TEST OF THE FAMA FRENCH THREE FACTOR MODEL IN STOCK EXCHANGE OF THAILAND IN ENERGY SECTOR

Ms. Manatsanan Srimarksuk

A Thesis Submitted in Partial Fulfillment of the Requirements For the Degree of Master of Business Administration

Department of International Business University of the Thai Chamber of Commerce

2007 @ Copyright of the University of the Thai Chamber of Commerce

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THESIS APPROVAL GRADUATE SCHOOL

Master of Business Administration

Degree International Business

Major Field

TEST OF THE FAMA FRENCH THREE FACTOR MODEL IN STOCK EXCHANGE OF THAILAND IN ENERGY SECTOR

Manatsanan Srimarksuk 2007 Name Graduation Year

Accepted by Graduate School, University of the Thai Chamber of Commerce in Partial Fulfillment of the Requirements for the Master’s Degree

………………………………. Dean, Graduate School (Dr. Ekachai Apisakkul) Thesis Committee ………………………………. Chairperson (Dr. Piraphong Foosiri)

……………………………… Thesis Advisor (Dr. Thasana Boonkwan)

………………………………. Thesis Co–advisor (Dr. Kittiphun Khongsawatkiat)

………………………………. Member (Dr. Phusit Wonglorsaichon)

………………………………. External Committee (Assoc.Prof.Sriaroon Resanond)

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Thesis Title: Test of Fama French Three Factor Model in

the Stock Exchange of Thailand in Energy Sector

Name: Ms. Manatsanan Srimarksuk

Degree: Master of Business Administration

Major Field: International Business

Thesis Advisor: Dr. Thasana Boonkwan

Thesis Co-Advisor: Dr. Kittiphun Khongsawatkiat

Graduate Year: 2007

ABSTRACT

The research is Test of Fama French Three Factor Model in the Stock

Exchange of Thailand (SET) in Energy Sector by having the objectives of the study is to

empirical examine Fama - French Three Factor Model that it suitable for explain and

predict the rate of return in energy sector in Stock Exchange of Thailand. Research

methodology needs to gather the secondary data since January 2003 to December

2007 which is collected the closing price of 12 securities in energy sector from Stock

Exchange of Thailand, book value and market value from each company and treasury

bills from Bank of Thailand (BOT).

The result show that adding two factors, namely the size factor and BE/ME

factor to the CAPM following Fama – French method improves the efficiency of

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capturing the risk and return in Stock Exchange of Thailand. Therefore, the study

confirms that the Fama – French three factor model can explains the common variation

in stock returns. However the result from the size factor (the average returns that has

small size minus big size : SMB) and value factor (the average returns that has high

BE/ME ratio minus low BE/ME ratio : HML) in this study isn’t in line with the hypothesis

of Fama and French because this study found that the security that has big size and

low BE/ME ratio will gave high stock returns. While the hypothesis of Fama – French

showed that the security that should gave high stock returns is the security that has

small size and high BE/ME ratio.

From this research is suggested that this study used only the securities that

traded in the energy sector from 2003 – 2007 so others can test Fama – French model

with the security in another sector or another year. Moreover there are many risk factors

facing companies such as economic situation, currency, market risk, etc. Therefore

should test the security with others risk to find the risk that may affected with the returns

of security. The study about asset pricing is not used only the Fama – French model

but also could be analyzed with more accuracy by developing with other theories, such

as CAPM and Arbitrage Pricing Theory (APT).

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หัวขอวิจัย: การทดสอบแบบจําลอง Fama – French Three Factor Model ใน

ตลาดหลักทรัพยแหงประเทศไทย ในกลุมพลังงาน

ชื่อ: นางสาว มนัสนันท ศรีหมากสุก

ปริญญา: บริหารธุรกิจมหาบัณฑิต

สาขาวิชา: ธุรกิจระหวางประเทศ

อาจารยที่ปรึกษา: ดร. ทรรศนะ บุญขวัญ

อาจารยที่ปรึกษารวม: ดร. กิตติพันธ คงสวัสด์ิเกยีรต ื

ปที่สําเร็จการศึกษา: 2550

บทคัดยอ

งานวิจัยนี้เปนการทดสอบแบบจําลอง Fama – French (Fama French Three factor

Model) ในตลาดหลักทรัพยแหงประเทศไทย ในกลุมพลังงาน โดยมีวัตถปุระสงคเพ่ือศึกษาวา

แบบจําลองมีความเหมาะสมในการอธิบาย และทาํนายอัตราผลตอบแทนหลักทรัพยในกลุม

พลังงาน ในตลาดหลักทรัพยแหงประเทศไทย โดยในการวิจัยใชขอมูลทตุิยภูมิตั้งแตเดือน

มกราคม 2546 ถึงเดือนธันวาคม 2550 ซึ่งเก็บขอมูลราคาปดของหลักทรัพย 12 ตัว ในกลุม

พลังงานจากตลาดหลักทรัพยแหงประเทศไทย, มูลคาทางบัญช ี และมูลคาตลาด จากแตละ

บริษัท และพนัธบตัรรัฐบาลจากธนาคารแหงประเทศไทย

จากผลการศกึษาพบวา ปจจัย 2 ตัวทีเ่พ่ิมเขาไป คือ ปจจัยขนาด (Size Factor) และ

อัตราสวนมูลคาทางบัญชีสวนดวยมูลคาตลาด (Market Factor) เขาไปในแบบจําลอง CAPM

(Capital Asset Pricing Model) ตามแนวทางของ Fama และ French นั้น มีนัยสําคัญตอการ

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จากการวิจัยนี้มีขอเสนแนะวา ในการศึกษานี้เปนการใชหลักทรัพยในกลุมพลังงาน ซึ่งมี

การซ้ือขายในป 2546 – 2550 เทาน้ัน ดังนั้นจึงควรทดสอบแบบจําลอง Fama – French กับ

หลักทรัพยในกลุมอ่ืนๆ และในปอ่ืนๆ ดวย นอกจากนั้นแลวจากการที่บริษทัตางๆ ตองพบกับ

ความเสี่ยงตางๆท่ีจะเกิดขึน้ เชน สถานะการณทางเศรษฐกิจ, คาเงิน, ความเสี่ยงทางการตลาด

เปนตน ดังนั้น จึงควรทดสอบแบบจําลองโดยใชความเสี่ยงอ่ืนที่อาจกระทบกบัอัตราผลตอบแทน

ของหลักทรัพยดวย การศึกษาเก่ียวกบัแบบจะลองราคาหลักทรัพยนั้นไมไดมีแคแบบจําลอง

Fama – French เทาน้ัน แตยังสามารถพิจารณาเพื่อใหเกิดความแมนยํามากขึ้น ดดยการ

พัฒนาทฤษฎอ่ืีนๆ เชน CAPM หรือ APT (Arbitrage Pricing Theory) เปนตน

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ACKNOWLEDGEMENTS

The thesis has been completed with the support and help from many people. I

would like to express my sincere thanks to all the people who directly or indirectly

encouraged and helped me.

