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MARKET BEHAVIOR AND TI-IE CAPITAL ASSET

PRICING MODEL IN THE SECURITIES EXCHANGE

OF THAILAND: AN EMPIRICAIL APPLICATION

PAIBOON SAREEWIWATTHANA AND R. PHIL MALONE'

INTRODUCTION

Security market behavior in foreign countries has recently become the focus of

empirical testing regarding market behavior and Capital Asset Pricing Model

(CAPM) theory. Over the last two decades a substantial amount of empirical

research has been undertaken to investigate market behavior in the US and

other major industrial countries of the world. While a limited number of

empirical works are available for the stock exchange of some less developed

countries, considerable testing still needs to be undertaken for the

underdeveloped capital markets of the world. The objective of this study is to

ascertain risk-return perceptions prevailing in the Securities Exchange of

Thailand (SET). An additional effort is made to determine how domestic in-vestors view risk and what the risk~return structure is within the SET.

REVIEW OF THE LITERATURE

771s Capital Asset Pricing Model

The traditional Capital Asset Pricing Model (CAPM) is developed mainly by

Sharpe (1964) and others. According to the CAPM, the equilibrium return of

asset I' is related to the return of the market portfolio by the equation: .

E(Rf,,) = Rh + Bi (E(Rm,l) _ Rn) (1)

where Rf, is the risk free rate of interest,

E(R,,,,,) is the expected return of the market portfolio, and B, is a measure of

systematic risk.

Under the CAPM, all residual or unsystematic risk is diversifiable and

investors hold portfolios that represent linear combinations of the risk free asset

and the market portfolio. A

' The authors are respectively, Assistant Professor at the University of Evansville; and Associate

Professor at the University of Mississippi. They wish to thank the anonymous referee for helpful

comments and suggestions. (Paper received August 1984, revisedjanuary 1985)


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]0umal1jBLu'ines.t Finance G1AccaunlI`ng, 12(3), Autumn 1985, 0306 686X $2.50

MARKET BEHAVIOR AND TI-IE CAPITAL ASSET

PRICING MODEL IN THE SECURITIES EXCHANGE

OF THAILAND: AN EMPIRICAIL APPLICATION

PAIBOON SAREEWIWATTHANA AND R. PHIL MALONE'

INTRODUCTION

Security market behavior in foreign countries has recently become the focus of

empirical testing regarding market behavior and Capital Asset Pricing Model

(CAPM) theory. Over the last two decades a substantial amount of empirical

research has been undertaken to investigate market behavior in the US and

other major industrial countries of the world. While a limited number of

empirical works are available for the stock exchange of some less developed

countries, considerable testing still needs to be undertaken for the

underdeveloped capital markets of the world. The objective of this study is to

ascertain risk-return perceptions prevailing in the Securities Exchange ofThailand (SET). An additional effort is made to determine how domestic in-

vestors view risk and what the risk~return structure is within the SET.

REVIEW OF THE LITERATURE

771s Capital Asset Pricing Model

The traditional Capital Asset Pricing Model (CAPM) is developed mainly by

Sharpe (1964) and others. According to the CAPM, the equilibrium return of

asset I' is related to the return of the market portfolio by the equation: .

E(Rf,,) = Rh + Bi (E(Rm,l) _ Rn) (1)

where Rf, is the risk free rate of interest,

E(R,,,,,) is the expected return of the market portfolio, and B, is a measure of

systematic risk.

Under the CAPM, all residual or unsystematic risk is diversifiable and

investors hold portfolios that represent linear combinations of the risk free asset

and the market portfolio. A

' The authors are respectively, Assistant Professor at the University of Evansville; and Associate

Professor at the University of Mississippi. They wish to thank the anonymous referee for helpful

comments and suggestions. (Paper received August 1984, revisedjanuary 1985)

439


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the Three Moment Version ty' CAPM

Empirical tests ofthe CAPM have shown that non-linear relationships between

risk as measured by beta coefficient and return may have existed in certain

periods and yielded an even better fit than the traditional CAPM. This leads to

the development of~a three moment CAPM, i.e., Arditti and Levy (1975), and

Krause and Litzenberger (1976). In general this version of CAPM states that

investors are willing to accept lower expected return for a possibility of very

high return. Thus, the exclusion ofa third moment parameter from the tradi-

tional tests would imply that the slope is biased if beta and the third moment

parameter are correlated. The proposed model is as follows:

E(R,-) = R/ + aB, + bG, (2)

where G, is a measure of skewness of return,

or E(R,~) = Rf + aB, + bK, (3)

where K, is a measure of coskewness.

