dividend changes and stock price informativeness: evidence
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DIVIDEND CHANGES AND STOCK PRICE
INFORMATIVENESS: EVIDENCE FROM THAILAND
BY
MISS JINTANA KONGVIJITWAT
AN INDEPENDENT STUDY SUBMITTED IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF MASTER OF SCIENCE
PROGRAM IN FINANCE (INTERNATIONAL PROGRAM)
FACULTY OF COMMERCE AND ACCOUNTANCY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2017
COPYRIGHT OF THAMMASAT UNIVERSITY
Ref. code: 25605902042141WZP
DIVIDEND CHANGES AND STOCK PRICE
INFORMATIVENESS: EVIDENCE FROM THAILAND
BY
MISS JINTANA KONGVIJITWAT
AN INDEPENDENT STUDY SUBMITTED IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF MASTER OF SCIENCE
PROGRAM IN FINANCE (INTERNATIONAL PROGRAM)
FACULTY OF COMMERCE AND ACCOUNTANCY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2017
COPYRIGHT OF THAMMASAT UNIVERSITY
Ref. code: 25605902042141WZP
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Independent study title DIVIDEND CHANGES AND STOCK PRICE
INFORMATIVENESS: EVIDENCE FROM
THAILAND
Author Miss Jintana Kongvijitwat
Degree Master of Science (Finance)
Major field/Faculty/University Master of Science Program in Finance
(International Program)
Faculty of Commerce and Accountancy
Thammasat University
Independent study advisor Associate Professor Seksak Jumreornvong, Ph.D.
Academic year 2017
ABSTRACT
This research aims to investigate private information in stock return and its
impact on dividend policy. The scope of this study is companies listed in SET and
MAI during 2007-2017. We use firm-specific variation and illiquidity ratio as the
private information measurement. The finding shows that companies listed in MAI
tend to convey more private information in stock return than companies listed in SET.
In case of companies listed in Dividend Universe, this research finds that companies
listed in Dividend Universe have less private information in stock return. Finally, the
result also finds that private information in stock return can affect the manager
decision on paying a dividend.
Keywords: Dividend Policy, Private Information, Logistic Regression
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ACKNOWLEDGEMENTS
This research cannot be complete without many helps and suggestions from
my advisor, MIF professors, MIF staff, my colleague and my close friends. Firstly, I
would like to appreciate my advisor, Associate Professor Seksak Jumreornvong, Ph.D.
who gives me the precious recommendation and always support this research from
start to finish. I am so thankful to all professors for providing the worth knowledge
and experience in finance. I am very cheerful to MIF Staff in providing all
accommodation and material for my research. Last but not least I am indebted to my
colleague and my close friends for their help and encouragement. Finally, I am very
grateful to my family who always supports and helps everything they can do.
Miss Jintana Kongvijitwat
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TABLE OF CONTENTS
Page
ABSTRACT (1)
ACKNOWLEDGEMENTS (2)
LIST OF TABLES (5)
LIST OF FIGURES (6)
LIST OF ABBREVIATIONS (7)
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 REVIEW OF LITERATURE 4
2.1 Dividend and Stock Return 4
2.2 Dividend, Stock Return and Private Information 5
2.3 Dividend and Crisis 6
CHAPTER 3 THEORETICAL FRAMEWORK 7
3.1 Dividend Policy 7
3.2 Dividend Signaling 7
3.3 Asymmetric Information 7
3.4 Catering Theory of Dividend 8
CHAPTER 4 DATA 9
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CHAPTER 5 RESEARCH METHODOLOGY 11
5.1 Dividend 11
5.2 Measurement of Private Information 11
5.3 Average Abnormal Return 12
5.4 Control Variables 12
5.5 Dummy Variable 13
5.6 Group of Sample 13
5.7 Baseline Specification 13
CHAPTER 6 EMPIRICAL RESULT 15
6.1 Descriptive Statistics 15
6.2 Firm-specific stock return variation and dividend 17
6.3 Illiquidity ratio and dividend 24
6.4 Private information, Dividend Universe and dividend 31
CHAPTER 7 CONCLUSION 42
REFERENCES 44
BIOGRAPHY 47
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LIST OF TABLES
Tables Page
5.1 Descriptive statistics from overall market and Dividend Universe 16
5.2 Result of logistic regression on dividend from firm-specific variation 18
5.3 Result of logistic regression on dividend from firm-specific variation 19
by company’s size
5.4 Result of logistic regression on dividend from firm-specific variation 22
during financial crisis.
5.5 Result of logistic regression on dividend from firm-specific variation 23
during financial crisis by company’s size
5.6 Result of logistic regression on dividend from illiquidity ratio 25
5.7 Result of logistic regression on dividend from illiquidity ratio 26
by company’s size
5.8 Result of logistic regression on dividend from illiquidity ratio 27
during the financial crisis
5.9 Result of logistic regression on dividend from illiquidity ratio 30
during the financial crisis by company’s size
5.10 Result of logistic regression on dividend universe 32
from firm-specific variation
5.11 Result of logistic regression on dividend universe from illiquidity ratio 35
5.12 Result of logistic regression on dividend universe from 38
firm-specific variation during the crisis
5.13 Result of logistic regression on dividend universe from illiquidity ratio 39
during the crisis
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LIST OF FIGURE
Figure Page
1.1 Dividend Trend during 2007-2017 2
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LIST OF ABBREVIATIONS
Symbols/Abbreviations Terms
Ab_illiq_chg Abnormal return and Illiquidity ratio
Ab_illiq_ln Abnormal return and Illiquidity ratio
Ab_return_chg Abnormal Return from change in price
Ab_return_ln Abnormal Return from log in price
Ab_roll_chg Abnormal return and Firm-specific
variation
Ab_roll_ln Abnormal return and Firm-specific
variation
D0709 Dummy variable for during the crisis
divpmt Dividend Payment
Illiq_chg Illiquidity ratio which return calculated
by change in price
Illiq_chg_ROA Illiquidity ratio and ROA
Illiq_ln Illiquidity ratio which return calculated
by log in price
Illiq_ln_ROA Illiquidity ratio and ROA
MAI Market for Alternative Investment
Market_Cap Market Capitalization
mtb Market-to-book ratio
ROA Return on Assets
Roll_chg Firm-specific variation which return
calculated by change in price
Roll_chg_ROA Firm-specific variation and ROA
Roll_ln Firm-specific variation which return
calculated by natural logarithm in price
Roll_ln_ROA Firm-specific variation and ROA
SET Stock Exchange of Thailand
TA Total Assets
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CHAPTER 1
INTRODUCTION
Dividend is a reward to shareholders who hold the shares of the company. It is
a part of a company’s profit which paid proportionally to the shareholder. There are 2
types of dividend which are cash dividend and stock dividend. The period of paying a
dividend can be i) annual payment – approved by shareholders’ meeting or ii) interim
payment – approved by the board of directors. Paying dividend or not paying dividend
can affect shareholder benefits, company’s ability and investors’ decision in trading
the security. Paying dividend impacts ability of company in term of reinvestment its
earning to generate higher income whereas shareholders are preferable getting the
dividend. As Baker and Wurgler (2004a), Li and Lie (2006) propose that companies
will adjust their dividend policy due to the investors demand. And Tangjitprom
(2011) studies Thai investors’ behavior. He finds that Thai investors prefer dividend
and also pay higher for stock paying a dividend. Furthermore, paying dividend helps
company to reduce the agency cost between shareholder and management and also
creates the value of the company. While Miller and Modigliani (1961) propose that
dividend policy cannot affect to stock price in the perfect capital market.
Dividend can imply market reaction. For example, paying a dividend can be
signal to the market that company can generate more profit or it conveys any private
information. Both managers and investors can respond differently to both public and
private information when they get the new information. There is several papers study
how manager makes dividend decision based on company’s profitability, company
growth, company’s earning and company’s size. Fama and French (2001) propose
that profitability, investment opportunities and size can affect decision on dividend.
Cesari and Meier (2015) study on how private information in stock return can
influence the change in dividend. They find that private information in stock price
positively affect dividend change.
In Thailand, catering theory from Baker and Wurgler (2004a) is used for
studying Thai investors’ behavior in dividend. Tangjitprom (2011) proposes that Thai
investors prefer dividend and they also pay higher for paying dividend shares even
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dividend is taxed. Moreover, Stock Exchange of Thailand Research provides an
alternative choice considered as a long-term investment to investors called “Dividend
Universe”. It is a list of potential companies which meet all four criteria. First,
profitable companies mean companies can continuously generate net profit each year.
Then, companies also have the positive cash flow from operating activities. Last but
not least is paying dividend companies mean companies need to pay dividend from
their operation at least one time each year. And the last is good governance companies
which mean that companies need to gain at least “Good” score from The National
Committee for Corporate Governance based on the latest assessment.
Figure 1.1: Dividend Trend during 2007-2017. The number of companies paying
dividend increase as the increase in number of companies listed in SET and MAI
Index.
As the dividend trend shows in figure 1.1, the number of companies listed in
both SET Index and MAI Index increase over decade year from 541 companies to 755
companies. The number of paying dividend companies also rises from 373 companies
to 559 companies which are around 60% growth. And companies listed in Dividend
Universe also go up from 123 companies to 191 companies which imply several
potential companies are in the market. It is fascinating to study how manager makes a
decision on dividend policy and there is private information in financial market or not.
Most people believe that the insider always knows more information than outsider but
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there is no evidence proving this belief. Then, this research help answering how
private information can influence manager makes decision on dividend.
The objectives of this research are to examine whether stock return carried
private information by measuring through 2 measurements which are firm-specific
stock return variation and illiquidity ratio. The second objective tries to answer
whether private information can affect manager decision on dividend by finding the
relationship between private information and dividend policy even during the regular
period or facing the crisis. If there is any private information related to dividend
policy, we can see how manager responds to the information when making dividend
decision. Furthermore, this research also looks into subsample group of Dividend
Universe to see whether stock price of companies with well-performed conveys any
private information and whether this information can affect dividend decision.
The contributions of this research are manager can explore the private
information conveyed in stock price and used it to make decision and investors can
forecast the future prospect of the company when manager announces paying
dividend.
In section 2 and 3, we review the relevant literature and theoretical framework.
Section 4 and 5 describe in data and methodology. For empirical result and conclusion
will be in section 6 and 7.
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CHAPTER 2
REVIEW OF LITERATURE
We analyze the related literature in the relationship among dividend policy,
stock return, private information and the financial crisis.
2.1 Dividend and Stock Return
Starting with Miller and Modigliani (1961) who propose that dividend policy
cannot affect share price in the perfect capital market. The result comes from three
assumptions. First, the capital market is perfect which refers to no one can get higher
than others and there is no transaction cost, no brokerage fee and no taxes. Second,
investors are rational behavior. They always prefer more to less and are indifferent
between dividend and capital gain. And the last is perfect certainty which means that
all cost of financing is the same no need to distinguish between debt financing and
equity financing. Baker and Wurgler (2004a) propose that company will adjust the
dividend policy due to the investors demand. They argue that company will pay
dividend when paying dividend stock price is high and will not pay dividend when
investors prefer nonpaying dividend stock. Model and empirical result of Baker and
Wurgler (2004a) are based on a binary model which is pay or not pay. Then, Li and
Lie (2006) extend the model from Baker and Wurgler (2004a) by adding the
magnitude of change in dividend level. And they find that the decision to pay or not to
pay, increase or decrease dividend and magnitude of the change rely on the dividend
premium.
