a test of life cycle of dividend and effect of financial

A TEST OF LIFE CYCLE OF DIVIDEND

AND EFFECT OF FINANCIAL CRISIS:

EVIDENCE FROM THAILAND

BY

MISS CHALITA NATIMAKUL

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 A TEST OF LIFE CYCLE OF DIVIDEND AND

EFFECT OF FINANCIAL CRISIS: EVIDENCE

FROM THAILAND

Author Miss Chalita Natimakul

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 study life cycle relationship with dividend payout policy in

Thailand. This study cover period between 1997 to 2017. This result evidence that

life cycle proxy as retained earnings/ total equity show positive significant between

life cycle and probability of dividend payout. Mature and old firm driven probability to

pay rather than young firm. In addition, this study finding that probability to pay

dividend is negatively affected by impact of Asian financial crisis but it not appears

significantly effect on impact of Global financial crisis in Thailand. After the financial

crisis, life cycle determinant of Asian financial crisis is changed while life cycle

determinant of Global financial crisis is not changed to determined dividend policy.

Keywords: Life cycle theory, Dividend Policy, Logit Model, Financial crisis

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ACKNOWLEDGEMENTS

I would like to express my very great appreciation to my main supervisor

Associate Professor Seksak Jumreornvong, Ph.D. for his suggestions during my research

development. He is generously given his valuable time with willingness.

My grateful thanks to all MIF professors for patient guidance about useful knowledge

that helping complete this research. I would like to thanks academic staff in MIF

department for helping and providing any accommodations. I wish to thanks my friends

my colleague and also my boss that valuable support me to achieve this research.

Finally, I would like to special thanks to my family that always encourage me

throughout my study.

Miss Chalita Natimakul

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TABLE OF CONTENTS

Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (2)

LIST OF TABLES (5)

LIST OF ABBREVIATIONS (6)

CHAPTER 1 INTRODUCTION 1

1.1 Introduction 1

CHAPTER 2 REVIEW OF LITERATURE 3

2.1 Dividend policy 3

2.2 Dividend policy and life cycle 3

2.3 Contribution 4

CHAPTER 3 THEORETICAL FRAMEWORK 5

3.1 Agency theory 5

3.2 Life cycle theory 5

3.3 Other relevance theories 5

3.3.1 Signaling theory 5

3.3.2 Dividend smoothing theory 6

3.4 Hypothesis 6

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CHAPTER 4 RESEARCH METHODOLOGY 7

4.1 Data 7

4.2 Methodology 7

4.3 Regression model 10

CHAPTER 5 RESULT 12

5.1 Sample statistic 12

5.2 Empirical results 16

CHAPTER 6 CONCLUSION 25

REFERENCES 26

BIOGRAPHY 29

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LIST OF TABLES

Tables Page

5.1 This table represent yearly proportion of dividend paid from total 12

observation in 1997-2017.

5.2 This table represent sub period of relationship between lifecycle stage 13

and proportion of dividend paid.

5.3 This table represent descriptive statistic of life cycle variable and other 14

control variables.

5.4 This table reports Pearson correlation coefficients of variables. 15

5.5 This table reports the logistic regression model to analyse decision 16

to pay dividend by testing all firm in 1997-2017.


to pay dividend by testing all firm in 1997-2017 in each life cycle stage.


to pay dividend by testing firm by separate into two period.


to pay dividend by testing firm by separate into two period

in each life cycle stage.

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LIST OF ABBREVIATIONS

Symbols/Abbreviations Terms

RETE Retained earnings to total equity

ROA Return on asset

SGR Sales Growth Ratio

LNTA Logarithm of total asset

CATA Cash balance or cash holding

ROAL1 Lagged return on asset

TETA Total equity to total asset

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CHAPTER 1

INTRODUCTION

1.1 Introduction

The Dividend policy is the top- list and active decision of corporate financial

policies for manager ( Lintner , 1956) Manager have to decide whether to distribute

profit to shareholder or retained earnings to future usage as investment decision in other

words to maintain sufficient cash to match their company needs. Both choices between

financing dividend policy to shareholder and retained earnings have to base on an

objective of maximizing shareholder wealth. According to shareholder will value stable

dividend payment stock rather than fluctuation then if management decide to change

dividend policy It will effect in stock valuation. This is the reason why dividend policy

is one of the key focus of management.

( Grullon & Michaely, 2002; DeAngelo, et al. , 2006; Faff, et al. , 2016) Many

previous study research investigates choices of firm dividend policy decision are follow

their corporate lifecycle. ( Miller & Friesen, 1984) Previous study characterized life

cycle stage by structure strategies and decision making. The life cycle stage of company

will have difference strategy of corporate dividend policy. Mature firm tend to pay

dividend rather than young firm according to young firm have low profitability and

retained earnings is important for finance their investment project.

( Gao & Alas, 2010) Some study summarized life cycle stage can be shifted by

strategy, structure, and situation. (Dickinson, 2011)the company might also face impact

from internal and external situation which life cycle of firm might shifted from phases

to difference phases according to those impact. Through company lifecycle, Manager

might be changed choice on dividend payment or corporate decision.

Some research studies have different aspects about company decision that

impact when company meet financial crisis. ( Lins, et al. , 2013) during the financial

crisis, Firm control by family owner mostly decide to cut dividend and use their

resource more appropriate than non- crisis situation. ( Nguyen & Tran, 2016) The

research find impact from global financial crisis. Firm will restructure by change their

dividend policy to balance their capital and cash flow in hand without consider life

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cycle. (Sun & Wang, 2015)In financial crisis period, firm decide to retained higher cash

due to external finance are too costly than internal finance effecting from crisis.

