Testing the Validity of Capital Asset Pricing Model:

Case Study on Indonesian Stock Market

Roberta Octami Sorongan

10436081

Bachelor Thesis in Economics and Finance

Supervisor:

dhr. dr. K.B.T. Boe Thio

Faculty of Economics and Business

University of Amsterdam

2014

Table of Content

1. Introduction ................................................................................................................ 3

2. Literature Review ....................................................................................................... 5

2.1 Development of Capital Asset Pricing Model .................................................... 5

2.2 Arguments against Capital Asset Pricing Model ................................................ 5

2.3 Arguments supporting Capital Asset Pricing Model .......................................... 6

2.4 Emerging Markets Perspective ........................................................................... 6

2.5 Previous Empirical Findings ............................................................................... 7

2.6 Performance of Capital Asset Pricing Model in Indonesia ................................. 8

3. Data and Methodology ............................................................................................. 10

3.1 Data Selection ................................................................................................... 10

3.2 Sub Periods and Portfolio Formation ................................................................ 10

3.3 Capital Asset Pricing Model Testing Procedures.............................................. 12

4. Empirical Results and Analysis ............................................................................... 13

4.1 Entire Period...................................................................................................... 13

4.2 Sub Periods ....................................................................................................... 14

4.3 Test of Security Market Line ............................................................................ 15

4.4 Test of Non-Linearity ........................................................................................ 16

4.5 Test of Non-Systematic Risk ............................................................................ 17

5. Conclusion ................................................................................................................ 19

5.1 Remarks on the Effect of 2008 Crisis ............................................................... 19

5.2 Limitations to this Study ................................................................................... 20

References ..................................................................................................................... 21

Appendix ....................................................................................................................... 23

2

1. Introduction

In making investment evaluations and decisions, it has been important to provide a

good estimation of expected return, price of stocks, or optimal portfolio. Investors and

financial managers are also seeking to minimize the level of risk when investing in a stock.

These issues can recently be solved using financial tools to determine an investment’s future

orientation. Modern finance theory has provided many insights into how stock prices are

formed and has provided a quantitative description for the risk structure of equilibrium

expected returns (Merton, 1980). As a result, a model is developed which is referred to as

Capital Asset Pricing Model, or CAPM.

CAPM was originally developed by Sharpe (1964) and Treynor (1961). In its most

elementary form, the equilibrium structure is defined by the following equation:

𝐸𝐸[𝑅𝑅𝑖𝑖] = 𝑅𝑅𝑓𝑓 + 𝛽𝛽𝑖𝑖�𝐸𝐸[𝑅𝑅𝑚𝑚] − 𝑅𝑅𝑓𝑓�

where 𝐸𝐸[𝑅𝑅𝑖𝑖] and 𝐸𝐸[𝑅𝑅𝑚𝑚] respectively denote the expected rate of return on stock i and market

portfolio; 𝑅𝑅𝑓𝑓 is the risk-free interest rate; and 𝛽𝛽𝑖𝑖 is the covariance of the return on stock i with

the return on the market divided by the variance of return on the market. Not only this

relationship can be used on the world of securities investment, but it has also been extended

to be applied in estimating a company’s cost of equity capital.

Nonetheless, CAPM has been tested and challenged empirically to evaluate its ability

in explaining risk and return relationship since its development. Many have argued and come

up with empirical results that indicate weak support for this model. Fama and French (2004)

mentioned that the record of the model is empirically poor as well as reflecting theoretical

failings as a result of many simplifying assumptions. As new financial markets emerge

around the world with their differences in system, potential return and risk structures, it is

getting more essential to test the validity of the model.

Earlier empirical studies of CAPM were mostly done on the US, UK, or European

market, but lately many studies have also been conducted on the emerging countries market,

especially in the Southeast Asia region. Garg (1998) suggested in his literature review that

most studies in the emerging market have resulted in the underperformance of CAPM in

explaining the risk return relationship.

However, one finding by Johnson and Soenen (1996) in Indonesian stock market for

the period December 29, 1990 through the end of 1993 indicates that most of the stocks are

not under- or overvalued according to CAPM. This evidence seems to support the model,

3

which is rather contrary to other findings in similarly emerging markets. This anomaly may

be interesting to be brought to attention. Thus, the purpose of this thesis is to investigate

whether the Capital Asset Pricing Model is relevant in estimating stocks return in Indonesian

stock market using more recent data.