For the study outside the United States, Dasilas and Leventis (2011) find that
there is a positive impact of dividend announcement on Greek stock return. And stock
price and trading volume also positively react to the dividend changes. Moreover,
dividend yield and percentage change in dividend can affect stock price during
dividend announcement. And Suwanna (2012) finds that there is a positive impact of
dividend announcement on Thai stock return. After company announces the dividend
payment, the result shows stock price reacts positively to that announcement.
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2.2 Dividend, Stock Return and Private Information
Dyl and Weigand (1998) propose that company which begins to pay dividend
may convey some information to market regarding the risk of the company. The
researchers find that 70% companies get less systematic and total risk after dividend
announcement. Dewenter and Warther (1998) propose that family companies
(Keiretsu-member) have asymmetric information and agency problem less than
Independent Japanese Companies and U.S. companies because Japanese stock price
has less reacted to dividend omission and initiation. And Cesari and Meier (2015)
study how private information impact dividend changes and find that stock return
positively relates to dividend change when stock price impounded more private
information. Moreover, Cesari and Meier (2015) also assume managers use the
private information learned from the market to adjust the current dividend.
To measure the private information, there are many researchers study in this
field. Starting with firm-specific variation, Roll (1988) he proposes that stock return
cannot be fully explained by economic factors and market factors. 𝑅2 is used to
measure the level of unexplained. The higher private information or low 𝑅2 gives
stock price more informative. Morck et al. (2000) study in variation of stock return in
different market and find that 𝑅2 is low in developed markets due to the intensive
legal protection whereas high 𝑅2 is in emerging markets due to poor protection to
shareholders and rumors can make stock price fluctuation. Their result is consistent
with Fernandes and Ferreira (2008) who also find out that stock price informativeness
will be stronger in emerging market. By the way, Durnev et al. (2004) study how
manager make the efficient decision in capital investment if stock price conveyed
private information. They use firm-specific variation to measure private information
in stock price and use Tobin’s q as robust checking. As the result of these
measurements tell the same results that manager will make more efficient investment
decision in capital budgeting when stock price is less informative. And Chen et al.
(2007) study how manager’s sensitivity to corporate investment when they know
there is private information in stock price. They use the probability of informed
trading (PIN) and firm-specific variation to measure private information and define
earnings surprise as the proxy of manager reaction to the information. The result
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shows that private information positively reflects investment decision. And they
believe that managers will be more sensitive only on new information.
On the other hand, Xing and Anderson (2011) argue that high or low firm-
specific variation can reflect either private information or public information which
implies the relationship between stock price and private information is steady. Their
result is consistent with U-shaped relation that shows an inverse relationship between
stock price synchronicity and public information.
For another measurement of private information, we use illiquidity ratio
introduced by Amihud (2002). This ratio reflects the impact of stock return from the
trading volume. The higher stock price reflects the higher probability of private
information in trading. Furthermore, this measurement also shows the negative
relationship between stock return and liquidity in stock. He believe that frequently
traded stock should give the lower return than rarely traded stock. Then, abnormal
return can compensate illiquidity in that stock. Moreover, this private information
measurement is used to measure in many fields. Ferreira et al. (2011) study how
private information in stock price affects the structure of the board of director. They
use the probability of informed trading (PIN), firm-specific variation and illiquidity
ratio to measure private information and the result shows that there is a negative
relationship between private information and board independence. When price is
more informative, there is less monitoring in the board of director. And Freasard
(2012) study how manager decides on cash saving when stock price conveyed private
information. He tests his hypothesis by using firm-specific variation, illiquidity ratio
and private information trading. Finally, he finds that manager is more sensitive to
cash saving when private information conveyed in stock price.
2.3 Dividend and Crisis
Hauserl (2013) studies how manager makes dividend decision during the
financial crisis. And he finds that companies tend to cut dividend during the financial
crisis or postpone paying dividend when companies facing the crisis. Cash ratio and
sale growth are used for make dividend decision even during or after the crisis.
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CHAPTER 3
THEORETICAL FRAMEWORK
3.1 Dividend Policy
The concept of dividend policy refers to a policy which companies use to
decide how much company willing to pay the dividend. Dividend is a part of
companies have the excess earning and would like to distribute to the shareholder of
the company as a portion of shareholding. Paying dividend is like a tradeoff between
paying dividend now and capital growth in future. It can be cost and benefit to the
company. For example, when company pays dividend company loses the chance to
create more excess earning or loss the opportunities to invest in other projects which
are more profitable. However, paying dividend helps company to reduce the agency
cost between shareholder and management and also creates the value of the company.
3.2 Dividend Signaling
The concept of dividend signaling refers to the announcement of paying a
dividend. It sends a good signal to investor that company has positive future prospect.
Then, the company’s stock will be attractive. Announcing dividend not only tells
positive prospect of the company but also tells the last performance and the growth of
the company. This signaling can boost the price of the company’s share to increase
while non-paying dividend or postpone paying dividend can signal to the investors
that company has some unplanned or problem which can devalue the stock price of
the company.
3.3 Asymmetric Information
The concept of asymmetric information refers to one party has more
information than the other party. The inequality of information can create two
problems which are the adverse selection and the moral hazard. Moral hazard can be
used to analyze the principal-agent problem which refers to one party called agents do
not act align with their responsibility and other parties called principals have to pay an
incentive to encourage them to take responsibility. Moreover, asymmetric information
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can occur in the company. For example, managers whom outsiders always believe
that carry more information. When manager decides to increase or decrease paying
dividend it can send a signal to the market.
3.4 Catering Theory of Dividend
This concept was introduced first time by Malcolm Baker and Jeffery Wurgler
in 2004. They propose that managers will adjust the dividend decision – pay or not
pay based on the investor demand. One of the measurement investor demands is
dividend premium which comes from the difference between the weighted average
market – to – book ratio of dividend payer and nonpayer. And the empirical result
shows that investor will put more premiums in stock price that pays dividend and
nonpayer manager would try to initiate paying a dividend vice versa if the premium is
low or low demand for dividend, manager would also omit dividend.
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CHAPTER 4
DATA
In this study, we separate data into 2 parts. The first part contains all
companies listed in SET and MAI which has 334 companies. We exclude all financial
sectors due to restriction and different criteria of paying a dividend. And second part
contains the companies listed in Dividend Universe which has 72 companies from 234
companies we choose companies which named in Dividend Universe list for three
years starting from 2015 to 2017. From 72 companies contain 69 companies listed in
SET Index and the rest 3 companies listed in MAI Index. Then, the subsample group
will be divided into 4 groups by ranking the total asset of the company. This list of
Dividend Universe comes from SET Research.1 The scope of this study is over the
period of 2007 to 2017.
For dividend, we collect the dividend per share for each company from
SETSMART. And all dividends are summed in the annual basis. For stock return and
market return, we collect the daily price of each company and market from
SETSMART and compute into yearly basis. We calculate return from 2 ways which
are the percentage change of the stock price along the time and the natural logarithm
of stock price. The reason behind using this method is we can consider the return as
the compounding return.
For control variables, we rely on the set of information following to Fama and
French (2001) and Cesari and Meier (2015) which includes dividend yield, market
capitalization, total assets, debt, cash, market-to-book ratio, and return on asset
(ROA). All control variables; we collect the data from EIKON. Dividend yield is the
return from dividend payment calculated from dividend per share divided by price per
share. For market capitalization, it is the market value of equity which comes from
price per share multiplied by number of share outstanding. It is used to measure
company’s size. While investment opportunities are measured by debt, cash and
market-to-book ratio. For debt, it comes from the long-term debts divided by total
assets. For cash, it comes from cash and short-term investments over total assets. And
1 https://www.set.or.th/th/setresearch/database/dividend_universe.html
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for market-to-book value comes from market value of equity divided by book value of
equity. For return on asset (ROA), it comes from operating income over total assets.
This variable is used to measure company’s profitability. For size, investment
opportunities and profitability are factors that relevant to Fama and French (2001) –
these characteristics of company affecting dividend decision. In addition, we also
include total assets as one of company’s size measurement. We also take natural
logarithm for market capitalization and total assets to deal with the scale effect
because sometimes the values of these items are too high or too small.
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CHAPTER 5
RESEARCH METHODOLOGY
5.1 Dividend
For testing the relationship in dividend payment of companies listed in SET
and MAI Index, we assign 1 for companies paying dividend on that year while 0 for
companies omitted dividend.
5.2 Measurement of Private Information
Starting with firm-specific variation, this measurement is introduced by Roll
(1988). It reflects the variation in stock return that cannot be explained by market
factors. This measurement can be defined as
Roll = ln((1 − 𝑅2)/𝑅2) (1)
where; 𝑅2 is calculated from 𝑟𝑖,𝑗,𝑡 = 𝑎𝑖 + 𝑏𝑖,𝑚𝑟𝑚,𝑡 + 𝜀𝑖,𝑡 (1.1)
𝑟𝑖,𝑡 = return of company i at time t
𝑟𝑚,𝑡 = market return at time t
In this measurement, we use this 𝑅2 to explain the relationship between
market return and stock return. To answer whether there is a private information in
stock return, we expect the value of 𝑅2 is low. Low 𝑅2 means market factors can
explain stock return less. Then, the lower 𝑅2 , the higher firm-specific variation and
the higher private information conveyed in stock price.
Illiquidity ratio, this measurement is introduced by Amihud (2002). It reflects
return over stock trading volume. This measurement can be defined as
𝐼𝐿𝐿𝐼𝑄𝑡 = 1
𝐷𝑖∑
|𝑟𝑖,𝑡|
𝑣𝑜𝑙𝑖,𝑡
𝐷𝑖
𝑡=1
(2)
where; 𝐷𝑖 = number of day observed from company’s i
𝑣𝑜𝑙𝑖,𝑡 = daily trading volume in dollar amount
𝑟𝑖,𝑡 = return of company i at time t
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In this measurement, we use illiquidity ratio to answer whether there is private
information in stock return. We expect the value of ILLIQ is high because stock price
which few trading but give high return will convey some inside information. As Kyle
(1985) states that insider traders always trade when stock price conveys some
information and private information will be included in stock price at the end of
trading.
5.3 Average Abnormal Return
For the abnormal return, we expect a positive relationship with dividend. The
reason is increase in dividend payment sends a good signal to investors. Then,
investors will reflect a positive reaction to stock price of the company as Baker and
Wurgler (2004a) and Li and Lie (2006) propose that investors prefer to pay more if
they demand company for paying dividend. We explore the average abnormal return
by following
𝐴𝐴𝑅𝑖,𝑡 =1
𝑛∑ 𝐴𝑅𝑖,𝑡
𝑛
𝑖=1
(3)
where; 𝐴𝑅𝑖,𝑡 is calculated from 𝑟𝑖,𝑡 − 𝑟𝑚,𝑡 (3.1)
5.4 Control Variables
For the control variables, we expect a positive sign among dividend yield,
market capitalization, total assets, return on assets (ROA) and dividend because Fama
and French (2001) propose that company with paying dividend usually have large size
and more profitable while non-paying dividend companies are likely small size, less
profitable and poor investment opportunities. Then, the relationship among debt, cash,
market-to-book ratio and dividend are opposite. As Fama and French (2001) state that
companies with high growth in investment tend to omit the dividend because they
spend their money to expand the business while paying dividend companies tend to
invest for new projects less. So, we expect the positive relationship among size,
profitability and dividend change while investment opportunities are a negative
relationship.
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5.5 Dummy Variable
To find the impact of manager’s decision on dividend during and after the
crisis, we assign 1 for the years during the financial crisis period which are 2007 –
2009. And assigned 0 for years after the crisis which are 2010 – 2017. This variable
helps explain how manager make a decision on dividend during the financial crisis
even there is private information.
