordina AMX IT Software & Services 13

62 SNS Reaal AMX Banks & Insurers 1

63 Ten Cate AMX Industry 10

64 Unit4 AMX IT Software & Services 13

65 USG People AMX Employment agency & Second 19

66 Vastned Retail AMX Real Estate 20

67 Vopak AMX Energy support 8

68 Wavin AMX Building support 3

69 Wessanen AMX Food 21


71

Appendix III B; Legend of Industry Codes In addition, a detailed deviation of industries and the sector codes are developed. As a result, 21

sectors were identified.

Sector code: Sector:

1 Banks & Insurers

2 Building

3 Building support

4 Chemically

5 Drinks

6 Electronics

7 Energy & Environment

8 Energy support

9 Pharmaceutics & Biotechnology

10 Industry

11 Investment company

12 IT Hardware

13 IT Software & Services

14 Media

15 Retail Food & Medicines

16 Steel & Metal

17 Transport & Storage

18 Telecommunication

19 Employment agency & Second

20 Real Estate

21 Food


72

Appendix IV; Variable Description Individual Listed Firms Benelux Indices19

Mnemonic: Name: Description:

P Price Data type (P) represents the official closing

price. This is the default data type for all

equities.

DPS Dividends Per Share Dividends per share are displayed gross,

inclusive of local tax credits where

applicable, except for Belgium where

dividends per share are displayed net.

EPS Earnings Per Share This is the latest annualized rate that may

reflect the last financial year or be derived

from an aggregation of interim period

earnings.

WC01706 Net Income Net income after preferred dividends

represents the net income after preferred

dividends that the company uses to

calculate its basic earnings per share.

WC04551 Total Cash Dividend Paid This variable represents the total common

and preferred dividends paid to

shareholders of the company. It excludes

dividends paid to minority shareholders

W09504 Dividend Payout Per Share (%) Dividends Per Share / Earnings Per Share *

100

DY Dividend Yield (%) The dividend yield expresses the dividend

per share as a percentage of the share price.

DWSL Sales („000) Gross sales and other operating revenue less

discounts, returns and allowances.

DWED EBITDA („000) The pre-tax income plus interest expense on

debt and depreciation, depletion and

amortization and subtracting interest

capitalized.

W09204 Earnings Yield Close (%) Earnings Per Share / Market Price-Year

End * 100. This item is also available at the

security level for 1987 and subsequent

years.

19 For the description of the variables this research has used descriptions given by DataStream 5.0.


73

Mnemonic: Name: Description:

DWND Net Debt („000) Total Debt minus Cash.

MTBV Market to Book Value The market value of the ordinary (common)

equity divided by the balance sheet value of

the ordinary (common) equity in the

company

DWTA Total Assets („000) The sum of total current assets, long term

receivables, investment in unconsolidated

subsidiaries, other investments, net property

plant and equipment and other assets

DWCX Capital Expenditures („000) Capital Expenditures represent the funds

used to acquire fixed assets other than those

associated with acquisitions. It includes but

is not restricted to: Additions to property,

plant and equipment. Investments in

machinery and equipment.


74

Appendix V; Scatter Plots Total Sample

Figure A; Scatter plot of the payout ratio (x-axis) versus the annualized 5 year expected

earnings growth for all individual firms in the three Benelux indices (AEX, AMX, and

BEL20) in 1995

Figure B; Scatter plot of the payout ratio (x-axis) versus the annualized 5 year expected


BEL20) in 2000


75

Figure C; Scatter plot of the payout ratio (x-axis) versus the annualized 5 year expected


BEL20) in 2005


76

Appendix VI; Scatter Plots of the Subsamples; the Netherlands versus Belgium

Figure A; Scatter plot of the payout ratio (x-axis) versus the annualized 1 year expected

earnings growth for all individual firms in the Dutch indices (AEX and AMX) in 1997

Figure B; Scatter plot of the payout ratio (x-axis) versus the annualized 1 year expected

earnings growth for all individual firms in the Belgian index (BEL20) in 1997

77

Appendix VII; Coefficients of the Single Yearly Model Total Sample


EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG1YR

(3 indices)