In particularly, I would like to thank you Dr.Thasana Boonkwan, my advisor and

I am also grateful to Dr. Kittiphun Khongsawatkiat, co- advisor and thorough to

Associate Professor Sriaroon Resanond, Dr Dr.Phusit Wonglorsaichon and

Dr. Piraphong Foosiri, members of my thesis committee, for their stimulating, valuable

comment and suggestion.

My thanks are extended to my friends and all staff members in Global MBA

program in University of Thai Chamber of Commerce for their sincerity and friendship.

Finally, I am most grateful to my lovely family for their understanding, helping

and encouragement.

Manatsanan Srimarksuk

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TABLE OF CONTENTS

Page

ENGLISH ABSTRACT………………………………………………………………..... iv

THAI ABSTRACT……………………………………………………………………….. vi

ACKNOWLEDGEMENT………………………………………………………………... viii

LIST OF TABLES………………………………………………………………………. xii

LIST OF FIGURES………………………..……………………………………………. xiii

Chapter

1. Introduction

1.1 Introduction to the Study……………………………………………. 1

1.2 Statement of the Problem…………………………………………… 5

1.3 Objectives of the Study…………………………………………....... 7

1.4 Scope of the Study……………………………………………......... 7

1.5 Expected Benefits………………………………………..………….. 8

1.6 Definition of Terms………………………………………………….. 8

1.7 Organization of the Study……………………………… …………. 13

2. Review of Literature

2.1 Theory…………………………………………………………………. 15

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TABLE OF CONTENTS (CONTINUED)

Chapter Page

2.1.1 CAPM (Capital Asset Pricing Model)………………………. 15

2.1.2 APT (Arbitrage Pricing Theory)……………………………... 18

2.1.3 Fama and French Three Factor Model……………………. 20

2.2 Research……………………………………………………………… 23

2.3 Energy Information Administration………………………………… 32

2.3.1 Background…………………………………………………….. 32

2.3.2 Oil………………………………………………………………... 32

2.3.3 Natural Gas……………………………………………………. 37

2.4 Company Profile……………………………………………………… 43

3. Methodology

3.1 Population…………………………………………………………….. 51

3.2 Fama – French Factor Model and Source of Variable………… 52

3.3 Test of the Model……………………………………………………. 58

3.4 Conceptual Framework……………………………………………… 61

4. Result of the Study

4.1 The Result of Setting the Group of Portfolio by

Fama – French Method………………………………………….… 63

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TABLE OF CONTENTS (CONTINUED)

Chapter Page

4.2 The Result of the Variable…………………………………………. 67

4.3 The Result from Tested of Model…………………………………. 70

5. Conclusion and Recommendation

5.1 Conclusion……………………………………………………………. 75

5.2 Recommendation…………………………………………………….. 78

5.3 Discussion…………………………………………………………….. 79

5.4 Suggestion for next study…………………………………………. … 81

BIBLIOGRAPHY……………………………………………………………………… … 82

APPENDICES

Appendix A…………………………………………………………………... … 87

Appendix B…………………………………………………………………….. 103

Appendix C…………………………………………………………………….. 106

BIOGRAPHY…………………………………………………………………………. … 108

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LIST OF TABLES

Table Page

4.1 The Amount of assets in each portfolio following the divided into

groups of Fama French method in the stock exchange of Thailand

in the energy sector from 2003 to 2007. ………………………………… 64

4.2 Excess Returns in Energy Sector 2003 – 2007…………………………. 65

4.3 The Relation Matrix of each Group 0f Portfolio………………………….. 66

4.4 Show the Relation of Variable in Fama – French Model……………….. 70

4.5 Show the result to check the problem of Autocorrelation………………. 71

4.6 Show the result to check the problem of Heteroscedasticity…………… 72

4.7 Show the result to check the problem of multicollinearity……………… 72

4.8 Show the result from study the factor that effected with

the rate of return in energy sector………………………………………. 73

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LIST OF FIGURES

Figure Page

2.1 Show Southeast Asia Oil Consumption and Net Oil Imports, 2006….. 33

2.2 Show Thailand’s Oil Production and Consumption, 1990-2006……..... 35

2.3 Show Southeast Asia Proven Natural Gas Reserves and Production… 38

2.4 Show Thailand’s Natural Gas Production and Consumption, 1994-2004... 39

3.1 Show the construct of six portfolios by Market Value and

Book to Market Value………………………………………………………. 56

4.1 SET 50 from 2003 – 2007………………………………………………. … 66

4.2 Average of SMB and HML in each year 2003 – 2007………………. … 69

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CHAPTER 1

INTRODUCTION

1.1 Introduction to the Study

The investors who interested to invest in the stock market have to evaluate the

level and predict the changeable of asset price. Due to, when they buy the security that

mean they pay money for return in the future from the profit dividend which the

company gain in each year. The investor has to consider that what factors have

effected to the level of cash flow that will receive in the future. However the

considerable those effect needed to use many data to evaluate, determine and offering

price to sell or buy the security. Therefore, the asset price is the result from analysis

and evaluated the effect of factor that will have the effected to cash flow and asset price

in the finally. By this reason, pricing asset model have the significant to determine the

asset price in the future and investor can use the model to plan and determine to invest

correctly.

In 1964 Sharpe, Lintner and Mossion developed the model “Capital Asset

Pricing Model: CAPM”, a linear cross – sectional relationship between asset return and

exposure to the single market factor (Sharp, 1964). This model was very popular and to

be a base of other models. The intuitive appeal of the CAPM is that it

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has a solid theoretical underpinning. Over the past three decades, many pricing

anomalies have been identified that if the investor will consider only the market risk, it

doesn’t suitable because they will confront with the other risks. So Merton offered to

use CAPM but based on the extra – market source of risk that calls “Multifactor CAPM”

that will consider the factor except market risk factor. This is the source of one popular

model call “Arbitrage Pricing Theory: APT” which is developed by Ross (1976). This

model is based on law of one price and indicates the relationship between economy risk

factors and asset returns. However it has problem about the determining of variable and

the model is almost sensitive with economy of each country.

In 1992, the result from the study of Fama and French are challenged with the

capability of prediction the asset returns of pricing model that used before and very

popular like CAPM. This result from the test of CAPM in US stock market in that time.

They found that CAPM is not confirmed or cannot explain the moveable of the average

of asset returns in US stock market and they believed that the risk factor of economy in

APT model have a affect with asset return but indirectly. While it have effect with other

factors such as company size, leverage, earning/price (E/P) and book to market

(BE/ME) ratio. Fama and French brought these factors to test the capability to explain

the average of asset return US stock market and found that it can explain the rate of

return better than CAPM’s factor. Fama and French (1993) confirm that portfolios

constructed to mimic risk factors related to market, size and value all help to explain the

random returns to well – diversified stock portfolios. Fama and French (1995) attempt to

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provided a deeper economic foundation for their three – factor pricing model by relating

the random return factors to earnings stocks. They claim that the behavior of stock

returns in relation to market, size and value factors is consistent with behavior earnings.