The Arbitrage Pricing TheoryRecent critisms of the CAPM, especially on the exact composition of the

market portfolio, sparks interest in an alternative asset pricing model. One of

these alternatives is the Arbitrage Pricing Theory (APT) developed by Ross

(1976). The appeal ofthe APT comes from its contention that composition for

bearing risk may be comprised of several risk premia, rather thanjust one risk

premium as in the CAPM. Furthermore, unlike the CAPM, definition ofthe

market portfolio is not required in the context of the APT.

Under the APT, returns of risky assets are related to a /c-factor linear

generating model:

Ri = E; + bf,iFi +` b;,tF/= + fi (4)

where IL] is the expected return on asset z`,

R- is the mean zero factor common to returns of all assets;

b,,, is the coefficient that measures the sensitivity of asset z' lo the

movement in common factor E,

and ei is a disturbance term.

The e, term is assumed to reflect the random influence of information

that is unrelated to other assets

1

DATA AND NATURE OF THE MARKET

The first securities exchange in Thailand was established by a group of

businessmen in 1962 as the Bangkok Stock Exchange (BSE). It was registered

first as a limited partnership and later as a limited company. It was operated


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mARKET BEHAVIOR AND CAPM IN SECURITIES EXCHANGE OF THAILAND 441

without neither interference nor support from the government. The operations

of the BSE, however, were disappointing with its failure to facilitate capital

market development due mainly to the economic structure and conditions of

the country during that time period.

However, as the number of businesses increased in the late 1960s, the

market for capital was not adequately served. As a result, the Securities

Exchange of Thailand (SET) was established in 1974 and the BSE discon-

tinued. Both corporate and government securities have been traded on the

SET. There are four types of corporate securities: 1) common stocks, 2) prefer-

red stocks, 3) debentures, and 4) unit trusts or mutual funds. The number of

companies initially listed on the SET was only ten companies; this number in-

creased to 81 in 1982.

Thin Market Characteristics

The SET has several characteristics in common with other thin markets in thata large number of stocks are inactive. Such market thinness characteristic has

several effects on market efficiency. Some individual securities are very inac-

tive and may not react fully to changes in available information. Inactivity

slows the speed of adjustment and may affect conclusion of efficiency. In addi-

tion, most companies that have their securities listed are not quite public com-

panies. As shown in the Appendix, even the thirty most active traded securities

are practically family controlled companies. Inactivity coupled with the fact

that information is not readily available may enable market participants to be

able to manipulate trading. Consequently, information available only to

insiders may also be used to generate abnormal returns. Thus, in general, the

SET appears to be less efficient than most other stock markets.

Special problems may also exist because of the lack of trading for some

securities in that when the market is moving, prices ofthese infrequently traded

securities do not promptly reflect the new market level, A major source of

potential bias is that the month-end quotes (used in this study) may not repre-

sent the outcomes oftransactions on the last day of the month. As a result, part

of a securitys actual return for any month may be reflected in the following

months measured return. This results in market returns constructed from

such security prices being biased with a positive serial correlation induced into

returns of these infrequently traded securities. Such implies that the estimated

variance and covariance will be positively related to trading frequency. Since

the mean beta of all securities is unity, infrequently traded securities have

estimated_ betas which 'are biased downward, while the frequently traded

securities betas are upward biased.

This problem can be minimized by employing a special procedure to

estimate beta coefficients developed by Dimson (1979) and others. In order for

the measure of security return to be matched with the corresponding months

market return, the previous months market return is included in the beta

estimation model:


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442 SAREEWIWATTHANA AND MALONE

RL, = a b,R,,,_, b2R,,|,_, ei (5)

where RL, = security excess return in time period l,

R,,,', = market excess return in time period t,

and R,,,_,_, = market excess return in time period I- 1.