Indonesia first established its stock exchange in 1914, but it was aroused only after the

deregulation actions in 1987 and 1989. Most of the foreign trades are conducted and

concentrated in the Jakarta Stock Exchange (JSX). As JSX may actually be one of Asia’s

smallest bourses, it is also one of the fastest-growing (Johnson and Soenen, 1996). In this

study, the test will be conducted on 38 stocks traded in LQ45 index per 2013, which is a

capitalization-weighted index of the most liquid and heavily traded stocks on the Indonesia

Stock Exchange (formerly Jakarta Stock Exchange). This index was launched in February

1997, and will firmly reflect the stock market condition in Indonesia as it covers at least 70%

of the stock market capitalization and transaction values in the Indonesian stock market.

This thesis will be organized as follows: the next section will provide a brief summary

of the literature review on fundamental background of CAPM and arguments on CAPM. It

will focus more on the emerging markets point of view and previous empirical findings,

including in Indonesia. Afterwards the methodology and data for the test will be discussed,

and then on the next section the empirical data results and analysis will be presented. Finally

we will come up with the conclusion and possibly further recommendation.

4

2. Literature Review 2.1 Development of Capital Asset Pricing Model

On their paper regarding CAPM, Black, Scholes, and Jensen (1972) discussed the

original development of the model by Sharpe (1964) and Treynor (1961). This model was

then extended and clarified by Lintner (1955a; 1965b), Mossin (1966), and Fama (1968a;

1968b), and Long (1972).

Alongside CAPM’s existence, there have been many developments, such as the

portfolio evaluation models by Treynor (1965), Sharpe (1966), and Jensen (1968; 1969).

These models are based on this asset pricing model or bear a close relation to it. There

have also been many added assumptions, they are:

• Investors are rational, risk-averse utility of terminal wealth maximizers, and can choose

between portfolios exclusively on the basis of mean and variance

• No taxes or transaction costs

• Investors have homogeneous views regarding the parameters of the joint probability

distribution of all security returns, and

• Investors can borrow and lend unlimited amount at a given risk-free interest rate

The last assumption regarding the risk-free borrowing and lending is the last phase

in the development of the Sharpe-Lintner CAPM model (Fama and French, 2004). Under

this condition, 𝐸𝐸[𝑅𝑅𝑍𝑍𝑍𝑍], the expected return on assets that are uncorrelated with market

return, must be equal to 𝑅𝑅𝑓𝑓, the risk-free rate. This relation between the expected return

and risk then becomes the following CAPM equation:

𝐸𝐸[𝑅𝑅𝑖𝑖] = 𝑅𝑅𝑓𝑓 + 𝛽𝛽𝑖𝑖�𝐸𝐸[𝑅𝑅𝑚𝑚] − 𝑅𝑅𝑓𝑓� (1)

Put into words, the expected return on stock i is the risk-free interest rate plus a risk

premium, which is the stock’s market beta times the premium per unit of beta risk.

2.2 Arguments against Capital Asset Pricing Model

Fama and French (2004) pointed out the failure of CAPM both empirically and

theoretically. They mentioned that the empirical record of the model is poor enough to be

applicable, and it may reflect theoretical failings as a result of the simplifying assumptions

stated previously. These assumptions are obviously not relevant in the real-world case.

Lumby and Jones (2003) admit to the fact that these assumptions are unrealistic, since

market inefficiencies are prevailing due to several causes such as government

interventions, protectionist rules and regulations, and other external factors.

5

In addition, Dempsey (2013) examined the CAPM theoretically and argued that the

model has actually reached the point where it should have been abandoned. The continued

defense of the CAPM by adding additional factors for unsystematic volatility, liquidity,

momentum, and so forth are typical Kuhn’s articulation of “normal science”. This refers to

how the single-factor CAPM has now become three, four, and even the latest five-factor

model by Fama and French.