( Hauser, 2013) also supported that financial crisis external fund are limited firm need

to reserve their cash to support their company to survive recession. (DeAngelo, et al. ,

2006) when firm meet financial distressed large firm tend to action faster than smaller

firm by massive dividend reduction. ( Bliss, et al. , 2015) Due to financial crisis cause

dividend payout reduction to reserve as their financing source of fund. These could

result the difference in decision of dividend payout policy changing during and impact

dividend decision after crisis.

In the past decade, Thailand have to deal with two biggest external shock which

are financial crises from Asian financial crisis to Global financial crisis. This research

focus on 2 different fundamental of external factor crisis comprise of Asian financial

crisis and Global financial crisis in a decade dividend policy strategy. First mentioned

crisis is Asian financial crisis which start from Thailand and expand the survivor to the

region while Global financial crisis starts from outside of the region and spread effect

to Thailand. Both crisis show that the impact of the Global financial crisis was much

less severe compared to the Asian financial crisis.

Therefore, this study would like to test the theory of dividend life cycle in

Thailand and whether Asian financial crisis and Global financial crisis have

significantly effect on dividend policy including impact from financial crisis whether

life cycle can explain dividend payout policy after crisis.

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CHAPTER 2

REVIEW OF LITERATURE

2.1 Dividend policy

The author found that studies of dividend are interested for many researchers

which are providing many evidences. (Modigliani & Miller, 1961) developed dividend

irrelevance proposition, the dividend payout not related with firm value.

In reality, firm meet transaction cost taxes and have to deal with the dividend

puzzle (Black, 1976). According that many previous literature examines whether firms

pay or not pay dividend and also conducted on the relationship between dividend policy

and factor determine dividend policy ( Denis & Osobov, 2008; Thanatawee, 2011;

Kouser, et al. , 2015) The dividend policy research developed and explained by many

theory including signaling theory, dividend smoothing and recent theory dividend life

cycle to show and explain characteristic and reason to pay or not to pay dividend payout.

2.2 Dividend policy and life cycle

A lot of research has also been conducted on topic of life cycle theory.

( Miller & Friesen, 1984) summarized the stage of life cycle stated that company face

experience and evolve various corporate decision through difference stage. The stage

of life cycle can be classified by strategy, structure, and situation. ( Miller & Friesen,

1984) suggests five stage of life cycle including birth growth mature revival decline.

However, ( Owen & Yawson, 2010) adjusted from previous life cycle suggestion into

three stage as young mature old.

Some researchers examine key life cycle determinants to explain decision to

pay dividend on different stage. ( Fama & French, 2001; Grullon & Michaely, 2002;

DeAngelo, et al. , 2006; Denis & Osobov, 2008) study three major characteristics

consisting of profitability, investment opportunities, size. The more profitability firm

the more possibility to pay dividend. Similarly, low investment firm tend to pay

dividend. ( DeAngelo, et al. , 2006) suggest retained earing/ total equity ratio. This

research summarizes positive relationship between earning to contribute capital mixed.

Mature firms that have high retained earnings/contributed ratio are expected to pay.

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A recent study ( Kouser, et al. , 2015; Attig, et al. , 2016; Major & Angback,

2017) extend dividend life cycle by finding relationship with life cycle and dividend

policy explaining relationship with global crisis. Due to severe of financing crisis might

stag business effect cash holding that would have expected to change dividend policy

follow their stage of life cycle firm. Company will be shifted life cycle to difference

stage but the result of previous research of dividend policy changing vary between

country.

2.3 Contribution

Mostly research in emerging market including Thailand ( Oonpipat, 2009;

Thanatawee, 2011) focus only dividend life cycle determinant without linkage with

financial crisis situation. this research will contribute by extend longer period test of

dividend life cycle in Thailand and examine relationship between life cycle dividend

policy and financial crisis situation.

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CHAPTER 3

THEORETICAL FRAMEWORK

3.1 Agency theory

This study involves mainly with dividend life cycle theory. Dividend life cycle

is the theory based on agency theory. ( Jensen & Meckling, 1976; Jensen, 1986)

developed Agency problem that explain the act of agent will do as their best interest

which cause conflicts of interest between manager and shareholder. Manager decision

might not decide on basis of maximize shareholder including the dividend policy

decision for example Manager might decide to investing in negative net present value

of project or use of their own benefit etc. ( Thanatawee, 2011) To reduce this agency

problem, Company can use dividend payment as a tool to decrease free cash flow of

company then manager will hold cash flow less to manage.

3.2 Life cycle theory

However, Company should concern on their stage that match with cash flow

internal usage before distribute as dividend payout ( Mueller , 1972) established Life

cycle theory to determine the characteristics and stage of firm dividend policy. Younger

firm have low cash flow and need more investment opportunities then this young firm

likely to pay dividend less than mature firm. Manager might not want company to grow

as mature stage cause agency theory suggest that manager expected to control power

and shareholder might get less dividend. The factor that represent the stage of life cycle

for this research is retained earnings to total equity. This ratio measures the stage of

firm such as mature firm tend to have higher retained earning rather than younger firm

then this ratio expected to be high level and also imply propensity to pay dividend.

