BUSN 89: Degree Project in Corporate andFinancial Management - Master Level

Department of Business Administration

Lund University

Spring 2016

Abnormal Dividend Increases – Do they Signal?An assessment of the signaling value in dividend increases which deviate from a firm’s historical

dividend policy

Advisor: Authors:Jankensgård, Håkan Fossum, Niklas

Fridlund, Johan

Skog, Magnus

ABSTRACT

Title: Abnormal Dividend Increases – Do they Signal? - An assessment of the signaling value in dividend increases which deviate from a firm’s historical dividend policy

Seminar date: 2016-06-02

Course: BUSN 89: Degree Project in Corporate and Financial Management – Master Level, 15 University Credit Points (15ECTS)

Authors: Fossum Niklas, Fridlund Johan, Skog Magnus

Advisor: Håkan Jankensgård

Key words: Historical dividend policy, signaling value, abnormal stock returns, earnings levels, regular dividend increases, abnormal dividend increases, Swedish market,

Purpose: The purpose of this thesis is to examine dividend increases that are large compared to the company’s historical dividend policy. The aim is to try to bring clarity to how this will affect the stock price and the earnings level of the company.

Theoretical In order to analyze the results theories such as sticky dividendperspectives: theory, market for lemons and dividend smoothing will be used.

Prior research will also be accounted for.

Methodology: Two quantitative studies have been performed. A calendar Time Approach study has been conducted to measure the performance of the stocks included in the sample. The earnings study has been conducted by using a model that assumes earnings to follow a random walk and therefore any earnings change becomes unexpected.

Empirical The analysis is based on companies listed on the Swedishfoundation: Small, Mid and Large Cap Stock Exchange.  One part of the results

is based on the performance between 2005 and 2013 and the other part between 2005 and 2015.

Conclusion: The findings of this study are that dividend increases that are large, compared to the firm’s historical dividend policy, are preceded by larger than normal earnings increases the year before. This is however not the case for the two years following the dividend increase. The study could not find that the signaling value is greater for stocks that increased their dividends more than what has been a regular increase for the company.

Table of Contents

1. Introduction....................................................................................................................11.1 Background........................................................................................................................................................11.2 Problem Discussion.........................................................................................................................................21.3 Research Purpose and Research Question...............................................................................................41.4 Delimitations......................................................................................................................................................4

2. Literature Review..........................................................................................................52.1 Theory..................................................................................................................................................................5

2.1.1 The Effective Market Hypothesis......................................................................................................52.1.2 Signaling Theory......................................................................................................................................62.1.3 Asymmetric Information.......................................................................................................................62.1.4 Sticky Dividends......................................................................................................................................72.1.5 Dividends and Risk.................................................................................................................................8

2.2 Earlier Research on Effects of Dividend Changes................................................................................92.2.1 Earnings and Dividend Increases.......................................................................................................92.2.3 Dividends and Stock Return..............................................................................................................10

2.3 Critique against Earlier Research.............................................................................................................10

3. Methodology.................................................................................................................123.1 Research Approach.......................................................................................................................................123.2 Data Collection and Sample Selection...................................................................................................123.3 Defining Abnormal Dividend Increases and Dummy Variable Creation...................................14

When defining a dividend increase this study does not include special dividends in the dividend per share..............................................................................................................................................................143.3.1 Increase of Dividend............................................................................................................................143.3.2 Larger Increase.......................................................................................................................................153.3.3 Two-Year Average...............................................................................................................................153.3.4 Five-Year Average...............................................................................................................................153.3.5 Quartile.....................................................................................................................................................153.3.6 Defining the Dividend Increase Size of a Dividend Initiation...............................................16

3.4 Sub-Study 1: Abnormal Dividend Increases Effect on Stock Performance..............................163.4.1 Long-Horizon Event Study................................................................................................................163.4.2 Firms Excluded from the Sample....................................................................................................18

3.5 Sub-Study 2: Abnormal Dividend Increases Effect on Earnings Levels....................................183.5.1 Model Definition...................................................................................................................................183.5.2 Firms Excluded from the Sample....................................................................................................19

3.6 Criticism against Research Approach.....................................................................................................203.6.1 Sub-Study 1.............................................................................................................................................203.6.2 Sub-Study 2.............................................................................................................................................213.6.3 Dummy Variables.................................................................................................................................21

4. Empirical Findings and Analysis...............................................................................224.1 Dividend Increases and Returns...............................................................................................................224.2 Dividend Increases and Earnings.............................................................................................................26

5. Concluding Discussion.................................................................................................305.1. Discussion.......................................................................................................................................................305.2 Future research...............................................................................................................................................32

References:..........................................................................................................................I

Appendices.......................................................................................................................VIAppendix A – Original Company Sample.....................................................................................................VIAppendix B – Included Companies Stock Performance Tests...............................................................IXAppendix C – Included Companies Earnings Tests...................................................................................XIAppendix D – Example Firm Hufvudstaden.............................................................................................XIIIAppendix E: Regressions with European Factors....................................................................................XIVAppendix F: Regressions with Swedish Factors......................................................................................XVIAppendix G: Heteroscedasticity Tests European Factors..................................................................XVIIIAppendix H: Heteroscedasticity Tests Swedish Factors........................................................................XXIAppendix I: Robust Standard Errors..........................................................................................................XXIV

1. Introduction

1.1 Background“New record in dividends expected this spring” (Goksör, 2014) “Time to buy stocks that pay

dividends” (Ericson, 2014) and “Dividends give fuel to the stock exchange” (Vilenius, 2016).

These are just a few of the headlines that have covered articles in Swedish newspapers during

the last couple of years. Dividends are as present in the world of finance today as they ever

have been, but the relevance of dividends is still widely debated. Some argue that dividends

are simply a way for firms to pay out residual cash flows to investors or a sign of too few

investment opportunities (Myers, 1984). Others state that there is something more to

dividends, that dividends convey information about the state of the company and what the

future holds. They argue that as management inherently possesses more information than

regular investors, dividends would be a way for management to signal their belief about

future performance.

Back in 1956 Lintner conducted a famous study where he argued that managers will only

increase their dividend level if they believe that they will not have to reduce it in the future

and that dividends would therefore convey information about future earnings. In contrast to

this, Miller and Modigliani (1961) argued that dividends are irrelevant and that shareholders

could just sell their company shares in order to receive a payout.

Another aspect of dividends is that it renders a double taxation for investors and therefore

some argue that dividends are bad news for investors. Other proponents argue that this is only

highlights the importance of dividends as they are being paid out even though double taxation

is in effect.

The dividend policy differs widely between firms and companies do assign varying

importance to dividends. The Swedish company Hufvudstaden has increased their dividend

every year since 2006, even during and the year following the financial crisis where dividend

increases were scarce in the Swedish market (Hufvudstaden AB, 2016). At the other end of

the spectrum, Acando has increased their dividend only once since they initiated dividend

payouts in 2008. A question that has been around for a long time is what an increase in

dividends signals to the market about future earnings and stock returns. If you take this one

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step further the question then becomes if a dividend increase by a company like

Hufvudstaden would have the same signal value as a dividend increase by Acando?

“Do you know the only thing that gives me pleasure? It's to see my dividends coming in.”

John D. Rockefeller (1901)

1.2 Problem DiscussionThe majority of the body of research points to the fact that there is no relationship between

future earnings and dividend increases. Merton Miller summed up the contemporary debate

regarding the relationship between earnings and dividend increases fairly well already in

1987 by stating, “…dividends are better described as lagging earnings than as leading

earnings.”

However, a special case where dividend changes play a part in both earnings decreases and

increases is that of dividend initiations and omissions. Several studies such as Healy and

Palepu (1988) have shown that dividend initiations/omissions are followed by an

increase/decrease in profit. Benartzi, Michaely and Thaler conducted a study in 1997

examining the same sample as the sample used by Healy and Palepu in their 1988 study.

They did not find, in line with much earlier research, any results that dividend increases

signal future increased earnings except for the rare case of a dividend initiation. Benartzi,

Michaely and Thaler sum up the results by stating: “We cannot explain why dividend

initiations should be so different from dividend increases, nor can we think of a reason why a

signaling model would make this prediction. Perhaps a firm can only send the dividend signal

once.”

Regarding stock returns, several studies have looked at the effect over a shorter period of

time following the dividend announcement and of dividend increases that are of a larger

quantity. Denis, Denis and Sarin (1994) looked at companies that did an increase of over 30

% and found that larger dividend increases led to larger stock returns on a 1 % significance

level.

Other similar studies have examined the effects on stock returns of these “large” dividend

increases instead in the long-term and have found a statistically significant abnormal return

for one, two and three years. Grullon, Michaely, and Swaminathan (2002) as well as

Michaely, Thaler, and Womack (1995) are some of the most famous examples. These studies

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categorize the companies that make “large” dividend increases as companies that increase by

a “large” value compared to the market.

In 2014 Michayluk, Neuhauser and Walker conducted a study examining if the market learns

to predict chain dividend increases, i.e. when a firm has increased dividends several years in a

row. They found that abnormal returns could be observed the first and second year in a row,

however further dividend increases were something that the market anticipated and therefore

had no effect to be seen on the stock price. However, the study did not take the size of the

increase into account, it focused on the number of dividend increases made.

To sum up, earlier research has studied whether larger dividend increases lead to larger stock

returns, if the effect is sustained over a longer period of time and the effect of dividend

initiations and omissions.

The authors of this paper hypothesize that defining large dividend increases with respect to a

firms historical dividend policy could capture a stronger signal to the market. This could also

be true even if the dividend increases are “large” compared to market in general, as long as

these “large” dividend increases have become the norm for the company. Further on, most

studies conducted on dividends have been using U.S data where firms pay quarterly

dividends. There is a possibility that Swedish firms due to less frequent dividend payouts

send stronger signals to the market.

In a study from Brav et al (2005), 89,6 % of managers responded that they try to maintain

smooth dividend streams and 80 % responded that they believe dividend policy convey

information to investors. In light of this it is not hard to speculate that when managers diverge

from their regular and smooth dividend policy by increasing dividends, management believe

that the company will experience a more beneficial future.

The question posed by Benartzi, Michaely and Thaler (1997) why dividend initiations should

signal earnings while a regular dividend increase does not, could be due to the fact that a

dividend initiation is the largest deviation from earlier firm dividend payouts. Therefore, this

paper will try to investigate if dividend increases, which differ from the companies’ dividend

history, have a signal value different from the signal value regular dividend increases have.

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1.3 Research Purpose and Research QuestionIn line with the content in section 1.2, we deem that there is a gap in the current research

regarding the effect of irregular dividend increases, which differ from the historical dividend

policy of a company. The study will therefore take a new approach in defining large dividend

increases and examine the signal value of such dividend increases.

The research questions are:

- How do dividend increases that are large, compared to the firms’ historical dividend

policy, signal future stock performance on the Swedish market?

- How do dividend increases that are large, compared to the firms’ historical dividend

policy, signal future earnings on the Swedish market?

1.4 DelimitationsTarget Group

The target group of this thesis is mainly academics who study the field of dividends. Our

findings can hopefully contribute to existing research and be of relevance to future studies of

dividends and their signaling value. However, as our research also could be used to construct

an investment strategy, this paper will be relevant for all people who invest in stocks. In line

with this, our study could further on be of relevance for management in their efforts to

construct a dividend policy that will maximize the value of their company.

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2. Literature ReviewThis chapter will discuss various reasons as to why companies adopt certain dividend

policies and what the scientific theories about these policies predict and interpret. Further

on, this chapter will also account for earlier research within the domain of dividend

increases with respect to both earnings, as well as stock returns.

2.1 Theory

2.1.1 The Effective Market Hypothesis

The theory about the effective market began to gain attention in the 1960`s and is probably

best known from the article “Efficient capital markets” by Eugene Fama in 1970. Fama

defines the market as effective when prices fully reflect all available information. If this

hypothesis holds, all performance by investors that is better than the average market is simply

due to luck. Fama categorize the level of market efficiency in to three subsets. These are

weak efficiency, semi-strong efficiency and strong efficiency.

The market that builds on weak market efficiency states that prices cannot be determined by

just looking at historical prices. In this scenario technical analysis of stocks, in which former

patterns are used to predict future patterns, is of no value.

The semi-strong market efficiency builds on the notion that prices fully reflect the

information obtained from historical prices and also all publicly available information such as

annual reports and statements from management. In this scenario the average investor cannot

on average beat the market as all public available information is already priced in to the

stocks.

The strong market efficiency states that even insiders of the company cannot beat the market.

However, this is a notion that Fama in his article from 1970 says does not hold as there

already then was evidence of this phenomenon not holding empirically.

Since Fama published his article, strategies have proven to be able to beat the effective

market hypothesis such as the momentum strategy (Marshall & Cahan, 2005). Fama has later

stated that he is aware of this but that the effective market hypothesis should more be used as

a proxy to which results can be compared against, than as a solid theory that always holds.