5.6 Group of Sample
To find the effect of company’s size on stock price informativeness and
manager’s decision on dividend, we rank the highest to lowest total assets of each
company and separate into 4 groups. Companies in group 1 - 4 define as large,
medium, small and micro companies respectively. We expect the smaller size of
companies carry higher private information and tend to omit dividend.
5.7 Baseline Specification
To explore the relationship between private information in stock return and
dividend by regressed the following model:
𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑦 = 𝛽0 + 𝛽1𝐴𝐴𝑅𝑖,𝑦 + 𝛽2𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝐼𝑛𝑓𝑜𝑖,𝑦 + 𝛽3(𝐴𝑅𝑅𝑖,𝑦 ×
𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝐼𝑛𝑓𝑜𝑖,𝑦) + 𝛽4𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑 𝑦𝑖𝑒𝑙𝑑𝑖,𝑦 +
𝛽5𝑆𝑖𝑧𝑒𝑖,𝑦 + 𝛽6𝐷𝑒𝑏𝑡𝑖,𝑦 + 𝛽7𝐶𝑎𝑠ℎ𝑖,𝑦 + 𝛽8𝑀𝑎𝑟𝑘𝑒𝑡 −
𝑡𝑜 − 𝑏𝑜𝑜𝑘𝑖,𝑦 + 𝛽9𝑅𝑂𝐴𝑖,𝑦 + 𝜀𝑖,𝑦
(4)
In addition, to investigate the relationship among private information, the
financial crisis and dividend, we regress the following model:
𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑦 = 𝛽0 + 𝛽1𝐴𝐴𝑅𝑖,𝑦 + 𝛽2𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝐼𝑛𝑓𝑜𝑖,𝑦 + 𝛽3(𝐴𝑅𝑅𝑖,𝑦 ×
𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝐼𝑛𝑓𝑜𝑖,𝑦) + 𝛽4𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑 𝑦𝑖𝑒𝑙𝑑𝑖,𝑦 +
𝛽5𝑆𝑖𝑧𝑒𝑖,𝑦 + 𝛽6𝐷𝑒𝑏𝑡𝑖,𝑦 + 𝛽7𝐶𝑎𝑠ℎ𝑖,𝑦 + 𝛽8𝑀𝑎𝑟𝑘𝑒𝑡 −
𝑡𝑜 − 𝑏𝑜𝑜𝑘𝑖,𝑦 + 𝛽9𝑅𝑂𝐴𝑖,𝑦 + 𝛽10(𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝐼𝑛𝑓𝑜𝑖,𝑦 ×
𝐷0709) + 𝛽11𝐷0709 + 𝜀𝑖,𝑦
(5)
Ref. code: 25605902042141WZP
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Both models use the logistic regression to answer whether private information
in stock return affects the chance in paying dividend during the financial crisis or in
normal period. Moreover, we expect a negative relationship among dividend, the
financial crisis and stock price informativeness. The reason is when stock price
conveys private information, managers seem to learn this information and make
dividend decision as Cesari and Meier (2015) proposed.
Ref. code: 25605902042141WZP
15
CHAPTER 6
EMPIRICAL RESULT
6.1 Descriptive Statistics
All variables are shown in table 5.1. There are 3,421 observations from 311
companies listed in SET Index while 253 observations from 23 companies listed in
MAI Index. The total observations in this research are 3,654 from 334 companies
operating during 2007 – 2017. On the average 79.17% companies listed in SET Index
pay dividend while companies listed in MAI Index pay dividend only 67.98%. For
private information measurement, the result shows that companies listed in MAI
Index have less 𝑅2 than companies listed in SET Index. It implies that stock price of
companies listed in MAI Index tend to hold more private information than companies
listed in SET Index. Using the illiquidity ratio also confirms that companies listed in
MAI Index provides higher ratio than companies listed in SET Index which also
implies that stock price of companies listed in MAI Index tend to hold more private
information likewise. Mostly companies listed in MAI Index are small and have been
listed in Index. Then, they may not provide the inside information such as the project
plan, target group and project value etc. to others.
For another group of sample is Dividend Universe. This group contains 792
observations from 72 companies which 759 observations or 69 companies listed in
SET Index and 33 observations or 3 companies listed in MAI Index. On the average
both Indexes tell that around 96% companies pay dividend. From private information
measurement, the results also are same as the overall group which means that
companies listed in both MAI Index and Dividend Universe have lower value of 𝑅2
but higher illiquidity ratio than companies listed in both SET Index and Dividend
Universe. Furthermore, companies listed in the group of Dividend Universe also have
higher 𝑅2 and lower illiquidity ratio than normal companies listed in SET and MAI
Index. This result implies that well-performed companies tend to carry less inside
information.
Moreover, both sample groups also show the similar result. First, companies
listed in SET Index have higher dividend yield than companies listed in MAI Index.
Ref. code: 25605902042141WZP
16
Due to the size, companies in SET have fewer investment opportunities but higher
profitability than companies listed in MAI. And companies listed in SET Index have
larger size than companies listed in MAI Index. Companies’ size is shown in term of
market capitalization and total assets.
Table 5.1 Descriptive statistics from overall market and Dividend Universe.
Overall Dividend Universe
SET MAI SET MAI
Company 311 23 69 3
Observation 3,421 253 759 33
divpmt 0.7919 0.6798 0.9631 0.9697
ab_return_chg 0.0171 0.0473 0.0214 0.0325
ab_return_ln -0.0142 -0.0383 0.0022 -0.0380
R2_chg 0.1133 0.0939 0.1355 0.0635
R2_ln 0.1157 0.0974 0.1373 0.0652
Roll_chg 3.0322 2.9001 2.8245 3.4087
Roll_ln 3.0062 2.8723 2.7984 3.3619
Illiq_chg 0.0002 0.0005 0.0001 0.0003
Illiq_ln 0.0002 0.0004 0.0001 0.0003
Div_yield 3.6521 3.1811 4.1420 4.4806
Mkt_cap (billion) 21.1871 1.1049 50.2726 1.3643
TA (billion) 24.3293 1.1147 53.5894 0.8319
Debt 9.6929 3.0667 11.9372 1.4669
Cash 11.1866 13.8118 11.7046 17.2903
mtb 2.1827 3.0666 2.4860 2.8932
ROA 7.5526 4.5088 11.2555 17.3565
Note: Dividend payment defines as managers are willing to pay dividend for that year. Average
abnormal return is the difference between stock return and market return. 𝑅2 tells the level of
unexplained in stock return and market return. Roll, firm-specific variation is the one of the private
information measurement from Roll (1988) and Illiq, illiquidity ratio tells the frequency of trading on
stock which introduced by Amihud (2002). There are dividend yield, market capitalization, total
assets, debt, cash, market-to-book ratio and return on assets are the control variables.
Ref. code: 25605902042141WZP
17
6.2 Firm-specific stock return variation and dividend
In order to answer whether there is private information in stock return affect
decision on dividend, we start with firm-specific stock return variation as a
measurement in private information. We use logistic regression to identify this
relationship. We focus on companies listed in SET and MAI Index operated during
2007 – 2017. The result from both markets is shown in table 5.2. Most models
demonstrate that dividend yield, company’s size (market capitalization, the natural
logarithm of market capitalization, total assets and the natural logarithm of total
assets), investment opportunity (debt) and company’s profit (ROA) significantly
affect dividend policy. Model 2, 4 and 6 from SET Index demonstrate there is a
positive relationship between dividend payment and these variables except debt. The
model tells that stock price which holds private information tends to pay dividend. As
it shows a statistically significant positive relationship between private information
and dividend. For MAI Index, there is no evidence showing the impact of private
information and dividend. Moreover, the result shows that dividend yield, company’s
size and profitability measurement are positively related to decision on dividend
whereas investment opportunities – debt, cash and market-to-book ratio show the
negative relationship with dividend policy.
Due to the significance in company’s size, we divide our data into 4 groups.
We rank the total assets from largest to lowest which group 1 indicates companies
with top 25% of total assets to group 4 with the least 25% of total assets. The different
companies’ size uses to find the effect of private information on dividend. The result
in table 5.3 show that size of companies significantly explains the relationship
between private information and dividend. Small and medium-sized tend to pay
dividend when their stock prices are more informative. For MAI Index, we cannot
find the relationship between private information and dividend. The statistic shows
private information insignificantly on dividend. We cannot predict the chance of
paying dividend from MAI Index but SET Index we can. And the finding also
demonstrates that companies with high profitability, big size and few investment
opportunities tend to pay dividend which is consistent with Fama and French (2001)
proposed.
Ref. code: 25605902042141WZP
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Table 5.2 Result of logistic regression on dividend from firm-specific variation.
Model 2 Model 4 Model 6
SET MAI SET MAI SET MAI
Intercept -11.17***
(1.0992)
-22.265***
(6.8719)
-7.707***
(1.081)
-9.7886
(8.1659)
-11.046***
(1.1055)
-20.437***
(6.6867)
ab_return_chg -0.3655
(0.6001)
1.5662
(1.1932)
0.1545
(0.6023)
1.0449
(1.1293)
ab_return_ln 0.00626
(0.6377)
-1.6028
(4.148)
roll_chg 0.0923***
(0.0338)
0.0894
(0.1854)
0.0629*
(0.0334)
-0.0414
(0.1741)
roll_ln 0.0874***
(0.033)
-0.00191
(0.149)
ab_roll_chg 0.0133
(0.1195)
-0.4189
(0.5491)
-0.0305
(0.1278)
-0.1545
(0.504)
ab_roll_ln -0.0105
(0.1402)
0.0949
(1.1764)
div_yield 1.2912***
(0.0645)
0.8087***
(0.1383)
1.3546***
(0.0665)
0.8021***
(0.1385)
1.2967***
(0.0644)
0.8051***
(0.1385)
lnmkt_cap 0.4642***
(0.0499)
1.0395***
(0.3369)
0.459***
(0.0501)
0.958***
(0.3317)
lnta 0.2919***
(0.0472)
0.4104
(0.3877)
debt -0.0124**
(0.00488)
0.0366
(0.0436)
-0.00864*
(0.00489)
0.0371
(0.0407)
-0.0123**
(0.00487)
0.0376
(0.0425)
cash -0.00618
(0.00634)
-0.00322
(0.0222)
-0.00045
(0.00627)
0.0196
(0.0203)
-0.00639
(0.00633)
-0.00172
(0.0221)
mtb -0.00119
(0.00456)
-0.00007
(0.00275)
-0.00109
(0.00391)
roa 0.0683***
(0.00984)
0.1134***
(0.0252)
0.0783***
(0.00987)
0.1255***
(0.026)
0.066***
(0.00978)
0.1146***
(0.0256)
Observations 3421 253 3421 253 3421 253
R-Square 0.4402 0.5262 0.4306 0.5065 0.44 0.5236
Pseudo R-
square 0.5672 0.5957 0.5505 0.5633 0.5669 0.5913
Likelihood
Ratio 1984.843 188.987 1926.504 178.696 1983.812 187.5909
Note: The model 2, 4 and 6 demonstrate companies listed in SET Index tend to pay dividend when
stock return conveyed more inside information whereas no evidence effect companies listed in MAI
Index. The standard error is reported in parenthesis.***, **, * defined as statistically significant at the
1%, 5% and 10%level.