3.3 Other relevance theories

3.3.1 Signaling theory

Adverse selection problem ( Akerlof , 1970) specified the term adverse

selection which means buyer and seller not have the same level of information. (Myers

& Majluf, 1984) further suggest that information receiveing between internal and

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external of firm are not equal. The result shown that managers gain information more

than shareholders that would be cause an agency problem due to unclear objective.

Therefore, when firm change dividend policy stock price will react from receiving

information. According to dividend payout give a future prospect of firm profitability

and view of cash flow that company retained for investment.

3.3.2 Dividend smoothing theory

( Lintner , 1956) Investor prefer to give higher value to stable dividend

payment firm. The value of stock will change if manager decide to cut or decrease

dividend per share as the relation with dividend signaling theory.

3.4 Hypothesis

From previous study and theory concept, this study objective would like to find

dividend lifecycle and financial crisis impact on dividend policy we can set criteria

hypothesis to find relationship as follows;

Hypothesis I

H0: The dividend policy determinant is not consistent with theory of life cycle.

H1: The dividend policy determinant is consistent with theory of life cycle.

Hypothesis II

H0: The dividend policy is not effected by financial crisis

H1: The dividend policy is effected by financial crisis

Hypothesis III

H0: According to the financial crisis the life cycle theory factors relationship to

dividend payout policy did not change after financial crisis.

H1: According to the financial crisis, the life cycle theory factors relationship

to dividend payout policy changed after financial crisis.

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CHAPTER 4

RESEARCH METHODOLOGY

4.1 Data

The author selected sample of company listed in Stock Exchange of Thailand

( SET) using data between period to 1997- 2017. This research examines 7,345 firm-

year observation corresponding 590 firms to test hypothesis. ( Henk & Megginson,

2008) Firm in the sample are listed and delisted to avoid survivorship bias. (DeAngelo,

et al. , 2006) The sample of firm must have non- missing value that used to calculated

and used as independent variable.

In this study, Financial firm are excluded from the sample according to financial

firm have regulated to control capital reserve more strictly and higher leverage which

are not comparable to other industries.

In addition, I also excluded data containing negative total equity. The reason

why omitted negative total equity is that sample with negative Total Equity tend to have

negative retained earning which will cause RETE turn positive ( denominator and

nominator are negative turns positive ratios) ( DeAngelo, et al. , 2006) The higher

positive RETE imply that company have high probability to pay dividend. It cannot be

the case of positive number that derive from data with either negative retained earnings

and total equity.

4.2 Methodology

The author examine that our approach is to developed logistic regression to

analyze main research hypothesis. This analysis will test secondary data in SAS to find

whether the result of dependent variable would be zero or one. According to the result

is non-linear OLS model should not applied to estimate. The model estimation is based

on previous research (DeAngelo, et al., 2006)and also extend variable to explain effect

of external factor which is financial crisis follow research of ( Kouser, et al. , 2015;

Major & Angback, 2017) The function of logit model is cumulative distribution as

follow

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From the formula, the observation Y is dependent variable dividend payout.

If Y equal to 1 company decide to pay while Y equal to zero company decide not to

pay. Z is index function that consists of independent variable, control variable and

interaction variable as follows

( DeAngelo, et al. , 2006; Kouser, et al. , 2015) The list of variables is choosing

for test hypothesis represents as following

Retained earnings to total equity are used as proxy of life cycle dividend

determinant.

( Owen & Yawson, 2010) suggest to considered stage of life cycle by separate

into 3 stages consist of old mature and young to test relationship. Old firm are in top 25

percentile of highest retained to earnings ratio. Young firm are in 25 percentiles from

bottom. Mature firm are in between. These variables are dummy variable coded as

Profitability measure by return on asset

Growth or Investment opportunity measure by sale growth rate

Size of company represent by

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Cash holding

Profitability lagged one year used as proxy for dividend signaling from

previous profitability given the firm increase high ratio of RETE

Total equity to total asset is control variable due to this variable might

effect to independent variable RETE

Lagged dividend payout is representing dividend smoothing theory to

explain that if previous year company paid dividend the dummy variable will turn to 1

otherwise zero

Crisis Determinants

The research hypothesis would like to find the effect of financial crises

for Asian financial crisis in 1997-1998 which represent by dummy variable D9798, and

Global financial crisis represent by subgroup in crisis year 2008-2009 using dummy

variable D0809. If dummy variable equal to 1 this represent the year that crisis exist

otherwise zero.

Interaction Variable

This time dummy variable ratio used as interaction term between other

variable to test dividend lifecycle impact after Asian financial crisis and Global

financial crisis.

After Asian financial crisis are in 1999- 2007 period while after Global

financial crisis is in period 2010- 2017 ( Tran, et al. , 2017) test dividend policy and

𝐶𝐴/𝑇𝐴 =𝐶𝑎𝑠ℎ

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡

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shareholder credit rights by how effect shareholder right that impact from global crisis.

Dummy variable of post- crisis are also used and stated interpretation that if dummy

variable is significant dividend policy will change between two periods. If the effect of

life cycle determinant on dividend policy are identical for both period, coefficient of

interactive term should be insignificant.

4.3 Regression Model

The following logistic regression models are explaining and answering

hypothesizes of study. model 1 and model 2 are test whether dividend policy

determinant is consistent with theory of life cycle.

Model 1

Model 2

To explore the effect of financial crisis to dividend policy, this research add

dummy variable represent D9798 Asian financial crisis ( Tom yum kung crisis) and

D0809 Global financial crisis ( Hamburger crisis) which are represent 2 crises adding

in model 3.