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2.1.2 Signaling Theory

Signaling theory stems from the works of Bhattacharya (1979) and Miller & Rock (1985).

These models rest on the basic assumption that dividends are not just a means of paying out

money to investors, but are something that also conveys information. Management has more

information than investors and thus are dividends a means to signal this information and their

faith in the future profitability of the company. In accordance with this, an increase in

dividends would signal that the company is undervalued and a decrease in dividends would

mean that the company is overvalued. Bhattacharya and Miller & Rock even hypothesize in

their models that management would keep a steady flow of dividends despite having to raise

costly external financing and give up on profitable investments.

There are different takes on what and why firms would want to signal by their dividends. For

example Allen, Bernardo and Welch (2000) suggests that firms pay high dividends to signal

that it is a quality company as high dividends attract institutional investors who conduct more

scrutinization of the company. Lower quality firms would thus not want to follow this

endeavor, as more scrutinization would be unbeneficial. The majority of signaling

hypothesizes do however speculate that changes in dividends signal changes in future cash

flows and has not anything to do with institutional investors.

Brav et al did a survey, in 2005, in which they interviewed managers about their views on

dividends. They found that 88,1% believe that there are negative consequences in reducing

dividends. This shows that dividends, at least to managers, have more value than just the cash

it is made of. However, the real implications and the validity of signal theory is something

that empirical testing has not been able to show a unanimous evidence of. In some instances,

such as dividend initiations, there seems to be a positive relationship with future earnings,

however with regular dividend increases the relationship is not as clear.

2.1.3 Asymmetric Information

Management does inherently sit on more information about the company than the investors.

This leads to an agency problem that results in some kind of uncertainty and heightened level

of risk for the investors. These types of agency problems increase when the company holds

more cash as investors are unsure if management will use the money for things such as

empire building and/or unprofitable investments. Many researchers like Fenn and Liang

(2001), Hu and Kumar (2004) have postulated that dividends are a way to decrease this

problem of asymmetric information. One of the more famous examples is Jensen’s free cash

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flow hypothesis, which states that a high level of debt is favorable as this disciplines

management to use their available cash wisely. Lastly, Leary and Michaely (2011) show that

dividend smoothing is most evident in firms with high asymmetry costs.

This line of reasoning is also applicable to dividends as this is another form of reducing

available cash for management and thus wasteful spending. Research like that of DeAngelo,

DeAngelo, and Skinner (2004) has shown that large, mature companies with lots of cash are

also the companies which have the highest payout ratio, probably because they are the

companies with the largest agency problems.

One of the more famous theories regarding information asymmetry is Akerlof’s theory (1970)

regarding “market for lemons”. This theory states that on a market with asymmetric

information between buyers and sellers, the “bad” merchandise will sell for the same price as

the “good” merchandise as there is no way to tell them apart. In order to mitigate this

relationship, the seller can certify his merchandise from a credible certifier and thus the buyer

will know that what is being bought is made of high quality which will raise the price. In

order for the certifier to send a credible signal it needs to be hard to mimic. Pertaining to

stocks, the question becomes if paying dividends becomes a way to certify that the quality of

the company is high.

Expanding further on the reasoning of agency problems is that of the influence of corporate

governance systems between different countries in its ability to reduce agency problems. This

theory builds on the research by La Porta et al (2000) and has been expanded upon by for

example Grullon and Michaely (2002). In countries with more concentrated ownership there

is less agency problems as large institutional investors do more monitoring by themselves and

thus there is a lesser need for high dividends.

2.1.4 Sticky Dividends

The theory of sticky dividends emerged from Lintners work in 1956. His findings supported

the notion that managers only increase dividends if they believe that this new level of payouts

can be sustained. Much literature support this conclusion and many have extended on these

findings like Fudenberg and Tirole (1995) who showed that managers calibrate earnings and

dividends to smooth them out in order to not get negative results that could lead to being

fired. Furthermore, Benartzi, Michaely and Thaler (1997) stated that dividend increasing

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firms are less likely to decrease their dividends in the future, indicating the fact that firms

only raise dividends when they feel certain that a future decrease will not be necessary.

Lambrecht and Myers (2012) hypothesize that smooth dividends can have something to do

with managers risk aversion compared to investors. This follows the reasoning that volatility

in the dividend stream can be followed by a volatility increase for the stock price. Lambrecht

and Myers deepen their studies in 2013 and show that even if the level of investment is very

volatile, the levels of dividends remain smooth. This smoothness is in part achieved by saving

cash for future down turns by the company, thus incurring so called financial slack for the

sake of dividends. Lastly, Leary and Michaely (2011) show that dividend smoothing is most

evident in firms with high asymmetry costs and that dividend smoothing has increased

steadily since the 1930´s.

2.1.5 Dividends and Risk

An interesting area of research regarding dividends is that of dividends and firm risk. There is

little research done in the area and to date there seem to be no comprehensive model to

measure this. Grullon, Michaely and Swaminathan (2002) studied the relationship between

changes in the level of dividends and systematic risk. They find that firms that increase their

dividends experience a lower level of systematic risk whereas firms that decrease dividends

experience increased systematic risk. Further on they found that dividend increasing firms

experienced a decline in revenue and an increase in stock price. Interestingly enough they

also found that the companies that increased their dividends and experienced the largest

decrease in systematic risk where the companies that performed the best over the subsequent

three years in respect to stock returns. An implication of this is that dividends do not signal

future increased profitability, but decreased risk and thus a decreased risk premium for the

company. This in turn will then raise the value of the company. Grullon, Michaely and

Swaminathan (2002) speculate that this should be the case as the value of a company is

basically decided by its current and future cash flows and/or its discount rate.

The reason for the reduction in risk is not entirely clear but some researchers speculate that

the dividend becomes the anchor to which the market gauges the stock price and as this value

changes infrequently the price stabilizes. Another explanation is being brought up by Grullon,

Michaely and Swaminathan (2002) who claim that reinvestment rates decline for large

companies which leads to excess cash, which is then being paid out and thus companies that

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pay large dividends are mature low risk companies. They call this the maturity hypothesis.

Thus, the decline in risk is because low risk companies pay out large dividends and not that

large dividends lead to low risk companies. The positive reactions to increasing dividends can

then, according to Grullon, Michaely and Swaminathan (2002), be because the market was

not fully aware of the decline in risk that the now more mature company will experience in

the future and thus ensues a positive stock market reaction.

2.2 Earlier Research on Effects of Dividend Changes

2.2.1 Earnings and Dividend Increases

Research like Benartzi, Michaely and Thaler (1997) has shown that firms cutting dividends

experience an earnings decrease in the year prior to reducing the dividend, and the same goes

for the year of the dividend cut, but the two following years actually show increased earnings.

Cutting dividends therefore can be seen as a lagged effect of earnings decreases. Regarding

dividend increases, Benartzi, Michaely and Thaler found that earnings had increased year t-1

and year 0. Benartzi, Michaely and Thaler measure the increase for year 0 as the last quarter

of the year of the dividend increase, which in effect makes the earnings measurement

primarily a gauge of the earnings the year prior to the dividend increase. After year 0 there

are no increase in earnings associated to dividend increases. Most studies confirm the

relationship that dividend increases do not signal future earnings, such as Grullon &

Michaely (2002), Pennman (1983) and DeAngelo & Skinner (1996). Grullon & Michaely

(2002) even shows that over a three-year period after an increase in dividends, earnings

decline and that the largest increases correspond to the largest declines in profitability.

However, there exist studies that contradict this as the one made by Nissim and Ziv in 2001.

Their study discard earlier researchers’ findings as they, according to Nissim and Ziv,

contained measurement errors like omitted variables and not taking in to consideration the

expected random drift and mean reversion that ensues. Nissim & Ziv’s standpoint has on the

other hand itself received criticism and other researchers like Grullon et al (2005) stated that

research that has been rigorous in its econometrics properties seem to have found results

conflicting with those of Nissim and Ziv (2001). For example Benartzi et al. (1997) used a

matched-sample study wherein non-dividend changing firms were matched to dividend

changing firms in terms of size, maturity, industry and earlier earnings.

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Thereby Grullon et al (2005) argue that they controlled for mean reversion and earning

patterns and found no abnormal change in earnings. In a different setting, DeAngelo,

DeAngelo, and Skinner (1996) examine firms that experience at least a decade of earnings

growth followed by a year of decline. Their test focuses on the dividend decision the year of

the earnings decline. They find no evidence that a dividend increase represents a reliable

signal of increased future earnings performance.

2.2.3 Dividends and Stock Return

The relationship between dividend increases and stock returns are also positive as has been

supported by (e.g. Charest, 1978; Kalay, 1985; and Nissim and Ziv, 2001). However, there

are researches contradicting these findings such as Alkebäck (1997) who over the period

1989 - 1994 could not find any support for dividend increases affecting the stock price on the

Swedish stock exchange.

An area where there is less conflicting evidence of the effect on stock returns is however once

again that of initiations and omission. Here there seem to be a positive relationship between

dividend initiations and increased stock returns and a negative one between dividend

omissions and stock returns. (Michaely, Thaler, and Womack, 1995)

Lastly, Eddy and Seifert (1988) have shown that abnormal returns for firms that increase their

dividends a lot are greater for smaller firms.

2.3 Critique against Earlier ResearchThere are a couple of studies that are similar to the one we intend to conduct, but none that is

identical.

Hersvall, Nilsson and Weibull (2008) examined the Swedish stock market by creating

different portfolios. They divided the market in to 5 quintiles and then put the companies that

increased their dividends the most into the fifth quintile. They then kept the portfolio in a

buy-and-hold strategy for 1 year, repeated the procedure and compared the return of the

stocks with the market index.

However, first of all they conducted all the transactions on the same date, even though

different stocks have different days when they announce their level of dividend payout. An

even larger error however was that they set the last day of June as their day to conduct the

transactions whereas the announcement day normally is in February. The study therefore

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didn`t capture the effect for the first 3-4 months and in our opinion becomes very flawed as

their presumed expectation then is that the market do not react for the first quarter of a year

following a dividend announcement. Brzeszczynski and Gajdka (2007) did a similar study on

the polish stock exchange with exactly the same flaw as they also measured from the last of

June each year. Lastly Elliot and Perman (2014) conducted a similar study but they also

conducted the transactions at dubious dates as they chose the last of May as the transaction

date for all their purchases.

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3. MethodologyThis chapter will describe the research methods used to answer the research questions posed

in the first section and thoroughly detail the data collection. The new take on abnormal

dividend increases as well as the definition thereof will be presented. Criticism and the

weaknesses of the research approach will also be discussed.

3.1 Research ApproachA wide variation of methods to examine the effect of dividend increases have been performed

and applied with varying results. The methods have varied depending on if the purpose was

to examine the effects of dividend increases on earnings or stock performance. This study

will examine both relationships and therefore it will be divided into two sub-studies, one

examining how abnormal dividend increases affect stock performance and the other

examining the effects of abnormal dividend increases on earnings. This will be achieved

through a quantitative study on dividend increases in the Swedish market.

3.2 Data Collection and Sample SelectionThe study will be conducted on the Stockholm Small, Mid and Large Cap between 2005 and

2015. Both sub-studies share the same original sample of firms, which can be seen in

Appendix A. Dead stocks, i.e. firms which for various reasons have been delisted during the

studied period, will be included in the stock performance study to increase reliability in the

empirical findings, but will not be included in the earnings study due to lack of time.

In order for a firm to be included in the original sample, the stocks need to fulfill the

following:

1. Be listed on Stockholm small, mid or large cap

2. Fiscal year end in December

3. Dividend is paid out once a year

If dividends are not paid in cash i.e. a firm paying stocks from another firm, it will not be

included as a dividend.

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Most of the data has been collected with the use of DataStream. In some cases DataStream

had limited data on the dividend announcement date and in those cases data were retrieved

from the website of the company.

Data collected from DataStream (variable code name in brackets):

Dividends per Share (DPS)

Announcement Date (DPSANCDT)

Special Dividends (WC05101)

Stock Prices (P)

Market Value at Year End (MV)

Net Income before Extraordinary Items (WC01551)

Data for the Fama French Three Factor Model (FF3FM) was collected from two different

sources. The European FF3FM data was collected from Kenneth French’s homepage. For the

Swedish FF3FM the data was gathered from Stefano Marmi’s, professor at Scuola Normale

Superiore di Pisa, homepage.

The FF3FM data variables included (Both European and Swedish):

Market Return

1-Month Treasury Bill Rate

Small minus Big Factor

High minus Low Factor

Each year starts by the date of the announcement. The announcement date is also needed in

order to calculate the monthly return of the stock. Each monthly return will end the same day

of the month as the announcement date, for example the 5th of February to the 5th of March,

except for the last examined month which will end the day before the following year’s

announcement date. This means that the stock price data had to be collected for the day of the

dividend announcement and also the day before the dividend announcement for all the years

between 2005 and 2016. When calculating the monthly returns, the dividend per share plus

the special dividend have been added to the stock price of April each year. The reason for the

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special dividends to be allocated in April is that most companies use this month to pay out

dividends (Bornold, 2016).

