dividend changes and stock price informativeness: evidence

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

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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


by company’s size


during financial crisis.


during financial crisis by company’s size

5.6 Result of logistic regression on dividend from illiquidity ratio 25


by company’s size


during the financial crisis


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

18

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

19

Table 5.3 Result of logistic regression on dividend from firm-specific variation by

company’s size

Medium Model 2 Model 4 Model 6


Intercept -21.333***

(4.1356)

-58.185**

(26.6389)

-16.3271*

(8.4324)

-55.5151

(115)

-21.310***

(4.1505)

-59.971**

(28.2502)


(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



(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)


(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


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


(1.1366)

-28.3726***

(8.1332)

-9.8993***

(1.1413)

-27.2143***

(8.0928)


(0.6173)

1.2606

(1.1913)


(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


(4.3582)

-62.7712**

(28.1918)

-18.4188***

(4.3676)

-61.5094**

(29.52)


(1.501)

9.8551

(8.9613)


(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)


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


(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


(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.




(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)




(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




Ref. code: 25605902042141WZP

29

Table 5.8 (continue)

Model 15 Model 16

SET MAI SET MAI


(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)


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




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)


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


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.




(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


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


Ref. code: 25605902042141WZP

33





(3.515)

-272.1

(2079.4)

-2.7835***

(1.0508)

0.7106

(177.6)

-20.101**

(7.9406)

-21.6819

(2429.6)


(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





Ref. code: 25605902042141WZP

34


Model 7 Model 8

SET MAI SET MAI


(1.0871)

4.7932

(339.5)

-12.1164***

(3.4447)

8.2549

(3751.3)


(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)


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


from model 7 - 8. The standard error is reported in parenthesis. ***, **, * defined as statistically


Ref. code: 25605902042141WZP

35

Table 5.11 Result of logistic regression on dividend universe from illiquidity ratio.



Intercept -1.7364**

(0.8772)

-12.6499

(122)

-14.0452*

(7.3177)

-225.8

(2362.5)

-2.1298**

(0.8699)

-29.226

(243.1)


(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



from model 9 – 11. The standard error is reported in parenthesis.***, **, * defined as statistically


Ref. code: 25605902042141WZP

36




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)


(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





Ref. code: 25605902042141WZP

37


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)


(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)


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


from model 15 - 16. The standard error is reported in parenthesis. ***, **, * defined as statistically


Ref. code: 25605902042141WZP

38

Table 5.12 Result of logistic regression on dividend universe from firm-specific

variation during the crisis.



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.



Intercept -3.5269**

(1.7442)

-12.4893

(229)

-21.4872**

(10.1567)

-369.7

(2409.1)

-3.7364**

(1.7071)

-3.7364**

(1.7071)


(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


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


Ref. code: 25605902042141WZP

40


Model 12 Model 13

SET MAI SET MAI


(5.4246)

-366.5

(7148.6)

-3.4491**

(1.746)

5.3475

(338.9)


(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)


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


Ref. code: 25605902042141WZP

41


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)


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


Ref. code: 25605902042141WZP

42

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

dividend changes and stock price informativeness: evidence

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