\

The addition ofthe lagged independent variable does not substantially affect

the statistical properties ofthe model and may reduce the residual variation (Ib-

botson, 1975). Since the market returns are independent for different time

periods, a model in equation (5) will not exhibit multicollinearity. The

estimated beta of security z' can then be obtained by summing the lagged and

unlagged betas.

The Data

The thirty most active securities whose prices are quoted continuously on the

Securities Exchange of Thailand during December 1978 through November1982 are selected for analysis. Name; industry, and percentage of stocks

outstanding controlled by major stockholders are reported in Appendix A.

Monthly returns are computed after adjusting for stock dividends and splits as

follows;

Rn = (Dig 'l' HJ ' R11-1)/P111-1

where DL, = dividend per share of the stock z' paid during month t,

and PL, = price per share of the stock z' at the end of month l.

Estimates of market returns are based on the weighted average of all the

monthly returns using market value as weights. The risk free rate used in this

study is the 180 day Thai treasury bill rate.

STATISTICAL ANALYSIS AND RESULTS

The Fama-MacBeth approach is utilized. Total risk, S(R,); skewness, G,-;

coskewness, Kf; beta coefficient, B;; and unsystematic risk, S(e;) are computed

using thirty monthly returns. For the next six months, these variables are used

as proxies for ex ante variables, During the same six-month period, the mean

rate of return is computed. The iterative procedure is employed by dropping

the first monthly return and adding the next monthly return. Thirteen testing

periods are generated for subsequent analysis.

Cross-sectional regressions are performed by regressing beta against the

mean return as follows:-

Ri=a+bBi+ei (6)

where R; = mean of six monthly rates of return.

All of the beta coefficients in Table 1 are significant at the 0.1 level except

one (period nine) and nine of them are significant at the 0.01 level. These


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MARKET BEHAVIOR AND CAPM IN SECURITIES EXCHANGE OF THAILAND 443

Table 1

Estimation ofthe Beta - Return Relationship R, = a + (JB,-

Where R, is the mean ofsix monthly returns

B, is the infrequently traded estimated beta

Period a I-value b I-value R2

1 0.0218 1.4279 0.0006 2.2287 0.1507

2 0.0182 2.1287" 0.0008 2.2070 0,1482

3 0.0143 1.7136" 0.0002 2.704-2' 0.1594

4 -0.0004 0.0529 0.0011 1.7674-" 0.1004

5 0.01856 1.6764 0.0012 2.1079 0.1365

6 0.0187 0.0588 0.0002 1.9906' 0.1239

7 0.0140 0.2637 0.0015 2.1810' 0.1452

8 - 0.0064 1.6388 0.0019 1.4-767' 0.07239 0.0075 1.7198" 0.0028 1.5934 0.0827

10 0.0135 2.5615" 0.0008 1.5011" 0.0723

11 0.0165 2.4741" 0.0017 1.6455' 0.0893

12 0.0222 3.1329"" 0.0022 2.6747' 0.2026

13 0.0197 3.6655"" 0.0007 2.1249' 0.1389

Average 0.0137 1.4716 0.0008 1.7424 0.1249

Significant at 0.1 level

" Significant at 0.01 level

results indicate that, in general, beta seems to be a good measure of systematic

risk and also that it is positively related to return.

Under the CAPM, the intercept term in equation (6) should be equal to the

risk free rate prevailing in the market during the time period tested and the

slope term should be equal to the market risk premium. However, as presented

in Table 1, the empirical intercepts are greater than the risk free rate in ten out

ofthirteen periods. The empirical slopes are less than the theoretical slopes in

nine out of thirteen periods. The results are consistent with empirical findings

in other countries, i.e., Black, Jensen, and Scholes (1972), Blume and Friend

(1973), and Fama and MacBeth (1973). A possible explanation is that there is

no real risk free rate of return. An alternative approach for the risk free rate

would be a return from a zero beta portfolio. The latter should be greater than a

theoretical risk free rate of return.