2.3 Arguments supporting Capital Asset Pricing Model

Despite many empirical evidences showing that CAPM has not been reliable in

estimating expected returns, there are still many defenses to this risk-return relationship.

Brown and Walter (2013) discussed the theoretical validity of CAPM to counter the

argument of Dempsey in 2013. They explain the problems with Dempsey’s previous claim

against CAPM.

First, they presume that the questionable validity is not within the model, but

within the empirical evidence itself. In 1977, Richard Roll concluded that many CAPM

tests were actually invalid due to the use of inefficient benchmark portfolios, whereas

CAPM requires the benchmark to be efficient. Second, the suggestion that investors do not

expect a compensation for unavoidable risk is contrary to the beliefs of the theorists and

practitioners, namely that for the investors risk matters such that ex ante, a risk premium

must exist.

Chan and Lakonishok (1993) also developed their defense to CAPM and beta as a

measure of risk. They tried to evaluate if there is truly sufficient evidence to dump beta. It

is rather difficult to draw any clear-cut conclusions from empirical research on stock

returns, due to the noise and constantly changing environment generating stock returns.

2.4 Emerging Markets Perspective

As this study of CAPM takes perspective from an emerging market like Indonesia,

it is important first to define the concept of emerging market. In the financial community,

there has been a significant amount of confusion to exactly characterize an “emerging

stock market”. The International Finance Corporation, World Bank, has employed a

definition that is broadly accepted. It states that within emerging countries, the market is

located in a low- or middle-economy, and there is a relatively low ratio of investable

market capitalization to its most recent Gross National Product (GNP).

6

There are also many other definitions regarding emerging market. Cavusgil (1987)

describe that essentially, emerging markets are high-growth developing countries that

represent attractive business opportunities for the Western firms. In addition, the

opportunities for future market expansion distinguish the emerging countries from the less

developed countries. The forms of economic stimulus, such as development of new

technologies, foreign investment, or external participation in their commercial affairs only

occur in countries with policies towards increased growth (Miller, 1998).

Up to now, Indonesia can be categorized as one of the emerging countries market

as its economy characteristics are in line with the aforementioned definition. Indonesia has

also been listed as a sample in many studies regarding emerging market, such as the one

conducted by Hartmann and Khambata in 1993.

In its relation to CAPM, pricing risky assets in the emerging market may be rather

problematic because institutional, political, and macroeconomic conditions are generally

volatile. This high volatility may have considerable impacts for the test of asset pricing

models. First, the parameters of both asset pricing models and expected returns are

unlikely to remain constant over time. Second, the distribution of asset returns does not

follow normal distribution (Brooks, Galagedera, and Iqbal, 2010).

Some of the CAPM assumptions also may raise concerns if applied to the emerging

market. Harvey (2000) emphasizes that the assumption of perfect capital market is a

serious problem in applying the model to emerging market. This assumption implies that

markets are perfectly integrated. In contrast, evidence shows that there is segmentation of

emerging markets from the global stock market, i.e. separation from the global market

from a pricing point of view (Drobetz, Stürmer, and Zimmermann, 2002). Therefore, the

model may not perform very well with these markets. This thesis will address this issue to

Indonesia.

2.5 Previous Empirical Findings

Throughout its existence, many empirical tests have been performed to evaluate

CAPM among many financial markets. In this paper we are going to review two studies

that represent the results of CAPM validity testing from opposing types of financial

markets. They are the developed financial market and the emerging financial market in

particular. Basically these studies employ similar method of testing as what we are going

to carry in this paper.

7

Modigliani et al. (1973) conducted the test of asset pricing model on the eight

major European stock markets. They are France, Italy, U.K., Germany, Netherlands,

Belgium, Switzerland, and Sweden. These countries are considered as having developed

financial markets. With U.S. market as the benchmark of consistency of the model, they

empirically show that on the whole, the European results are comparable with the U.S.

This implies that CAPM is relevant when applied to these markets.

On the contrary, Aljinović and Džaja (2013) tested the CAPM on the emerging

markets of the Central and Southeastern Europe, including returns from Croatia, Czech

Republic, Hungary, Poland, Turkey, Serbia, Bulgaria, Romania, and Bosnia and

Herzegovina financial market in the sample. The test of the validity of beta, efficient

frontier, as well as the cross sectional analysis suggested that the CAPM is not sufficient to

assess the price of capital assets on the observed markets.

