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
3.4.2 Hypothesis 2: The Payout Ratio is positively correlated to the Expected Future
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’
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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).
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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
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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)
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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.
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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
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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
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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.
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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.
3.4.1 Hypothesis 1: The Payout Ratio is positively correlated to the Expected Future
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-.
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3.4.2 Hypothesis 2: The Payout Ratio is positively correlated to the Expected Future
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.
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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.
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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.
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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
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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.
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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
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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).
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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
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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.
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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.
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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
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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)”.
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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.
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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
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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.
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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).
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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)
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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
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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.
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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
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) -0,376 -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)
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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.
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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
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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
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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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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,
EEG5YRt= (EPSt+5/EPSt -1)/5. The same method is used to calculate EEG1YR and EEG3YR.
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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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.
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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)
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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.
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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
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 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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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.
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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,
EEG5YRt= (EPSt+5/EPSt -1)/5. The same method is used to calculate EEG1YR and EEG3YR.
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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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
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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.
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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
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 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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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
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 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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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.
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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
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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
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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.
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References
Anonymous, written by many scholars, the 1911 Encyclopedia Britannica. Volume V08,
pp. 332, Horace Everett Hooper
Anonymous (2010, February 4). Shell bevriest dividend. Het Financieele Dagblad.
Retrieved from http://www.fd.nl
Arnott, R.D. and Asness, C.S. (2003), Surprise! Higher dividends = Higher Earnings
Growth. Financial Analysts Journal Jan/Febr 2003, pp. 70-87
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
Bagwell, L.S. and Shoven, J.B. (1989), Cash Distributions to Shareholders. The Journal
of Economic Perspectives, Vol. 3, No. 3, pp. 129-140
Benartzi, S., Michaely, R. and Thaler, R. (1997), Do Changes in Dividends Signal the
Future or the Past? The Journal of Finance, Vol. 52, No. 3, pp. 1007-1034
Bhattacharya, S. (1979) Imperfect Information, Dividend Policy, and the Bird-in-Hand
Fallacy. Bell Journal of Economics, vol. 10, no. 1 (Spring), pp. 259-270
Brennan, M.T. and Thakor, A.V. (1990), Shareholder preferences and Dividend Policy.
The Journal of Finance, Vol. 45, No. 4, pp. 993-1018
Bulan, L., Subramaniam, N. and Tanlu, L. (2007), On the timing of Dividend Initiations.
Financial Management, Winter 2007, pp. 31-65
Brüderl, J. (May 2005), Panel Data Analysis. University of Mannheim
DeAngelo, H., DeAngelo, L. and Stulz, R.M. (2006), Dividend policy and the earned/
contributed capital mix: a test of the life-cycle theory. Journal of Financial
Economics 81, pp. 227-254
Master Thesis Financial Management 2011
63
Fama, E. and French, K. (2001), Disappearing dividends: changing firm characteristics
or lower propensity to pay? Journal of Financial Economics 60, pp. 3-43
Fargher, N.L. and Weigand, R.A. (2009), Cross-sectional differences in the profits,
returns and risk of firms iniating dividends. Managerial Finance, Vol. 35, No. 6,
pp.509-530
Frankfurter, G.M. and Wood, B.G. (2003), Dividend Policy Therory & Practice.
Academic Press, Elsevier Science (USA)
Gordon, M.J. (1962), The investment, financing, and valuation of the corporation.
Homewood, IL: Richard D. Irwin
Grullon, G., Michaely, R. and Swaminathan, B. (2002), Are Dividend Changes a Sign of
Firm Maturity? The Journal of Business, Vol. 75, No. 3, pp. 387-424
Gwilym, O., Seaton, J., Suddason, K. and Thomas, S. (2006), International Evidence on
the Payout Ratio, Earnings, Dividends, and Returns. Financial Analysts Journal,
Vol. 62, No. 1, CFA Institute, pp. 36-53
Hsiao, C. (1986), Analysis of panel data. Cambridge University Press, Chapter 1
Jensen, M.C. (1986), Agency costs of free cash flow, Corporate Finance and Takeovers.
American Economic Review, vol. 76, no. 2, pp. 323-329
Lease, R.C. et al. (2000), Dividend policy – Its impact on firm value. Harvard Business
School Press, United States of America
Lintner, J. (1956), Distribution of Incomes of Corporations among Dividends, Retained
Earnings, and Taxes. American Economic Review, vol. 46, no. 2, pp. 97-113
Myers, S.C. and Majluf, N.S. (1984), Corporate financing and investment decisions
when firms have information the investors do not have. Journal of Financial
Economics 13, 187-221.
Master Thesis Financial Management 2011
64
Miller, M.H. and Modigliani, F. (1961), Dividend Policy, Growth, and Valuation of
Shares. The Journal of Business, Vol. 34, No. 4, pp. 411-433
Nissim, D. and Ziv, A. (2001), Dividend Changes and Future Profitability. The Journal
of Finance, Vol. 56, No. 6, pp. 2111-2133
Pesaran, M.H., Shin, Y. and Smith, R.P. (1999), Pooled mean Group Estimation of
Dynamic Heterogeneous Panels. The Journal of the American Statistical
Association, Vol. 94, No. 446, pp. 621-634
Zhou, P., CFA and Ruland, W. (2006), Dividend Payout and Future Earnings Growth.
Financial Analysts Journal, Vol. 62, No. 3, CFA Institute, pp. 58-69
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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.
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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
= number of shares in issue on day t
= unadjusted share price on day t
n = number of constituents in index
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
= unadjusted share price on day t
= number of shares in issue on day t
= earnings per share on day t. (Negative earnings per share
are treated as zero)
n = number of constituents in index
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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
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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
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Firm
Code: Firm name: Index: Sector:
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
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Firm
Code: Firm name: Index: Sector:
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
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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
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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.
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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.
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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
earnings growth for all individual firms in the three Benelux indices (AEX, AMX, and
BEL20) in 2000
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Figure C; 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 2005
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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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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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.
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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
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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
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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Panel B: Annualized 3-year Expected Earnings Growth
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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
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Panel C: Annualized 5-year Expected Earnings Growth
***, **,* Significant at a 0,001, 0,01 and 0,05 level (two-tailed)
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
Master Thesis Financial Management 2011
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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
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