Model 3

𝐷𝐴𝐹𝑇𝐸𝑅_𝑇𝑌𝐾 = 1 𝑎𝑓𝑡𝑒𝑟 𝐴𝑠𝑖𝑎𝑛 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠

𝐷𝐴𝐹𝑇𝐸𝑅_𝐻𝐵𝐺 = 1 𝑎𝑓𝑡𝑒𝑟 𝐺𝑙𝑜𝑏𝑎𝑙 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑟𝑖𝑠𝑖𝑠

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In addition, to answer third hypothesis that after facing Asian financial crisis

the life cycle theory relationship to dividend payout did change or not. The following

model will be separated into two period of time. Model 4 test Asian financial crisis

period in 1997-2007 and model 5 test Global financial crisis period in 2008-2017.

Model 4 (Asian financial crisis)

Model 5 (Global financial crisis)

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CHAPTER 5

RESULTS

5.1 Sample Statistic

Table 5.1 This table represent yearly proportion of dividend paid from total observation

in 1997-2017

Year

# of

dividend

paid

Total

Observation

% of firm

Dividend

paid

1997 18 22 81.82%

1998 15 54 27.78%

1999 77 206 37.38%

2000 97 230 42.17%

2001 131 246 53.25%

2002 148 258 57.36%

2003 186 288 64.58%

2004 224 326 68.71%

2005 266 379 70.19%

2006 275 393 69.98%

2007 274 393 69.72%

2008 289 399 72.43%

2009 286 410 69.76%

2010 310 417 74.34%

2011 334 428 78.04%

2012 346 436 79.36%

2013 364 457 79.65%

2014 378 483 78.26%

2015 394 506 77.87%

2016 405 512 79.10%

2017 388 502 77.29%

Total 5,205 7,345 70.86%

NOTE: This data is from Thomson Reuters Eikon Database.This sample is included non-financial listed

firms in SET from period 1997-2017 exclduing missing data. Inaddition, year 2017, total observation of

company decrease according to property fund have merge and turn to REIT including some firms are not

provided annual report due to their financial problem (availability as of May 7,2018).

From table 5. 1, The statistic reports percentage of dividend paid at 70. 86%.

Distribution of dividend sorted ascending since the beginning of year 1998

(Asian financial crisis) but in year 2009 (Global financial crisis) show lower dividend

payment then later year become gradually increasing.

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Table 5.2 This table represent sub period of relationship between lifecycle stage and

proportion of dividend paid.

Earned equity as a fraction Earned equity as a fraction

of Total Equity (RE/TE) of Total Assets (RE/TA)

Young Mature Old Young Mature Old Total

Panel A : Total Period 1997-2017

Total

period

Period

1997-2017

No. Pay 463 3013 1729 417 3044 1744 5205

% of Pay 25% 83% 95% 23% 83% 95%

Total Firm 1874 3648 1823 1837 3672 1836 7345

Panel B : Asian financial crisis

During

crisis

Period

1997-1998

No. Pay 16 14 3 18 11 4 33

% of Pay 33% 58% 100% 35% 58% 80%

Total Firm 49 24 3 52 19 5 76

After

crisis

Period

1999-2007

No. Pay 92 1102 484 98 1007 573 1678

% of Pay 11% 80% 92% 12% 78% 94%

Total Firm 805 1386 528 819 1290 610 2719

Panel C: Global financial crisis

During

crisis

Period

2008-2009

No. Pay 40 334 201 38 336 201 575

% of Pay 22% 80% 94% 21% 81% 93%

Total Firm 178 418 213 177 416 216 809

After

crisis

Period

2010-2017

No. Pay 315 1563 1041 263 1690 966 2919

% of Pay 37% 86% 96% 33% 87% 96%

Total Firm 842 1820 1079 789 1947 1005 3741


firms in SET from period 1997-2017 exclduing missing data. Life cycle stages including young mature

old are ranked by retained earning to capital mixed. This ratio of dividend payer shown in each sub

period.

This table 5. 2 present relationship between lifecycle stage and proportion of

payer ranging by earned to capital mixed comparing in difference sub period. Life cycle

stage in this study consist of young mature and old which are distinct stages by earned

to capital mixed. Proportion of payer gradually increase related to life cycle stage.

Mature and old firm have high proportion of dividend payer in every period.

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Table 5. 3 This table represent descriptive statistic life cycle variable and other contol

variables


firms in SET from period 1997-2017 exclduing missing data. This mean and median of each variable

shown different between dividend payers and non-dividend payers.

In Table 5.3 described value of mean and median of sample with total investigated

period and sub-period. The main variable of interest is RETE showing in panel A have

higher mean and median of dividend payer firm through every period.

In panel B shows other control variable including profitability (ROA) and size

of company ( Lnasset) previous year profitability ( ROAL1) total equity to total asset

(TETA) and cash balance (CATA) which median or mean of dividend payer also higher

than non- dividend payer. Meaning that higher statistics are predicted high probability

to pay dividend.

SGR which represent as investment opportunities value of mean of SGR of

non-dividend payer is follow higher than dividend payer whereas during crisis median

of dividend payer higher than non-dividend payer which contrary to expectation.

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Table 5.4 This table reports Pearson correlation coefficients of variables.