3.3 Defining Abnormal Dividend Increases and Dummy Variable CreationIn order to examine if and how abnormal dividend increases signal future stock performance

and/or earnings it is necessary to define an abnormal dividend increase. Since earlier research

mostly compared dividend increases against the market whereas this study intends to

compare dividend increases of a firm to its dividend history, it is necessary to create a

variable, which then defines an abnormal dividend increase. Therefore we intend to create a

set of dummy variables which all have different definitions of abnormal dividend increases

and then test whether any of the dividend increases send a signal. The dummy variables will

be created to detect different levels of abnormal dividend increases. The first dummy

variable, if the dividend has increased, is only considered to be a “regular dividend increase”,

whereas all the other dummy variables is considered to be “abnormal dividend increases”.

The size of the dividend increase will be calculated as:

(1) Dividend Increase=Dividend per sharet−Dividend per sharet−1

Dividend per sharet−1

When defining a dividend increase this study does not include special dividends in the

dividend per share.

3.3.1 Increase of Dividend

The first dummy variable is also the simplest, if the company has increased its dividend

compared to the previous year it will be included in the sample and receive a dummy value of

one. In order for any of the following dummy variables’ conditions to be answered this

definition has to be true as well. This variable has been widely used and tested by earlier

studies, it is however necessary to include it due to the reason that if a regular dividend

increase potentially signals any significant signal value found for the abnormal dividend

increases could be explained by this.

3.3.2 Larger Increase

The second dummy variable requires that the investigated year’s dividend increase is larger

than the previous year’s. If a firm increases the dividend by 10% each year, all of these

dividend increases would get the value 0 for this dummy variable, however if a 10% dividend

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increase would be followed by a 12% dividend increase, the 12% dividend increase would be

included in the sample. Since this definition compares growth to growth the dividend of the

previous two years will be necessary. If the data for dividend per share for any of the

previous two years are missing the dummy variable will automatically be zero.

3.3.3 Two-Year Average

Precisely as for the aforementioned dummy variables this definition requires that the dividend

data for the two previous years are present. If the dividend increase is larger than the previous

two-year’s average dividend increase, the requirements are met and the investigated company

and year will receive a dummy value of one. This variable is relevant in respect to the fact

that the last couple of years are most relevant as management probably has been the same

during this period and that this time period is freshest in the mind of investors and

management.

3.3.4 Five-Year Average

The requirements of this dummy variable is not far from the last mentioned, but since it is

comparing dividends with regards to the five-year average, and the first year with calculated

growth is 2005, the first year possible to meet all the conditions is 2010. The number of

observations will therefore be significantly fewer than for the other dummy variables.

3.3.5 Quartile

The last dummy variable has a more academic approach since it would not be possible to

replicate it practically as an investment. It requires that the dividend increase is among the

fourth quartile of both the company’s historical and known future dividend increases. There

is no restriction on how far back in time the dividends will be gathered, if DataStream has

data going back further than 2004 it will be included, otherwise data from 2004 will be the

starting point. This dummy variable is the only one that cannot be used as a portfolio strategy

and will only answer the question if the year following one of the company’s largest dividend

increases will result in a higher return than the market. The reason for its inclusion is that the

largest dividend increases in the firm history should have the highest signaling value if the

firms´ historical dividend policy is an important determiner of signaling value.

The quartile dummy variable incorporates the longest period of time for the dividend history

and could thus be argued is the most relevant one. But the fact that it contains such a long

period of time is simultaneously one of its shortcomings. If the company had a very

15

aggressive dividend policy under a certain management 20 years ago most of the

observations ending up in the highest quartile will then be during a time period that we do not

measure and thus will not be included in our sample. What happens then is that we do not

include these observations in the quartile and thus the effect becomes that we get a smaller

sample, not that we get a more erroneous one.

3.3.6 Defining the Dividend Increase Size of a Dividend Initiation

Since the percentage increase of a dividend initiation is not defined, calculating the average

dividend increase where a firm has initiated a dividend is not possible. Therefore we define a

dividend initiation i.e. when a firm pays a dividend following a year where they did not pay a

dividend, to a dividend increase of 2000 %. The number 2000 % is arbitrarily chosen and

could have been any extremely high number. The purpose is simply to make it in to the

largest increase possible for a certain stock, since earlier research has concluded that dividend

initiations are the strongest signal a firm can send.

3.4 Sub-Study 1: Abnormal Dividend Increases Effect on Stock Performance

3.4.1 Long-Horizon Event Study

In order to examine the effect of dividend increases on share price the most popular method is

the event study. Event study methodology is described by Mackinlay (1997) as when stock

performance is examined before as well as after an event and the returns between firms

experiencing an event to those not experiencing one are compared. This is usually done by

assessing it with cumulative abnormal returns in the days following the event. However this

study aims to examine stock performance the year following the event and thus the method

used will be a long-horizon event study where focus is somewhat different compared to a

normal event study.

In a long-term event study it is more important to control the abnormal returns for factors not

pertaining to the event since there could be and likely is more noise in one year abnormal

returns compared to daily abnormal returns (Kothari and Warner, 2006).

The two main approaches to a long term event study on abnormal returns are the Buy and

Hold Abnormal Return Approach or the Calendar Time Approach. This study will use the

Calendar Time Approach, which was first used by Jaffe (1974), to examine abnormal returns

16

following an event. Both methods are valid to use in order to examine long term returns and

both models suffer from bad-model problem to some extent (Fama, 1998). The reason for

using Calendar time approach is that it is more time efficient and in the opinion of the authors

easier to apply to this example.

In a Calendar Time Approach, portfolios are set up consisting of firms which go through the

event which is to be examined. The returns of the portfolio are then to be controlled for with

a model for expected returns such as CAPM or Fama French Three Factor Model (FF3FM).

Thereafter the portfolio is tested for whether there are abnormal returns present which the

model for expected returns cannot explain. The portfolio is rebalanced once a month to

include new firms which have gone through the event intended to be examined. The firms are

kept in the portfolio for the time period intended, therefore it follows that if the purpose is to

examine one year returns after an event, the firms going through the event are kept in the

portfolio for one year and then removed.

Essentially this study will put together five equally weighted portfolios, each based on a

different dummy variable defining abnormal dividend increases, and then regress the monthly

portfolio returns on the market and the two additional factors from the FF3FM using an OLS

regression. The intercept of the regression will denote if there are abnormal returns present.

This results in the following regression specification:

(2) RP,t−RF , t=α P+ βP ( RM ,t−RF ,t )+sP SMB+hP HML+εP ,t

RP = Monthly Portfolio Return

RF = The 1-Month Treasury Bill Rate

RM = The Monthly Return on a Value Weighted Index

SMB = The Difference in Returns between Small and Big Stocks

HML = The Difference in Returns between High and Low Book Equity/Market Equity

The alpha will then be tested to see if it is significantly different from zero, i.e. if there are

monthly returns, which cannot be explained by the FF3FM.

The reason we use FF3FM is that it better explains returns than CAPM (Fama & French,

1992), which is as earlier argued central when doing a long-horizon event study. The FF3FM

will be used twice, once with European market factors and once with Swedish market factors.

17

The Swedish regression is constrained to 2013 since we did not manage to retrieve data for

the factors for a longer time period, however the results could perhaps explain some returns

which the European factors might miss as stocks on the Swedish exchange are studied. The

reason for using both Swedish and European factors is due to a study performed by Griffin

and Lemmon (2002) where they found that local market factors better explain variation in

returns than global ones. However due to the limitations in data and time we decided to

perform a regression with European factors as well.

3.4.2 Firms Excluded from the Sample

The original sample included 354 companies (See Appendix A), but some of them were

found inadequate and were excluded from both sub-studies. Most of the companies which

were excluded from the stock performance tests were excluded because they were lacking

dividend payments for the examined period. Other reasons were for example; unavailable

information about the announcement day and no observed dividend increases. In total 210

companies were examined in the stock performance study (See Appendix B).

3.5 Sub-Study 2: Abnormal Dividend Increases Effect on Earnings Levels

3.5.1 Model Definition

This sub-study will use a similar model like the simpler model used by Benartzi, Michaely

and Thaler (1997). That model assumes earnings to follow a random walk and therefore any

earnings change is unexpected. Consequently the definition of unexpected earnings change in

the model is UE = Earningst-Earningst-1/(Market cap0). The study by Benartzi, Michaely and

Thaler divided dividend increases into quintiles depending on the size of the dividend

increase in percentage in comparison to the market. They then tested if average unexpected

earnings for dividend increasing stocks were significantly different from the average

unexpected earnings for non-dividend increasing stocks.

This paper will use the same type of model as the simple one used by Benartzi, Michaely and

Thaler (1997). However instead of dividing the dividend increases into quintiles determined

by size the dummy variables explained in section 3.3 will be used. Thereafter the average

unexpected earnings will be calculated for each dummy variable in either true or false states.

The unexpected earnings changes will be calculated for one year before the dividend

increase, the year following the dividend increase and then one year after the dividend

increase.

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Benartzi, Michaely and Thaler (1997) used a two-tailed students t-test to infer if the average

earnings change was significant. This study will use a two-tailed Welch’s t-test, which is a

two-sample t-test for samples with unequal variances and size (Welch, 1947). It is possible

that the variance for firms increasing their dividends is different than those not increasing

which is why we choose this test.

The formula for the test is:

(3)T−stat=

(UETRUE )−(UE FALSE)

√ sTRUE2

NTRUE+

sFALSE2

N FALSE

The t-stat is then compared to the two-tailed t-distribution with v degrees of freedom.

(4) υ=( STRUE

2

NTRUE+

sFALSE2

N FALSE)

2

STRUE4

NTRUE2 ∗υTRUE

+SFALSE

4

N FALSE2 ∗υFALSE

υTRUE=N TRUE−1 υFALSE=N FALSE−1

With the help of a t-distribution a p-value will be retrieved which indicates on which level

there is a difference in the two samples.

3.5.2 Firms Excluded from the Sample

As in sub-study 1, the original sample consisted of 354 companies, however some were

excluded. Reasons for exclusion from the earnings tests were mostly due to earnings or

market value not being retrievable from DataStream or that the firm was delisted during the

study period. The earnings study included 279 companies (See Appendix C).

3.6 Criticism against Research Approach

3.6.1 Sub-Study 1

The main criticism against sub-study 1 is that the Swedish FF3FM data which will be used to

control the stock returns is constrained to 2013. This means that the observations will be

fewer for the Swedish market regressions which could affect the results. Secondly, the data

used for the Swedish FF3FM is secondary data from a less renowned source and thus the

19

reliability and to some extent the validity of the Swedish portfolio regressions are somewhat

weaker. Thirdly the use of European FF3FM is also not optimal as pointed out by Griffin and

Lemmon (2002) since the country specific factors better explain variation in returns than

regional or global data,

Fama (1998) argued that the use of Calendar Time Approach will suffer from three issues

which could lead to complications when inferring significance of abnormal returns.

1. Since the firms in the portfolio change through time, so do the estimates of the slopes

on the risk factors included to model expected returns

2. Since the number of firms change through time there is residual heteroscedasticity

3. Most importantly according to Fama (1998), the model for expected returns is not a

perfect story

Issue number one noted by Fama is definitely a necessary consideration when reviewing the

results. Issue number two has been accounted for and tested using Breusch-Pagan Godfrey

tests which can be seen in Appendices G and H. The third issue is something we attempt to

take into consideration by using both regional and country specific factors to model expected

returns for the portfolios.

However it can also be argued that the inclusion of additional risk factors, such as the

momentum factor which further explains variations in returns (Carhart, 1997), would better

model expected returns. We however believe that for the purpose of this study, the two

additional factors added by the Fama and French model from 1992 should be enough, since

FF3FM is still used in studies surrounding abnormal returns.

The rebalancing of the portfolio is also an aspect which can be criticized, since most of the

stocks are included in the middle of the month when the dividend increase is announced. This

could lead to that the model for expected return fails to account for all of the variation in

stock returns since the three factors “market”, “SMB” and “HML” are measured in the

beginning of each calendar month.

3.6.2 Sub-Study 2

The model used to predict earnings is rather simplistic in nature and relies heavily on the

assumption that earnings follow a random walk and therefore any change in earnings would

indicate an unexpected earnings change i.e. it would not be necessary to control for other

20

factors. In itself the assumption is perhaps not completely wrong; however it has been

questioned and also challenged by Fama (1998). Therefore, inference on earnings levels may

be incorrect, especially when judging if it signals informational content. However the

important inference to make is whether the dummy variables in some way predict an

increased future earnings level and to that end the model should still hold.

Another issue is that the study relies almost exclusively on second hand data which has been

retrieved from DataStream. Therefore, if there is an error in the data from DataStream, a

replication of this study with a different data source could yield different results.