Ref. code: 25605902042141WZP
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Table 5.3 Result of logistic regression on dividend from firm-specific variation by
company’s size
Medium Model 2 Model 4 Model 6
SET MAI SET MAI SET MAI
Intercept -21.333***
(4.1356)
-58.185**
(26.6389)
-16.3271*
(8.4324)
-55.5151
(115)
-21.310***
(4.1505)
-59.971**
(28.2502)
ab_return_chg -0.7786
(1.3304)
8.6486
(8.6866)
-0.4298
(1.2669)
3.0956
(6.5349)
ab_return_ln -0.2693
(1.3756)
4.8733
(9.7562)
roll_chg 0.1351*
(0.0748)
0.0741
(0.4593)
0.1637**
(0.0736)
-0.1733
(0.3576)
roll_ln 0.1362*
(0.0742)
-0.4284
(0.5188)
ab_roll_chg -0.00026
(0.3114)
-2.625
(2.9326)
-0.0347
(0.2861)
-0.6017
(2.1878)
ab_roll_ln -0.0767
(0.3459)
-2.8542
(3.5359)
div_yield 0.9449***
(0.0937)
1.1002***
(0.3312)
1.0027***
(0.0959)
1.1855***
(0.362)
0.9518***
(0.0935)
1.1474***
(0.3449)
lnmkt_cap 0.9463***
(0.1943)
2.8134**
(1.3511)
0.9448***
(0.1947)
2.9455**
(1.433)
lnta 0.6648*
(0.3727)
2.595
(5.5051)
debt -0.0127
(0.00865)
0.1536
(0.1175)
-0.0179**
(0.0089)
0.107
(0.0976)
-0.0126
(0.00864)
0.1672
(0.1169)
cash -0.0133
(0.015)
-0.0686
(0.0556)
0.00406
(0.0146)
-0.0243
(0.0468)
-0.0141
(0.015)
-0.0657
(0.0533)
mtb -0.1053
(0.0832)
-0.2746
(0.579)
0.1481**
(0.0731)
0.0483
(0.0765)
-0.1096
(0.0833)
-0.3297
(0.5442)
roa 0.0521***
(0.0188)
0.0226
(0.0494)
0.0684***
(0.0187)
0.083
(0.0602)
0.0499***
(0.0188)
0.043
(0.0565)
Observations 855 63 855 63 855 63
R-Square 0.3809 0.5498 0.3641 0.4713 0.3803 0.5475
Pseudo R-
square 0.4838 0.6080 0.4568 0.4855 0.4829 0.6041
Likelihood
Ratio 409.9212 50.2776 387.0428 40.1482 409.1998 49.9579
Note: Focus on model 2, 4 and 6 the result shows medium companies can reflect a positive relationship
between private information in stock return and dividend. The standard error is reported in
parenthesis.***, **, * defined as statistically significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
20
Table 5.3 (Continue)
Small Model 2 Model 4 Model 6
SET MAI SET MAI SET MAI
Intercept -22.3145***
(5.2391)
-77.4516
(49.4401)
-18.7469*
(10.9552)
-284.8*
(156.1)
-21.8133***
(5.2152)
-181.4
(110.6)
ab_return_chg -2.3012*
(1.3899)
-4.1982
(7.7542)
-1.4392
(1.3661)
-5.9066
(7.2503)
ab_return_ln -1.7669
(1.364)
15.5997
(21.6016)
roll_chg 0.097
(0.0601)
-0.1025
(0.6518)
0.1062*
(0.0601)
-0.4146
(0.5892)
roll_ln 0.1063*
(0.0612)
0.408
(0.3544)
ab_roll_chg 0.2335
(0.3389)
0.5964
(1.8767)
0.104
(0.3301)
1.3141
(1.7468)
ab_roll_ln 0.2218
(0.3368)
-8.1831
(6.7618)
div_yield 0.8038***
(0.0857)
2.5267**
(1.2223)
0.8392***
(0.087)
1.7371**
(0.7445)
0.8152***
(0.0859)
4.3403*
(2.291)
lnmkt_cap 1.018***
(0.2546)
3.7562
(2.4451)
0.991***
(0.2526)
8.7846
(5.4)
lnta 0.7919
(0.5042)
13.7963*
(7.5781)
debt -0.0247**
(0.0108)
-0.1434
(0.3403)
-0.0319***
(0.0108)
0.1666
(0.2942)
-0.0251**
(0.0108)
-0.7885
(0.7465)
cash -0.0214
(0.0151)
-0.2605
(0.1751)
-0.00644
(0.0148)
-0.0812
(0.0759)
-0.0221
(0.015)
-0.5632
(0.3483)
mtb -0.0525
(0.1085)
0.0128
(0.3346)
0.3056***
(0.1176)
0.0228
(0.1889)
-0.0565
(0.1035)
-0.0294
(0.4453)
roa 0.1488***
(0.0264)
0.4861*
(0.271)
0.1596***
(0.026)
0.3411*
(0.1777)
0.1443***
(0.0262)
0.922*
(0.5236)
Observations 856 64 856 64 856 64
R-Square 0.4176 0.6309 0.4084 0.635 0.416 0.652
Pseudo R-
square 0.5441 0.7449 0.5283 0.7533 0.5413 0.7889
Likelihood
Ratio 462.7974 63.7891 449.3567 64.5087 460.3954 67.556
Note: Focus on model 2, 4 and 6, only model 4 and 6 show small companies can reflect a positive
relationship between private information in stock return and dividend. Standard error is reported in
parenthesis.***, **, * defined as statistically significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
21
In addition, we explore the relationship between private information in stock
return on dividend decision during financial crisis. We assign the dummy variable,
D0709 which 1 represents year during crisis and 0 for otherwise. We test this
relationship in both SET and MAI Index. The result shows that during the crisis, there
is no result explained the relationship between private information in stock price and
dividend in MAI Index. No effect on dividend paying for companies listed in MAI
Index due to the insignificant of the testing relationship. However, in SET Index this
testing confirms the strong relationship between private information and dividend.
The probability to pay dividend increase as the significant in private information.
Model 2 and 6 in table 5.4 demonstrate that positive effect of stock price
informativeness on dividend policy. The more inside information in stock return, the
higher chance companies paying dividend. While the relationship between the crisis
and dividend is negative. It means that the chance of paying dividend will be less
when companies face the crisis. Moreover, the high dividend yield, big companies
and more profitability also affect the chance of paying dividend. When we focus on
the period of financial crisis, the interactive term of private information in stock return
and financial crisis (roll_chg_d0709 and roll_ln_d0709) are insignificant. It implies
that private information in stock price cannot be explained the chance of paying
dividend during the crisis.
However, the significant effect of company’s size on dividend policy, we
separate our data into 4 groups to find the impact of stock price informativeness in
different company’s size on dividend decision. The result in table 5.5 shows small
companies has a positive relationship on dividend payment. They tend to pay
dividend when their stock prices carry more inside information. While the crisis
shows a negative effect on dividend which is consistent with the previous result in
table 5.4. On the other hand, the interactive term between private information and the
crisis show insignificant relationship with dividend. It means that we cannot forecast
the probability of paying dividend when stock price more informativeness during the
financial crisis.
Ref. code: 25605902042141WZP
22
Table 5.4 Result of logistic regression on dividend from firm-specific variation during
financial crisis.
Model 2 Model 6
SET MAI SET MAI
Intercept -9.9906***
(1.1366)
-28.3726***
(8.1332)
-9.8993***
(1.1413)
-27.2143***
(8.0928)
ab_return_chg -0.4076
(0.6173)
1.2606
(1.1913)
ab_return_ln -0.0714
(0.653)
-1.1413
(4.3079)
d0709 -0.8372***
(0.2827)
-1.332
(1.621)
-0.8256***
(0.2784)
-0.7939
(1.4958)
roll_chg 0.0704*
(0.0388)
-0.0617
(0.2067)
roll_ln 0.067*
(0.0384)
-0.0965
(0.1442)
roll_chg_d0709 0.0569
(0.0706)
0.6587
(0.4768)
roll_ln_d0709 0.0539
(0.0696)
0.5383
(0.4391)
ab_roll_chg 0.0187
(0.1262)
-0.2264
(0.5445)
ab_roll_ln -0.00365
(0.1455)
-0.0765
(1.2287)
div_yield 1.2971***
(0.0645)
0.8068***
(0.1442)
1.3024***
(0.0644)
0.7837***
(0.141)
lnmkt_cap 0.4184***
(0.0512)
1.3585***
(0.4028)
0.4144***
(0.0514)
1.3005***
(0.4027)
debt -0.0111**
(0.00489)
0.0173
(0.0456)
-0.0111**
(0.00488)
0.0184
(0.0443)
cash -0.00722
(0.00635)
-0.00683
(0.0232)
-0.00742
(0.00634)
-0.00422
(0.0231)
mtb -0.0016
(0.00555)
-0.1525
(0.1279)
-0.00162
(0.00496)
-0.1329
(0.127)
roa 0.0704***
(0.01)
0.1173***
(0.0265)
0.0682***
(0.01)
0.1205***
(0.0275)
Observations 3421 253 3421 253
R-Square 0.4428 0.5325 0.4427 0.5301
Pseudo R-
square 0.5718 0.6064 0.5715 0.6022
Likelihood
Ratio 2000.9881 192.3783 1999.9008 191.0658
Note: The model 2 and 6 demonstrate companies listed in SET Index tend to pay dividend when stock
return conveyed more inside information while the financial crisis causes companies to omit a
dividend. There is no effect on dividend for companies listed in MAI Index. Standard error is reported
in parenthesis. ***, **, * defined as statistically significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
23
Table 5.5 Result of logistic regression on dividend from firm-specific variation during
financial crisis by company’s size.
Small Model 2 Model 6
SET MAI SET MAI
Intercept -18.4184***
(4.3582)
-62.7712**
(28.1918)
-18.4188***
(4.3676)
-61.5094**
(29.52)
ab_return_chg -0.8134
(1.501)
9.8551
(8.9613)
ab_return_ln -0.5597
(1.5088)
3.312
(11.0266)
d0709 -1.4703**
(0.5964)
-4.4709
(4.1651)
-1.5047**
(0.5923)
-2.4926
(3.5179)
roll_chg 0.1594*
(0.0913)
-0.3881
(0.5653)
roll_ln 0.1635*
(0.0933)
-0.5656
(0.5471)
roll_chg_d0709 -0.0495
(0.1636)
1.5058
(1.1722)
roll_ln_d0709 -0.0393
(0.1634)
1.0291
(1.0567)
ab_roll_chg -0.0424
(0.4219)
-2.7364
(3.0316)
ab_roll_ln -0.0553
(0.4201)
-2.1484
(3.9134)
div_yield 1.0011***
(0.0979)
1.2588***
(0.429)
1.0071***
(0.0978)
1.1753***
(0.4042)
lnmkt_cap 0.8176***
(0.2044)
3.0964**
(1.4336)
0.8165***
(0.2046)
3.0404**
(1.4927)
debt -0.0114
(0.00909)
0.1052
(0.1424)
-0.0111
(0.00907)
0.1251
(0.1418)
cash -0.0175
(0.0156)
-0.0655
(0.0623)
-0.0184
(0.0155)
-0.0686
(0.0567)
mtb -0.1024
(0.0847)
-0.336
(0.6185)
-0.1064
(0.0847)
-0.3127
(0.5505)
roa 0.0683***
(0.0206)
0.0256
(0.0554)
0.0669***
(0.0206)
0.0393
(0.0594)
Observations 855 63 855 63
R-Square 0.3969 0.5641 0.3966 0.5565
Pseudo R-
square 0.5103 0.6326 0.5098 0.6195
Likelihood
Ratio 432.4007 52.3081 431.9273 51.2283
Note: Focus on model 2 and 6, the result shows that only small companies listed in SET Index can
reflect the positive relationship between private information in stock return and dividend while the
crisis negatively reflects. Standard error is reported in parenthesis. ***, **, * defined as statistically
significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
24
6.3 Illiquidity ratio and dividend
For an alternative measurement of private information, we use illiquidity ratio
to measure whether the daily stock return over its trading volume conveys private
information. By using this ratio, we find the significantly negative relationship
between illiquidity ratio and dividend policy as the model 15 in table 5.6 shown. The
model demonstrates private information has a significant negative effect on paying
dividend. It implies companies with high illiquidity ratio tend to keep or omit
dividend which is consistent with Cesari and Meier (2015). Moreover, companies
with high illiquidity ratio also imply that their stock prices tend to convey more
private information. Even result from illiquidity ratio contradict the result from firm-
specific variation, we find the estimated coefficients of control variables in both
measurements are the same. Dividend yield, company’s size, and ROA significantly
affect decision on dividend. The positive relationship tells that companies with high
dividend yield, large size and more profit tend to pay dividend.