Fama and French (1996) conjecture that the superior returns that smaller sized firms

and firms with high book – to – market obtain is a premium for increased distress risk.

Due to the lack of a theoretical justification for the model, many researchers have

attracted the risk based explanation that Fama and French (1996) propose. However

the study of three – factors of Fama and French in Thailand have not much. Thus, this

paper will study and test the Fama – French model in the Stock Exchange of Thailand

in the energy sector to indicate the ability to explain the rate of return in stock

exchange.

Mr. Thanaporn Wisarutapong (April 2007), Director of Energy Group and

Securities Analysis Department, SCB Securities said the stock in energy sector can

divided into 4 groups:

(1) Oil and Gas – PTT, PTTEP

(2) Refining – TOP, RRC, BCP, IRPC

(3) Mining – BANPU, LANNA

(4) Utility – EGCO (EGCOMP), RATCH, GLOW

In this period, the oil price still increasing because the critical situation between

Iran (the country who is the fourth in the world to produce and export the crude oil) and

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the Western Countries. It affected to oil price that still at 60 dollars per barrel or

increase about 7-8 dollars per barrel from the beginning of the year. It regards as the oil

price is increasing rapidly. The other factors that important and impact to increasing of

oil price is come from quarter 2/2007, while many refineries in Asia were closed the

refinery to maintenance, it effect that the crude oil didn’t enough to the consumer

demand. That will make the close price is increase. He expects that the price of crude

oil in 2007 will average at 50 dollar per barrel.

In refining group which the investor always invest when the price of crude oil is

increase. Mr. Thanaporn said except the profit of refining will come from the different of

petroleum oil and the quantity of oil in the world market too.

In utility, expect that will receive the good result from auction of IPP in April and

buy the utility from SPP too. SCB Securities think that the securities in utility are like

investment in bond because it has income from the license in the long term. So it has a

stable income and the business growth doesn’t exciting. Therefore, it is suitable for the

investors who want to receive the consistent return. The security that is interesting is

GLOW because it has an opportunity to growth higher than other securities.

In mining that has an advantage from adjusting of coal in the half of beginning

of the year. Because the big coal consumer that is Japan negotiated to buy coal with

the entrepreneur in the last year (2006) and the oil price is increase. So the consumer

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changes to use coal to compensate. Therefore they think that it will be the advantage of

the mining in the long time. SCB Securities give the interested to BANPU because they

believe that it can expand the production capability and distribute income from utility

business.

1.2 Statement of the Problem

Fama and French are study this model for many times to test and confirm their

model that it can affirm the ability to predict the return on asset efficiently. So this model

is beginning to prevalent in many countries and has many people study this model with

the stock market. For example, Gregory Connor and Sanjay Sehyalz and Robert Faff

tested the model Fama – French in the stock market of India and Australia. The result

of study found that Fama – French Model can explain the fluctuation of asset return in

stock market better than CAPM.

In Thailand have many person brought CAPM and APT model to test the ability

to predict the rate of return in the stock market. For example, Nanthiya Chanthiratikul

and Nuttapong Ruzhe in 1999 to 2004. They found that CAPM cannot explain the

significant of rate of return. In the same time, APT can explain in some period and have

more sensitive. However the study of Fama – French Three Factors Model in Thailand

stock market didn’t distinctly. So this study will examine Fama – French Three Factors

Model that it suitable for explain and predict the rate of return in Thailand stock market.

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Moreover the reason that investor should have a model or tool for predict the

rate of return because nowadays the economic situation in Thailand and in the world

aren’t stable so they should consider more and more for example the oil situation in the

world on 6 – 12 May 08. The crude oil price in West Texas Intermediate adjusted

pricing to the highest in memorable events continuously on May 5, 2008. By increasing

more than 4 US dollar per barrel and the closing price was 125.96 US dollar per barrel

on May 2, 2008. Because the speculation the profit of Head Fund supplementing with

the investor interested to invest in the commodity, especially the oil, after the foresting

that the crude oil pricing still adjusted to increasing continuously because the

developing countries such as China, India and Middle East still want to used oil

increasing because of growing economic. Although the crude oil pricing will increase in

this period while the supply of crude oil especially from outside the OPEC Group such

as Mexico and Russian can’t expand as more as forecasting. Because they confront

with a technical problem and lose money from foreign countries, therefore it has some

forecasting that the crude oil supply will be strained after the quality of Finland refinery

was closed to maintenance and Scotland refinery was temporary closed because the

worker were fetch out. Their make the market predict that the crude oil price will

increase.

The gasoline pricing in Singapore market was increasing to 11 US dollar per

barrel and closing price was 131.29 US dollar per barrel on May 5, 2008. As the

gasoline pricing was increased, it was an important factor to push the gasoline pricing

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higher. However the gasoline market in the provincial in this period weren’t good when

compared with in April and this period in last year because demand of gasoline in USA

still slow down even though it approach to summer travel season. It has a cause from

the economic in USA is slow down and the oil price still high.

Source: www.thannews.th.com

1.3 Objectives of the Study

To empirically examines the Fama – French three factors model of stock return

for Thailand.

1.4 Scope of the Study

This study is use the energy sector that traded in January 2003 to 2007 and

collected Market to Book Value data from data base of the Stock Exchange of Thailand

and cut the assets that have Market to Book Value in minus. Twelve securities in

energy sector of the companies registering on the Stock Exchange of Thailand (SET)

would be employed in the empirical study as follows:

1. BAFS Bangkok Aviation Fuel Services PCL.

2. BANPU Banpu Public Company Limited

3. BCP The Bangchak Petroleum Public Company Limited

4. EASTW Eastern Water Resources Development and Management PLC.

5. EGCO Electricity Generating Public Company Limited

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6. IRPC IRPC Public Company Limited

7. LANNA The Lanna Resources Public Company Limited

8. PTT PTT Public Company Limited

9. PTTEP PTT Exploration and Production Public Company

10. RATCH Ratchaburi Electricity Generating Holding Public Co., LTD.

11. STRD Sino-Thai Resources Development Public Company Limited

12. SUSCO Siam United Services Public Company Limited

1.5 Expected Benefits

1. As this study will know the ability and suitability of the Fama – French model

for the Stock Exchange of Thailand.

2. Augmenting the method to forecast the rate of asset returns except the

method that use before like CAPM (Capital Asset Pricing Method) and APT

(Arbitrage Pricing Theory)

1.6 Definition of Terms

Portfolio - The appropriate mix of or collection of investments held by an

institution or a private individual. In building up an investment portfolio a financial

institution will typically conduct its own investment analysis, whilst a private individual

may make use of the services of a financial advisor or a financial institution which offers

portfolio management services. Holding a portfolio is part of an investment and risk-

limiting strategy called diversification. By owning several assets, certain types of risk (in

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particular specific risk) can be reduced. The assets in the portfolio could include stocks,

bonds, options, warrants, gold certificates, real estate, futures contracts, production

facilities, or any other item that is expected to retain its value.

Return - The gain or loss of a security in a particular period. The return consists

of the income and the capital gains relative on an investment. It is usually quoted as a

percentage.