Survivorship bias is also an issue for sub-study 2 as firms that were delisted during the study

period were not included. There is a possibility that firms which were delisted and therefore

not included could have increased dividends, but have been in more distress than firms which

were not delisted during the period. It should be said that distress and bankruptcy are not the

only reasons for a firm to be delisted as it could also have been acquired; however it is

something which needs to be considered.

3.6.3 Dummy Variables

The definition of abnormal dividend increases in regard to a firm’s earlier dividend increases

is somewhat arbitrary as earlier research has not been made in this area before. Therefore this

study is, to the knowledge of the authors, the first one using this definition for abnormal

dividend increases and therefore the validity and reliability of the measures can be

questioned. To mitigate as much of these potential problems as possible four different

definitions of abnormal dividend increases with respect to firm dividend policy will be tested.

21

4. Empirical Findings and AnalysisThis chapter will present the results from the research studies regarding earnings and stock

returns. The results will then be analyzed with the help of the theoretical frameworks

presented in chapter two.

4.1 Dividend Increases and ReturnsBelow are the results for the tests on how the dividend increases affected the stock value. The

tables are made up as follows: Dummy Increase demonstrates the portfolio consisting of

stocks which increased their dividends with any value. Dummy Larger Increase portfolio is

made up of stocks that have increased their dividends, in percent, more than the dividend

increase was the previous year. Dummy Two-Year Average is the portfolio consisting of

stocks which increased the dividends more than the average dividend increase the last two

years in percent. Dummy Five-Year Average shows the result of the portfolio consisting of

stocks with a dividend increase larger than the average dividend increase the last five years

and Dummy Quartile is the portfolio made up of stocks which made dividend increases in

that year which were in the highest quartile with respect to their dividend increase history.

The first dummy variable, if the dividend has increased, will further on in the text be referred

to as a “regular dividend increase”, whereas all the other dummy variables will be referred to

as “abnormal dividend increases”.

Table 1

European FactorsDummy Variable

α β s hIncrease Coefficient 0,8623 0,4563 1,0840 -0,1080

Prob. 0,0064 0,0000 0,0000 0,5222Larger Increase Coefficient 0,8174 0,4665 1,1130 -0,1042

Prob. 0,0164 0,0000 0,0000 0,5545Two-Year Avarage Coefficient 0,8781 0,4421 1,1550 0,0013

Prob. 0,0077 0,0000 0,0000 0,9928Five-Year Average Coefficient 0,9017 0,4131 0,8568 -0,1165

Prob. 0,0419 0,0001 0,0007 0,5931Quartile Coefficient 0,9919 0,4548 1,1505 -0,0947

Prob. 0,0024 0,0000 0,0000 0,5874

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Table 2

Swedish FactorsDummy Variable

α β s hIncrease Coefficient 0,4885 0,6565 0,3066 -0,0845

Prob. 0,2066 0,0000 0,0027 0,6015Larger Increase Coefficient 0,5004 0,6695 0,3352 -0,0193

Prob. 0,2472 0,0000 0,0027 0,9179Two-Year Avarage Coefficient 0,5317 0,6577 0,3238 -0,0295

Prob. 0,2185 0,0000 0,0037 0,8745Five-Year Average Coefficient 0,3242 0,6238 0,1896 -0,3007

Prob. 0,6553 0,0002 0,3496 0,4005Quartile Coefficient 0,6281 0,6936 0,3351 -0,0400

Prob. 0,1120 0,0000 0,0013 0,8082

When looking at the abnormal returns of the dividend increase variables controlling for the

European market, there is a positive abnormal return for all variables on a 5 % level with the

regular dividend increase, two-year average and quartile dummies all significant on the 1 %

level. The differences between the alphas of the variables are quite small but the portfolio

based on the quartile variable shows the highest abnormal return. The portfolio, which

included stocks increasing the dividend more than the preceding year, was the portfolio

rendering the lowest abnormal return. However the differences between the variables are too

small to draw any conclusions regarding whether abnormal dividend increases better predict

stock price signaling than regular dividend increases. It can be seen however that the

portfolio based on the quartile dummy has the highest alpha and therefore the highest

estimated abnormal returns.

Looking at the portfolios controlling with the Swedish FF3FM model between 2005 and

2013, the results differ compared to when controlling with the European FF3FM. The

abnormal return for the portfolios controlled with the Swedish FF3FM averaged

approximately half the abnormal return compared to when controlled for with the European

FF3FM. Also, none of the alphas were significant when using the Swedish FF3FM. When

controlling the returns for the Swedish market and Swedish Fama and French factors the best

performing portfolio is once again the one containing firms chosen by the quartile variable. In

this test the portfolio based on companies which increased the dividend more than their five-

year average dividend increase performed worst. However, once again, the differences were

too small to draw any statistically significant conclusions.

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To summarize, the test using the European FF3FM control variables showed a statistically

significant abnormal return for all the portfolios. The test using the Swedish FF3FM showed

a higher return than the market, but not a statistically significant abnormal return for any of

the portfolios. What needs to be taken into consideration is however that when controlling for

the Swedish FF3FM there were at most 96 observations as the data sample for the Swedish

FF3FM was for the years between 2005 and 2013. This does affect the p-values, but it is

unlikely that the results from the regressions would have been different if the sample period

had been the same as for the European FF3FM. Further on, no abnormal dividend increases

showed statistically significant abnormal returns. Lastly, even though the quartile variable

seems to have performed slightly better, we cannot find any stronger signaling value for

abnormal dividend increases compared to regular dividend increases.

The results do not unanimously confirm that the signal theory for dividends is prevalent on

the Swedish stock exchange. Our findings do not support our hypothesis that management

would send stronger signals to the market if they increase the level of dividends more than

they have historically done. Brav et al.’s (2005) findings that the majority of management

think that dividends convey information is thus not something that in practice is applicable to

abnormal dividend increases, at least not regarding future earnings or stock performance. One

possible reason for this is that management cannot regulate the strength of the signal to the

market as you either signal or you do not. Perhaps the larger dividend increases are instead

just due to a rare increased level of cash flow that results in an abnormal dividend increase.

An additional reason to why this result is interesting is that dividend initiations become a

larger fraction of the abnormal dividend increases than for the regular dividend increases.

This is because there are more observations contained in the regular dividend increase

sample, but both the regular dividend increase sample as well as the abnormal dividend

increase samples contain all initiations. This means that dividend initiations become a larger

part of the abnormal dividend increases than it becomes for the regular dividend increases. As

dividend initiations have proven several times to be associated with abnormal stock returns

this should have increased the return for the dummy variables compared to the regular

increases. Even though this is the case, the abnormal dividend increases could not indicate a

better result than that of the regular increases.

Stemming from the findings of La Porta et al (2000) and further developed by Grullon &

Michaely (2012), another potential reason could be that countries with large institutional

24

investors do not take as much consideration to dividends as countries with dispersed

ownership do (La Porta et al, 2000). Companies with a dispersed ownership would in theory

want to overcome the agency problem by sending stronger signals to the market because a

dispersed ownership structure leads to less scrutinization of the company. From the American

stock exchange, a country with a relatively dispersed ownership structure, there has been

evidence that larger dividend increases also lead to larger abnormal stock returns.

Scandinavian countries do, however, have a less dispersed ownership structure (La Porta

2000). Merging this fact with the theories, once again, found in La Porta et al (2000) could be

one of the explanations to why the abnormal dividend increases did not perform better than

the regular dividend increases on the Swedish stock exchange. It is simply more important to

overcome the informational asymmetry on the American stock exchange as investors

themselves conduct less scrutinization. The question whether the portfolios formed based on

the dummy variables would perform better than regular dividend increases in countries with a

more dispersed ownership is something that future research would have to bring clarity to.

One advantage with analyzing the Swedish stock exchange regarding dividends is that it is

possible to look at how an event will affect the following four quarters. In the US for

example, where most prior research has been conducted, dividends are paid each quarter.

This means that the effect of a dividend decision is harder to study beyond a quarter as the

next dividend decision will influence the results. The findings from the quarterly return over

the 10 years are as follows:

Table 3

Quarter Increase Larger Increase

Increase Two–Year Average

Increase Five-Year Average

Increase Quartile Market

Q1(Mar-May) 2,70% 3,16% 3,33% 2,37% 3,09% 1,85%Q2(Jun-Aug) -0,70% -1,24% -1,24% -0,72% -0,69% -0,72%Q3(Sep-Nov) 0,37% 0,14% 0,18% 0,66% 0,57% 0,15%Q4(Dec-Feb) 2,30% 2,22% 2,14% 2,97% 2,23% 1,21%

All returns in the table are the average monthly return during the quarter specified

As the sample is made up of only 33 observations for each quarter it is hard to draw any

statistically significant conclusions from the sample. However, there seems to be a pattern

that quarter 1 and quarter 4 are performing better for the dividend increase portfolios, even if

the quarterly market returns are subtracted. If there had been better results only for the quarter

following the dividend announcement, one could argue that there is a three-month drift in the

25

price reaction after the dividend announcement is made which thereafter fades away.

However, as the last quarter is performing almost as good as the first this reasoning seems

invalid. The reason for the improved performance in the fourth quarter is unclear, but perhaps

investors expect that a large earnings increase accompanied by a large dividend increase will

follow. As such, investors start to incorporate this into the stock price. This is however pure

speculation by the authors of this paper.     

4.2 Dividend Increases and EarningsBelow are the statistics for the earnings sub-study. Table 4 describes how much earnings

increased/decreased scaled by market value and table 5 shows the t-stat and p-value for the

different tests. The label “true” comprises the stocks included in the dummy variable while

the stocks labeled “false” contain the result for the ones which were not included in the

dummy variable.

Table 4

Earnings Tests Year -1 Year 0 Year +1Dummy Increase T-stat 3,2926 -1,8817 -1,3450

P-value 0,0010 0,0600 0,1788D.F. 1888 2110 2062

Dummy Larger Increase T-stat 3,6180 -1,5757 -1,1054P-value 0,0003 0,1156 0,2692D.F. 994 573 1284

Dummy Two-Year Average T-stat 4,1763 -2,1949 -1,9101P-value 0,0000 0,0283 0,0565D.F. 762 1503 852

Dummy Five-Year Average T-stat 2,9195 -1,3097 -0,7294P-value 0,0037 0,1908 0,4663D.F. 493 660 349

Dummy Quartile T-stat 3,4250 -2,5882 -1,5302P-value 0,0007 0,0102 0,1267D.F. 276 249 415

Table 5

Earnings Stats Year-1 Year 0 Year +1Dummy Increase - True 2,89% -0,10% 0,25%Dummy Increase - False -0,33% 1,68% 1,60%Dummy Larger Increase - True 3,89% 0,05% 0,07%Dummy Larger Increase - False 0,12% 1,39% 1,19%Dummy Two-Year Average - True 4,60% -0,59% -0,88%Dummy Two-Year Average - False -0,09% 1,31% 1,26%

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Dummy Five-Year Average - True 4,55% 0,12% 0,30%Dummy Five-Year Average - False 0,83% 1,55% 1,36%Dummy Quartile - True 5,76% -1,70% -0,62%Dummy Quartile - False 0,25% 1,29% 1,30%

The results for the year before the dividend increase are quite apparent, there is a clear pattern

of increased earnings. Of all the variables, the regular dividend increase had the lowest

earnings level change the year before the dividend increase and the quartile dummy had the

highest earnings level change. The difference is quite large as the earnings level increase for

the quartile dummy is more than twice the size of the regular dividend increase. There is in

fact quite a large difference between all the abnormal dividend increases and the regular

dividend increase.

The year of the dividend increase did render in less apparent results than the year prior to the

increase, but a pattern could still be discerned. For all the variables, including the regular

increases, the companies that did not increase their dividend performed better in respect to

earnings on a 10 % level for all variables except for the five-year average dummy variable

which did not show any significant results. The year after the dividend increase has a similar

pattern to the year of the dividend increase. All variables, once again, show that the firms,

which did not increase their dividends, had a better earnings development compared to those

that did increase their dividends regardless of the type of variable. The results were, however,

not as clear and they were only statistically significant for the two-year average variable. The

fact that the results were worse for earnings the year of the increase compared to the year

after is quite interesting. This indicates that management does not signal better earnings

performance even in a shorter perspective.

The results are in line with most prior studies such as Benartzi, Michaely and Thaler (1997).

Our study is thus another one going against the findings of Nissim and Ziv (2001) who found

that increased dividends signal an increase in future earnings. One of the more notable results

from our study is that the year prior to the dividend increase earnings had increased more for

the abnormal dividend increases than for the regular dividend increases, but less the two

years after the increase. The results show that dividend increases that positively diverge from

the history of the company do not correspond to increased future earnings, but instead to

increased prior earnings. Signaling theory states that it should be the other way around. The

authors of this paper hypothesized beforehand that future earnings would be greater the

27

bigger the increase in dividends had been compared to historical dividend increases, but

instead the results show the opposite.