Companies’ size is one factor affected dividend decision. We look into the
size of the company by ranking companies’ total assets from highest to lowest and
separate into 4 groups. Then, we use logistic regression to indicate the relationship
again. The result in table 5.7 shows the negative relationship between private
information and dividend in small companies. When share price of small companies
convey more private information, managers tend to keep or omit dividend rather than
pay it which is consistent with Fama and French (2001). However, all testing results
show that there is insignificant relationship between private information and dividend
in MAI Index. We cannot find any effect on dividend except dividend yield and ROA
which have positively affected.
Ref. code: 25605902042141WZP
25
Table 5.6 Result of logistic regression on dividend from illiquidity ratio.
Model 15
SET MAI
Intercept -1.0959***
(0.1227)
-1.9852***
(0.5836)
ab_return_ln 0.6623*
(0.3701)
-1.8141
(1.8269)
Illiq_ln -159.4*
(93.4827)
43.6997
(137.7)
ab_illiq_ln -719.8
(452.8)
64.9283
(863.8)
div_yield 1.3766***
(0.0668)
0.7897***
(0.1386)
ta 4.12E-12***
(1.55E-12)
4.38E-10
(3.3E-10)
debt -0.00243
(0.00463)
0.0348
(0.0415)
cash -0.00506
(0.00615)
0.0159
(0.0199)
mtb -0.00015
(0.00228)
-0.0102
(0.049)
roa 0.0755***
(0.00973)
0.1346***
(0.0263)
Observations 3421 253
R-Square 0.4267 0.5059
Pseudo R-square 0.5438 0.5622
Likelihood Ratio 1903.014 178.3586
Note: The model 15 only demonstrate companies listed in SET Index tend to omit dividend when stock
return conveyed more private information. No effect on companies listed in MAI Index can explain
except dividend yield and ROA. The standard error is reported in parenthesis. ***, **, * defined as
statistically significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
26
Table 5.7 Result of logistic regression on dividend from illiquidity ratio by company’s size.
Small Model 15
SET MAI
Intercept -1.3249***
(0.4237)
-2.4324
(5.0888)
ab_return_ln 0.2195
(0.7729)
-1.6854
(3.5717)
Illiq_ln -1376.6***
(332)
-1243.3
(1630.2)
ab_illiq_ln -4102.7***
(1149.1)
2005.3
(9504.9)
div_yield 1.062***
(0.1007)
1.1865***
(0.3533)
ta 8.08E-11
(5.32E-11)
7.02E-10
(4.36E-09)
debt -0.0168*
(0.00915)
0.1033
(0.0923)
cash 0.00191
(0.0144)
-0.0382
(0.0457)
mtb 0.1334*
(0.0747)
0.0531
(0.0692)
roa 0.0598***
(0.0189)
0.0871*
(0.0526)
Observations 855 63
R-Square 0.3681 0.48
Pseudo R-square 0.4632 0.4982
Likelihood Ratio 392.4512 41.1941
Note: Focus on model 15 the result shows that only small companies listed in SET Index can
negatively reflect the relationship between private information in stock return and dividend. The
standard error is reported in parenthesis. ***, **, * defined as statistically significant at the 1%, 5% and
10%level.
Besides, we also explore the relationship between private information in stock
return on dividend decision during the financial crisis in both SET Index and MAI
Index. We assign the dummy variable, D0709 which 1 represent year during crisis
and 0 for otherwise. The result shows in table 5.8 that during the crisis, there is no
model explained the relationship between private information in stock return and
dividend. Moreover, we find that during the crisis company tends to omit dividend as
it shows negatively significant from testing the regression. However, the finding
Ref. code: 25605902042141WZP
27
shows the positive relationship among dividend yield, company’s size, profit and
dividend which is consistent with Fama and French (2001).
Table 5.8 Result of logistic regression on dividend from illiquidity ratio during the
financial crisis.
Model 9 Model 10 Model 11
SET MAI SET MAI SET MAI
Intercept -0.9432***
(0.1268)
-1.9061***
(0.5153)
-9.2294***
(1.0626)
-26.4832***
(7.7831)
-0.9342***
(0.1268)
-1.8439***
(0.6354)
ab_return_chg 0.0271
(0.3457)
0.6345
(0.5846)
-0.16
(0.3539)
0.6617
(0.5799)
0.0765
(0.3457)
0.732
(0.5004)
d0709 -0.9005***
(0.17)
0.2899
(0.6936)
-0.7384***
(0.1754)
0.5564
(0.7671)
-0.9696***
(0.1689)
-0.1125
(0.6754)
Illiq_chg 27.6407
(157.6)
-133.8
(1185.4)
133.7
(164.2)
69.6908
(1273.3)
10.2471
(157.3)
-762.5
(828.8)
Illiq_chg_d0709 44.0929
(184.6)
120.9
(1211.7)
53.7688
(188.1)
58.96
(1288.7)
61.202
(184.5)
737.3
(866.8)
ab_illiq_chg 411.4
(1060.5)
125.5
(1038.1)
347.5
(994.7)
div_yield 1.3422***
(0.0655)
0.803***
(0.1392)
1.3124***
(0.0647)
0.7921***
(0.1385)
1.3721***
(0.0666)
0.8123***
(0.1421)
mkt_cap 1.73E-11***
(4.38E-12)
1.32E-09***
(4.42E-10)
lnmkt_cap 0.3928***
(0.0496)
1.2549***
(0.3858)
ta 3.79E-12**
(1.57E-12)
4.60E-10
(3.72E-10)
debt -0.0055
(0.00472)
0.00895
(0.0453)
-0.012**
(0.00484)
0.0304
(0.0458)
-0.00197
(0.00466)
0.0267
(0.0444)
cash -0.00756
(0.00627)
-0.0015
(0.0219)
-0.0071
(0.00639)
-0.0026
(0.0219)
-0.0065
(0.00622)
0.0148
(0.0206)
mtb -0.00135
(0.00516)
-0.2355**
(0.1175)
-0.00297
(0.00737)
-0.1454
(0.1153)
-0.00055
(0.00317)
-0.011
(0.0524)
roa 0.0749***
(0.01)
0.1171***
(0.0239)
0.0699***
(0.01)
0.1081***
(0.0242)
0.0824***
(0.0101)
0.1308***
(0.0255)
Observations 3421 253 3421 253 3421 253
R-Square 0.4363 0.5276 0.4423 0.5294 0.4321 0.5092
Pseudo R-square 0.56041422 0.5979682 0.57085991 0.60110132 0.55321362 0.56760471
Likelihood Ratio 1961.1328 189.7096 1997.6862 190.7031 1935.9344 180.0767
Note: The result shows there is no evidence in both SET Index and MAI Index explained the
relationship between stock price informativeness and dividend. The standard error is reported in
parenthesis. ***, **, * defined as statistically significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
28
Table 5.8 (continue)
Model 12 Model 13 Model 14
SET MAI SET MAI SET MAI
Intercept -6.2416***
(0.9825)
-9.3592
(8.6431)
-0.9271***
(0.1275)
-2.0051***
(0.528)
-9.1402***
(1.0683)
-26.437***
(7.7677)
ab_return_chg 0.1459
(0.3496)
0.6823
(0.509)
ab_return_ln 0.392
(0.3811)
-1.6694
(1.9031)
0.094
(0.3886)
-1.682
(1.9133)
d0709 -0.9255***
(0.1697)
-0.1416
(0.6891)
-0.8768***
(0.17)
0.2464
(0.6782)
-0.7236***
(0.1753)
0.5656
(0.7643)
Illiq_chg 97.352
(161.8)
-780.1
(828.3)
Illiq_ln -28.8368
(169.5)
-184.8
(1190.2)
98.55
(173.1)
22.6554
(1282.4)
Illiq_chg_d0709 26.1179
(185.9)
753.1
(867.1)
Illiq_ln_d0709 23.7048
(186.9)
283.3
(1203.1)
26.9947
(189.3)
155.9
(1295.1)
ab_illiq_chg 386.9
(1013.7)
ab_illiq_ln -476.2
(413.2)
8.031
(979.7)
-285
(382.2)
163.7
(1110)
div_yield 1.3563***
(0.0658)
0.8136***
(0.1426)
1.3456***
(0.0654)
0.7959***
(0.1402)
1.3165***
(0.0645)
0.784***
(0.1398)
mkt_cap 1.7E-11***
(4.34E-12)
1.30E-
09***
(4.42E-10)
lnmkt_cap 0.3886***
(0.0497)
1.2469***
(0.3855)
lnta 0.2437***
(0.0443)
0.3872
(0.4141)
debt -0.00845*
(0.00488)
0.035
(0.0428)
-0.00552
(0.00473)
0.0115
(0.0445)
-0.0119**
(0.00484)
0.0318
(0.0448)
cash -0.00319
(0.00634)
0.0178
(0.0208)
-0.00778
(0.00626)
-0.00162
(0.0219)
-0.00724
(0.00637)
-0.0022
(0.0219)
mtb -0.00054
(0.00345)
-0.0127
(0.0521)
-0.00125
(0.00444)
-0.2189*
(0.1215)
-0.00224
(0.00602)
-0.13
(0.1171)
roa 0.079***
(0.01)
0.1254***
(0.0259)
0.0716***
(0.01)
0.123***
(0.0253)
0.0676***
(0.00998)
0.114***
(0.0257)
Observations 3421 253 3421 253 3421 253
R-Square 0.4358 0.508 0.4363 0.527 0.4421 0.529
Pseudo R-
square 0.5594 0.56565 0.5604 0.5969 0.5704 0.6003
Likelihood
Ratio 1957.7575 179.4557 1961.1808 189.3857 1996.3439 190.4598
Note: The result shows there is no evidence in both SET Index and MAI Index explained the
relationship between stock price informativeness and dividend. The standard error is reported in
parenthesis. ***, **, * defined as statistically significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
29
Table 5.8 (continue)
Model 15 Model 16
SET MAI SET MAI
Intercept -0.9138***
(0.1275)
-1.8759***
(0.6278)
-6.207***
(0.9829)
-9.2232
(8.6224)
ab_return_ln 0.4831
(0.381)
-1.9296
(1.8458)
0.5071
(0.3852)
-2.0096
(1.8456)
d0709 -0.9425***
(0.169)
-0.1885
(0.6632)
-0.9002***
(0.1699)
-0.197
(0.6734)
Illiq_ln -52.4889
(169.7)
-842.1
(824.8)
49.6092
(173.5)
-835.7
(827.7)
Illiq_ln_d0709 41.5582
(186.8)
920.4
(844.1)
-1.7358
(188.9)
918.2
(845.6)
ab_illiq_ln -531 (421.8) 44.7317
(934.4)
-442.9
(405.8)
78.9807
(934)
div_yield 1.3751***
(0.0665)
0.8065***
(0.144)
1.3588***
(0.0656)
0.8096***
(0.1443)
ta 3.76E-12**
(1.57E-12)
3.85E-10
(3.50E-10)
lnta 0.243***
(0.0444)
0.3747
(0.4132)
debt -0.00207
(0.00467)
0.033
(0.0435)
-0.00853*
(0.00489)
0.0392
(0.0423)
cash -0.00677
(0.0062)
0.0154
(0.0205)
-0.00339
(0.00633)
0.0183
(0.0207)
mtb -0.00059
(0.0031)
-0.00113
(0.0496)
-0.00062
(0.00352)
-0.00314
(0.0499)
roa 0.0787***
(0.0101)
0.1373***
(0.027)
0.0756***
(0.01)
0.132***
(0.0273)
Observations 3421 253 3421 253
R-Square 0.4322 0.5079 0.4358 0.5072
Pseudo R-
square 0.55337565 0.56549285 0.55956465 0.56436758
Likelihood
Ratio 1936.5019 179.4063 1958.1596 179.0492
Note: The result shows there is no evidence in both SET Index and MAI Index explained the
relationship between stock price informativeness and dividend. The standard error is reported in
parenthesis. ***, **, * defined as statistically significant at the 1%, 5% and 10%level.