Intercept (α) -0,271 0,067 -0,019 -0,044 0,058 -0,157 -0,606 -0,042 0,128 -0,038 0,109 -0,074

β (PR) 1,014*** 0,409 0,819*** -0,006 0,223 0,705** 0,960*** -0,129 -0,035 0,844*** 0,610** 0,492*

T-test 4,720 1,781 5,023 -0,026 1,560 3,013 5,437 -0,753 -0,135 4,590 2,953 2,560

R2 0,271 0,049 0,286 0,000 0,042 0,135 0,326 0,011 0,000 0,281 0,131 0,139

N 62 63 65 67 58 60 63 52 54 56 60 60

EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG3YR

(3 indices)

Intercept (α) -0,142 0,098 -0,078 0,106 -0,142 -0,352 -0,262 0,208 0,131 -0,002 0,176 -0,024

β (PR) 1,015*** 0,295 0,650*** -0,048 0,217** 0,814*** 0,638*** -0,047 0,364 0,658*** -0,069 0,408**

T-test 5,500 1,155 7,000 -0,265 3,207 5,111 5,276 -0,275 1,482 6,585 -0,532 3,113

R2 0,335 0,038 0,438 0,001 0,155 0,310 0,313 0,002 0,041 0,445 0,005 0,189

N 62 63 65 67 58 60 63 52 54 56 60 60

EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG5YR

(3 indices)

Intercept (α) -0,160 0,137 -0,104 0,073 -0,221 0,130 -0,136 0,237 0,074 -0,210 0,011 -0,015

β (PR) 1,015*** 0,104 0,341*** -0,209 0,637*** -0,121 0,648*** -0,040 0,259 0,485*** 0,132 0,296**

T-test 5,448 0,712 5,635 -1,020 8,172 -0,686 5,530 -0,283 1,478 6,162 1,296 2,949

R2 0,331 0,008 0,335 0,016 0,544 0,008 0,334 0,002 0,040 0,413 0,028 0,187

N 62 63 65 67 58 60 63 52 54 56 60 60

78

Appendix VIII; Coefficients of the Single Yearly Models Dutch vs Belgian Indices

Panel A: Dutch indices (AEX and AMX)

EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG1YR

(AEX and AMX)

Intercept (α) 0,077 0,205 -0,052 0,064 0,029 -0,396 -0,649 -0,208 0,091 0,102 0,122 -0,056

β (PR) -0,050 -0,137 0,816** -0,290 0,270 0,183 0,988*** 0,065 -0,196 0,644 0,395 0,244

T-test -0,163 -0,697 2,797 0,519 1,473 -0,867 5,980 0,430 -0,687 1,722 1,360 1,079

R2 0,001 0,010 0,145 0,030 0,053 0,019 0,460 0,005 0,014 0,078 0,046 0,078

N 47 48 48 49 41 41 44 36 35 37 40 42

EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG3YR

(AEX and AMX)

Intercept (α) 0,130 0,089 -0,076 0,045 0,161 -0,147 -0,345 -0,032 0,108 0,174 0,136 0,022

β (PR) 0,091 0,145 0,543** 0,055 0,244** 0,052 0,694*** 0,100 0,236 0,303 0,073 0,231

T-test 0,521 1,006 3,344 0,258 2,751 0,342 7,843 -0,237 1,002 1,708 0,376 1,719

R2 0,006 0,022 0,196 0,001 0,162 0,003 0,594 0,002 0,030 0,077 0,004 0,100

N 47 48 48 49 41 41 44 36 35 37 40 42

EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG5YR

(AEX and AMX)

Intercept (α) 0,100 0,168 -0,161 -0,078 -0,273 0,213 -0,220 0,102 0,022 -0,125 0,126 -0,011

β (PR) 0,101 -0,049 0,461*** 0,095 0,755*** -0,481 0,724*** 0,039 0,269 0,294* -0,161 0,186*