As pointed out by Miller and Scholes (1972), an inappropriate functional

form in the specification of the risk-return relationship might bias the cross-

sectional test. In order to test for the non-linearity in the relationship, a

generalized functional form is employed.

Following Box and Cox (1964), Zarembka (1968), and Lee (1977), a func-

tional form specification ofthe risk-return relationship is defined as


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Rf = a + bB," (7)

where /l. is the functional form parameter to be estimated. The relationship

reduces to the linear form when /1 is equal to one. In other words, equation (7)

includes the linear relationship form as a special case and provides a generalized

functional form. .

In order to allow equation (7) to be continuous at /l = 0 and stochastic, the

transformation procedures should be employed as

Rf = a' bB_fl) e,-

where Rf = (Rf_,)/J.,

Bra) " (Bi-/)/'11

a == *(ab)-1]/1,

and ei ~ N(0, S,).

Using the maximum likelihood method, a maximum logarithmic function

for determining the functional form isLmax (2) = ~ nlog 51(1) + (A - 1) X log IT, + const. (8)

where n -= sample size,

S,(}.) = estimated regression residual standard error.

The optimum value ofl is obtained from the value of). that maximizes the

logarithmic likelihood. Following the likelihood ratio method, an approximate

confidence for it is

Lmax(}'i) _ Lmas


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Table 2

Estimation of /1 of the Functional Form for the

Beta - Return Relationship

R.

=G+

115.1

Where R, is the mean ofsix monthly returns


Peliod Estimated Critical Region Critical Region

at 0.1 level at 0.01 level

1 0.6403 0.274~ 1.018 0.094-1.198

2 0.8732 0.771 - 1.004 0.622- 1.157

3 0.8165 0.618-1.054 0.414- 1.259

'1- 1.1325 0.773- 1.437 0.584-1.6215 0.7349 0.458-1.017 0.241-1.214

5 0.8780 0.537-1.203 0.291 -1.454

7 0.7915 0.511-1.040 0.321-1.248

8 0.4352 0.320-0,561 0.182-0.700

9 2.4824 2.210-2.584 2.077-2.713

10 1.5890 1.258-1.852 1.077-2.025

11 0.7432 0.620-0.873 0.449-1.294

12 0.8743 0.641-1.126 0.476-1.294

13 0.7932 0.478-1.112 0.350-1.239

Table 3

Estimation ofthe Risk - Return Relationship

R, = a + bB, + cS(,)

Where R, is the mean of six monthly returns


S(e,) is the unsystematic risk measure

Period a I-ratio b I-ratio c I-ratio F I?

1 -0.0253 -2.6534" 0.0007 2.4-122" 0.0059 2.1819" 3.47" 0.1767

2 -0.0191 -1.6370' 0.0003 2.0438" 0.0021 2.050l" 2.44' 0.1217

3 ~0.0l17 - 1.0313 0.0010 2.3562" 0.0179 1.7985" 3.59" 0.1880

4 0.0206 0.0553 0.0008 1.84-10 0.0111 1.5166' 2.84" 0.1425

5 0.0222 1.6816 0.0012 l.7614' 0.0177 1.482' 2.78 0.1411

6 0.0244 2.0293" 0.0023 2.2317" 0.0051 2.1076' 6.84" 0.3128

7 0.0281 1.9364" 0.0034 3.4956 0.0024 3.6988" 11.94" 0.4451

8 0.0264 1.8233" 0.0023 2.1981" 0.0041 1.7598" 1.88 0.0907

9 0.0280 1.8655" 0.0014 1.1405 0.0009 1.7989 1.65 0.0777

10 0.0366 3.1266" 0.0007 2.2194' 0.0094 1.9607" 2.96" 0.1508

11 0.0016 2.0044" 0.0009 0.6829 0.0079 1.4320' 3.92" 0.1902

12 0.0279 2.7319" 0.0013 3.l2B6" 0.0075 2.8675" 7.89" 0.3530

13 0.0214 4.1480" 0.0015 2.9014" 0.0057 3.1884" 6.55" 0.3023Average 0.0169 1.2712 0.0012 2.0710 0.0055 2.0030 4.46 0.1902


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' Significant at 0.1 level



can tentatively be rejected and that unsystematic risk appears to be a significant

determinant of return. To support this contention, the total risk, represented

by the standard deviation of return, is regressed on return to see how signifi-

cant the relationship is.