Focusing on company’s size, we have 4 groups of sample which separated
from ranking the highest to lowest total assets. By testing these different groups, we
find that private information in stock return during the financial crisis negatively
explain the chance of paying dividend especially private information in medium-sizes
companies. The more stock price of medium companies in SET Index during the
financial crisis conveyed private information, the higher chance of company omits the
dividend. This result comes from the statistically significant on crisis and dividend.
Ref. code: 25605902042141WZP
30
And the interactive term of private information and crisis shows a negative
significance as table 5.9 shows.
Table 5.9 Result of logistic regression on dividend from illiquidity ratio during the
financial crisis by company’s size.
Medium Model 15 Model 16
SET MAI SET MAI
Intercept -1.0252**
(0.4513)
-1.8211
(5.273)
-10.1778
(8.7871)
-13.0101
(110.7)
ab_return_ln 0.0046
(0.8334)
-2.2813
(3.9205)
0.00145
(0.8337)
-2.2448
(3.9122)
d0709 -1.7251***
(0.3586)
-0.3402
(1.6302)
-1.724***
(0.3585)
-0.31
(1.6343)
Illiq_ln 671
(1006.3)
-5370
(6105.4)
696.6
(989.1)
-5311.4
(6091.1)
Illiq_ln_d0709 -1876*
(1065.7)
4672.1
(6258.2)
-1905.3*
(1048.9)
4611.1
(6245.9)
ab_illiq_ln -4934.7***
(1136.6)
-85.8508
(6972.6)
-4930.4***
(1135.2)
-90.696
(6943.9)
div_yield 1.1318***
(0.1058)
1.2586***
(0.4267)
1.1313***
(0.1058)
1.2553***
(0.4249)
ta 6.1E-11
(5.54E-11)
2.22E-10
(4.57E-09)
lnta 0.4236
(0.3893)
0.5487
(5.3095)
debt -0.0146
(0.00955)
0.1044
(0.1051)
-0.0146
(0.00955)
0.1025
(0.1051)
cash -0.00435
(0.0151)
-0.0428
(0.0487)
-0.00445
(0.0151)
-0.0428
(0.0485)
mtb 0.0942
(0.0746)
0.0537
(0.0719)
0.094
(0.0744)
0.0537
(0.0718)
roa 0.0797***
(0.021)
0.0892
(0.0546)
0.0797***
(0.0209)
0.0894
(0.0545)
Observations 855 63 855 63
R-Square 0.3912 0.4864 0.3911 0.4865
Pseudo R-
square 0.5007 0.5077 0.5007 0.5078
Likelihood
Ratio 424.2685 41.9802 424.2336 41.9885
Note: The result finds that during the crisis stock price of medium-sized companies tends to hold more
private information which causes the manager omits the dividend. The standard error is reported in
parenthesis.***, **, * defined as statistically significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
31
6.4 Private information, Dividend Universe and dividend
At first, we focus on the companies listed in SET and MAI Index. Now, we
will scope down into companies listed in Dividend Universe. By answer whether
private information in stock prices of companies listed in Dividend universe can affect
manager’s decision on dividend. We run the logistic regression and the result shows
that private information has insignificant effect on dividend policy from both SET
Index and MAI Index. We cannot use the impact of return carried private information
to determine the chance of paying dividend. In table 5.10, it demonstrates the private
information measured by firm-specific variation measurement while table 5.11
demonstrates private information measured by illiquidity ratio. The estimated
coefficient of private information also provides the same direction as the previous
result. We find the positive sign for firm-specific variation and negative sign for
illiquidity ratio. Only dividend yield, company’s size and ROA have positive impact
on dividend.
Moreover, we also find whether private information in stock return during the
crisis can affect the dividend payment. We define the dummy variable, D0709 to the
baseline regression by assign 1 for stock during 2007 – 2009 and 0 for otherwise. By
using firm-specific variation, we find that model 1 and 5 in table 5.12 show the
positive relationship between private information and dividend. The previous result
implies that the more private information conveyed in stock return the higher
probability of paying dividend. And these models show no effect of the crisis on
dividend decision. While model 7 shows a positive relationship between stock price
informativeness and dividend and a negative relationship between the crisis and
dividend. However, by using illiquidity ratio the result in table 5.13 shows the
positive relationship between private information in stock return and dividend. It
indicates the higher private information contained in stock return, the higher chance
of companies paying a dividend. By contrast, the financial crisis has a negative effect
on the chance of paying dividend. The interactive term of private information and
crisis shows negatively significant. It implies that during the crisis stock prices of
companies with carried more inside information tend to omit the dividend.
Ref. code: 25605902042141WZP
32
Table 5.10 Result of logistic regression on dividend universe from firm-specific variation.
Model 1 Model 2 Model 3
SET MAI SET MAI SET MAI
Intercept -2.7895***
(1.0569)
-9.1756
(168)
-19.5989**
(7.9775)
-204.3
(1428.2)
-3.1292***
(1.0844)
-28.2207
(243.5)
ab_return_chg -6.8729*
(4.0971)
79.6567
(1008.3)
-6.4323*
(3.8323)
82.2831
(899.5)
-5.1804
(3.6064)
81.816
(1478.5)
roll_chg 0.087
(0.1422)
-0.5164
(22.8988)
0.0992
(0.1511)
-0.2936
(22.4617)
0.0843
(0.1418)
0.7968
(44.7635)
ab_roll_chg 0.8385
(0.5928)
-17.3917
(296)
0.8169
(0.5501)
-20.8694
(231.4)
0.6317
(0.5301)
-19.8116
(374.4)
div_yield 2.4789***
(0.4899)
2.8849
(28.3751)
2.5035***
(0.5032)
2.7427
(18.5332)
2.5585***
(0.5058)
3.3103
(21.9316)
mkt_cap 1.31E-10*
(7.05E-11)
9.78E-09
(1.03E-07)
lnmkt_cap
0.8285**
(0.3836)
9.9862
(70.1001)
ta
6.77E-11
(4.30E-11)
debt -0.00023
(0.0343)
0.2278
(23.8168)
-0.0171
(0.0388)
-0.0341
(22.2813)
-0.00827
(0.0372)
-0.3838
(28.3099)
cash -0.00065
(0.0543)
0.0587
(3.2547)
-0.0135
(0.0559)
0.0593
(3.2415)
0.0072
(0.0571)
-0.00887
(5.3147)
mtb 0.3154
(0.3759)
0.5323
(72.2282)
0.2076
(0.3776)
1.3087
(39.4781)
0.5862*
(0.3481)
5.859
(38.3284)
roa 0.1439**
(0.071)
-0.2113
(17.5147)
0.1399**
(0.0695)
-0.3924
(13.0747)
0.1418**
(0.0707)
-0.2401
(17.1658)
Observations 759 33 759 33 759 33
R-Square 0.2115 0.2377 0.2119 0.2377 0.2086 0.2377
Pseudo R-square 0.752247 0.999442 0.753916 0.999331 0.740814 0.999442
Likelihood Ratio 180.3462 8.9577 180.7456 8.9562 177.6044 8.957
Note: There is no evidence showing the relationship between private information and dividend policy
from model 1 - 3. The standard error is reported in parenthesis.***, **, * defined as statistically
significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
33
Table 5.10 (Continue)
Model 4 Model 5 Model 6
SET MAI SET MAI SET MAI
Intercept -11.8409***
(3.515)
-272.1
(2079.4)
-2.7835***
(1.0508)
0.7106
(177.6)
-20.101**
(7.9406)
-21.6819
(2429.6)
ab_return_chg -5.336
(3.5909)
80.9067
(1048.7)
ab_return_ln
-5.2699
(3.8495)
-31.2639
(822.8)
-5.1129
(3.6049)
-31.2258
(694.6)
roll_chg 0.0653
(0.1423)
0.4879
(29.6008)
roll_ln
0.0824
(0.1407)
-1.161
(25.6011)
0.0953
(0.1503)
-1.0421
(25.0819)
ab_roll_chg 0.6258
(0.5159)
-21.0454
(275.9)
ab_roll_ln
0.6032
(0.5307)
0.9863
(217.7)
0.6272
(0.5045)
1.0082
(183.8)
div_yield 2.7589***
(0.5214)
3.3583
(19.9518)
2.5187***
(0.4884)
2.108
(17.6578)
2.5408***
(0.5008)
1.9951
(14.6417)
mkt_cap
1.31E-10*
(6.97E-11)
9.63E-10
(8.99E-
08)
lnmkt_cap
0.8531**
(0.3806)
1.0995
(118.3)
lnta 0.4199***
(0.1516)
12.5916
(97.3622)
debt 0.00556
(0.0322)
-0.3047
(23.1476)
-0.00858
(0.0326)
0.3759
(17.7573)
-0.026
(0.0374)
0.3849
(16.3778)
cash -0.00019
(0.0574)
0.1094
(3.8119)
-0.00598
(0.0543)
0.1642
(7.0793)
-0.0185
(0.056)
0.16
(7.222)
mtb 0.5909*
(0.329)
6.0696
(34.0277)
0.2977
(0.3718)
1.6169
(40.1599)
0.184
(0.3713)
1.6386
(39.549)
roa 0.1274*
(0.0678)
-0.3532
(14.8449)
0.1335*
(0.069)
-0.178
(8.1814)
0.1328*
(0.0684)
-0.1439
(8.7094)
Observations 759 33 759 33 759 33
R-Square 0.209 0.2377 0.2101 0.2377 0.2108 0.2377
Pseudo R-square 0.742078 0.999331 0.746691 0.999554 0.749653 0.999554
Likelihood Ratio 177.9079 8.9561 179.014 8.9588 179.7235 8.9585
Note: There is no evidence showing the relationship between private information and dividend policy
from model 4 - 6. The standard error is reported in parenthesis.***, **, * defined as statistically
significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
34
Table 5.10 (Continue)
Model 7 Model 8
SET MAI SET MAI
Intercept -3.1588***
(1.0871)
4.7932
(339.5)
-12.1164***
(3.4447)
8.2549
(3751.3)
ab_return_ln -3.7544
(3.4097)
-39.5694
(900.9)
-3.8618
(3.3887)
-34.7531
(819.9)
roll_ln 0.0872
(0.142)
-1.2983
(26.4409)
0.0563
(0.1381)
-1.1267
(25.6078)
ab_roll_ln 0.4551
(0.4881)
1.9995
(213)
0.4246
(0.4722)
1.5306
(200.1)
div_yield 2.5977***
(0.5016)
1.693
(21.6358)
2.814***
(0.5197)
1.915
(22.9417)
ta 7.12E-11*
(4.21E-11)
-3.17E-09
(2.08E-07)
lnta
0.4336***
(0.1481)
-0.364
(176.6)
debt -0.0169
(0.0351)
0.5769
(18.757)
-0.00289
(0.0308)
0.4546
(17.8476)
cash 0.00306
(0.0573)
0.2547
(5.4493)
-0.00476
(0.0577)
0.2109
(6.2045)
mtb 0.571*
(0.3428)
1.4614
(45.8781)
0.5803*
(0.3216)
1.7977
(50.7282)
roa 0.1335*
(0.0692)
-0.1422
(7.6726)
0.1186*
(0.0661)
-0.1546
(8.4371)
Observations 759 33 759 33
R-Square 0.2075 0.2377 0.2077 0.2377
Pseudo R-
square 0.736418 0.999554 0.737035 0.999554
Likelihood
Ratio 176.5509 8.9585 176.6992 8.9585
Note: There is no evidence showing the relationship between private information and dividend policy
from model 7 - 8. The standard error is reported in parenthesis. ***, **, * defined as statistically
significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
35
Table 5.11 Result of logistic regression on dividend universe from illiquidity ratio.