These findings support the theory that firms paying abnormal dividend increases just intend

to pay out their extra cash flow that have resulted from abnormal earnings increases. It also

supports the notion earlier, described by Miller (1987), that dividends are better described as

lagging earnings than as a predictor of future earnings. The results are further on in line with

the theory of Jensen’s free cash flow hypothesis that management wants to reduce wasteful

spending as earnings increase.

If we look at the results through the lens of Akerlof’s theory on adverse selection and assume

that dividend increases would be a way to signal that it is a good company and not a bad one,

perhaps the signal a dividend increase sends should be compared to the dividend increases of

the entire market and not the company. Since a certification should be hard to mimic it is

perhaps irrelevant how much the dividend increase diverges from the firms’ earlier dividend

increases. Instead the more important aspect could be how big the dividend increase and the

signal is compared to the dividend increases of the entire market.

In regards to sticky dividends the results do not contradict the theory. The companies that do

abnormal dividend increases do not show future earnings increases, which they experienced

the year before the dividend increase. The dummy increase, the dummy five-year average and

the dummy larger increase, which had the lowest amount of earnings increase before the

dividend increase, manage to maintain their newly achieved earnings level. However the

dummy two-year average and the dummy quartile increases, which followed the largest

earnings increases, were followed by a two year decline in earnings. Thus sticky dividends

could perhaps be questioned as a firm which experiences earnings decline would in the future

possibly have to cut dividends. However, it could also be that sticky dividends still hold and

that firms’ management making the most abnormal dividend increases suffered from

excessive optimism and/or overconfidence when increasing the dividend levels.

Our research does not contradict that the market would behave in a semi-strongly effective

way regarding stock returns after dividend increases. In our study we do not include the

returns corresponding to the day of the announcement, which means that any abnormal return

thereafter would have been due to the market not being effective. But as our study could not

unanimously find any evidence of this being the case no conclusion can be drawn about the

lack of effectiveness of the market in Sweden.

28

29

5. Concluding DiscussionThis chapter will summarize the results from chapter four in light of the research questions

posed in chapter one. The authors of this paper will discuss the potential reasons for the

results and give recommendations on future research questions which were found during the

study.

5.1. DiscussionThe purpose of this thesis was to examine if dividend increases, which are large in

comparison to a firm’s historical dividend increases, indicate better future performance. In

order to investigate this, our research questions were: “How do dividend increases that are

large, compared to the firms’ historical dividend policy, signal future stock performance on

the Swedish market?” and “How do dividend increases that are large, compared to the firms’

historical dividend policy, signal future earnings on the Swedish market?”.

The most important finding of our research is that dividend increases diverging from the

history of the company do not result in abnormal stock returns compared to regular dividend

increases. This can conclude that the signal to the market does not get stronger even if the

dividend increase is larger than the historical dividend increases have been. The reason for

the abnormal dividend increases should thus be searched for elsewhere.

As earnings increase significantly the year before the abnormal dividend increase it is highly

likely that management simply has made increased profits and deem that the best course of

action is to pay out parts or all of these large profits in dividends. The possibility that the

abnormal dividend increases are made with the intention by management to send a stronger

signal to the market cannot be supported by our findings. The authors of this paper

hypothesized in the problem discussion that a stronger signaling value would be found with

the new definition of an abnormal dividend increase compared to earlier research, which

mainly looked at large increases compared to the market. Perhaps the dividend increases that

positively diverge from the company history did not have the effect that we expected. In light

of the theories of Akerlof (1970), that a signal must be credible and hard to mimic, the

dividend increase might need to be compared to the market instead of the company’s history.

It could be that it is more difficult to diverge from the dividend increases of the market

30

compared to deviating from the firm’s own history and thus the signal when diverging from

the market would be stronger.

Furthermore, there are observed differences between the stocks that increase their dividends

and the ones that do not, just not unanimously statistically significant ones. Thus, the

conclusion to draw from this thesis is not to avoid an investment strategy building on

dividend increases, but neither is it that you surely can beat the market by adopting this way

to invest. However, to build the strategy on large dividend increases diverging from the

history of the company does not seem to be a better strategy than investing in stocks that do

regular dividend increases. The portfolio that performed the best, the quartile portfolio, is

made purely in an academic purpose as it is impossible to replicate it for an investor and is

thus not relevant as an investment strategy. The other variables showed a performance very

closely related to the regular dividend increases.

The signaling values for future earnings were unanimously not supported by our research.

The earnings increases seemed to be smaller the two years following the dividend increase

for the stocks that increased their dividends than for the ones that did not. Therefore, like

much prior research, it can be concluded that dividend increases seem to indicate lagged

earnings instead of future ones. Hence, the possibly better performance of stocks cannot be

explained by increased earnings, but, perhaps, rather in line with the research by Grullon,

Michaely and Swaminathan (2005), i.e. due to a decreased risk premium for the stocks.

A reason that could have affected the significance of the results is that our research was

limited to the years between 2005 and 2015; a longer study period could have given more

conclusive results. This pertains especially to whether the dividend increases per se resulted

in an abnormal return as all variables showed an abnormal return, but not unanimously

statistically significant result. However, it is not likely that it would have affected the main

topic of this paper, as to whether stocks with abnormal dividend increases perform better than

stocks with regular dividend increases. The difference between the regular dividend increases

and the dividend increases diverging from the history of the company are likely not a

question of statistical significance, but rather lack of existence.

5.2 Future researchThe subject of dividends has for a long time been a heavily researched topic. Our research

could not find any conclusive results in support of our research questions, but the results of

31

the sub-studies could still be of value. A more rigorous test of the effect for the different

quarters with a larger sample could be an interesting aspect to test. A more detailed study of

how long the possibly positive effect of dividend increases sustains for stock returns could be

of interest for a potential investor. In a shorter perspective it is very possible that investors

could achieve an abnormal return on the Swedish stock exchange when buying stocks that

have experienced abnormal dividend increases, as our sample indicated that the first quarter

and fourth quarter performed better than the second and third quarters.

Future research could further on through our sample investigate if there is a difference

between small and big companies when it comes to the effect of dividend increases that are

larger than what has been the norm historically. Eddy and Seifert (1988) have shown that

larger dividend increases are associated with larger stock returns for small companies

compared to big companies.  It is possible that this would be the case for dividends diverging

from the history of the company as well and perhaps the effect would be even larger for small

companies increasing their dividend in this way.  The companies that are most likely to pay

out dividends are, in line with the maturity hypothesis, large companies. It is consequently

rarer for small companies to pay out dividends. Perhaps the effect of dividends increases

would therefore become more significant for small stocks.

32

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IV

Data Sources:

DataStream (Thomson Reuters)

Stefano Marmi

http://homepage.sns.it/marmi/Data_Library.html#Sweden

Kenneth French

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html (Fama/French

European 3 Factors)

V

Appendices

Appendix A – Original Company SampleCompany SampleAARHUSKARLSHAMN EAST CAPITAL EXPLORER LINDAB INTERNATIONAL ROTTNEROSABB LTD N (OME) ELANDERS LINDEX DEAD - 21/01/08 SAABACADEMEDIA DEAD - 10/09/10 ELECTRA GRUPPEN LUCARA DIAMOND (OME) SAGAXACANDO ELECTROLUX LUNDIN GOLD (OME) SAK I DEAD - 04/07/11ACAP INVEST DEAD - 27/11/14 ELEKTA LUNDIN MINING SDB SALUS ANSVAR

A-COM DEAD - 01/02/13ELEKTRONIKGRUPPEN BK DEAD - 28/09/11 LUNDIN PETROLEUM SANDVIK

ACSC DEAD - 31/12/07 ELOS MEDTECH LUXONEN SDB SARDUS DEAD - 30/04/07

ADDNODE ELTELMALMBERGS ELEKTRISKA SAS

ADDTECH ENDOMINESMAXIM PHARMS (OME) DEAD - 02/01/06 SCA

AF ENEA MEDASCAN MIN. DEAD - 10/12/07

AFFARSSTRA. DEAD - 01/02/10 ENIRO MEDCAP SCANDI STANDARD

AFRICA OIL (OME) ENQUEST (OME)MEDICOVER HOLDING SDB DEAD - 08/12/06

SCANDIC HOTELS GROUP

AINAX DEAD - TAKEOVER 870812

ENTRACTION HOLDING DEAD - 01/08/11 MEDIVIR SCANIA DEAD - 06/06/14

ALFA LAVAL EOLUS VIND B MEKONOMENSEAMLESS DISTRIBUTION

ALIMAK GROUP EPISURF MEDICAL MERTIVA SEBALLTELE ALLM.SVEN.TELAB ERICSSON

METRO INTL.SDB DEAD - 01/06/12 SEB 'C'

ANOTO GROUP ETRION (OME) MICRO SYSTEMATIONSECO TOOLS DEAD - 05/02/12

ARCAM EWORK GROUP MIDSONA SECTRAARCTIC PAPER (OME) FABEGE MIDWAY HOLDINGS SEMAFO (OME)ARISE FAGERHULT MILLICOM INTL.CELU.SDR SEMCONARTIMPLANT DEAD - 02/08/13 FAST PARTNER MOBERG PHARMA SENEA DEAD - 27/12/06ASSA ABLOY FASTIGHETS BALDER MODERN TIMES GP.MTG SENSYS GATSO

ASTRAZENECA (OME)FAZER KONFEKTYR SERVICE DEAD - 26/01/09

MODUL 1 DATA DEAD - 14/03/11 SIGMA B DEAD - 22/05/13

ATLAS COPCO FEELGOOD SVENSKA MQ HOLDING SINTERCAST

ATRIUM LJUNGBERG FENIX OUTDOOR INTL MSC GROUPSKANDIA FORSAKRINGS DEAD - 06/06/06

AUDIODEV DEAD - 18/06/09FINNVEDEN DEAD - DEAD 21/02/05 MULTIQ INTERNATIONAL

SKANDITEK INDRI.FRV. DEAD - 25/01/10

AVAILO DEAD - 07/07/14 FORMPIPE SOFTWARE MUNKSJO (OME) SKANSKAAVANZA BANK HOLDING G5 ENTERTAINMENT MUNTERS SKFAVEGA GROUP GAMBRO DEAD - 20/07/06 MYCRONIC SKISTAR

AXFOOD GETINGENAN RESOURCES DEAD – T/O SOFTRONIC

AXIS GEVEKO DEAD - 16/09/15NARKES ELECTRISKA DEAD - 03/11/06 SPORTAMORE

B&B TOOLS GHP SPECIALTY CARE NCC SSABBALLINGSLOV INTL. DEAD - 13/12/08

GLOCALNET DEAD - T/O BY 255248 NEDERMAN HOLDING

STOCKWIK FORVALTNING

BE GROUPGORTHON LINE DEAD – MERG NEFAB DEAD - 03/12/07 STORA ENSO 'R'

BEIJER ALMA GUNNEBO NEONET DEAD - 08/06/10STRALFORS DEAD - 19/06/06

BEIJER REF AB HALDEX NET INSIGHT STRAXBERGS TIMBER HAVSFRUN INVESTMENT NETENT STUDSVIKBESQAB PROJEKT & FASTIGH HEBA

NETONNET DEAD - 11/04/11 SWECO

VI

BIACORE INT DEAD - 13/09/06 HEMFOSA FASTIGHETER NEUROVIVE PHARMA. SWEDBANKBILIA HENNES & MAURITZ NEW WAVE GROUP SVEDBERGS I DALSTORPBILLERUD KORSNAS HEXAGON NGEX RESOURCES (OME) SWEDISH MATCHBIOINVENT INTL. HEXATRONIX GROUP NIBE INDUSTRIER SWEDISH ORPHAN BIO.BIOLIN SCI DEAD - 23/02/11 HEXPOL NILORNGR DEAD -01/07/09 SWEDOLBIOPHAUSIA DEAD - 26/07/11 HIFAB GROUP NOBIA SVENSKA HANDBKN.BIOTAGE HIQ INTERNATIONAL NOBINA SWITCHCOREBLACK EARTH FARMING SDB

HL DISPLAY DEAD - 20/09/10 NOKIA (OME) SVOLDER

BLACKPEARL RES. SDR HMS NETWORKSNOKIA SDB DEAD - 04/06/07 SYSTEMAIR

BOLIDEN HOGANAS DEAD - 21/10/13 NOLATOTECHNOLOGY NEXUS DEAD - 28/09/09

BONG HOIST FINANCE NORDAX GROUP TELE2

BORAS WAFVERI HOLMEN NORDEA BANKTELELOGIC DEAD - T/O BY 906187

BOULE DIAGNOSTICS (WI)HOME PROPERTIES DEAD - DEAD 11/05/09 NORDIC MINES TELIA COMPANY

BRAVIDA HOLDING HQ NORDNETTELIGENT DEAD - 04/11/08

BRINOVA FASTIGHETER DEAD - 24/09/12 HQ FONDER DEAD - 26/10/05 NOTE THULE GROUPBRIO DEAD - 13/06/11 HUFVUDSTADEN 'C' NP3 FASTIGHETER TIC. TR. DEAD - 12/04/10BROSTR. DEAD - 02/03/09 HUSQVARNA OASMIA PHARMA. TIETO CORP. (OME)BTS GROUP I A R SYSTEMS GROUP ODD MOLLY INTL. TIVOX DEAD - 26/08/05