Required Rate of Return - The rate of return needed to induce investors or

companies to invest in something.

Expected Return - The average of a probability distribution of possible returns,

calculated by using the following formula:

E(R) = Σ (Pi x Ri)

Rate of Return - The gain or loss of an investment over a specified period,

expressed as a percentage increase over the initial investment cost. Gains on

investments are considered to be any income received from the security, plus realized

capital gains.

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Risk - The chance that an investment's actual return will be different than

expected. This includes the possibility of losing some or all of the original investment. It

is usually measured by calculating the standard deviation of the historical returns or

average returns of a specific investment.

Beta - A measure of the volatility, or systematic risk, of a security or a portfolio

in comparison to the market as a whole. Also is known as "beta coefficient".

Correlation - The tendency of two variables to move together.

Security - An instrument representing ownership (stocks), a debt agreement

(bonds), or the rights to ownership (derivatives).

Stock - A type of security that signifies ownership in a corporation and

represents a claim on part of the corporation's assets and earnings. There are two main

types of stock: common and preferred. Common stock usually entitles the owner to vote

at shareholders' meetings and to receive dividends. Preferred stock generally does not

have voting rights, but has a higher claim on assets and earnings than the common

shares. For example, owners of preferred stock receive dividends before common

shareholders and have priority in the event that a company goes bankrupt and is

liquidated. Also is known as "shares" or "equity".

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Cost of common stock - The return required by the firm common stockholders.

It is usually calculated using CAPM or the dividend growth model.

Diversification - A risk-management technique that mixes a wide variety of

investments within a portfolio. The rationale behind this technique contends that a

portfolio of different kinds of investments will, on average, yield higher returns and pose

a lower risk than any individual investment found within the portfolio. Diversification

strives to smooth out unsystematic risk events in a portfolio so that the

positive performance of some investments will neutralize the negative performance of

others. Therefore, the benefits of diversification will hold only if the securities in the

portfolio are not perfectly correlated.

Security Market Line (SML) - The line that graphs the systematic, or

market, risk versus return of the whole market at a certain time and shows all risky

marketable securities.

Market Risk Premium - The difference between the expected return on a

market portfolio and the risk-free rate.

Capital Asset Pricing Model (CAPM) - A model that describes the relationship

between risk and expected return and that is used in the pricing of risky securities.

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Market Value - The estimated amount for which a property should exchange on

the date of valuation between a willing buyer and a willing seller in an arms-length

transaction after proper marketing wherein the parties had each acted knowledgably,

prudently, and without compulsion.

Book Value - the value carried on the bookkeeping records of an economic

entity such as an individual, corporation, government, or other organization. Depending

on the circumstances, assets and liabilities may be valued on a balance sheet at actual

value (cash and cash equivalents), acquisition cost, depreciated value, amortized value,

depleted value, or market value.

Price/Book Value Ratio (P/BV Ratio)

The current market price of a stock divided by the company's book value per share.

P/BV ratio = Market price

Book value

A P/BV ratio indicates how much investors pay for what would be left of the

company if it went out of business immediately. If stocks are trading for less than their

book value (P/BV is less than 1), it generally implies that the stocks are undervalued.

However, it may also tell differently that investors expect the company to have a very

poor return on its assets. P/BV ratio may not be so meaningful if a company has a large

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percentage of intangible assets as they are very difficult to quantify, thereby making the

book value uncertain.

1.7 Organization of the study

This research is consisted of 5 chapters as follows:

Chapter 1 General Introduction

Chapter 2 Literature Review

Chapter 3 Research Methodology

Chapter 4 Research Results and Discusses

Chapter 5 Conclusion and Recommendation

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CHAPTER 2

REVIEW OF LITERATURE

The essential for investment of investor is to understanding and analysis the

portfolio before decides to invest. The investment analysis can divide in two parts that

are:

Technical Analysis is the method to analysis the moveable of stock price in the

past to forecast asset price in the future.

Fundamental Analysis is the method to analysis the basic factor that effect to

asset price such as type of business, asset, debt, liquidity, profitability and dividend.

As this study, the analysis of asset price model is the fundamental analysis.

As the result of study of Eugene.F Fama and Kenneth R.French are challenged

the ability of forecasting the rate of asset return of pricing model that use before like

CAPM. Fama and French found that beside of stock’s CAPM beta, the factors that

affected to security market line (SML) or the line that show relationship of rate of return

are size of the company and book to market ratio.

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2.1 Theory

2.1.1 CAPM (Capital Asset Pricing Model)

The first theory of Asset Pricing Model that very popular is Capital Asset

Pricing Model: CAPM. The originator who found this theory is William F.Sharp, John

Lintner and Jan Mossion in 1964. They developed this model from Markowitz Portfolio

Theory to explain asses the rate of return or asset price and portfolio in capital market

from the risk of assets or portfolio, the hypotheses of the theory consist of (Charles

P.Jones, 2002):

(1) The investor will consider the rate of return that they will receive and the risk

from investment in the same security and homogeneous expectations. So we can call

the investor “Risk Averter” that means before they will invest, they will compare

between expected return and the risk securities. They will choose to invest in the

security that have the lowest risk when the security have equal rate of return and

expected to get equally. Anyway, they will choose to invest in the security that has the

highest return when have the equal risk.

(2) The investor have an equal period and time.

(3) The investor can loan by risk free and can borrow by risk free. Risk Free

Rate: Rf will equal no matter they will loan or borrow. Risk free rate of every investor

will be equal.

(4) No exchange cost.

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(5) No income tax involved: investor have the different from investment and

dividend yield.

(6) No inflation rate.

(7) More investor; the consideration of one investor don’t effected to the asset

price in the market and investor will be a price taker, cannot set the price.

(8) The market is equilibrium.

Even though the above hypotheses are difficult to practice but it can help the

investor to understand the relationship easily and can improved to use with the real

data.

CAPM equation:

E (Rit) = Rft + [ E (Rm) – Rft ] βi + ε it

Where :

E (Rit) = Expected return on asset i at t time

Rft = Risk free rate at t time

E (Rm) = Expected market rate of return

βi = Beta of asset i

ε it = Residual term of portfolio i

From the equation show that CAPM is the anomaly which has only one factor

that will effected to the rate of asset return that is market risk. That means the market

risk factor can be affected by other factors and it is only factor that will effected to the

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rate of asset returns. Afterward, Merton was presented the theory “Multifactor Asset

Pricing Model”. He found that Markowitz Portfolio Theory and CAPM considered that

the investor will consider only market risk and use only variance of expected return are

not suitable because investor will confront with the others factors too. Therefore Merton

was presented to use CAPM but based on the extra – market source of risk call

“Multifactor CAPM” that can consider the other factors except only market risk. Under

the concept, the rate of return is come from;

(1) The rate of return which happen is for compensate the risk from internal

market that can evaluate by risk premium.

(2) The rate of return which happen is for compensate the risk from external

factors.