RETE RETA AGE ROA SGR LNASSET ROAL1 TETA CATA DIVDUM

RETE 1

RETA 0.06302 1

AGE 0.00063 0.0538 1

ROA 0.02025 0.01457 -0.02495 1

SGR 0.00053 -0.00986 -0.00499 0.00122 1

LNASSET 0.0025 0.14296 -0.00921 -0.00629 0.00311 1

ROAL1 0.0098 0.07236 -0.01273 0.02803 -0.00507 0.01437 1

TETA 0.07704 0.28351 0.00356 0.10703 -0.01816 -0.29639 0.05912 1

CATA 0.02065 0.11942 -0.01134 0.1033 -0.00969 -0.1138 0.0421 0.34645 1

DIVDUM 0.05267 0.41122 -0.05539 0.09884 -0.03726 0.15688 0.04537 0.26658 0.09604 1

NOTE: Pearson Correlation Coefficients, N = 6,874

The result in table 5.4 is report correlation metric between variable. The statistic

numbers above have no high correlation between any variables.

According to ( DeAngelo, et al. , 2010) used AGE as a proxy to test life cycle

with seasoned equity offering then this table show correlation of age with other variable

to see relationship. ( Faff, et al. , 2016) Some researchers argue that AGE is not good

proxy to classified life cycle of company according to maturity cannot be proof by age

of company including age in this study represent listed year of company not an actual

operating year. Moreover, relationship in correlation are opposite from expected

such as longer listed firm have negative relationship with profitability. Therefore, this

research is focus on retained earnings to total equity as lifecycle proxy.

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5.2 Empirical Results

Table 5.5 This table reports the logistic regression model to analyse decision to pay

dividend by testing all firm in 1997-2017.

(1) ∆Prob (2) ∆Prob (3) ∆Prob

rete 2.6848*** 0.3641 1.4546*** 0.0010 1.4051*** 0.0010

(0.0906) (0.0902) (0.0903)

roa 0.0299*** -0.0005 0.0176*** 0.0005 0.0171*** 0.0005

(0.00221) (0.00175) (0.00173)

sgr -0.0036 0.0190 0.0019 0.0281 0.0018 0.0291

(0.00369) (0.00358) (0.00358)

lnasset 0.1399*** 0.3641 0.1728*** 0.0001 0.1828*** 0.0001

(0.0231) (0.03) (0.0303)

roal1 0.0009 0.0002 0.0009 0.0002

(0.000956) (0.000918)

teta 1.2844*** 0.2756 1.2181*** 0.2608

(0.199) (0.2)

cata 0.2103 -0.0026 0.2516 0.0033

(0.3409) (0.3415)

divdum 2.756*** 0.6076 2.7978*** 0.6106

(0.0778) (0.0789)

d9798 -1.3296*** -0.3002

(0.3189)

d0809 -0.1289 -0.0096

(0.1209)

Intercept -2.6839*** -5.4418*** -5.6058***

(0.5079) (0.707) (0.7125)

observation 7,345 7,345 7,345

cox-snell r2 0.3001 0.4339 0.4352

psuedo r2 0.2957 0.4716 0.4735

Wald 1024.0045*** 2027.8266*** 2038.4321***

Note: The model is composed of model 1 to model 3. The dependent variable is equal to 1 if dividend is

likely to pay. The result shown number of coefficient and level of significant are reported as follows

0.1(*) ,0.05(**) ,0.01(***) including standard error are shown in parenthesis. ∆Prob is shown marginal

effect.

In logistic regression of model 1 presented in table 5. 5 shown that the main

focus of life cycle variable represents by RETE is significant. This show empirical

result that higher retained earning company would have high probability to pay.

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High ROA, represents profitability, and larger size of company are positive

related with dividend decision. In contrast for SGR variable is not significant which is

not consistent with ( DeAngelo, et al. , 2006) but if compare the result with previous

research in Thailand ( Oonpipat, 2009) and Pakistan ( Kouser, et al. , 2015) found

similarly insignificant. It can be explained not to be applicable as ( Mueller , 1972)

suggest that young firm expected to be reinvest more until company mature will lower

their investment and likely to pay dividend.

The result in model 2 show that the major focus variable which is RETE still

significant. profitability and size variable also confirmed significant. SGR presented

insignificant result matching with model 1.

In model 2 is also included control variables suggested by ( DeAngelo, et al. ,

2006) including previous year profitability, lagged dividend, cash balance, total equity

to total asset. (Lintner , 1956; DeAngelo, et al., 2006)Lagged dividend (Divdum) show

that firm with last year recored dividend payment still continue to pay dividend this

year. TETA is also present positive impact the probability to pay divided. ROAL1 is

not significant which inconsistent with (DeAngelo, et al., 2006)arguing that this factor

is strongly determined probability to pay dividend. CATA is insignificant.

Therefore, significant life cycle variable can be concluded that we can reject

null hypothesis. While SGR ROAL1 and CATA are not determined propensity to pay

dividend in Thailand.

In model 3 ( DeAngelo, et al. , 2006; Kouser, et al. , 2015; Bliss, et al. , 2015;

Major & Angback, 2017) test whether time dummy variables that we included for

finding relationship with financial crisis and dividend payout policy. dummy variable

D9798 have significant relationship whereas D0809 result shown insignificant.

This means that Asian financial crisis negatively affected dividend policy and dividend

policy may have changed. On the other hand, Global financial crisis did not affect

decision of dividend policy of Thai firms. The reason why Global financial crisis is not

effect is that this study is not included financial secture in sample and the real cause of

credit financial crisis in 2008 came from financial sector in United Stage thus it might

be a reason why credit crisis in 2008 is effect more on developed country that have

connection with financial institution sector in United Stage than Thailand.