2.6 Performance of Capital Asset Pricing Model in Indonesia

In the Indonesian market itself, a survey was conducted among the companies by

Leon, et. al. (2008) to investigate which capital budgeting practice is mostly used by the

executives. This study reveals that only 14.7% of the respondents indicated that their

companies use CAPM to estimate the cost of equity capital. This appears to be the average

number compared to emerging South East Asian countries such as Malaysia with 6.2%,

Singapore with 24.1%, Philippines with 24.1%, and Hong Kong with 24.1% as shown in

another study conducted by Kester et. al. (1999). Not surprisingly, all these numbers are

relatively low compared to the application of CAPM in other developed countries market,

where it is reported that the model is used by 72.7% of the Australian companies (Kester

et. al., 1999), 73% of the US and Canadian companies (Harvey, 2001), and 47% of the

companies in the UK (McLaney et. al., 2004).

Despite many studies that have been conducted to test the validity of CAPM in the

emerging markets, scarcely any was conducted in Indonesia. However, in the paper

presented by Johnson and Soenen (1996) regarding the risk and return characteristics in

the Jakarta Stock Exchange, a short test of CAPM was performed and resulted in quite

unexpected conclusion. Using weekly returns from 75 leading stocks traded on the Jakarta

Stock Exchange during the period December 29, 1990 through the end of 1993, they

interpret that in most cases:

• beta coefficients are positive;

8

• alpha estimates do not differ significantly from zero; and

• investors are compensated only for bearing systematic risk and not for the non-

systematic one.

These are according to what would be expected in the theory. In shorts, it indicates that

most stocks are not under- or overvalued according to CAPM and this model is rather

relevant to be used in the Indonesian market.

9

3. Data and Methodology 3.1 Data Selection

This study uses monthly stock prices for the stocks traded in LQ45 index. LQ45

will also be selected as a proxy to the market portfolio as it represents the largest

companies from various economy sectors. Originally, this index consists of 45 most

heavily traded stocks on the Indonesia Stock Exchange. However, only 38 out of 45

companies have the available data required for the entire testing period, which is January

2010 to December 2013. Thus we are going to narrow down the test only to these

companies1.

Here monthly returns are used instead of daily returns as conducted by Dimson

(1979) and Cohen, Hawanini, and Maier (1983). The main purpose is to decrease the thin-

trading effect, or the intervaling effect. The closing price of the last trading day in the

month is used to calculate the monthly returns based on the following equation:

𝑅𝑅𝑖𝑖,𝑡𝑡 = 𝑃𝑃𝑡𝑡−𝑃𝑃𝑡𝑡−1+𝐷𝐷𝑡𝑡𝑃𝑃𝑡𝑡−1

(2)

where 𝑅𝑅𝑖𝑖,𝑡𝑡 is the return of stock i at time t, 𝑃𝑃𝑡𝑡 is stock price at time t, 𝑃𝑃𝑡𝑡−1 is the stock price

at time t – 1, and 𝐷𝐷𝑡𝑡 is the amount of dividends paid on stock i at time t. The data on these

returns was retrieved from finance.yahoo.com and was already adjusted for dividends and

splits.

The value of risk-free rate will be according to the BI Rate, which is the policy rate

that reflects the monetary policy stance adopted by Bank Indonesia (i.e. Indonesia’s

central bank) and announced to the public2. This rate is announced by the Board of

Governors of Bank Indonesia in each of monthly Board of Governors Meeting.

3.2 Sub Periods and Portfolios Formation

The testing will employ the method used by Black, Jensen, and Scholes (1972). In

general, the test will be done within the entire period of January 2010 – December 2013,

as well as four equally divided sub periods, each containing 24 months, summarized in the

1 Sample companies are listed in Appendix 1. These are the companies which data on returns are available for the whole sets of estimation and testing period, which is from January 1, 2008 to December 31, 2013. 2 Complete risk-free rates are shown in Appendix 2. The monthly data is obtained from Bank Indonesia’s website (www.bi.go.id) and it is actually yearly rate. Therefore, in order to adjust it to monthly rate the following formula is used:

(1 + 𝑅𝑅𝑓𝑓)(1 12� ) − 1

10

table below. The purpose of doing this division is to test the stationarity of the empirical

relations.