T-test 0,643 -0,206 3,850 1,066 7,410 -1,637 8,191 0,334 1,585 2,214 -1,154 2,027

R2 0,009 0,001 0,244 0,024 0,585 0,064 0,615 0,003 0,071 0,123 0,034 0,161

N 47 48 48 49 41 41 44 36 35 37 40 42



79

Panel B: Belgian Index (BEL20)

EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG1YR

(BEL20)

Intercept (α) -0,408 0,237 0,091 -0,309 0,122 -0,245 -0,415 0,500 0,103 -0,198 0,166 -0,032

β (PR) 1,524*** 0,667 0,786** 0,844 0,118 1,065*** 0,635 -1,179 0,500 0,964*** 0,886** 0,619*

T-test 5,134 1,374 3,098 1,869 0,527 4,788 0,679 0,585 1,000 6,582 3,196 2,621

R2 0,670 0,127 0,390 0,179 0,018 0,574 0,026 0,225 0,056 0,718 0,362 0,304

N 15 15 17 18 17 19 19 16 19 19 20 18

EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG3YR

(BEL20)

Intercept (α) -0,140 0,303 0,026 0,229 -0,098 -0,328 0,118 0,293 0,105 -0,119 0,195 0,053

β (PR) 1,393*** 0,347 0,646*** -0,240 0,158 1,022** -0,009 0,298 0,781 0,830*** -0,239 0,453

T-test 3,977 0,806 4,660 -0,677 1,641 3,519 -0,012 0,434 1,393 7,653 -1,530 1,988

R2 0,549 0,048 0,591 0,028 0,152 0,421 0,000 0,013 0,102 0,775 0,115 0,254

N 15 15 17 18 17 19 19 16 19 19 20 18

EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG5YR

(BEL20)

Intercept (α) -0,119 0,213 -0,042 0,400 -0,105 0,182 0,375 0,489 0,136 -0,253 -0,089 0,108

β (PR) 1,368** 0,173 0,290** -0,930 0,372*** -0,042 -0,402 -0,158 0,311 0,571*** 0,468*** 0,184

T-test 3,762 0,874 3,803 -1,248 4,736 -0,157 -0,584 -0,289 0,753 5,504 4,148 1,937

R2 0,521 0,056 0,491 0,089 0,599 0,001 0,020 0,006 0,032 0,641 0,489 0,268

N 15 15 17 18 17 19 19 16 19 19 20 18



80

Appendix IX; Coefficients of the Single Yearly Models Large- vs Medium-Cap

Panel C: AEX (Large-cap firms)

EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG1YR

(AEX)

Intercept (α) -0,210 -0,027 0,609 -0,265 -0,174 0,033 -0,918 -0,248 0,221 0,117 0,409 -0,041

β (PR) 0,577 0,346 -0,979 0,470 1,051*** -0,360 1,204*** 0,171 -0,225 0,194 0,355 0,255

T-test 1,509 1,523 -1,851 1,217 4,790 -0,862 6,380 0,898 -0,742 0,648 0,617 1,284

R2 0,094 0,095 0,135 0,058 0,534 0,036 0,705 0,051 0,035 0,027 0,022 0,163

N 24 24 24 26 22 22 19 17 17 17 19 21

EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG3YR

(AEX)

Intercept (α) 0,087 0,098 0,178 -0,144 -0,326 -0,228 -0,453 0,051 0,066 0,140 0,279 -0,023

β (PR) 0,082 -0,009 -0,130 0,458 0,614*** 0,246 0,793*** 0,102 0,183 0,132 -0,017 0,223

T-test 0,350 -0,034 -0,351 1,792 6,520 1,723 6,456 1,172 1,318 0,686 -0,036 1,781

R2 0,006 0,000 0,006 0,118 0,680 0,129 0,710 0,084 0,104 0,030 0,000 0,170

N 24 24 24 26 22 22 19 17 17 17 19 21

EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG5YR

(AEX)

Intercept (α) 0,054 0,320 -0,105 -0,148 -0,114 0,358 -0,329 -0,011 -0,074 -0,143 0,255 0,006