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dividend by testing all firm in 1997-2017 in each life cycle stage.

Panel A: Model 1 (1) ∆Prob (1) ∆Prob (1) ∆Prob

rete_old 1.9868*** 0.3461

(0.1105) rete_mature 1.0086*** 0.1766

(0.0576) rete_young -2.8911*** -0.3834

(0.0738)

roa 0.0562*** -0.0008 0.0623*** -0.0010 0.00612 -0.0004

(0.00331) (0.00337) (0.00297)

sgr -0.0048 0.0270 -0.00598 0.0396 -0.00265 0.0239

(0.00406) (0.00404) (0.00388)

lnasset 0.1549*** 0.3461 0.2263*** 0.1766 0.1804*** -0.3834

(0.0201) (0.0199) (0.0226)

Intercept -3.0422*** -4.8092*** -2.1853***

(0.4417) (0.4399) (0.4975)

observation 7345 7345 7345

cox-snell r2 0.1608 0.1403 0.2905

psuedo r2 0.1453 0.125249553 0.284364475

Wald 796.4641*** 858.8042*** 1963.3469***

Panel B: Model 2 (2) ∆Prob (2) ∆Prob (2) ∆Prob

rete_old 1.337*** 0.0009

(0.1275) rete_mature 1.0173*** 0.0009

(0.0761) rete_young -2.1013*** 0.0005

(0.0844)

roa 0.0127*** 0.0005 0.0109*** 0.0005 0.00301*** 0.0005

(0.00349) (0.00354) (0.00116)

sgr 0.0029 0.0237 0.00249 0.0313 0.00314 0.0200

(0.00364) (0.00354) (0.00367)

lnasset 0.2196*** 0.0866 0.2809*** 0.1193 0.2274*** -0.3205

(0.0284) (0.0288) (0.0304)

roal1 0.00233* 0.0002 0.00245 0.0002 0.00101 0.0002

(0.00134) (0.00125) (0.00075)

teta 2.2476*** 0.2620 2.3533*** 0.2747 2.0634*** 0.2139

(0.1858) (0.1905) (0.192)

cata -0.1705 -0.0329 0.2314 0.0277 0.0361 -0.0318

(0.3323) (0.3332) (0.3504)

divdum 3.0588*** 0.5890 3.1753*** 0.5830 2.8003*** 0.4710

(0.0742) (0.0754) (0.0782)

Intercept -7.1181*** -8.9167*** -6.1922***

(0.6652) (0.6822) (0.7129)


cox-snell r2 0.4100 0.4143 0.4484

psuedo r2 0.4372 0.443280602 0.492989398

Wald 2264.0398*** 2275.2497*** 2174.093***

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Table 5.6 (Continued)

Panel C: Model 3 (3) ∆Prob (3) ∆Prob (3) ∆Prob

rete_old 1.3064*** 0.0009

(0.1281) rete_mature 1.0089*** 0.0009

(0.0764) rete_young -2.0643*** 0.0004

(0.0851)

roa 0.0116*** 0.0005 0.00951*** 0.0005 0.00296*** 0.0005

(0.00343) (0.0034) (0.00114)

sgr 0.0027 0.0248 0.00236 0.0321 0.00301 0.0206

(0.00365) (0.00355) (0.00365)

lnasset 0.2348*** 0.0824 0.2947*** 0.1173 0.2375*** -0.3156

(0.0287) (0.0291) (0.0307)

roal1 0.00221* 0.0002 0.00233* 0.0002 0.000972 0.0002

(0.00128) (0.0012) (0.000731)

teta 2.1393*** 0.2488 2.2279*** 0.2608 1.9712*** 0.2063

(0.1865) (0.1913) (0.1934)

cata -0.0937 -0.0260 0.2929 0.0326 0.0675 -0.0283

(0.3337) (0.3346) (0.3512)

divdum 3.114*** 0.5927 3.2307*** 0.5862 2.8397*** 0.4750

(0.0754) (0.0766) (0.0791)

d9798 -1.8165*** -0.2805 -1.9507*** -0.2809 -1.2487*** -0.1704

(0.2779) (0.2884) (0.3116)

d0809 -0.1057 -0.0112 -0.1324 -0.0107 -0.2135* -0.0195

(0.1177) (0.1178) (0.1228)

Intercept -7.3787*** -9.1296*** -6.3523***

(0.6713) (0.6886) (0.7197)


cox-snell r2 0.4132 0.4177 0.4498

psuedo r2 0.4417 0.448142866 0.495079801

Wald 2268.9716*** 2284.2061*** 2188.3731***

Note: The life cycle stage represent by old mature or young variable is dummy variable equal to 1.

Panel A represent is represent model 1. Panel B is show result of model 2. Panel C show result of

model 3 The dependent variable is equal to 1 if dividend is likely to pay. the result shown number of

coefficient and level of significant are reported as follows 0.1(*) ,0.05(**) ,0.01(***) including standard

error are shown in parenthesis. ∆Prob is shown marginal effect.

In logistic regression of model 1 presented in table 5. 6 shown that the main

focus of life cycle variable represents by RETE is still significant. This table show

consistent with life cycle theory that young firm have lower probability to pay dividend.

while mature firm and old firm evidence positive relationship with higher ability to pay

dividend. The marginal effect of young firm provides lower likelihood at - 0. 3834

than mature firm which statistically shown at 0.1766.