R, = a + bS(R}) + 2, (11)

where S(R,) represents total risk ofsecurity z`.

As reported in Table 4, the results show that in all thirteen periods S(R,) coef-

ficients are significant at the 0.1 level with eleven significant at the 0.01 level.

The R-Squares from equation (11) are greater than that of equation (6) in

twelve out of thirteen periods. Based on these results, it is logical to conclude

that the hypothesis of beta as a complete measure of risk can be rejected. The

return in the SET appeared to be more closely related to total risk than to thebeta coefficient.

Table 4

Estimation of the Standard Deviation - Return Relationship

= a + b S(R,-)

Where R, is the mean of siic monthly returns

S(R,) is the standard deviation of return

R,

Period a t-value b l-value R2

1 -0.0041 3.2615" 0.3708 2.7083"' 0.2076

2 -0.0200 2.4469 0.3990 2.4568" 0.1773

3 -0.0211 L7560" 0.3359 1.8B51" 0.1368

4 -0.0076 0.6743 0.2835 1.8851" 0.1126

5 0.0211 1.0928 0.0510 2.944" 0.2364

6 0.0228 1.1958 0.0164 2.3787" 0.2017

7 0.0160 1.6457' 0.0186 2.0795" 0.1528

8 0.0097 0.8556 0.0081 1.6809' 0.0849

9 0.0215 L5380' 0.2788 1.5015' 0.0745

10 0.0108 2.7085 0.0179 1.7985"' 0.1034

11 0.0117 2.2918*' 0.1125 3.4632" 0.2999

12 0.0242 3.3931" 0.0174 2.6768 0.2035

13 0.0255 2.0654 0.0213 3.8732 0.3421

Average 0.0098 0.6652 0.1057 2.2191 0.1795



A possible explanation is that investors hold inadequately diversified port-

folios, thus the unsystematic risk of securities contributes to portfolio risk and

investors with risk aversion may require compensation in the form of higher

expected return for unsystematic risk and consequently, for total risk. Inade-

quately diversified portfolios may be held because of transaction costs, infor-mation costs, or heterogeneous expectations in risk and return. In addition, the


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SET appears to be a market that is dominated by small investors who hold

single or a few securities rather than institutions or large investors who hold

portfolios. Total risk or variation in return may be a better measure ofrisk than

the beta coefficient.

Yet, the observed relationship may also result from the correlation of a

measure of unsystematic risk with a missing variable where the missing

variable is a significant determinant of return. One possible missing variable is

skewness. Consequently, the hypothesis that skewness or coskewness is a rele-

vant pricing parameter is tested by separately adding measures of skewness and

coskewness to the traditional CAPM.

R; = a + bBi -1- CG, + ei (12)

where G, = 2(R,|,-E.)/[z


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Table 6

Estimation of the Beta, Coskewness and Return Relationship

Ri = a + bB,< + cK,

Where R; is the mean of six monthly returns


K, is the coskewness measure

Period a I-value b I-value c I-value F-value fl?

1 -0.0283 - L3240' 0.0022 1.3276' 0.0064 1.0177 3.11" 0.1587

2 -0.0103 -1.2315 0.0098 l.3802 -0.0090 2.4468 4.35" 0.2171

3 -0.0774 -2.5456" 0.0112 1.3254 -0.0141 1.6067 3.58" 0.1804

4 0.0012 0.1503 0.0123 1.43l7' - 0.0007 0.2334 1.38 0.0603

5 0.0074 0.8314 0.0212 0.3160 - 0.0078 2.1409" 3.23" 0.16426 0.0113 1.3971 0.0091 1.4267' - 0.0054 1.8441 2.12 0.1047