Table 1

Beta estimation, portfolio formation, and testing periods

Sub Period 1 Sub Period 2 Sub Period 3 Sub Period 4

Beta estimation period

2008-2009 2009-2010 2010-2011 2011-2012

Portfolio formation and testing period

2010 2011 2012 2013

Number of securities

38 38 38 38

The test is based on the time series regressions introduced by Black et al (1972).

We begin by estimating the coefficient 𝛽𝛽𝑖𝑖 (identified as estimate �̂�𝛽𝑖𝑖) by regressing 𝑟𝑟𝑖𝑖,𝑡𝑡 to

𝑟𝑟𝑚𝑚,𝑡𝑡 for sub period 1 (2008-2009) on the following equation:

𝒓𝒓𝒊𝒊,𝒕𝒕 = 𝜶𝜶𝒊𝒊 + 𝜷𝜷𝒊𝒊𝒓𝒓𝒎𝒎,𝒕𝒕 + 𝒆𝒆𝒊𝒊,𝒕𝒕 (3)

This equation is basically obtained by assuming that the stocks are priced in the market

such that equation (1) holds over each short time interval (in this case a month), then we

can do the test by rearranging the traditional form of the model and adding an intercept 𝛼𝛼𝑖𝑖.

𝑟𝑟𝑖𝑖,𝑡𝑡 simply represents expected excess returns on stock i at time t, 𝐸𝐸�𝑅𝑅𝑖𝑖,𝑡𝑡� − 𝑅𝑅𝑓𝑓, while 𝑟𝑟𝑚𝑚,𝑡𝑡

represents expected excess market returns at time t, 𝐸𝐸�𝑅𝑅𝑚𝑚,𝑡𝑡� − 𝑅𝑅𝑓𝑓.

These securities were then ranked from the on the basis of estimates �̂�𝛽𝑖𝑖 from

highest to the lowest, which then were assigned to six equally-weighted portfolios, with

each containing 6 to 7 stocks. Combining stocks into portfolio will diversify away most of

the firm-specific part of returns, and therefore will enhance the precision of the beta

estimates and the expected rate of return on the portfolios (Michailidis et. al., 2006). The

return in each of the next 12 months (year 2010) for each of the six portfolios was

calculated. This process was then repeated for the next sub periods.

The following step is to estimate the portfolio beta, �̂�𝛽𝑝𝑝 according to the equation

below:

11

𝑟𝑟𝑝𝑝,𝑡𝑡 = 𝛼𝛼𝑝𝑝 + 𝛽𝛽𝑝𝑝𝑟𝑟𝑚𝑚,𝑡𝑡 + 𝑒𝑒𝑝𝑝,𝑡𝑡 (4)

where 𝑟𝑟𝑝𝑝,𝑡𝑡 is the average portfolio excess returns at time t and 𝛽𝛽𝑝𝑝 is the portfolio

beta. Once again this process is repeated for the next sub periods and the whole period.

3.3 Capital Asset Pricing Model Testing Procedures

The first test to conduct is on the ex-post Security Market Line (SML) for the

testing period by regressing the portfolio excess returns (𝑟𝑟𝑝𝑝,𝑡𝑡) against the portfolio betas

(𝛽𝛽𝑝𝑝) on the equation below:

𝑟𝑟𝑝𝑝 = 𝛾𝛾0 + 𝛾𝛾1𝛽𝛽𝑝𝑝 + 𝑒𝑒𝑝𝑝 (5)

Corresponding to the traditional form of the asset pricing model, it implies that the

intercept 𝛾𝛾0 in (5) should be equal to zero and the slope 𝛾𝛾1 should be equal to 𝑅𝑅�𝑍𝑍, the

average excess return on the market portfolio.