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In model 2 and model 3 also appear significant in RETE and other variables are

present level of significant similar to previous summary. Therefore, this can be

concluded that life cycles determinant consistent with (DeAngelo, et al., 2006) evidence

that mature firm have high probability to pay dividend rather than infusion stage firm.


dividend by testing firm by separate into two periods.

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Note: The Asian financial crisis period is tested by Model 4. The Global Financial crisis period is tested

by Model 5. DAFTER_TYK which is equal to 1 if sample are in year after Asian financial crisis

(1999-2007) and zero for during crisis period (1997-1998). DAFTER_HBG is equal to 1 if sample are

in year after Global financial crisis ( 2010- 2017) and zero for during crisis period ( 2008- 2009) .

The dependent variable is equal to 1 if dividend is likely to pay. The result shown number of coefficient

and level of significant are reported as follows 0.1(*),0.05(**) ,0.01(***) including standard error are

shown in parenthesis. ∆Prob is shown marginal effect.

To further test whether financial crisis have impact on life cycle determinant of

dividend payout, DAFTER dummy is added in model 4 and model 5 using as an

interactive term that multiply with variable in model 2.

The result of regression model 4 illustrate that RETE variable is no longer

significant but RETE interact with DAFTER_TYK become significant. It represents

that life cycle determinant is change after financial crisis, so Asian financial crisis effect

to dividend decision change.

The coefficient of previous profitability ( ROAL1) and interactive term of

ROAL1 are highly significant. It means that dividend signaling from profitability can

highly explain in Asian financial crisis. It has notice that ROAL1 change sign when

interactive with after financial crisis meaning previous profitability tend to lower their

probability to pay dividend. The result of Divdum are also significant indicating that

previous dividend can explain probability to pay dividend. firm maintain to pay

dividend to smooth stream of dividend cashflow.

In contrast of life cycle determinant in model 5 testing in Global financial crisis

RETE still significant while RETE interact with DAFTER_HBG is not significant. The

result shown that after financial crisis dividend policy determined by life cycle is not

changed. ( Major & Angback, 2017) also found Sweden firm can explain dividend

possibility to pay by life cycle determinant after crisis while in Pakistan (Kouser, et al.,

2015) RETE have significant change in dividend policy which not follow life cycle

theory.

Therefore, Asian financial crisis have life cycle determinant of

dividend policy shift whereas retained earning can be explained propensity to pay

dividend after Global financial crisis. Life cycle determinant still hold after Global

financial crisis.

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dividend by testing firm by separate into two periods in each life cycle stage.

Asian Financial Crisis

(1999-2007) (4) ∆Prob (4) ∆Prob (4 ∆Prob

rete_old 0.7247 0.1464

(1.315)

rete_mature 0.7428 0.145

(0.6381)

rete_young -0.9561 -0.1945453

(0.6548)

roa -0.0207 -0.0022 -0.0283 -0.003 -0.0306 -0.0039811

(0.0237) (0.025) (0.0252)

sgr 0.0751 0.0616 -0.1338 0.031 -0.1748 0.0196344

(0.7362) (0.7675) (0.7689)

lnasset 0.1058 0.0171 0.1397 0.022 0.1464 0.0226846

(0.198) (0.202) (0.2018)

roal1 0.1025*** 0.0109 0.0957*** 0.010 0.0927*** 0.0093361

(0.0351) (0.0344) (0.035)

teta -0.2558 0.0244 -0.4871 -0.022 -0.6261 -0.0520707

(1.4224) (1.4667) (1.4775)

cata 1.6428 0.4021 1.5514 0.363 1.3367 0.3312612

(2.7512) (2.8046) (2.8196)

divdum 2.6318*** 0.3512 2.6056*** 0.350 2.5663*** 0.3418547

(0.8472) (0.8361) (0.8355)

roa*dafter_tyk 0.0308 0.0030 0.0331 0.004 0.0326 0.0042494

(0.0242) (0.0252) (0.0252)

sgr*dafter_tyk -0.0992 -0.0627 0.0923 -0.032 0.1189 -0.0202373

(0.7382) (0.7699) (0.772)

lnasset*dafter_tyk 0.1284 0.0116 0.1097 0.009 0.0504 -0.0041858

(0.2031) (0.2073) (0.208)

rete*dafter_tyk 0.4595 -0.0654 0.5758 0.019 -1.5169 -0.1902839

(1.3309) (0.6498) (0.6722)

roal1*dafter_tyk -0.1024*** -0.0108 -0.0954*** -0.010 -0.0923*** -0.009294

(0.0351) (0.0344) (0.035)

teta*dafter_tyk 3.2983 0.3524 3.348 0.380 2.7565* 0.2612855

(1.4567) (1.5019) (1.5171)

cata*dafter_tyk -1.9117 -0.4540 -1.4642 -0.401 -1.0696 -0.3284179

(2.8159) (2.8712) (2.8936)

divdum*dafter_tyk 0.5345 0.2401 0.6537 0.220 0.2065 0.1020993

(0.8561) (0.8458) (0.846)

dafter_tyk -2.3695 -0.2272 -2.2779 -0.193 0.0367 0.2546799

(5.0012) (5.1276) (5.085)

Intercept -5.4516 -6.3403 -5.5481

(4.8878) (5.0099) (4.9475)

observation 2,795 2,795 2,795

cox-snell r2 0.4585 0.4742 0.5078

psuedo r2 0.4593 0.481394029 0.530768312

Wald 898.465*** 895.4147*** 860.0952***

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Table 5.8 (Continued)