Merton’s concept equation is given by :

E(Rp) = RF + βp,m[E(RM) – RF] + βp,F1[E(RF1) - RF] + βp,F2

[E(RF2) - RF] + …. + βp,Fk [E(RFk) - RF]

Where : Rf = Risk – free return

F1,F2…Fk = Factor or extra – market source of risk

k = Amount of risk

βp,m = Sensitive of portfolio by the market

βp,Fk = Sensitive of investor by factor k

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E (RFk) = Expected return of k factor

E (Rp) = Expected return of portfolio

E (Rm) = Expected return to market

Which βp,F1[E(RF1) - RF] + βp,F2 [E(RF2) - RF] + …. + βp,Fk [E(RFk) - RF]

is the total extra – market sources of risk.

From the equation can explain that if investors consider other external factors

except the internal market risk, expected return of portfolio (E (Rp)) will include return

which happened from compensate the risk of each factor. That mean if don’t have the

extra – market sources of risk, equation (1) will be:

E(Rp) = RF + βp[E(RM) - RF]

So this equation is the predication the return of CAPM concept.

2.1.2 APT (Arbitrage Pricing Theory)

The concept of Multifactor Asset Pricing Model is the theory that very popular.

The other theory that is well-known like CAPM is Arbitrage Pricing Theory: APT, this

model is based on the law of one price which developed by Ross. APT is the model

that shows the relationship between expected rate of return and risk while CAPM will

identify only the market risk that effected to the rate of return on asset. However ATP is

not identifying the relationship of portfolio clearly like CAPM. By the way, recognized

that have many risks that can effect to the rate of asset return.

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APT has assumption that the return on asset has linear relationship with the

indexes, which each index is the representative of factor that each factor has the

influence to the return on asset. Under the law of one price, investor will buy or sell

asset which the asset that will affected from the factor will have the same pattern

should give the equal rate of expected return both buying and selling for make a profit

from the different price in each arbitrage until the asset price is equal. This is the

process to create the determinant of asset price.

By the way, APT doesn’t have hypothesis in :

(1) The investor will consider the portfolio by estimate from the expected of

return and standard deviation of rate of return in 1 period of investment.

(2) No taxation.

(3) The interest of loaning and borrowing is equal to the interest free.

(4) The way to choose portfolio of investor is based on expected rate of return

and the fluctuation.

APT’s hypothesis which like CAPM is :

(1) The investor has to predict the risk and rate of return in the same.

(2) The investor doesn’t like the risk but want to get high utility.

(3) Perfect Market

(4) The rate of return on asset is from factor model.

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The APT’s equation is given by :

E (Rit ) = Rf + βi1 E( λ1 ) + βi2 E(λ2 ) + …… + βik E( λk )

Where :

E (Rit ) = Expected Rate of Return of Asset I at t time

Rit = Risk Free Rate at t time

λ = Risk Premium of each Factor

βi = Beta of Asset i

However the problem of APT is this model cannot determine the economic

factor, that make a model has more sensitive in each period in different economic. That

make each period has variable of economic which effected or influenced to the rate of

return on asset are different.

2.1.3 Fama and French Three Factor Model

In 1992, Fama and French developed model to analysis the rate of asset return

in the stock market. They find that the economic factor or the market factor have effect

to the rate of asset return but indirectly. It will effected to the company overall operation

such as company growth, debt, sales and profit. Therefore, they developed Fama

French Factor Model: Three Factor Model to test hypothesis: the factor that effected to

the security market line (SML) by three hypothesis following :

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(1) Beta of asset from CAPM that show the market risk factor that effect to the

portfolio.

(2) Size of the company: the realized stock return of a company depends on the

absolute size of the firm’s equity market capital (smaller firms having higher returns).

From historical researched Banz (1981) found that portfolio that have lower BE will get

high rate of return. In stead of the portfolio that have higher BE will get low rate of

return. The smaller firms have statistically higher returns.

(3) Book to market ratio : BE/ME. Many practitioners maintain that the value of a

stock, as determined by reference to some measures of its fundamentals such as

accounting book value relative to its market, is useful in determining future return. Value

stocks that have high ratios of fundamental-to-market value of equity may perform well

in the future, while glamour stocks that have low ratios of fundamental-to-market value

seem to perform poorly in the future.

Fama and French tested their hypothesis and found that business that is smaller

and have high book to market ratio will give the rate of return higher than average that

is followed with the hypothesis. Anyway it is unusual because they didn’t found the

relationship between Beta (ß) and the rate of return. As the hypothesis, the asset that

have high Beta means it has a high risk but it doesn’t give rate of return higher than

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average returns. In turn, the lower beta asset didn’t give the rate of return lower than

average return (Fama and French, 1992).

In 1993, Fama and French developed the three factors model based on the

study in the past. They developed three factors model and determine the equation and

method more clearly.

The first factor is Market Risk Premium that can calculate by market rate of

return minus risk – free return proposed by the CAPM.

The second factor is setup by divided the asset return in to two groups follow by

company size that is small asset and big asset. And calculated average of the returns

of both groups and bring the result from the simple average of the returns of the small

stock portfolio minus the simple average of the returns of the big stock portfolio call

“Return of Small Size minus Return of Big Size: SMB”.

The third factor is setup by allocated portfolio by BE/ME ratio. The first group is

30% of all assets that have high BE/ME ratio. Second group is 30% of all assets that

have low BE/ME ratio. After that, calculate the average of the returns of both groups by

minus the simple average of the returns of the high BE/ME portfolio and the simple

average of the returns of the low BE/ME portfolio. So we will get the factor call “HML

(Return of High BE/ME ratio minus Return of Low BE/ME ratio)”.

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As the hypothesis, the equation is given by :

( ki – kRF ) = ai + bi ( kM - kRF ) + ci ( kSMB ) + di ( kHML ) + ei,

When :

ki = The Return on Asset of the portfolio i

kRF = The Return on the Risk – Free Asset

kM = The Return on the Market Portfolio

kSMB = The Mimicking Portfolio for the Stock Factor

kHML = The Mimicking Portfolio for the Book – to -

Market Factor

ai = The abnormal mean return of portfolio i, which equals

zero under the hypothesis pricing model.

bi, ci, di = The market, size and value factor exposures of portfolio i

ei = The mean – zero asset – specific return of portfolio i

2.2 Research

The beginning of prediction of stock price of investor was started in 1602. When

the stock market opened and does the business in part of stock market in Amsterdam,

Netherlands and use a long time for 100 years to develop the stock market call “The

Stock Exchange” that located the first place at England until now. The investment in

stock market of investor also related with the consideration of stock price that they want

to invest because it will effect to the rate of return. For the asset pricing model that

have many people studied and showed the significant that to be a beginning and base

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of Fama French Factor Model is CAPM that is a model which have a Single Factor

Model and APT model which is a Multi Factor Model. So, we can review the literature of

the model following:

Modern cost – of – capital theory commences with Modigliani and Miller (1958,

1963), who use arbitrage argument to model the effect of leverage changes on a firm’s

cost of equity and its weighted average cost of capital in the form of Modigliani – Miller

Propositions II and III. Several subsequent models have dealt with all factors affecting a

firm’s cost of equity; including the capital asset pricing model (CAPM) of Sharpe (1946),

Lintner (1965), and Mossin (1966). This model is showed a linear cross – sectional

relationship between mean excess returns and exposures to the market factor that we

can predict the risk and the rate of return that the investor will receive by this model.