β (PR) 0,138 -0,458 0,226 0,233 0,400* -0,644 0,789*** 0,130 0,367** 0,254** -0,306 0,103

T-test 0,607 -1,343 0,844 1,757 2,725 -1,419 6,288 1,670 2,190 3,467 -0,894 1,445

R2 0,016 0,076 0,031 0,114 0,271 0,091 0,699 0,157 0,242 0,445 0,045 0,199

N 24 24 24 26 22 22 19 17 17 17 19 21



81

Panel D: AMX (Mid-cap firms)

EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG1YR

(AMX)

Intercept (α) 0,228 0,341 -0,206 0,404 0,158 0,182 -0,416 -0,112 -0,067 0,002 -0,138 0,034

β (PR) -0,314 -0,391 1,397*** -1,011*** -0,408* 0,132 0,525 -0,268 -0,103 1,164 0,430 0,105

T-test -0,694 -1,345 4,603 -5,396 -2,693 0,118 1,347 -0,900 -0,175 1,690 1,495 -0,177

R2 0,022 0,076 0,491 0,581 0,299 0,001 0,073 0,045 0,000 0,137 0,105 0,166

N 23 24 24 23 29 19 25 19 18 20 21 22

EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG3YR

(AMX)

Intercept (α) 0,134 0,108 -0,138 0,246 -0,022 0,019 -0,234 0,210 0,106 0,179 0,023 0,057

β (PR) 0,163 0,234 0,752*** -0,336 -0,075 -0,491 0,432 -0,204 0,358 0,482 0,117 0,130

T-test 0,534 1,522 4,801 -1,043 -0,695 -1,397 0,446 -0,611 0,608 1,644 0,799 0,601

R2 0,133 0,095 0,512 0,049 0,028 0,103 0,206 0,021 0,023 0,131 0,033 0,121

N 23 24 24 23 29 19 25 19 18 20 21 22

EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average

EEG5YR

(AMX)

Intercept (α) 0,130 0,094 -0,163 -0,002 -0,409 0,091 -0,157 0,176 0,205 -0,113 0,039 -0,010

β (PR) 0,109 0,172 0,575*** -0,042 1,062*** -0,385 0,678*** -0,054 0,013 0,332 -0,097 0,215

T-test 0,492 0,519 4,720 -0,372 10,337 -1,491 3,827 -0,185 0,035 1,229 -1,018 1,645

R2 0,011 0,012 0,503 0,007 0,863 0,116 0,389 0,002 0,000 0,077 0,052 0,185

N 23 24 24 23 29 19 25 19 18 20 21 22



82

Appendix X; Cross Sectional Regressions with Dividend Yield for the Three Indices

EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG1YR

Intercept (α) 0,358 0,301 0,347 0,033 0,190 0,066 -0,066 0,215 0,404 0,177 0,548 0,234

β (PR) -8,691* -2,419 -2,398 -3,423 -1,243 2,476 -3,925 -7,861** -7,643** 7,817 -6,969 -3,116

T-test -2,458 -0,604 -0,535 -1,364 -0,364 0,454 -0,764 -3,363 -2,700 1,234 -1,114 -1,053

R2 0,091 0,006 0,005 0,028 0,002 0,004 0,009 0,185 0,123 0,027 0,021 0,046

N 62 63 65 67 58 60 63 52 54 56 60 60

EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG3YR

Intercept (α) 0,371 0,273 0,209 0,167 -0,068 0,080 0,127 0,485 0,499 0,328 0,360 0,257

β (PR) -4,640 -1,986 -1,739 -3,361 0,986 -4,275 -3,600 -7,328** -4,510 0,599 -8,506* -3,487

T-test -1,413 -0,603 -0,606 -1,620 0,572 -1,036 -1,039 -3,104 -1,586 0,151 -2,387 -1,152

R2 0,032 0,006 0,006 0,039 0,006 0,018 0,017 0,162 0,046 0,000 0,089 0,038

N 62 63 65 67 58 60 63 52 54 56 60 60

EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG5YR

Intercept (α) 0,347 0,274 0,006 0,073 0,104 0,285 0,295 0,455 0,327 0,088 0,222 0,225