Global Financial Crisis

(2008-2017) (5) ∆Prob (5) ∆Prob (5) ∆Prob

rete_old 0.6097 0.0593

(0.3787)

rete_mature 0.4856 0.070

(0.2391)

rete_young -1.2063*** -0.2531838

(0.3024)

roa 0.0376*** 0.0037 0.0389*** 0.004 0.0232* 0.0019088

(0.0136) (0.0137) (0.0138)

sgr 0.0007 -0.0005 0.00485 0.000 0.0113 0.0012049

(0.0317) (0.0318) (0.0286)

lnasset 0.1300 0.0146 0.1438 0.019 0.1184 0.0111539

(0.0897) (0.0899) (0.0916)

roal1 0.1448*** 0.0070 0.1516*** 0.007 0.1418*** 0.0061686

(0.0201) (0.0203) (0.0201)

teta 1.2102 0.1506 1.0998* 0.147 0.8428 0.0922996

(0.6009) (0.6068) (0.6149)

cata 0.0437 -0.0758 0.4223 0.000 0.4227 -0.011434

(1.1059) (1.1117) (1.1493)

divdum 2.7184*** 0.5051 2.7846*** 0.505 2.6052*** 0.4394914

(0.2389) (0.2377) (0.242)

roa*dafter_hbg -0.0319 -0.0030 -0.0325 -0.003 -0.0193 -0.0014374

(0.0139) (0.014) (0.0139)

sgr*dafter_hbg 0.0099 0.0020 0.00532 0.001 -0.0022 0.000028634

(0.0324) (0.0326) (0.0296)

lnasset*dafter_hbg 0.1038 0.0079 0.1646 0.012 0.1488 0.0095183

(0.1006) (0.101) (0.1032)

rete*dafter_hbg 0.5307 0.0098 0.2247 0.014 -0.3971 -0.001324

(0.4244) (0.2655) (0.3286)

roal1*dafter_hbg -0.0829*** -0.0052 -0.0881*** -0.005 -0.099*** -0.0048205

(0.0213) (0.0215) (0.0213)

teta*dafter_hbg -0.0256 -0.0139 0.3404 0.013 0.739 0.0751175

(0.6608) (0.6702) (0.6774)

cata*dafter_hbg -0.4779 0.0436 -0.5404 0.024 -0.8652 -0.0455787

(1.21) (1.2153) (1.2541)

divdum*dafter_hbg 0.2538 0.0820 0.2935 0.081 0.2034 0.052911

(0.2646) (0.2637) (0.2689)

dafter_hbg -1.9688 -0.1758 -3.5046 -0.280 -2.979 -0.2297124

(2.347) (2.3713) (2.42)

Intercept -4.8619 -5.3901 -3.9611*

(2.0884) (2.1024) (2.1449)

observation 4,550 4,550 4,550

cox-snell r2 0.3923 0.392 0.4087

psuedo r2 0.4596 0.459265138 0.484951608

Wald 1228.4111*** 1229.1434*** 1202.7653***

Note: The Asian financial crisis period is tested by Model 4. The Global Financial crisis period is tested

by Model 5. DAFTER_TYK which is equal to 1 if sample are in year after Asian financial crisis

(1999-2007) and zero for during crisis period (1997-1998). DAFTER_HBG is equal to 1 if sample are

in year after Global financial crisis ( 2010- 2017) and zero for during crisis period ( 2008- 2009) .

Life cycle stage represent by old mature or young variable is dummy variable equal to 1. ∆Prob is

marginal effect.

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The regression result of model 4 illustrate that RETE variable is no longer

significant but RETE interact with DAFTER_TYK become non significant for sub

group stage. While model 5 RETE have no significant change after financial crisis.

Life cycle still explain after global financial crisis.

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CHAPTER 6

CONCLUSIONS

This research provides evidence and test whether life cycle theory explained

dividend policy in Thailand including to compare effect of Asian financial crisis and

Global financial crisis. This study uses logistic regression to test hypothesis of dividend

policy during 1997-2017.

Our finding is that the main life cycle determinant retained earning to total

equity found significant which can be explained relationship between life cycle and

dividend payout policy. These results are follow life cycle theory. Firm with higher size

and probability are likely to pay dividend. While investment opportunity or growth is

not significant. Mature company have higher propensity to pay rather than young firm.

These results are consistent with ( Oonpipat, 2009) Thai market. This study also

contributes from previous research by adding more control variables and still found

significant result of life cycle determinant variable.

This study also found difference effect between two crises Asian financial crisis

is negatively effect decision to pay dividend while Global financial crisis is not affect

decision to pay dividend in Thailand.

Moreover, I found more evidence on dividend policy changing after financial

crisis. Life cycle determinant are shifted after Asian financial crisis on the other hand

life cycle determinant are continuing to explain after Global financial crisis.

According to extended long term of study period of dividend in Thailand, this

research can compare globally result with other countries and support investor to use

life cycle determinant as part of their investment decision.

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BIOGRAPHY

Name Miss Chalita Natimakul

Date of birth November 29, 1991

Educational attainment

2010-2013 : THAMMASAT UNIVERSITY

Bachelor of Economics,

Finance Major

Work Experiences June 2014 – December 2017

Real Estate Investment Expert

Muangthai Life Assurance Public Company limited.

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a test of life cycle of dividend and effect of financial

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