Model 9 Model 10 Model 11
SET MAI SET MAI SET MAI
Intercept -1.7364**
(0.8772)
-12.6499
(122)
-14.0452*
(7.3177)
-225.8
(2362.5)
-2.1298**
(0.8699)
-29.226
(243.1)
ab_return_chg -2.0574
(2.4099)
25.4361
(254)
-2.1363
(2.3765)
-0.5896
(204.7)
-1.5889
(2.2188)
1.7573
(260)
Illiq_chg -1327.6
(1119.1)
-4203.1
(272846)
-1039.6
(1373.5)
25668
(338076)
-1418.4
(1199.9)
13602.8
(369022)
ab_illiq_chg 603.8
(5800.4)
-54530.8
(3690479)
721.5
(6731.2)
349725
(4549375)
-355.5
(6128.8)
186719
(4981095)
div_yield 2.4687***
(0.4844)
2.6079
(19.705)
2.5591***
(0.5089)
2.1821
(25.116)
2.582***
(0.5065)
3.2677
(27.7937)
mkt_cap 9.29E-11
(5.87E-11)
1.17E-08
(9.49E-08)
lnmkt_cap
0.5891*
(0.3539)
10.4475
(117.4)
ta
4.80E-11
(3.84E-11)
1.22E-08
(2.02E-
07)
debt -0.0171
(0.0334)
0.3078
(14.1201)
-0.0174
(0.0359)
1.085
(18.3554)
-0.0186
(0.036)
0.5625
(18.5202)
cash -0.0141
(0.054)
-0.0215
(4.5925)
-0.0183
(0.0551)
-0.0154
(4.939)
-0.00866
(0.0564)
-0.1153
(8.271)
mtb 0.2147
(0.3707)
-1.7598
(69.9598)
0.2631
(0.3527)
2.1686
(70.9599)
0.4813
(0.3232)
6.1676
(49.8302)
roa 0.1209*
(0.066)
0.3774
(8.8188)
0.1151*
(0.0661)
0.0983
(10.7324)
0.1237*
(0.0677)
0.1515
(13.8767)
Observations 759 33 759 33 759 33
R-Square 0.2116 0.2377 0.2101 0.2377 0.2092 0.2377
Pseudo R-square 0.752618 0.999331 0.746804 0.999219 0.743204 0.999331
Likelihood Ratio 180.4346 8.9568 179.0409 8.9554 178.1778 8.9563
Note: There is no evidence showing the relationship between private information and dividend policy
from model 9 – 11. The standard error is reported in parenthesis.***, **, * defined as statistically
significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
36
Table 5.11 (Continue)
Model 12 Model 13 Model 14
SET MAI SET MAI SET MAI
Intercept -10.3603**
(4.1198)
-303.5
(3673.5)
-1.7548**
(0.8829)
7.6902
(195.5)
-14.3636**
(7.3061)
-3.1344
(2158.4)
ab_return_chg -1.7261
(2.1982)
6.0554
(640.3)
ab_return_ln
-1.7963
(2.3164)
4.696
(341.4)
-1.9085
(2.296)
2.8078
(343.8)
Illiq_chg -1416.1
(1253.9)
7722.7
(1143867)
Illiq_ln
-1329.8
(1141.9)
-6412.1
(77979.6)
-1032.7
(1354.9)
-6272.3
(88179.7)
ab_illiq_chg -200.4
(6583.3)
102552
(15564997)
ab_illiq_ln
815.9
(5474)
-46500.2
(590268)
1112.5
(6203.2)
-44751
(654468)
div_yield 2.7065***
(0.5085)
2.8776
(24.1607)
2.4911***
(0.4845)
1.0372
(20.0321)
2.5823***
(0.5086)
1.0496
(21.7817)
mkt_cap
9.41E-11
(5.85E-11)
6.17E-10
(6.68E-
08)
lnmkt_cap
0.603*
(0.353)
0.5416
(101.4)
lnta 0.3844**
(0.1826)
13.7642
(180.3)
debt -0.00942
(0.0317)
0.7275
(31.0308)
-0.0195
(0.0329)
0.9108
(52.0664)
-0.0199
(0.0353)
0.9283
(53.35)
cash -0.012
(0.0567)
0.00894
(8.6161)
-0.0165
(0.054)
-0.00702
(3.0588)
-0.0206
(0.0551)
-0.00723
(3.4531)
mtb 0.5282*
(0.3136)
5.7212
(91.5274)
0.2086
(0.3717)
-0.1178
(33.0065)
0.2579
(0.3526)
-0.0283
(39.7711)
roa 0.114*
(0.0651)
0.4111
(20.4406)
0.1175*
(0.0644)
-0.0527
(7.3373)
0.1119*
(0.0645)
-0.0509
(7.7569)
Observations 759 33 759 33 759 33
R-Square 0.209 0.2377 0.2112 0.2377 0.2098 0.2377
Pseudo R-square 0.742141 0.999442 0.751129 0.999331 0.745361 0.999442
Likelihood Ratio 177.9229 8.9571 180.0781 8.9568 178.6945 8.9571
Note: There is no evidence showing the relationship between private information and dividend policy
from model 12 - 14. The standard error is reported in parenthesis.***, **, * defined as statistically
significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
37
Table 5.11 (Continue)
Model 15 Model 16
SET MAI SET MAI
Intercept -2.1444**
(0.8703)
6.9867
(404.7)
-10.5472***
(4.0459)
-5.9828
(3216.7)
ab_return_ln -1.2831
(2.1727)
0.9017
(399.6)
-1.4934
(2.1518)
2.7059
(341.7)
Illiq_ln -1492.7
(1207)
-6393.7
(98784.3)
-1488.5
(1248.8)
-6307
(87415.5)
ab_illiq_ln -209.5
(5771.6)
-46058.5
(729286)
60.974
(6088.6)
-45103.1
(631139)
div_yield 2.6035***
(0.5061)
1.1873
(31.8609)
2.7354***
(0.5091)
1.0707
(24.4034)
ta 4.89E-11
(3.77E-11)
2.29E-10
(2.29E-10)
lnta
0.3918**
(0.1791)
0.6572
(148.2)
debt -0.0213
(0.0352)
0.8158
(48.2703)
-0.0121
(0.0312)
0.9435
(53.3544)
cash -0.0108
(0.0564)
-0.00554
(4.399)
-0.0137
(0.0569)
-0.00113
(4.2667)
mtb 0.481
(0.3229)
0.2994
(56.8102)
0.5311*
(0.3129)
0.2008
(49.4095)
roa 0.1207*
(0.066)
-0.0657
(8.2765)
0.1116*
(0.0637)
-0.0543
(7.5829)
Observations 759 33 759 33
R-Square 0.2089 0.2378 0.2086 0.2377
Pseudo R-
square 0.741703 0.999665 0.740497 0.999442
Likelihood
Ratio 177.8176 8.9591 177.5291 8.9572
Note: There is no evidence showing the relationship between private information and dividend policy
from model 15 - 16. The standard error is reported in parenthesis. ***, **, * defined as statistically
significant at the 1%, 5% and 10%level.
Ref. code: 25605902042141WZP
38
Table 5.12 Result of logistic regression on dividend universe from firm-specific
variation during the crisis.
Model 1 Model 5 Model 7
SET MAI SET MAI SET MAI
Intercept -5.0624**
(2.5441)
-8.925
(599.7)
-5.1203*
(2.6246)
-1.6069
(219.5)
-5.0472**
(2.3649)
6.8408
(505.8)
ab_return_chg -15.5655**
(7.0833)
78.5535
(2567.9)
ab_return_ln
-12.7725**
(6.0634)
-10.9422
(1412.2)
-10.7329**
(5.2771)
-35.361
(2268.4)
d0709 -3.2596
(2.4105)
-0.7597
(609.9)
-3.0161
(2.3975)
4.3472
(329.7)
-4.2492*
(2.4935)
0.88
(506.5)
roll_chg 1.1299*
(0.6668)
-0.4182
(59.7291)
roll_ln
1.1634*
(0.6916)
-0.2649
(85.5908)
0.9636*
(0.585)
-0.3205
(108.1)
roll_chg_d070
9
-0.8042
(0.6759)
0.142
(97.2964)
roll_ln_d0709
-0.8341
(0.6988)
-1.4873
(120.4)
-0.5767
(0.5904)
-1.3484
(178)
ab_roll_chg 2.0952*
(1.0756)
-16.2013
(632.5)
ab_roll_ln
1.6098*
(0.869)
-3.9475
(379.4)
1.4071*
(0.8389)
1.3746
(595.1)
div_yield 3.5608***
(1.0669)
2.7761
(33.0003)
3.4184***
(0.9837)
2.5403
(26.1614)
3.5634***
(1.0426)
1.7894
(39.7496)
mkt_cap 2.05E-10*
(1.14E-10)
1.08E-08
(1.46E-07)
2.02E-10*
(1.13E-10)
3.02E-09
(1.93E-07)
ta
1.25E-10*
(6.66E-11)
-4.39E-09
(2.89E-07)
debt 0.00132
(0.0476)
0.1019
(23.3748)
-0.0132
(0.0451)
0.1736
(21.3503)
-0.0395
(0.0511)
0.7113
(32.1456)
cash -0.0564
(0.0714)
0.0711
(2.8952)
-0.0544
(0.0699)
0.028
(6.8171)
-0.0572
(0.07)
0.1719
(9.0057)
mtb 0.2851
(0.4275)
-0.1469
(80.4437)
0.2546
(0.4333)
1.1845
(51.8393)
0.5897
(0.3793)
0.8126
(80.8981)
roa 0.3782***
(0.1386)
-0.1884
(17.37)
0.3541***
(0.1294)
-0.2418
(12.376)
0.3868***
(0.1418)
-0.1477
(10.6364)
Observations 759 33 759 33 759 33
R-Square 0.2375 0.2377 0.2364 0.2378 0.2356 0.2378
Pseudo R-
square 0.8583 0.9994 0.8537 0.9997 0.8504 0.9997
Likelihood
Ratio 205.7705 8.9574 204.6624 8.9592 203.878 8.959
Note: Model 1 and 5 show the positive relationship between private information and dividend. The
crisis cannot affect dividend decision. Model 7 shows a positive relationship between stock price
informativeness and dividend and a negative relationship between the crisis and dividend. The standard
error is reported in parenthesis.***, **, * defined as statistically significant at the 1%, 5% and
10%level.