β (PR) -4,414 -3,603 1,059 -3,065 -1,459 -8,276* -4,926 -5,794** -2,980 -1,437 -6,130* -3,730

T-test -1,333 -1,461 0,615 -1,281 -0,541 -2,240 -1,455 -2,929 -1,467 -0,474 -2,167 -1,339

R2 0,029 0,034 0,006 0,025 0,005 0,080 0,034 0,146 0,040 0,004 0,075 0,043

N 62 63 65 67 58 60 63 52 54 56 60 60



83

Appendix XI; Pearson’s Correlation Matrix

All independent variables (variables of interest and control variables) are tested for correlation. The first horizontal row of each

variable presented the Pearson correlation, the second row gave information about the significance (2-tailed) of the variables and the

last row presents the number of observations.


Variables PR DY EY SA EB ND TA MT CX

PR

1,000

62

0,237

0,064

62

-0,118

0,362

62

0,067

0,620

58

0,108

0,435

54

0,022

0,870

59

0,079

0,550

59

-0,109

0,402

61

0,103

0,436

59

DY

0,237

0,064

62

1,000

62

0,628***

0,000

62

0,108

0,422

58

0,135

0,329

54

0,147

0,266

59

0,125

0,345

59

-0,324*

0,011

61

0,104

0,432

59

EY

-0,118

0,362

62

0,628***

0,000

62

1,000

62

-0,026

0,848

58

-0,009

0,951

54

0,096

0,470

59

0,023

0,865

59

-0,291*

0,023

61

-0,046

0,730

59

SA

0,067

0,620

58

0,108

0,422

58

-0,026

0,848

58

1,000

58

0,968***

0,000

54

0,512

0,255

58

0,475***

0,000

58

-0,058

0,664

58

0,943***

0,000

58

EB

0,108

0,435

54

0,135

0,329

54

-0,009

0,951

54

0,968***

0,000

54

1,000

54

0,124

0,371

54

0,462***

0,000

54

-0,063

0,651

54

0,988***

0,000

54

ND

0,022

0,870

59

0,147

0,266

59

0,096

0,470

59

0,512

0,255

58

0,124

0,371

54

1,000

59

0,898***

0,000

59

-0,122

0,357

59

0,033

0,806

59

TA

0,079

0,550

59

0,125

0,345

59

0,023

0,865

59

0,475***

0,000

58

0,462***

0,000

54

0,898***

0,000

59

1,000

59

-0,131

0,323

59

0,354**

0,006

59

MT

-0,109

0,402

61

-0,324*

0,011

61

-0,291*

0,023

61

-0,058

0,664

58

-0,063

0,651

54

-0,122

0,357

59

-0,131

0,323

59

1,000

61

-0,053

0,690

59

CX

0,103

0,436

59

0,104

0,432

59

-0,046

0,730

59

0,943***

0,000

58

0,988***

0,000

54

0,033

0,806

59

0,354**

0,006

59

-0,053

0,690

59

1,000

59


84

Appendix XII; Coefficients of the Single Yearly Expanded Models for the Total Sample

Panel A: Annualized 1-year Expected Earnings Growth

EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG1YR

Intercept (α) 0,126 0,332 0,362 -0,184 0,055 -0,029 -0,590 -0,096 0,038 -0,088 0,613 0,049

β1 (PR) 1,391*** 0,753* 0,898*** 0,308 0,221 0,757*** 1,026*** -0,011 0,536 0,936*** 0,869*** 0,699**

t-stat (PR) 7,174 2,639 5,376 1,167 1,347 2,882 5,117 -0,080 1,746 4,917 3,805 3,281

β2 (DY) -16,856*** -12,034* -12,347** -1,646 -3,940 -3,986 -8,751 -5,124* -10,274** -0,448 -20,724** -8,739

t-stat (DY) -5,264 -2,097 -2,895 -0,502 -0,933 -0,645 -1,638 -2,301 -2,777 -0,078 -3,003 -1,895