The next step is to run a test of non-linearity between the portfolio excess returns

and portfolio betas using the following equation:

𝑟𝑟𝑝𝑝 = 𝛾𝛾0 + 𝛾𝛾1𝛽𝛽𝑝𝑝 + 𝛾𝛾2𝛽𝛽𝑝𝑝2 + 𝑒𝑒𝑝𝑝 (6)

CAPM hypothesis is that the portfolio returns and betas are linearly related with each

other, which means that the slope 𝛾𝛾2 should be equal to zero.

The last is to test whether the portfolio excess returns are determined solely by the

systematic risk (i.e. non-systematic risk does not exist). Here we regress the portfolio

excess returns, 𝑟𝑟𝑝𝑝 to the residual variance of portfolio excess returns, 𝜎𝜎2(𝜀𝜀𝑝𝑝) in the

equation:

𝑟𝑟𝑝𝑝 = 𝛾𝛾0 + 𝛾𝛾1𝛽𝛽𝑝𝑝 + 𝛾𝛾2𝛽𝛽𝑝𝑝2 + 𝛾𝛾3𝜎𝜎2(𝜀𝜀𝑝𝑝) + 𝑒𝑒𝑝𝑝 (7)

Again, if CAPM holds true, then 𝛾𝛾3 should also equal to zero.

All the tests mentioned above are also repeatedly performed for both the entire

period and each sub period. To sum up, concerning the validity of CAPM in Indonesia,

here we are going to test whether:

• the intercept equals to zero;

• average risk premium exists;

• the relation between the return and risk is linear; and

• beta is the only risk variable.

Each of the hypotheses testing will be conducted using two-tailed t-tests at the 95%

confidence level.

12

4. Empirical Results and Analysis 4.1 Entire Period

Using the monthly returns of 4-year on each of the six portfolios constructed as

explained previously, we then estimate the least-squares parameters which results in 𝛼𝛼�𝑝𝑝

and �̂�𝛽𝑝𝑝 in equation (4) for each of the six portfolios (p = 1, 2, …, 6) using all 4-year of

monthly data. The idea is that the first portfolio contains the stocks with highest betas

while the sixth portfolio contains the stocks with lowest betas. The advantage of using this

approach is the unbiased and efficient properties. In this case, where the number of stocks

in each sample is relatively small, the trade-off between these properties becomes crucial

(Modigliani et al., 1973). The results are summarized in the following table3:

Table 2

Statistics for Time Series Tests, Entire Period (January 2010 – December 2013)

Item

Portfolio Number

1 2 3 4 5 6 Market

𝜷𝜷�𝒑𝒑 1.393 1.324 1.048 1.009 1.134 0.754 1.000 𝜶𝜶�𝒑𝒑 0.019 0.012 0.008 0.017 0.019 0.021

𝒕𝒕�𝜶𝜶�𝒑𝒑� 2 2.02 1.23 2.32 3 2.56

𝒓𝒓(𝑹𝑹� ,𝑹𝑹�𝑴𝑴) 0.750 0.859 0.785 0.712 0.806 0.566 𝑹𝑹� 2.367% 1.689% 1.152% 2.114% 2.304% 2.403% 0.363% 𝝈𝝈 0.096 0.080 0.069 0.073 0.073 0.069 0.052

The estimated risk coefficients, �̂�𝛽𝑝𝑝, range from 1.393 for portfolio 1 to 0.754 for

portfolio 6. In general these coefficients are getting lower as we move from the first

through the sixth portfolio, except for portfolio 5 in which beta is relatively higher. The

significance tests of 𝛼𝛼�𝑝𝑝, given by the t-values 𝑡𝑡�𝛼𝛼�𝑝𝑝�, show that 5 out of 6 coefficients have

t-values greater than 1.96, which is the critical value for 5% significance level. The

correlation coefficient between portfolio return and market return, 𝑟𝑟(𝑅𝑅�,𝑅𝑅�𝑍𝑍), is also given

in the table. The numbers appear to be lower than expected, with portfolio 2 being the

highest at 0.859.

3 For complete average monthly returns on the six portfolios, see Appendix 3. These portfolios indeed have different composition of company stocks for each sub period.

13

However, it may be too early to derive a conclusion from this entire period result,

due to a possibility of the existence of some non-stationarity in the relations, as well as the

lack of more complete aggregation (Black et al., 1972). Therefore, we need to do the

testing for each of the sub periods.