Ref. code: 25605902042141WZP
39
Table 5.13 Result of logistic regression on dividend universe from illiquidity ratio
during the crisis.
Model 9 Model 10 Model 11
SET MAI SET MAI SET MAI
Intercept -3.5269**
(1.7442)
-12.4893
(229)
-21.4872**
(10.1567)
-369.7
(2409.1)
-3.7364**
(1.7071)
-3.7364**
(1.7071)
ab_return_chg -7.3709**
(3.7579)
25.9122
(438.2)
-8.4023**
(3.9585)
15.1727
(392.6)
-7.1254**
(3.5942)
-7.1254**
(3.5942)
d0709 -3.2459**
(1.37)
1.9114
(279.7)
-3.6387**
(1.4723)
3.3139
(238.3)
-3.7556**
(1.5367)
-3.7556**
(1.5367)
Illiq_chg 319121*
(184388)
-3943.2
(1091610)
324640*
(184003)
2277.7
(940283)
307149*
(176245)
307149*
(176245)
Illiq_chg_d0709 -319835*
(184552)
-2203.6
(1133092)
-325154*
(184167)
-4879.7
(963085)
-307667*
(176363)
-307667*
(176363)
ab_illiq_chg 4216.1
(4992.6)
-78775.6
(1566834)
5471.8
(5436.7)
-34071.1
(1669479)
3685.9
(5222.8)
3685.9
(5222.8)
div_yield 3.0812***
(0.7927)
3.0783
(45.8015)
3.3079***
(0.8652)
2.1691
(47.8828)
3.1662***
(0.8037)
3.1662***
(0.8037)
mkt_cap 1.21E-10
(8.49E-11)
1.38E-08
(8.73E-08)
lnmkt_cap
0.847*
(0.4471)
17.8989
(118.2)
ta
8.63E-11
(5.75E-11)
8.63E-11
(5.75E-11)
debt -0.0107
(0.0477)
0.0881
(24.4012)
-0.0151
(0.0479)
0.3881
(25.1924)
-0.0338
(0.0549)
-0.0338
(0.0549)
cash 0.0324
(0.0851)
0.0504
(4.138)
0.0523
(0.0872)
0.0356
(4.6748)
0.021
(0.089)
0.021
(0.089)
mtb 0.6145
(0.4532)
-2.9907
(61.9879)
0.6344
(0.4467)
-3.3895
(69.3233)
0.8471*
(0.4637)
0.8471*
(0.4637)
roa 0.3187***
(0.1188)
0.2083
(15.0099)
0.3447***
(0.1241)
0.4602
(15.8223)
0.3516***
(0.1267)
0.3516***
(0.1267)
Observations 759 33 759 33 759 759
R-Square 0.2365 0.2377 0.237 0.2377 0.2362 0.2362
Pseudo R-square 0.8542 0.9994 0.8564 0.9994 0.8530 0.8530
Likelihood Ratio 204.7767 8.9574 205.3088 8.9575 204.5036 204.5036
Note: The result during the financial crisis shows the negative relationship on dividend from model 9 -
11. The standard error is reported in parenthesis. ***, **, * defined as statistically significant at the
1%, 5% and 10%level.
Ref. code: 25605902042141WZP
40
Table 5.13 (Continue)
Model 12 Model 13
SET MAI SET MAI
Intercept -15.628***
(5.4246)
-366.5
(7148.6)
-3.4491**
(1.746)
5.3475
(338.9)
ab_return_chg -8.1408**
(3.8863)
12.5531
(667.8)
ab_return_ln
-6.5818*
(3.6165)
0.2532
(367.8)
d0709 -3.941***
(1.5042)
-1.1676
(506.7)
-3.281**
(1.3802)
1.3194
(208.5)
Illiq_chg 301108*
(180306)
-757.8
(981087)
Illiq_ln
298219*
(176350)
-1766.6
(461561)
Illiq_chg_d0709 -301702*
(180465)
-722.7
(1081670)
Illiq_ln_d0709
-298546*
(176342)
-3543.9
(480256)
ab_illiq_chg 4775.1
(5719.5)
-25531.6
(2543261)
ab_illiq_ln
3644.7
(4691.4)
-43153.7
(837876)
div_yield 3.384***
(0.8474)
2.7366
(79.5938)
3.0985***
(0.7934)
1.2246
(56.6545)
mkt_cap
1.24E-10
(8.47E-11)
1.70E-09
(1.64E-07)
lnta 0.5562**
(0.2198)
16.9675
(362.1)
debt -0.0094
(0.0456)
0.6105
(59.8442)
-0.016
(0.0461)
0.4604
(75.5357)
cash 0.0503
(0.0904)
0.084
(20.0518) 0.023 (0.085)
-0.00612
(4.0234)
mtb 0.8372*
(0.4583)
4.3734
(139.7)
0.5229
(0.4369)
-0.0439
(50.0976)
roa 0.347***
(0.1231)
0.5793
(19.0603)
0.3096***
(0.117)
-0.0861
(12.4239)
Observations 759 33 759 33
R-Square 0.236 0.2377 0.2352 0.2378
Pseudo R-square 0.8524 0.9993 0.8491 0.9997
Likelihood Ratio 204.3444 8.9569 203.5635 8.9598
Note: The result during the financial crisis shows the negative relationship on dividend from model 12
- 13. The standard error is reported in parenthesis. ***, **, * defined as statistically significant at the
1%, 5% and 10%level.
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Table 5.13 (Continue)
Model 14 Model 15
SET MAI SET MAI
Intercept -22.6457**
(10.4498)
-59.2828
(6668.5)
-3.697**
(1.6978) 8.16 (689.5)
ab_return_ln -7.9405**
(3.9327)
-0.1421
(370.8)
-6.3887*
(3.4802)
-1.6195
(422.3)
d0709 -3.759**
(1.5239)
1.9556
(274.5)
-3.8085**
(1.5656)
-0.8365
(170.5)
Illiq_ln 313844*
(181642)
-722.7
(495587)
289917*
(170890)
-3311
(415787)
Illiq_ln_d0709 -313767*
(181599)
-4176.9
(496182)
-290111*
(170868)
-2185.3
(466197)
ab_illiq_ln 5683.1
(5203.4)
-40395.4
(1025965)
3276.2
(4821.7)
-44701
(1090707)
div_yield 3.3797***
(0.8946)
1.256
(62.9454)
3.1938***
(0.8109)
0.9137
(79.4966)
lnmkt_cap 0.899**
(0.457)
3.1977
(314.9)
ta
8.78E-11
(5.65E-11)
-5.62E-10
(3.75E-07)
debt -0.0199
(0.0465)
0.4406
(81.1595)
-0.0393
(0.0535)
0.8492
(88.3273)
cash 0.0429
(0.0875)
-0.0074
(3.9882)
0.0111
(0.088)
-0.0105
(4.145)
mtb 0.5519
(0.4269)
-0.1354
(51.0319)
0.7759*
(0.4468) 0.1094 (102)
roa 0.3416***
(0.1252)
-0.0919
(13.4052)
0.3458***
(0.1273)
-0.00914
(15.1711)
Observations 759 33 759 33
R-Square 0.2359 0.2378 0.235 0.2378
Pseudo R-square 0.8520 0.9997 0.8479 0.9998
Likelihood Ratio 204.2636 8.9597 203.2773 8.96
Note: The result during the financial crisis shows the negative relationship on dividend from model 14
- 15. The standard error is reported in parenthesis. ***, **, * defined as statistically significant at the
1%, 5% and 10%level.
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CHAPTER 7
CONCLUSION
This study tries to study the effect on stock price carried private information
and decision on dividend. We use the observation from companies listed in SET Index
and MAI Index which are 334 companies during 2007-2017. From 334 companies
there are 72 companies are listed in Dividend Universe. We find that companies listed
in SET Index pay dividend more than companies listed in MAI. While, stock price of
companies listed in MAI Index tend to hold more private information than companies
listed in SET Index due to the lower value of 𝑅2 and higher value of illiquidity ratio.
Comparing to the group of Dividend Universe, we find that companies listed in the
group of Dividend Universe tend to carry less inside information due to the higher
value of 𝑅2 and lower value of illiquidity ratio.
Firstly, we try to regress the baseline model (equation 4) with the overall
sample. And we find that private information measurement by using firm-specific
stock return variation indicates a positive relationship between private information
and dividend. The more inside information conveyed in stock return, the higher
chance of companies paying dividend. Focusing on the company’s size we find that
small and medium-sized companies positively affect the chance of paying dividend.
For an alternative measurement of private information, we use illiquidity ratio. The
result shows that private information in stock return has a negative relationship with
dividend. This finding suggests that only companies conveyed more private
information tend to keep or omit dividend. And when we focus on the company’s
size, we also ensure that when stock price of small companies convey more private
information, the probability of companies paying dividend will be less. The manager
tends to omit or keep dividend rather than pay it. The estimated coefficients of control
variables of both measurements show positive relationship with paying dividend only
dividend yield, company’s size and ROA. While, other variables show insignificant
relationship. High dividend yield and profitability companies tend to pay dividend.
Secondly, we try to find the relationship between private information during
the financial crisis and dividend by using the baseline model (equation 5). By using
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firm-specific variation we find that the financial crisis has a negative impact on
dividend while private information in stock return has a positive impact on dividend.
There is no relationship showing private information affects the probability of paying
dividend during the financial crisis. Only small companies whose stock price
conveyed more private information tend to pay dividend. On the other hand, using
illiquidity ratio we find a negative relationship between financial crisis and dividend.
No effect of private information on dividend. However, we find that companies,
especially medium-sized companies tend to omit dividend when their stock price
convey more private information during the crisis.
Thirdly, we try to regress the baseline model with the subsample group,
Dividend Universe. We find that private information cannot be used to determine the
chance of paying dividend. But when we add the interactive term of private
information and the financial crisis to test the relationship between private
information in stock return and dividend during the crisis. Using firm-specific stock
return variation, we find a positive relationship between private information and
dividend. No effect of private information on dividend decision during the crisis.
While using illiquidity ratio, we find a negative relationship between dividend and
stock price informativeness during the crisis. It means that companies tend to omit
their dividend during the crisis when more inside information conveyed in stock price.
For the overall finding, the result shows that during the crisis companies tend
to omit or keep dividend. The effect of private information on dividend decision is
still questionable. Some models demonstrate private information positively relate to
the chance of paying dividend while some models demonstrate a negative relationship
between private information in stock return and dividend. Then, it is interesting for
further study due to the yearly basis on variables and ambiguous conclusion on effect.
Increasing number of observation, the frequency of basis and extension the time
length might give the significant result in private information.
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BIOGRAPHY
Name Miss Jintana Kongvijitwat
Date of birth February 19, 1991
Educational attainment
June 2009 – March 2013: Assumption University,
Business Administration, major in Financing
Work position Loan Administration in Corporate Lending
TISCO Bank Public Company Limited
Work Experiences March 2013 – Present
Loan Administration in Corporate Lending
TISCO Bank Public Company Limited
Ref. code: 25605902042141WZP
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