Anyway, this model is become popular and have many researcher that bring CAPM to

empirical studied such as Blume and Friend (1970), Miller and Scholes (1972), Blume

and Husick (1973), Fama and Macbeth (1973). Their methology which is still widely

used is based on two regressions which are time series and cross-section. Paper that

use this approach include Chan, Hamao and Lakonishok (1991), Jegadeesh (1992),

Davis (1994), Jagannathan and Wang (1996). The most study used monthly data and in

the first time, they preferred to estimated beta of each portfolio by find the relationship

between the mean excess return and the market ratio. Firstly contemporaneous betas

are estimated using rolling time – series regressions of excess stock returns. Then

estimated stock betas are used to predict the one – period ahead cross – section of

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stock returns by regressing portfolio returns on their betas to estimate market risk

premium.

The results are :

(1) The intercept is different from zero in significantly but in the real, it should

equal to zero following by CAPM.

(2) CAPM can explain in the short period.

(3) The cross – section regression is suitable for data and in the long time, the

rate of market return is higher than the risk – free rate.

(4) The other factors can explain the average of the returns better than used

only beta. For example, Basu (1977) found that portfolio that has low price/earnings will

get the rate of return higher than beta. Banz (1981) and Reinganum (1992) found that

the size of company is important that is the small firms have statistically to get high

returns. In 1974 Pettit, R. Richardson – Westerfield, Randolph is study the prediction in

investment in the asset by using CAPM and Market Model by tested the efficiency of

both model. From the study founded that the high risk investment was better than

prediction when the market risk is lower than the risk-free return. But it was bad more

than prediction when the market return is higher than the risk-free return. A part of low

risk investment will opposite with a part of high risk investment.

When the studied in analysis of investment by the asset pricing model is

popular, it has the other method to determine the stock price; Arbitrage Pricing Theory

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(APT) developed by Ross (1976). This model is based on the law of one price that the

model showed the relationship between the external – risk factors and the rate of asset

return. The study of Ronald J.Balvers, Thomas F Cosimo and Bill Mcdonald in 1990

were studied the prediction of stock return in stock market. They showed the theory that

the rate of return can predict base on the prediction of the economic result. The test

can confirm that the stock return can predict by the function of total result. However the

test used annual data of the result and the stock return in 1947 – 1987, found that the

result in present period can predict by the fluctuation of stock price more than 20%. In

1993, Mei and Korajozyd and Viallet are studied the ability to explain the return on

asset between CAPM and APT and found that APT can explain the return on asset

better than CAPM.

From the historical studied found that only market factor in CAPM cannot

explain the rate of return on asset. In the same time, even though APT can explain

better than CAPM but it cannot identify the certainly variable because it depends on the

suitable of economy. In 1992, Fama and French studied the efficiency of CAPM. They

found that estimates the return on asset by CAPM that use only market factor cannot

predict the return on asset in US stock market. Fama and French test to bring other

factors that can explain the return on asset better than the factor that use in CAPM.

Exposure to two factors, a company size factor and a book to market factor, often

called a “value” factor, explain a significant part of the cross – sectional dispersion in

average returns to include in CAPM equation called “Three Fame French Factor”. The

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study result of Fama and French’s study showed that the new model can predict the

return on asset in US stock market significantly. They study continuous to show the

insufficient of CAPM and APT, demonstrate that their model can explain the return on

asset. In 1993, Fama and French continued to study the risk exposure in asset return

and bond by determine 5 risk factors in stock market and bond market. That is 3 risk

factors in stock market are the market, size and value exposures and 2 risk factors in

bond market are withdraw time and common risk. The result of the study can concluded

that 5 risk factors they set up can explain the rate of return in stock market and bond

market in significant. In 1995, Fama and French studied the multifactor explanations of

asset pricing anomalies. From the historical study, Fama and French found that the

average return on asset have relationship with the business such as size, earning /

price, cash flow / price, book to market equity, past sale growth, long term past return,

short term past return. These will show the average of asset return in each asset and

clear that it cannot explain by CAPM that is the unusual of anomalies (Fama and

French, 1995). From the studied by used the Fama French Factor showed that the

anomalies can explain the moveable of value very well and the unusual that happen in

CAPM don’t happen in Fama French Factor. It has the unusual only the short term

past return that can explain not much. Morever, they study the same method in ICAPM

and APT. Anyway, the result is the same. In 1998, Fama and French studied to

compared between value and growth rate in stock market in 1975 – 1995, the different

between the rate of return around the world is about 7.68% per year and found that

have 12 stock markets from 30 stock markets that the book – to – market doesn’t show

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the growth rate of asset and the international capital asset pricing model cannot explain

the value premium. Anyway, when combined the 2 factors of Fama and French, found

that it can explain the value premium in international rate of return.

In the similar time, have many people studied Fama French Factor Model

worldwide and bring this model to empirical study to compare with CAPM or another

models in many countries except US stock market or tested the ability of Fama French

Model to predict the return on asset. For example, Jason Halliwell, Richard Heaney,

Julia Sawiki (1998) use Australian data from 1981 to 1991 to examine the explanatory

power of book – to – market and size using the Fama and French method (1993). They

report that the size and the market risk premiums are statistically significant in

explaining as in the Fama – Farench paper, it is not statistically significant.

In 2001, Gregory Connor and Sanjat Sehgal tested Fama French three – factor

model with the rate of return in Indian stock market. They found that all three Fama –

French factors, market, size and value have a pervasive influence on random returns in

the Indian stock market. The one – factor CAPM relationship for average returns can be

rejected, but the three – factor model cannot. There is some weak evidence for market,

value and size factors in earnings stock. They found no evidence that the common risk

factors in one – year – ahead earnings growth rates are related to the common factors

in current portfolio returns.

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Faff (2001) use Australian data over the period January 1991 to April 1999 to

examine the power of the Fama French three – factor model. He found strong support

for the Fama and French three factor model, but found a significant negative rather than

the expected positive, premium to small size stocks. Faff conjectures that his results are

consistent with evidence from other markets, on a reversal of the size effect.

In the same year, Souad Ajili (2001) was tested Fama French Factor Model and

Capital Asset Pricing Model in stock market of France. He creates a portfolio following

Fama and Freach (1993) by size and value in the market. The result of study found that

Fama French Factor can explain the fluctuation of return on asset in stock market of

France better than CAPM. Moreover he found that both assets pricing model can

explain the changeable of return on asset.

Maroney and Protopapadakis (2002) tested the FF three – factor model on

stocks exchanges of Australia, Canada, Germany, France, Japan, the UK and the US.

The size effect and the value premium survive for all countries examined. They

conclude that the size and BE/ME effects are international in character. Using a

Stochastic Discount Factor (SDF) model, and a variety of macroeconomic and financial

variables. Do not price assets better than the Fama and French three – factor model.