7 0.0225 1.1524 -0.0110 -2.7033" -0.0122 1.5768 4.54" 0.2248

B 0.0321 1.8694 0.0174 1.8976 - 0.0038 2.2496 5.73" 0.2727

9 0.0129 0.5016 - 0.0104 - 1.0363 0.0082 0.9903 2.46' 0.1238

10 0.0333 2.2900 0.0205 1.3956' - 0.0017 0.4377 0.89 0.0263

11 0.0359 1.5016 0.0198 l.6065' -0.0010 2.2143 5.56" 0.2665

12 0.0360 3.1618 0.0106 2.1894' -0.0112 2.7956 6.98" 0.3166

13 0.0258 1.5678 0.0097 1.4125' -0.0012 2.1948 4.34" 0.2170

Avcrigt 0.0079 0.7168 0.0080 0.8294 - 0.0036 - 1.4410 3.40 0.1640



significant- at the 0.1 level in ten periods with four periods significant at the 0.01

level. The skewness coefficients are significant at the 0.1 level in twelve periods

with ten periods significant at the 0.01 level. Compared to equation (6),

adjusted R-Squares are greater under equation (12) in eleven out of thirteen

periods. The evidence indicates that skewness may be a relevant parameter in

determ;ning return. This is consistent with the empirical evidence found by

Arditti and Levy (1975) for the New York Stock Exchange.

In equation (13), the beta coefficients are significant at the 0.1 level in ten

periods with nine periods significant at the 0.01 level. The coskewness coeffi-

cients are significant at the O;1"level in ten periods with nine periods significant

at the 0.01 level. Compared to equation (6), the coskewness model has a greater

adjusted R-Square in ten out of thirteen periods. It appears that coskewness is a

relevant determinant of return. Since both skewness and coskewness coeffi-

cients are significant, the effort is made to identify the more appropriate one.

Since the adjusted R-Squares from the skewness model are greater in ten out of'

thirteen periods, it is selected as a better variable. Thus it appears that the three

moment version of the CAPM fits the SET quite well, indicating that in addi-

tion to aversion to risk, investors in the SET have a preference for positiveskewness.


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The Arbitrage Pricing Theory (APT) is used to test the risk-return relation~

ship. The 36 monthly rates of return on securities (from December 1978 to

LJ

MARKET BEHAVIOR AND CAPM IN SECURITIES EXCHANGE OF THAILAND 44-9

Table 7

Regressions of Excess Returns on Factor Loadings

One Factor Model

ExRi = a

-0.0114 0.14-58

(~ 03555) (3.0769) *

R2 = 0.2527 F~value = 9.47"

Two Factor Model

ExR,- = a + bjn- + cF2_,-0.0359 0.1463 - 0.1184

(13084) (3.3516)'* (-2.4-193)

R2 =- 0.3663 F-value = 8.58"

Three Factor Model

ExR, == a + bF,_,~ + cF2_, + dF3_|~

0.0167 0.0983 - 0.1334 0.1034

(0.5325) (2.1702) (- 3.9055) (1.7052)'

1? = 0.3723 F-value = 6.16"

Four Factor Model

ExRi = a + bF,_,~ + cF.2_i + dF3_, +, eF4',-

0.0170 0.1617 -0.0673 0.0604 0.0727

_ (0.6663) ' (3_4402)~~ (~1.7274)' (1.72s5)' (O.973O)

E -= 0.3997 F-value = 5.36"

ExR; - Excess return ofthe mean of six monthly returns above the risk free rate

Fm; - Factor loading on factor 1'



\

May 1982) are analyzed and a number of common factors is determined. A

Chi-Square test is used to signal the adequacy of common factors. The null

hypothesis of four common factors cannot be rejected, indicating a sufficient

number of common factors at the 0.1 level of significance. It thus appears that

four common factors may be the determinants of return in the SET. Cross-

Sectional regression is then used to evaluate the significance of these four com-

mon factors. The mean of six monthly excess returns over the period of_]une

1982 to November 1982 is used as an independent variable in the following

regression model.


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