β3 (SA) 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000

t-stat (SA) -1,308 -0,742 -1,596 -1,572 1,000 -0,906 -0,252 -0,953 0,524 0,145 0,573 -0,462

β4 (ND) 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000

t-stat (ND) 1,036 -0,118 -0,711 1,967 -0,078 0,142 1,097 0,211 1,489 -0,546 -0,137 0,396

β5 (MT) -0,014 -0,021 -0,021* 0,012 0,003 0,003 0,059 0,093*** 0,056 -0,006 -0,028 0,012

t-stat (MT) -0,796 -1,069 -2,160 1,867 0,190 0,110 1,377 4,766 1,572 -0,223 -0,540 0,463

Adjusted R2 0,517 0,050 0,355 0,082 0,004 0,067 0,395 0,455 0,198 0,320 0,183 0,239

N 58 60 61 63 64 56 59 49 52 53 56 57



85

Panel B: Annualized 3-year Expected Earnings Growth


EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG3YR

Intercept (α) 0,214 0,303 0,093 0,142 -0,191 -0,091 -0,002 0,111 0,223 0,191 0,340 0,121

β1 (PR) 1,279*** 0,576* 0,715*** -0,020 0,137 1,000*** 0,780*** 0,125 0,969** 0,790*** 0,035 0,581***

t-stat (PR) 7,194 2,480 7,537 -0,086 1,904 6,176 5,645 0,788 3,210 7,567 0,236 3,877

β2 (DY) -12,581*** -9,031 -7,854** -3,240 0,960 -12,123** -10,434** -4,566 -11,240* -8,104* -9,499* -7,974*

t-stat (DY) -4,286 -1,934 -3,240 -1,130 0,520 -3,180 -2,838 -1,756 -3,087 -2,565 -2,151 -2,332

β3 (SA) 0,000 0,000 0,000 0,000 0,000*** 0,000 0,000 0,000 0,000 0,000 0,000 0,000

t-stat (SA) -1,851 -1,807 0,106 1,033 3,702 -1,250 -0,490 -0,387 -0,340 -0,277 0,912 -0,059

β4 (ND) 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000

t-stat (ND) 0,194 0,462 0,074 0,516 0,598 0,352 0,415 0,127 -0,014 -0,576 -1,246 0,082

β5 (MT) -0,022 -0,015 -0,008 0,001 0,001 0,004 -0,001 0,076** 0,018 -0,006 0,018 0,006

t-stat (MT) -1,370 -0,933 -1,489 0,174 0,219 0,251 -0,027 3,330 0,508 -0,401 0,538 0,073

Adjusted R2 0,495 0,069 0,493 -0,015 0,278 0,400 0,379 0,260 0,154 0,520 0,074 0,282

N 58 60 61 63 64 56 59 49 52 53 56 57


86

Panel C: Annualized 5-year Expected Earnings Growth


EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG5YR

Intercept (α) 0,145 0,358 -0,036 0,180 -0,133 0,260 0,251 0,182 0,171 0,010 0,287 0,152

β1 (PR) 1,277*** 0,368* 0,357*** -0,172 0,783*** 0,028 0,872*** 0,066 0,672** 0,602*** 0,264* 0,465***

t-stat (PR) 6,858 2,094 5,591 -0,641 10,418 0,147 6,929 0,513 3,125 7,257 2,377 4,061

β2 (DY) 12,148*** -9,891** -2,193 -3,855 -6,460*** -8,378 -13,360*** -3,636 -7,792** -8,600** -10,985** -5,727**

t-stat (DY) -3,950 -2,796 -1,345 -1,156 -3,346 -1,864 -3,987 -1,715 -3,002 -3,427 -3,266 -2,714

β3 (SA) 0,000 0,000 0,000 0,000 0,000* 0,000 0,000 0,000 0,000 0,000 0,000 0,000

t-stat (SA) -0,804 -0,366 -0,471 0,324 -2,171 -0,590 -0,547 -0,747 0,005 -0,049 0,805 -0,419