BUFABIAR SYSTEMS DEAD - T/0 690556 OEM INTERNATIONAL TOBII AB

BULTEN IBS DEAD - 01/09/11 OPTIMAIL DEAD - 24/01/06 TRACTIONBURE EQUITY ICA GRUPPEN OPUS GROUP TRADEDOUBLERBYGGMAX GROUP IMAGE SYSTEMS ORC GR. DEAD - 12/03/12 TRANSCOM WW

CAPIO INDL.& FINL.SYS. ORESUND INVESTMENTTRANSCOM WWD.SDB.A DEAD - 25/11/14

CAPIO DEAD - 20/11/06 INDUSTRIVARDEN OREXO TRELLEBORGCARDO DEAD - 26/04/11 INDUTRADE ORIFLAME HOLDING TRENTIONCASHGUARD DEAD - MERGED 257541

INTENTIA INTL. DEAD - T/O 14746M ORTIVUS

TRICORONA DEAD - 23/08/10

CATELLA INTRUM JUSTITIA OSCAR PROPERTIES TRIGON AGRI

CATENA INVESTOROXIGENE (OME) DEAD - 01/07/10

TRIO INFO.SYSTEMS DEAD - 14/08/06

CAVOTEC INWIDO PANDOX TROAX GROUP

CELLAVISION INVISIO COMMUNICATIONSPARTNERTECH DEAD - 20/07/15 TURNIT DEAD - T/O

CISION DEAD - 25/08/14 ITAB SHOP CONCEPT PEAB TV4 DEAD - 05/03/07

CLAS OHLSONJEEVES INFO.SYSTEMS DEAD - 22/06/12 PERGO DEAD - 02/04/07 UNIBET GROUP SDB

CLX COMMUNICATIONS JM PLATZER FASTIGHETER UNIFLEXCOLLECTOR KABE HUSVAGNAR POOLIA WALLENSTAM

COM HEM HOLDINGS KAPPAHLPOWERWAVE TECH. (OME) DEAD - 10/06/06 VBG GROUP

CONCENTRIC KARO PHARMA PRECISE BIOMETRICS VICTORIA PARK

CONCORDIA MARITIMEKAROLIN MACHINE TOOL DEAD - 04/02/08 PREVAS VICTORIA PARK B

CONSILIUMKAROLINSKA DEVELOPMENT (WI) PRICER

WIHLBORGS FASTIGHETER

COREM PROPERTY GROUPKAUPTHING BANK DEAD - 10/12/08 PROACT IT GROUP VIKING SUPPLY SHIPS

C-RAD KINNEVIK PROBI WISE GROUPCUSTOS DEAD - 02/01/07 KLIPPAN DEAD - 05/05/06 PROFILGRUPPEN VITEC SOFTW. GR.CYBERCOM GROUP EUROPE DEAD - 04/01/16 KLOVERN

PROTECT DATA DEAD - 13/02/07 VITROLIFE

D CARNEGIE & CO DEAD - 24/12/08 KLOVERN QLIRO GROUP VLT DEAD - 03/11/08DEDICARE KNOW IT RATOS WM-DATA DEAD - T/ODGC ONE KUNGSLEDEN RAYSEARCH LABS. VOLVODIN BOSTAD SVERIGE DEAD - 30/09/09 LAGERCRANTZ GROUP RECIPHARM AB

VOSTOK GAS SDB DEAD - 02/02/09

VII

DOMETIC GROUPLAMMHULTS DESIGN GROUP REJLERS B VOSTOK NEW VENT. SDR

DORO LATOUR INVESTMENT RESCO DEAD - 19/04/06 XANO INDUSTRI

DUNI LB ICON DEAD - 27/07/06 REZIDOR HOTEL GROUPXPONCARD DEAD - 20/06/08

DUROCLEDSTIERNAN DEAD - 17/05/10

RIDDARH. RES. DEAD - 28/11/05

DUSTIN GROUP LIFCO B RETAIL AND BRANDS

VIII

Appendix B – Included Companies Stock Performance TestsCompanies Incl Stock Perf. TestsAVEGA GROUP HEXAGON ORESUND INVEST. AUDIODEV 'B' DEAD - 18/06/09

ELECTROLUX NCCTELIA COMP./TELIAS AVAILO DEAD - 07/07/14

BE GROUP HEXPOLORIFLAME HOLDING AXIS

BEIJER ALMA HIQ INTERNATIONAL PEABBALLINGSLOV INTL. DEAD - 13/12/08

BEIJER ELECTRONICS HMS NETWORKS POOLIA BOSS MEDIA DEAD - 21/04/08BEIJER REF HOLMEN PREVAS BROSTROM DEAD - 02/03/09BILLERUD KORSNÄS IAR SYSTEMS GROUP PROBI CARDO DEAD - 26/04/11CATENA KUNGSLEDEN SENSYS GATSO CISION DEAD - 25/08/14BILIA HUSQVARNA PROACT IT GROUP D CARNEGIE & CO DEAD - 24/12/08DUNI MIDWAY HOLDINGS SWECO DAGON DEAD - 11/06/12EAST CAP. EXPL. MTG SWEDISH MATCH DIN BOSTAD SV DEAD - 30/09/09

BIOGAIA ICA GRUPPEN PROFILGRUPPENFAZER KONFEKT SER. DEAD - 26/01/09

BIOTAGE INDL. & FINL. SYS. RATOS FENIX OUTDOOR 'B' DEAD - 07/07/14BJORN BORG INDUTRADE RAYSEARCH LABS. GAMBRO 'A' DEAD - 20/07/06BOLIDEN INTELLECTA REJLERS B HL DISPLAY 'B' DEAD - 20/09/10BONG INTRUM JUSTITIA REZIDOR HOT. GR. HOGANAS 'B' DEAD - 21/10/13BETSSON HUFVUDSTADEN PRICER HOME PROP. - DEAD 11/05/09BOULE DIAGNOSTICS INVESTOR ROTTNEROS IBS 'B' DEAD - 01/09/11

BTS GROUP ITAB SHOP CONCEPT SAABJEEVES INFO.SYSTEMS DEAD - 22/06/12

BUFAB JM SAGAX KARLSHAMNS DEAD - 14/11/05

BULTEN KABE HUSVAGNAR SANDVIKKAROLIN MACHINE TOOL DEAD - 04/02/08

BURE EQUITY KINNEVIK SECURITAS NEFAB 'B' DEAD - 03/12/07BYGGMAX GROUP KLOVERN SEMAFO NEONET DEAD - 08/06/10

CASTELLUM KNOW IT SEMCONNOBEL BIOCARE (OME) DEAD - 12/05/08

CELLAVISIONLAMMHULTS DESIGN GROUP SINTERCAST OMX DEAD - 05/05/08

COMHEM HOLDINGS LATOUR INVESTMENT SEB ORC GROUP DEAD - 12/03/12CONCENTRICS LINDAB INT. SKANSKA PARTNERTECH DEAD - 20/07/15CONCORDIA MARITIME LOOMIS SKF PROFFICE 'B' DEAD - 22/02/16CONSILIUM LUNDBERGFOR. SOFTRONIC PROTECT DATA DEAD - 13/02/07COREM PROP. GR. MALMBERGS ELEK SSAB Q-MED DEAD - 28/03/11CTT SYSTEMS MEDA STORA ENSO READSOFT 'B' DEAD - 06/10/14DEDICARE MEKONOMEN STUDSVIK SCANIA 'B' DEAD - 06/06/14DIOS FASTIGHETER MICRO SYSTEMATION SCA SECO TOOLS 'B' DEAD - 05/02/12DORO MIDSONA SV. HANDBKN. SIGMA B DEAD - 22/05/13DGC ONE MELKER SCHORLING SVEDB. I DALS SKANDIA FORS DEAD - 06/06/06

DUROC MILLICOM INTL.CELU. SWEDBANKSKANDITEK INDRI.FRV. DEAD - 25/01/10

ELANDERS MSC GROUP SWEDOL TRICORONA DEAD - 23/08/10ELECTRA GRUPPEN MYCRONIC TELE2 TV4 'A' DEAD - 05/03/07AARHUSKARLSHAMN FABEGE NOBIA UNIBET GROUPABB FAGERHULT NOKIA UNIFLEXACANDO FAST PARTNER NOLATO VBG GROUPADDNODE FASTIGHETS BALDER NORDEA VIKING SUPPLY SHIPSAF FEELGOOD SVENSKA NORDNET VITEC SOFTWARE GROUPALFA LAVAL FORMPIPE SOFTWARE NOTE VITROLIFEELOS MEDTECH NEDERMAN HOLDING TIETO CORP. VLT 'B' DEAD - 03/11/08ALLTELE GETINGE NOVESTRA VOLVO

IX

ALLM.SVEN.TELEABASSA ABLOY GUNNEBO NOVOTEK WALLENSTAMATRIUM LJUNGBERG HALDEX ODD MOLLY INTL. WIHLBORGS FASTIGHETER

AUTOLIV HEBAOEM INTERNATIONAL WISE GROUP

AVANZA BANK HOLD. HENNES & MAURITZ OPUS GROUP XANO INDUSTRIENIRO NETENT TRACTION XPONCARD DEAD - 20/06/08

ERICSSON NEW WAVE GROUP TRADEDOUBLER

EWORK SCANDINAVIA NIBE INDUSTRIER TRELLEBORG

X

Appendix C – Included Companies Earnings TestsCompanies Incl. Earnings TestsAARHUSKARLSHAMN DEDICARE KINNEVIK PROFILGRUPPENABB DGC ONE KLOVERN QLIRO GROUPACANDO DIOS FASTIGHETER KNOW IT RATOSACTIVE BIOTECH DOMETIC GROUP KUNGSLEDEN RAYSEARCH LABS.ADDLIFE DORO LAGERCRANTZ GROUP RECIPHARM ABADDNODE DUNI LAMMHULTS DES. GR. REJLERS BADDTECH DUROC LATOUR INVESTMENT RESURS HOLDINGAF DUSTIN GROUP LIFCO B REZIDOR HOTEL GROUPAFRICA OIL EAST CAP. EXPL. LINDAB INT. RETAIL AND BRANDSALFA LAVAL ELANDERS LOOMIS ROTTNEROSALIMAK GROUP ELECTRA GRUPPEN LUCARA DIAMOND SAABALLTELE ALLM.SVEN.TELEAB ELECTROLUX LUNDBERGFORETAGEN SAGAXANOTO GROUP ELEKTA LUNDIN GOLD INC SANDVIKARCAM B ELOS MEDTECH LUNDIN MINING SDB SASARCTIC PAPER ELTEL AB LUNDIN PETROLEUM SCAARISE AB ENDOMINES MALMBERGS ELEKT. SCANDIC HOTELSASSA ABLOY ENEA MEDA SCANDIC STANDARDASTRA ZENECA ENIRO MEDCAP SEAMLESS DISTR.ATLAS COPCO ENQUEST MEDIVIR B SEBATRIUM LJUNGBERG EOLUS VIND MEKONOMEN SECTRAATTENDO EPISURF MEDICAL MELKER SCHORLING SECURITASAUTOLIV ERICSSON MICRO SYSTEMATION SEMAFOAVANZA BANK HOLD. ETRION CORP MIDSONA SEMCONAVEGA GROUP EWORK SCAND. MIDWAY HOLDINGS SENSYS GATSOAXFOOD FABEGE MILLICOM INTL.CELU. SINTERCASTB&B TOOLS FAGERHULT MOBERG PHARMA SKANSKABACTIGUARD HOLD FAST PARTNER MODERN TIMES GP.MTG SKFBE GROUP FASTIGHETS BALDER MQ HOLDING SKISTARBEIJER ALMA FEELGOOD SVENSKA MSC GROUP SOFTRONICBEIJER ELECTRONICS FENIX OUTDOOR MULTIQ INT. SPORTAMOREBEIJER REF FINGERPRINT CARDS MUNKESJO(OME) SSABBERGS TIMBER FORMPIPE SOFT. MYCRONIC STOCKWIK FORVALT.BESQAB P G5 ENTERTAINMENT NCC STORA ENSOBETSSON GARO NEDERMAN HOLDING STUDSVIKBIOGAIA GRANGES NEUROVIVE PHARMA. SVEDBERGS I DALST.BLACK EARTH FARMING HEBA NOBIA SVENSKA HANDBKN.BILIA GETINGE NET INSIGHT B SWECOBILLERUD KORSNÄS GLOBAL HEALTH P. NETENT SWEDBANKBIOINVENT INTL. GUNNEBO NEW WAVE GROUP SWEDISH MATCHBIOTAGE HALDEX NGEX RESOURCES SWEDISH ORPHAN BIOBJORN BORG HANSA MEDICAL NIBE INDUSTRIER SWEDOLBLACKPEARL RES. HEMFOSA NOBINA SYSTEMAIRBOLIDEN HENNES & MAURITZ NOKIA TELE2BONG HEXAGON NOLATO TELIA COMP/TELIASBOULE DIAGNOSTICS HEXATRONIX GROUP NORDAX GROUP TETHYS OILBRAVIDA HOLDING HEXPOL NORDEA THULE GROUPBTS GROUP HIQ INTERNATIONAL NORDIC MINES TIETO CORPORATIONBUFAB HMS NETWORKS NORDNET TOBII ABBULTEN HOIST FINANCE NOTE TRACTIONBURE EQUITY HOLMEN NOVESTRA TRADEDOUBLERBYGGMAX GROUP HUFVUDSTADEN NOVOTEK TRANSCOM WORCAMURUS HUMANA NP3 FASTIGHETER TRELLEBORGCAPIO HUSQVARNA OASMIA PHARMA TRENTIONCASTELLUM IAR SYSTEMS GROUP ODD MOLLY INTL. TRIGON AGRICATENA ICA GRUPPEN OEM INTERNATIONAL TROAX GROUP