In 2003, Drew and Veeraraghavan compare the explanatory power of the single

index model with the multifactor asset pricing model of Fama and French (1993) for

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Hong Kong, Malaysia and the Philippines. They find that small and high book – to –

market equity firms generate higher returns than big and low book – to – market equity

firms and conclude that the size effect and the value premium are present in these

markets. They also find that the FF three – factor model explains the variation in returns

better than single index model. They suggest that the premium is a compensation for

risk that is not captured by the CAPM.

Gaunt (2004) studies the Fama French (FF) three – factor model on the

Australian Stock Exchange (ASX) for a sample of 6,814 companies over the period

January 1993 to December 2001. He finds that beta risk tends to be greater for smaller

companies and those with lower BM ratios. However, the study does not find a strong

small firm effect but there is evidence of the BM/ME effect increasing monotonically

from the lowest to the highest book – to – market equity portfolios. Overall, the

evidence indicates that the three – factor model provides a better explanation of

observed Australian stock returns than the CAPM.

In 2005, Sanil K Bundoo studies and attempt an augmentation of Fama French

three – factor model on the Stock Exchange of Mauritius (SEM) by use the listed

companies from January to December over the period 1998 to 2004. He finds that the

Fama and French three factor model holds the SEM. In other words, both a size effect

and a book – to – market equity are present on SEM. The augmented Fama and

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French model shows that the time variation in betas is priced, but the size and book –

to – market equity effects are still statistically significant.

From the studied and reviewed the historical literature both Thailand and

Foreign, can summarize into two points that are:

The first point – The empirical study about the capability of CAPM in Stock

Market of many countries. The results of studies are matching that is CAPM cannot

explain the fluctuation of the rate of return in Stock Market efficiently. However it still

cannot find the other models to compensate CAPM. CAPM is need and important to

use extensively both in the past and nowadays.

The second point – The empirical study about the capability of Fama – French

in many countries. The results of studies are matching with the capability of Fama –

French that can explain the fluctuation of the rate of return in Stock Market better than

CAPM. But Fama – French doesn’t popular or use extensively because it has more

complicated than CAPM and didn’t have the financial theory to support this model like

CAPM.

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2.3 Energy Information Administration (EIA)

2.3.1 Background

Thailand has limited domestic oil production and reserves, and imports make up

a significant portion of the country’s oil consumption. Thailand holds large proven

reserves of natural gas, and natural gas production has increased substantially over the

last few years. However, the country still remains dependent on imports of natural gas

to meet growing domestic demand for the fuel.

In September 2006, a military coup overthrew the government of Prime Minister

Thaksin Shinawatra. The change in leadership did not have any immediate impact on

oil or natural gas production. During 2006, Thailand’s real gross domestic product

(GDP) grew by an estimated 5.0 percent, right on trend with average 5-year growth

levels.

2.3.2 Oil

According to Oil & Gas Journal (OGJ), Thailand held 290 million barrels of

proven oil reserves as of January 2007. In 2006, Thailand produced an estimated

336,000 barrels per day (bbl/d) of total oil liquids, of which 130,000 bbl/d was crude oil,

76,000 bbl/d was lease condensate, and 111,000 bbl/d was natural gas liquids, and the

remainder was refinery gain. Thailand consumed an estimated 922,000 bbl/d of oil in

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2006, leaving net imports of 586,000 bbl/d, the second largest among Southeast Asian

countries.

Figure 2.1 Show Southeast Asia Oil Consumption and Net Oil Imports, 2006. Source: EIA Short-Term Energy Outlook (Feb.2007)

Sector Organization

The oil industry in Thailand is dominated by PTT, formerly the Petroleum

Authority of Thailand. Although PTT is considered a national oil company (NOC), the

company underwent a partial privatization in 2001, during which 32 percent of its equity

was sold through the Bangkok Stock Exchange. However, Thailand’s Supreme

Administrative Court (SAC) is currently considering a proposal to reverse the sale of

PTT’s shares. The court is set to issue a verdict by mid-2007, which could see PTT

forced to repurchase the shares sold to investors in 2001. In a similar case, the SAC

ruled in March 2006 that the privatization of EGAT, the national power utility, was

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carried out illegally, forcing the company to delist its shares from the Thailand’s stock

exchange.

Thailand’s oil sector is open to foreign involvement, although foreign companies

often work in joint ventures with PTT Exploration and Production (PTTEP), PTT’s

upstream subsidiary. Foreign companies supply the bulk of Thailand’s domestic oil

production, with the largest volumes coming from Chevron. Some analysts speculate

that if PTT’s privatization is reversed, foreign company interest in Thailand’s oil sector

could wane, although this remains to be seen. PTT has a considerable presence in

Thailand’s downstream sector, with stakes in all four of the country’s refineries as well

as equity interests in downstream subsidiaries Thai Oil Company (Thaioil) and the Thai

Petroleum Pipeline Company (Thappline).

The Energy Policy and Planning Office (EPPO), which is part of Thailand’s

Ministry of Energy, oversees all aspects of the country’s energy policies, including the

oil, natural gas, and power sectors. The National Economic and Social Development

Board overseas large energy infrastructure projects and also assists in the policy

planning process.

Exploration and Production

Thai oil production has risen in the last few years, although production remains

well below consumption levels. About 85 percent of the country’s crude oil production

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comes from offshore fields in the Gulf of Thailand. Chevron is the largest oil producer in

Thailand, accounting for more than three-fourths of the country’s crude oil and

condensate production during 2006. Significant oil fields in Thailand include Chevron’s

Benchamas, Pailin, and Fiel fields, as well as PTTEP’s Bongkot and Sirikit fields.

Figure 2.2 Show Thailand’s Oil Production and Consumption, 1990-2006. Source: EIA International Energy Annual 2004; Short-Term Energy Outlook (Feb.2007)

PTTEP and various foreign companies continue to aggressively explore

for oil reserves throughout Thailand, although companies have had much more success

locating additional natural gas reserves in recent years. Thailand announced the results

of the country’s 19th Petroleum Concession Bidding Round in November 2006. PTTEP

added several new blocks to its upstream portfolio, and the Thai government awarded

exploration rights to several small foreign and private oil companies, including Pan

Orient Energy, Pearl Oil, Northern Gulf Oil, and Occidental Petroleum.

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Overseas E&P

PTTEP officials have announced plans to increase the company’s upstream

activities abroad, noting that domestic exploration and production (E&P) potential is

becoming increasingly limited. To date, much of PTTEP’s overseas investments have

focused on other Southeast Asian countries, including Burma, Cambodia, Indonesia,

and Malaysia. However, PTTEP has also invested in E&P projects in Algeria, Iran, and

Oman, and has considered upstream investments in several other countries.

Pipelines

PTT established its subsidiary, Thai Petroleum Pipeline Company (Thappline), in

1991 to develop the country’s first oil pipeline. The main trunk line runs from the Sri

Racha Oil Terminal in the south to the northern Lumlukka and Saraburi terminals. All

told, Thappline’s oil pipeline infrastructure consists of the 160-mile trunk line and 70

miles of additional local spurs, which most analysts consider inadequate to meet the

country’s growing oil demand requirements. Thailand does not currently have any

international oil pipeline connections.