β4 (ND) 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000

t-stat (ND) 0,374 0,270 0,954 0,370 0,638 0,252 0,164 -0,127 -1,122 -0,571 -0,760 0,040

β5 (MT) -0,017 -0,021 -0,007 -0,005 0,006 0,010 -0,025 0,069*** 0,005 -0,008 -0,018 -0,001

t-stat (MT) -0,988 -1,673 -1,811 -0,817 0,914 0,543 -0,933 3,746 0,196 -0,636 -0,710 -0,197

Adjusted R2 0,453 0,055 0,358 -0,034 0,633 0,014 0,470 0,309 0,153 0,497 0,132 0,276

N 58 60 61 63 64 56 59 49 52 53 56 57


87

Appendix XIII; Coefficients of the Single Yearly Limited Expanded Models for the Total Sample

EEG1YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG1YR

Intercept (α) 0,043 0,177 0,115 -0,002 0,118 -0,105 -0,316 0,201 0,248 0,026 0,440 0,086

β1 (PR) 1,376*** 0,688* 0,918*** 0,164 0,265 0,747** 1,122*** 0,044 0,616* 0,883*** 0,879*** 0,700***

t-stat (PR) 7,452 2,572 5,565 0,680 1,761 3,000 6,407 0,267 2,032 4,355 4,073 3,469

β2 (DY) -15,632*** -8,778 -8,315* -4,317 -3,181 -2,814 -12,415** -8,075** -12,108** -2,854 -17,779** -8,752*

t-stat (DY) -5,739 -1,924 -2,167 -1,519 -0,902 -0,520 -2,959 -3,240 -3,440 -0,475 -2,888 -2,343

Adjusted R2 0,516 0,075 0,315 0,005 0,021 0,109 0,392 0,152 0,157 0,257 0,215 0,201

N 62 63 65 67 58 60 63 52 54 56 60 60

EEG3YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG3YR

Intercept (α) 0,080 0,182 0,026 0,144 -0,129 -0,144 -0,043 0,446 0,248 0,195 0,351 0,123

β1 (PR) 1,271*** 0,507* 0,726*** 0,108 0,226*** 0,982*** 0,761*** 0,122 0,995*** 0,780*** 0,072 0,595***

t-stat (PR) 7,376 2,279 7,994 0,538 3,148 6,317 6,438 0,735 3,495 7,594 0,521 4,221

β2 (DY) -11,051*** -6,672 -6,418*** -3,948 -0,670 -11,233** -9,363** -7,921** -11,714*** -8,821** -9,398* -7,928**

t-stat (DY) -4,348 -1,758 -3,038 -1,677 -0,398 -3,325 -3,300 -3,162 -3,548 -2,900 -2,365 -2,711

Adjusted R2 0,479 0,055 0,495 0,013 0,127 0,402 0,399 0,137 0,200 0,503 0,062 0,261

N 62 63 65 67 58 60 63 52 54 56 60 60

EEG5YR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average

EEG5YR

Intercept (α) 0,057 0,218 -0,084 0,097 -0,093 0,284 0,119 0,426 0,153 -0,016 0,188 0,123

β1 (PR) 1,265*** 0,31 0,356*** -0,112 0,727*** 0,003 0,791*** 0,093 0,687** 0,605*** 0,275* 0,455***

t-stat (PR) 7,200 1,836 5,634 -0,485 9,963 0,019 7,207 0,670 3,351 7,867 2,633 4,172

β2 (DY) -10,796*** -6,467* -1,234 -2,455 -6,777*** -8,300* -10,907*** -6,248** -7,954** -8,746*** -9,520** -7,219**

t-stat (DY) -4,165 -2,247 -0,840 -0,904 -3,965 -2,105 -4,141 -2,973 -3,346 -3,839 -3,188 -2,883

Adjusted R2 0,465 0,055 0,321 -0,002 0,632 0,047 0,465 0,120 0,182 0,523 0,146 0,269

N 62 63 65 67 58 60 63 52 54 56 60 60

the relationship between firm‟s dividend policy and

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