XI

CAVOTEC IMAGE SYSTEMS OPUS GROUP UNIBET GROUPCELLAVISION INDL. & FINL. SYS. ORESUND INVEST UNIFLEXCLOETTA INDUTRADE ORIFLAME HOLDING VBG GROUPCLX COMMUNICATIONS INTELLECTA ORTIVUS A VENUE RETAIL GROUPCOLLECTOR INTRUM JUSTITIA OSCAR PROPERTIES VICTORIA PARKCONCENTRICS INWIDO PEAB VIKING SUPPLY SHIPSCONSILIUM ITAB SHOP CONCEPT POOLIA VITEC SOFTWARE GR.COOR SERVICE MAN JM PRECISE BIOMETRICS VITROLIFECOREM PROPERTY GR. KABE HUSVAGNAR PREVAS VOLVOC-RAD B KAPPAHL PRICER VOSTOK NEW V. SDRCLAS OHLSON INDUSTRIVARDEN A OREXO WALLENSTAMCOMHEM HOLDINGS INVESTOR PANDOX WIHLBORGS FAST.CONCORDIA MARITIME INVISIO COMM. PLATZER FAST. WISE GROUPCTT SYSTEMS KARO PHARMA AB PROACT IT GROUP XANO INDUSTRI

D CARNEGIE & CO B KAROLINSKA DEV. PROBI

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Example Firm: HufvudstadenDate DPS Special

DividendsStock price Return Increase Larger

IncreaseLarger than 2-year average

Larger than 5-year average

Quartile

2004M12 1,2 1,22005M0 1,3 1,3 49 1 0 0 0 02005M1 1,3 1,3 52,25 6,42 1 0 0 0 02005M2 1,3 1,3 53 3,71 1 0 0 0 02005M12 1,3 1,3 57 1,45 1 0 0 0 02006M0 1,45 1,45 56 1 1 1 0 12006M1 1,45 1,45 62 10,34 1 1 1 0 12006M2 1,45 1,45 63 3,59 1 1 1 0 12006M12 1,45 1,45 86,75 7,38 1 1 1 0 12007M0 1,6 1,6 83,5 1 0 1 0 02007M1 1,6 1,6 89,25 6,46 1 0 1 0 02007M2 1,6 1,6 82,5 -6,21 1 0 1 0 02007M12 1,6 1,6 63,75 13,20 1 0 1 0 02008M0 1,75 1,75 64,25 1 0 0 0 02008M1 1,75 1,75 64,25 -0,17 1 0 0 0 02008M2 1,75 1,75 60,5 -3,29 1 0 0 0 02008M12 1,75 1,75 51 4,07 1 0 0 0 02009M0 1,9 1,9 51,5 1 0 0 0 02009M1 1,9 1,9 45,2 -12,25 1 0 0 0 02009M2 1,9 1,9 46,5 7,07 1 0 0 0 02009M12 1,9 1,9 53,75 -1,83 1 0 0 0 02010M0 2,1 2,1 54,75 1 1 1 1 12010M1 2,1 2,1 59 7,75 1 1 1 1 12010M2 2,1 2,1 60 5,24 1 1 1 1 12010M12 2,1 2,1 72,25 -5,38 1 1 1 1 12011M0 2,3 2,3 72,35 1 0 0 0 02011M1 2,3 2,3 76,15 5,24 1 0 0 0 02011M2 2,3 2,3 75,8 2,56 1 0 0 0 02011M12 2,3 2,3 74,8 6,48 1 0 0 0 02012M0 2,45 2,45 74,6 1 0 0 0 02012M1 2,45 2,45 75,05 0,60 1 0 0 0 02012M2 2,45 2,45 70,35 -3,00 1 0 0 0 02012M12 2,45 2,45 85,45 4,53 1 0 0 0 02013M0 2,6 2,6 84,9 1 0 0 0 02013M1 2,6 2,6 87,8 3,42 1 0 0 0 02013M2 2,6 2,6 83,2 -2,28 1 0 0 0 02013M12 2,6 2,6 90,65 3,72 1 0 0 0 02014M0 2,75 2,75 91,35 1 0 0 0 02014M1 2,75 2,75 92,85 1,64 1 0 0 0 02014M2 2,75 2,75 90,85 0,81 1 0 0 0 02014M12 2,75 2,75 113 8,13 1 0 0 0 02015M0 2,9 2,9 116,4 1 0 0 0 02015M1 2,9 2,9 112,5 -3,35 1 0 0 0 02015M2 2,9 2,9 121 10,13 1 0 0 0 02015M12 2,9 2,9 114,3 -0,54 1 0 0 0 0

Appendix D – Example Firm Hufvudstaden

XIII

Appendix E: Regressions with European FactorsDependent Variable: DUMMY_INCREASEMethod: Least SquaresDate: 05/29/16 Time: 16:18Sample (adjusted): 1 132Included observations: 132 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.862336 0.309054 2.790242 0.0061HML -0.107974 0.168055 -0.642491 0.5217

MKT_RF 0.456347 0.065516 6.965387 0.0000SMB 1.084044 0.160542 6.752409 0.0000

R-squared 0.456759    Mean dependent var 1.168256Adjusted R-squared 0.444027    S.D. dependent var 4.704102S.E. of regression 3.507548    Akaike info criterion 5.377546Sum squared resid 1574.770    Schwarz criterion 5.464903Log likelihood -350.9180    Hannan-Quinn criter. 5.413044F-statistic 35.87433    Durbin-Watson stat 2.038624Prob(F-statistic) 0.000000

Dependent Variable: DUMMY_HIGHER_INCREASEMethod: Least SquaresDate: 05/23/16 Time: 16:59Sample (adjusted): 1 120Included observations: 120 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.817357 0.335538 2.435962 0.0164HML -0.104176 0.175745 -0.592765 0.5545

MKT_RF 0.466476 0.068507 6.809134 0.0000SMB 1.113021 0.169483 6.567166 0.0000

R-squared 0.465793    Mean dependent var 1.072069Adjusted R-squared 0.451978    S.D. dependent var 4.888804S.E. of regression 3.619109    Akaike info criterion 5.443098Sum squared resid 1519.363    Schwarz criterion 5.536015Log likelihood -322.5859    Hannan-Quinn criter. 5.480832F-statistic 33.71483    Durbin-Watson stat 2.081595Prob(F-statistic) 0.000000

Dependent Variable: DUMMY_2_YEAR_AVERAGEMethod: Least SquaresDate: 05/23/16 Time: 17:01Sample (adjusted): 1 120Included observations: 120 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.878068 0.323758 2.712114 0.0077HML 0.001297 0.143706 0.009025 0.9928

MKT_RF 0.442068 0.055086 8.024995 0.0000SMB 1.155030 0.165880 6.963025 0.0000

XIV

R-squared 0.482090    Mean dependent var 1.102785Adjusted R-squared 0.468695    S.D. dependent var 4.852730S.E. of regression 3.537186    Akaike info criterion 5.397305Sum squared resid 1451.356    Schwarz criterion 5.490222Log likelihood -319.8383    Hannan-Quinn criter. 5.435039F-statistic 35.99232    Durbin-Watson stat 2.026749Prob(F-statistic) 0.000000

Dependent Variable: DUMMY_5_YEAR_AVERAGEMethod: Least SquaresDate: 05/23/16 Time: 17:03Sample (adjusted): 1 72Included observations: 72 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.901665 0.434754 2.073966 0.0419HML -0.116479 0.216954 -0.536881 0.5931

MKT_RF 0.413149 0.099249 4.162744 0.0001SMB 0.856814 0.240702 3.559642 0.0007

R-squared 0.312626    Mean dependent var 1.321104Adjusted R-squared 0.282301    S.D. dependent var 4.126361S.E. of regression 3.495735    Akaike info criterion 5.394917Sum squared resid 830.9709    Schwarz criterion 5.521398Log likelihood -190.2170    Hannan-Quinn criter. 5.445269F-statistic 10.30909    Durbin-Watson stat 2.158797Prob(F-statistic) 0.000011

Dependent Variable: DUMMY_QUARTILEMethod: Least SquaresDate: 05/23/16 Time: 17:05Sample (adjusted): 1 132Included observations: 132 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.991932 0.320234 3.097518 0.0024HML -0.094738 0.174135 -0.544047 0.5874

MKT_RF 0.454819 0.067887 6.699701 0.0000SMB 1.150523 0.166349 6.916304 0.0000

R-squared 0.452251    Mean dependent var 1.302206Adjusted R-squared 0.439414    S.D. dependent var 4.854178S.E. of regression 3.634435    Akaike info criterion 5.448619Sum squared resid 1690.767    Schwarz criterion 5.535977Log likelihood -355.6088    Hannan-Quinn criter. 5.484117F-statistic 35.22796    Durbin-Watson stat 2.146940Prob(F-statistic) 0.000000

XV

Appendix F: Regressions with Swedish FactorsDependent Variable: INCREASEMethod: Least SquaresDate: 05/23/16 Time: 17:33Sample (adjusted): 1 96Included observations: 96 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.488478 0.384083 1.271801 0.2066HML -0.084472 0.161175 -0.524101 0.6015

MKT_RF 0.656546 0.068632 9.566164 0.0000SMB 0.306613 0.099325 3.086963 0.0027

R-squared 0.499217    Mean dependent var 0.896831Adjusted R-squared 0.482887    S.D. dependent var 5.104560S.E. of regression 3.670718    Akaike info criterion 5.479425Sum squared resid 1239.624    Schwarz criterion 5.586273Log likelihood -259.0124    Hannan-Quinn criter. 5.522615F-statistic 30.57077    Durbin-Watson stat 2.369635Prob(F-statistic) 0.000000

Dependent Variable: HIGHER_INCREASEMethod: Least SquaresDate: 05/23/16 Time: 17:35Sample (adjusted): 1 84Included observations: 84 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.500444 0.429318 1.165673 0.2472HML -0.019257 0.186156 -0.103448 0.9179

MKT_RF 0.669498 0.073902 9.059257 0.0000SMB 0.335248 0.108309 3.095278 0.0027

R-squared 0.508986    Mean dependent var 0.658413Adjusted R-squared 0.490573    S.D. dependent var 5.340926S.E. of regression 3.812041    Akaike info criterion 5.560654Sum squared resid 1162.532    Schwarz criterion 5.676407Log likelihood -229.5475    Hannan-Quinn criter. 5.607186F-statistic 27.64273    Durbin-Watson stat 2.448979Prob(F-statistic) 0.000000

Dependent Variable: _2_YEAR_AVERAGEMethod: Least SquaresDate: 05/23/16 Time: 17:36Sample (adjusted): 1 84Included observations: 84 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.531731 0.428738 1.240223 0.2185HML -0.029464 0.185905 -0.158491 0.8745

MKT_RF 0.657724 0.073802 8.911951 0.0000SMB 0.323814 0.108163 2.993754 0.0037

XVI

R-squared 0.500579    Mean dependent var 0.694079Adjusted R-squared 0.481851    S.D. dependent var 5.288637S.E. of regression 3.806898    Akaike info criterion 5.557954Sum squared resid 1159.398    Schwarz criterion 5.673707Log likelihood -229.4341    Hannan-Quinn criter. 5.604486F-statistic 26.72850    Durbin-Watson stat 2.407315Prob(F-statistic) 0.000000

Dependent Variable: _5_YEAR_AVERAGEMethod: Least SquaresDate: 05/23/16 Time: 17:38Sample (adjusted): 1 36Included observations: 36 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.324218 0.719529 0.450597 0.6553HML -0.300703 0.352925 -0.852032 0.4005

MKT_RF 0.623789 0.148204 4.209002 0.0002SMB 0.189620 0.199773 0.949179 0.3496

R-squared 0.356768    Mean dependent var 0.815206Adjusted R-squared 0.296465    S.D. dependent var 4.779533S.E. of regression 4.008928    Akaike info criterion 5.719364Sum squared resid 514.2882    Schwarz criterion 5.895311Log likelihood -98.94856    Hannan-Quinn criter. 5.780774F-statistic 5.916258    Durbin-Watson stat 2.144475Prob(F-statistic) 0.002489

Dependent Variable: QUARTILEMethod: Least SquaresDate: 05/23/16 Time: 17:40Sample (adjusted): 1 96Included observations: 96 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.628094 0.391449 1.604538 0.1120HML -0.039994 0.164266 -0.243474 0.8082

MKT_RF 0.693649 0.069948 9.916598 0.0000SMB 0.335106 0.101230 3.310346 0.0013

R-squared 0.518126    Mean dependent var 1.040775Adjusted R-squared 0.502413    S.D. dependent var 5.303541S.E. of regression 3.741111    Akaike info criterion 5.517416Sum squared resid 1287.624    Schwarz criterion 5.624264Log likelihood -260.8360    Hannan-Quinn criter. 5.560606F-statistic 32.97374    Durbin-Watson stat 2.440749Prob(F-statistic) 0.000000

XVII

Appendix G: Heteroscedasticity Tests European FactorsEuropean Factors IncreaseHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 3.118524     Prob. F(3,128) 0.0284Obs*R-squared 8.990792    Prob. Chi-Square(3) 0.0294Scaled explained SS 8.179449    Prob. Chi-Square(3) 0.0424

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/29/16 Time: 16:18Sample: 1 132Included observations: 132

Variable Coefficient Std. Error t-Statistic Prob.