Downstream Activities

According to OGJ, Thailand had 729,100 bbl/d of refining capacity at four

facilities as of January 2007. The largest refinery is the 301,000-bbl/d plant at Map Ta

Phut, which is owned by Alliance Refining Company (ARC), a joint venture between

Chevron and PTT. Other refineries include the ExxonMobil-operated plant at Sri Racha

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(173,500 bbl/d capacity), Thai Oil Company’s facility at Sri Racha (192,850 bbl/d), and

PTT’s Bangkok refinery (61,750 bbl/d).

The Thai government has introduced tax subsidies to encourage oil companies

to develop additional refining capacity in the country, both to meet expected higher

demand for petroleum products domestically but also to serve export markets in the

region. The government hopes to promote Thailand as an oil refining and trading center

that would rival Singapore.

2.3.3 Natural Gas

According to OGJ, Thailand held 14.8 trillion cubic feet (Tcf) of proven natural

gas reserves as of January 2007. Almost all of the country’s natural gas fields are

located offshore in the Gulf of Thailand. Natural gas production has risen steadily in

recent years, although not enough to keep up with the growth in domestic consumption.

Thailand produced 790 billion cubic feet (Bcf) of natural gas in 2004, while consuming

1,055 Bcf. The country showed net natural gas imports of 265 Bcf in 2004, which

consisted mostly of piped imports from Burma.

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Figure 2.3 Show Southeast Asia Proven Natural Gas Reserves and Production. Source: Oil & Gas Journal; EIA International Energy Annual 2004

Sector Organization

PTTEP has a stake in many of Thailand’s natural gas producing fields, including

Bongkot, the countries largest. Foreign companies, however, supply the bulk of

Thailand’s natural gas output. Chevron is the largest foreign operator, accounting for 70

percent of the country’s natural gas production from 22 offshore fields. PTT has a

leading position in mid- and downstream natural gas activities, including much of

Thailand’s domestic transmission and distribution infrastructure.

Exploration and Production

There are several ongoing projects that will increase Thailand’s natural gas

supplies in the next few years. The largest of these is PTTEP’s Arthit project, which is

off the coast of Songkhla, about 350 miles south of Bangkok. The company expects to

start production at the main Arthit field during the first quarter of 2008 at a rate of 330

million cubic feet per day (MMcf/d). PTTEP was originally scheduled to bring the field

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online in April 2007, but the company stated that equipment shortages would delay the

start-up. PTTEP also has plans to begin natural gas production from the Arthit North

field during the second half of 2008, initially producing at a rate of 120 MMcf/d.

Figure 2.4 Show Thailand’s Natural Gas Production and Consumption, 1994-2004

Source: EAI International Energy Annual 2004

Malaysia-Thailand Joint Development Area

Ongoing projects from the Malaysia-Thailand Joint Development Area (JDA),

located in the lower part of the Gulf of Thailand, will also increase natural gas supplies

to Thailand in future. The area is divided into three blocks, Block A-18, Block B-17, and

Block C-19, and is administered by the Malaysia-Thailand Joint Authority (MTJA), with

each country owning 50 percent of the JDA’s hydrocarbon resources. Sources estimate

that the JDA holds 9.5 Tcf of proved plus probable natural gas reserves, and some

analysts speculate that the area could hold as much as 24 Tcf total in-place reserves.

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Production at Block A-18 started in 2005, although all natural gas from the field

currently goes to Malaysia. Thailand will begin taking deliveries from A-18 in April 2007,

once PTT completes a pipeline linking the JDA with its Arthit field pipeline system. Initial

flows are expected to be 200 MMcf/d, eventually rising to 400 MMcf/d in 2008, and 550

MMcf/d by the end of 2010. Thailand was not originally scheduled to receive natural gas

from Block A-18 until 2008, but decided to bring the schedule forward to make up for

delays at the Arthit field.

The Carigali-PTTEP Operating Company (CPOC), a joint venture of the E&P

arms of the Thai and Malaysian national oil companies, operates Blocks B-17 and C-19

in the JDA. CPOC expects to start natural gas production from B-17 in the second half

of 2009 at an initial rate of 270 MMcf/d, with plans to increase output to 420 MMcf/d in

2010. CPOC also plans to start production at the smaller Block C-19 sometime after

2009, although no production timetable is set.

Pipelines

Whereas Thailand’s oil pipeline system is rather limited in scale, the country’s

natural gas transmission infrastructure is much more advanced. PTT Natural Gas

Distribution (PTTNGD) currently has more than 2,300 miles of total natural gas

transmission pipelines throughout the country. Thailand has two major natural gas

pipelines linking the offshore Erawan field with Rayong, with a combined capacity of

2.65 Bcf/d. PTTNGD completed construction on the country’s third major natural gas

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pipeline in early 2007, which will pump natural gas from the new Arthit project to the

east coast town of Pattaya. The line will have an initial capacity to handle 700 MMcf/d,

eventually expanding to 1.9 Bcf/d in late 2008, when PTT installs a compressor unit.

However, the company expects that the pipeline will only run at 60 percent capacity for

2007 and 2008, and is expected to run at its full 1.9 Bcf/d capacity beginning in 2010.

International Connections

The 410-mile Thai-Burmese natural gas pipeline, running from Burma's Yadana

gas field in the Andaman Sea to an Electricity Generating Authority of Thailand (EGAT)

power plant in Ratchaburi province, was completed in mid-1999. As a result of the

Asian Financial Crisis and Thailand’s ensuing currency crash, Thailand did not begin to

fully honor its gas purchase agreement until 2002, when it renegotiated the contract.

Today, the Ratchaburi power station consumes only 150 MMcf/d of natural gas rather

than the originally contracted amount of 525 MMcf/d.

As part of the ongoing projects in the JDA described above, the Trans-Thailand-

Malaysia (TTM) Gas Pipeline System has been developed to connect the JDA natural

gas fields with each country’s domestic transmission system. The TTM pipeline currently

delivers natural gas to the Malaysian market, and will begin sending natural gas to

Thailand in April 2007. The effort is a major component of the proposed “Trans-ASEAN

Gas Pipeline” (TAGP) system, which envisions the establishment of a transnational

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pipeline network linking the major natural gas producers and consumers in Southeast

Asia.

Liquefied Natural Gas

PTT has established a subsidiary, PTTLNG, to study the feasibility of building a

liquefied natural gas (LNG) import and storage terminal in Thailand. PTTLNG is

considering a 5 million metric ton per year (244 Bcf/y) facility at Map Ta Phut in

Rayong. In October 2006, PTTLNG opened a pre-qualification tender process to gauge

supplier interest in the proposed terminal. If the company proceeds with the plan, it

expects the LNG facility to be ready by 2011. PTT would also start construction in 2008

or 2009 on a fourth major pipeline to deliver regasified natural gas from