C 12.00468 1.433442 8.374723 0.0000HML -1.031246 0.779467 -1.323015 0.1882

MKT_RF -0.095132 0.303876 -0.313061 0.7547SMB -1.898566 0.744618 -2.549718 0.0120

R-squared 0.068112    Mean dependent var 11.93008Adjusted R-squared 0.046271    S.D. dependent var 16.65852S.E. of regression 16.26856    Akaike info criterion 8.446180Sum squared resid 33877.24    Schwarz criterion 8.533538Log likelihood -553.4479    Hannan-Quinn criter. 8.481678F-statistic 3.118524    Durbin-Watson stat 2.103554Prob(F-statistic) 0.028447

European Factors Larger IncreaseHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.808681    Prob. F(3,116) 0.1494Obs*R-squared 5.362320    Prob. Chi-Square(3) 0.1471Scaled explained SS 4.607667    Prob. Chi-Square(3) 0.2029

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 14:02Sample: 1 120Included observations: 120

Variable Coefficient Std. Error t-Statistic Prob.

C 12.50345 1.582549 7.900831 0.0000HML -0.944893 0.828894 -1.139944 0.2567

MKT_RF 0.162905 0.323112 0.504176 0.6151SMB -1.678713 0.799357 -2.100079 0.0379

R-squared 0.044686    Mean dependent var 12.66135Adjusted R-squared 0.019980    S.D. dependent var 17.24248S.E. of regression 17.06936    Akaike info criterion 8.545213Sum squared resid 33798.12    Schwarz criterion 8.638129Log likelihood -508.7128    Hannan-Quinn criter. 8.582946

XVIII

F-statistic 1.808681    Durbin-Watson stat 2.223720Prob(F-statistic) 0.149447

European Factors Two Year AverageHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.310916    Prob. F(3,116) 0.2743Obs*R-squared 3.934954    Prob. Chi-Square(3) 0.2686Scaled explained SS 3.141548    Prob. Chi-Square(3) 0.3703

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 14:02Sample: 1 120Included observations: 120

Variable Coefficient Std. Error t-Statistic Prob.

C 12.17574 1.447495 8.411592 0.0000HML 0.251634 0.642496 0.391650 0.6960

MKT_RF 0.105624 0.246287 0.428867 0.6688SMB -1.401015 0.741638 -1.889082 0.0614

R-squared 0.032791    Mean dependent var 12.09463Adjusted R-squared 0.007777    S.D. dependent var 15.87633S.E. of regression 15.81447    Akaike info criterion 8.392493Sum squared resid 29011.30    Schwarz criterion 8.485409Log likelihood -499.5496    Hannan-Quinn criter. 8.430227F-statistic 1.310916    Durbin-Watson stat 2.351780Prob(F-statistic) 0.274252

European Factors Five Year AverageHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 2.477718    Prob. F(3,68) 0.0686Obs*R-squared 7.094853    Prob. Chi-Square(3) 0.0689Scaled explained SS 6.065679    Prob. Chi-Square(3) 0.1085

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 14:02Sample: 1 72Included observations: 72

Variable Coefficient Std. Error t-Statistic Prob.

C 10.94453 1.941557 5.636987 0.0000HML -1.981744 0.968890 -2.045375 0.0447

MKT_RF 0.056062 0.443234 0.126485 0.8997SMB -1.310304 1.074946 -1.218949 0.2271

R-squared 0.098540    Mean dependent var 11.54126Adjusted R-squared 0.058769    S.D. dependent var 16.09152S.E. of regression 15.61151    Akaike info criterion 8.387847Sum squared resid 16572.92    Schwarz criterion 8.514329

XIX

Log likelihood -297.9625    Hannan-Quinn criter. 8.438200F-statistic 2.477718    Durbin-Watson stat 2.431918Prob(F-statistic) 0.068614

European Factors QuartileHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.067238    Prob. F(3,128) 0.3655Obs*R-squared 3.221196    Prob. Chi-Square(3) 0.3588Scaled explained SS 3.376301    Prob. Chi-Square(3) 0.3372

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 14:02Sample: 1 132Included observations: 132

Variable Coefficient Std. Error t-Statistic Prob.

C 12.85183 1.690243 7.603544 0.0000HML -0.658780 0.919108 -0.716760 0.4748

MKT_RF 0.011414 0.358315 0.031854 0.9746SMB -1.429967 0.878016 -1.628634 0.1058

R-squared 0.024403    Mean dependent var 12.80884Adjusted R-squared 0.001537    S.D. dependent var 19.19783S.E. of regression 19.18307    Akaike info criterion 8.775767Sum squared resid 47102.73    Schwarz criterion 8.863125Log likelihood -575.2007    Hannan-Quinn criter. 8.811266F-statistic 1.067238    Durbin-Watson stat 2.193183Prob(F-statistic) 0.365479

XX

Appendix H: Heteroscedasticity Tests Swedish FactorsSwedish Factors IncreaseHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.490355    Prob. F(3,92) 0.2224Obs*R-squared 4.449233    Prob. Chi-Square(3) 0.2169Scaled explained SS 4.577669    Prob. Chi-Square(3) 0.2055

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 13:59Sample: 1 96Included observations: 96

Variable Coefficient Std. Error t-Statistic Prob.

C 13.42233 2.017475 6.653033 0.0000HML 1.011493 0.846606 1.194762 0.2353

MKT_RF -0.601070 0.360504 -1.667305 0.0989SMB -0.512165 0.521726 -0.981675 0.3288

R-squared 0.046346    Mean dependent var 12.91275Adjusted R-squared 0.015249    S.D. dependent var 19.42990S.E. of regression 19.28119    Akaike info criterion 8.796910Sum squared resid 34202.30    Schwarz criterion 8.903758Log likelihood -418.2517    Hannan-Quinn criter. 8.840100F-statistic 1.490355    Durbin-Watson stat 2.210169Prob(F-statistic) 0.222375

Swedish Factors Larger IncreaseHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.479400    Prob. F(3,80) 0.6975Obs*R-squared 1.483442    Prob. Chi-Square(3) 0.6861Scaled explained SS 1.551536    Prob. Chi-Square(3) 0.6704

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 13:57Sample: 1 84Included observations: 84

Variable Coefficient Std. Error t-Statistic Prob.

C 14.16393 2.403933 5.891980 0.0000HML 0.821082 1.042365 0.787710 0.4332

MKT_RF -0.395789 0.413810 -0.956452 0.3417SMB -0.208181 0.606471 -0.343266 0.7323

R-squared 0.017660    Mean dependent var 13.83967Adjusted R-squared -0.019178    S.D. dependent var 21.14347S.E. of regression 21.34525    Akaike info criterion 9.005983Sum squared resid 36449.56    Schwarz criterion 9.121736Log likelihood -374.2513    Hannan-Quinn criter. 9.052515

XXI

F-statistic 0.479400    Durbin-Watson stat 2.178114Prob(F-statistic) 0.697520

Swedish Factors Two Year AverageHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.490535    Prob. F(3,80) 0.6898Obs*R-squared 1.517276    Prob. Chi-Square(3) 0.6783Scaled explained SS 1.623839    Prob. Chi-Square(3) 0.6540

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 13:50Sample: 1 84Included observations: 84

Variable Coefficient Std. Error t-Statistic Prob.

C 14.13513 2.424679 5.829691 0.0000HML 0.665740 1.051361 0.633217 0.5284

MKT_RF -0.456747 0.417381 -1.094318 0.2771SMB -0.183602 0.611705 -0.300149 0.7648

R-squared 0.018063    Mean dependent var 13.80235Adjusted R-squared -0.018760    S.D. dependent var 21.33031S.E. of regression 21.52946    Akaike info criterion 9.023169Sum squared resid 37081.40    Schwarz criterion 9.138922Log likelihood -374.9731    Hannan-Quinn criter. 9.069701F-statistic 0.490535    Durbin-Watson stat 2.181323Prob(F-statistic) 0.689841

Swedish Factors Five Year AverageHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.511168    Prob. F(3,32) 0.6775Obs*R-squared 1.646298    Prob. Chi-Square(3) 0.6489Scaled explained SS 1.614149    Prob. Chi-Square(3) 0.6562

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 13:49Sample: 1 36Included observations: 36

Variable Coefficient Std. Error t-Statistic Prob.

C 15.48303 4.185244 3.699433 0.0008HML 2.483979 2.052838 1.210022 0.2351

MKT_RF -0.440254 0.862048 -0.510708 0.6131SMB -0.204364 1.162007 -0.175872 0.8615

R-squared 0.045730    Mean dependent var 14.28578Adjusted R-squared -0.043732    S.D. dependent var 22.82477S.E. of regression 23.31852    Akaike info criterion 9.240812Sum squared resid 17400.11    Schwarz criterion 9.416759

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Log likelihood -162.3346    Hannan-Quinn criter. 9.302222F-statistic 0.511168    Durbin-Watson stat 2.463849Prob(F-statistic) 0.677454

Swedish Factors QuartileHeteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.450471    Prob. F(3,92) 0.2333Obs*R-squared 4.335543    Prob. Chi-Square(3) 0.2274Scaled explained SS 6.375849    Prob. Chi-Square(3) 0.0947

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 05/24/16 Time: 13:50Sample: 1 96Included observations: 96

Variable Coefficient Std. Error t-Statistic Prob.

C 14.16632 2.506943 5.650834 0.0000HML 1.069739 1.052004 1.016858 0.3119

MKT_RF -0.842850 0.447967 -1.881498 0.0631SMB -0.451078 0.648304 -0.695782 0.4883

R-squared 0.045162    Mean dependent var 13.41275Adjusted R-squared 0.014026    S.D. dependent var 24.12889S.E. of regression 23.95908    Akaike info criterion 9.231345Sum squared resid 52811.43    Schwarz criterion 9.338193Log likelihood -439.1046    Hannan-Quinn criter. 9.274535F-statistic 1.450471    Durbin-Watson stat 2.126702Prob(F-statistic) 0.233303

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Appendix I: Robust Standard ErrorsEuropean Factors IncreaseDependent Variable: DUMMY_INCREASEMethod: Least SquaresDate: 05/24/16 Time: 14:04Sample (adjusted): 1 132Included observations: 132 after adjustmentsWhite heteroskedasticity-consistent standard errors & covariance

Variable Coefficient Std. Error t-Statistic Prob.

C 0.862336 0.311315 2.769977 0.0064HML -0.107974 0.168237 -0.641799 0.5222

MKT_RF 0.456347 0.070072 6.512577 0.0000SMB 1.084044 0.152628 7.102527 0.0000

R-squared 0.456759    Mean dependent var 1.168256Adjusted R-squared 0.444027    S.D. dependent var 4.704102S.E. of regression 3.507548    Akaike info criterion 5.377546Sum squared resid 1574.770    Schwarz criterion 5.464903Log likelihood -350.9180    Hannan-Quinn criter. 5.413044F-statistic 35.87433    Durbin-Watson stat 2.038624Prob(F-statistic) 0.000000    Wald F-statistic 31.28716Prob(Wald F-statistic) 0.000000

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