4.2 Sub Periods

After dividing into four equal sub periods each containing 24 months, we repeat

the estimation process for each of the sub periods. The table below presents a summary of

regression on equation (4) calculated using the data for each of these sub periods, as well

as for each of the six portfolios4: Table 3

Statistics for Time Series Tests, Per Sub Period

Item Sub

Periods Portfolio Number

1 2 3 4 5 6 Market

𝜷𝜷�𝒑𝒑

1 1.150 1.510 1.136 1.078 0.976 0.204 1.000 2 1.455 1.239 0.690 0.807 1.064 0.971 1.000 3 1.190 1.278 1.369 1.188 0.625 0.872 1.000 4 1.643 1.223 1.276 0.762 1.597 0.650 1.000

𝜶𝜶�𝒑𝒑

1 0.021 0.007 0.003 0.056 0.038 0.061 2 0.019 0.011 0.008 0.002 0.011 0.013 3 0.042 0.027 0.000 0.000 0.015 0.027 4 0.000 -0.001 0.017 0.009 0.020 -0.007

𝒕𝒕�𝜶𝜶�𝒑𝒑�

1 1.36 0.41 0.31 3.19 2.91 2.13 2 1.09 1.68 0.60 0.27 1.63 1.15 3 1.74 1.94 0.01 -0.02 1.56 3.13 4 0.02 -0.15 1.13 0.44 1.12 -0.83

𝑹𝑹�

1 4.438% 3.712% 2.596% 7.705% 5.790% 6.537% 1.991% 2 1.562% 0.822% 0.672% 0.017% 0.837% 1.129% -0.214% 3 4.628% 3.190% 0.486% 0.391% 1.703% 3.031% 0.347% 4 -1.0715 -0.970% 0.853% 0.343% 0.887% -1.087% -0.672%

𝝈𝝈

1 0.077 0.097 0.070 0.079 0.066 0.089 0.054 2 0.105 0.078 0.061 0.054 0.068 0.070 0.060 3 0.094 0.071 0.068 0.061 0.041 0.047 0.042 4 0.107 0.071 0.083 0.075 0.101 0.042 0.052

4 Composition of the companies for each portfolio per sub period is available on Appendix 4.

14

From the table we can see that the data for �̂�𝛽𝑝𝑝 indicates that the risk coefficients �̂�𝛽𝑝𝑝

were non-stationary throughout the period. The sections for 𝛼𝛼�𝑝𝑝 and 𝑡𝑡�𝛼𝛼�𝑝𝑝� indicate that the

critical intercepts were also non-stationary for portfolio number 4, 5, and 6, which contain

the lower beta stocks. There seems to be no obvious patterns that can be derived from

these values. However, we can see that the standard deviations of each regression are

relatively small, which provide a major improvement of grouping the data into sub

periods.

4.3 Test of Security Market Line

As previously explained, the traditional model of CAPM implies that the intercept

𝛾𝛾0 in equation (5) should be equal to zero and the slope 𝛾𝛾1 should be equal to the mean

excess return on the market portfolio. On the entire period, the average monthly excess

return of the market portfolio was 𝑅𝑅�𝑍𝑍 = 0.145%. Therefore, the theoretical values of both

the intercept and slope should be respectively

𝛾𝛾0 = 0 and 𝛾𝛾1 = 0.145%

The t-values are obtained as the following:

𝑡𝑡(𝛾𝛾�0) =𝛾𝛾�0

𝑠𝑠(𝛾𝛾�0)=

0.0220.012

= 1.867

𝑡𝑡(𝛾𝛾�1) =𝛾𝛾1 − 𝛾𝛾�1𝑠𝑠(𝛾𝛾�1)

=0.00145 − (−0.002)

0.011= 0.338

They appear to be relatively small and not rejected at 5% significance level (t-critical =

±1.96). This test is again repeated on every sub periods which is summarized in the

following table.

Table 4

Statistics for Security Market Line Tests

Item

Time Period Sub Periods Total

Period 1 2 3 4 𝜸𝜸�𝟎𝟎 0.074 -0.005 -0.027 -0.007 0.022

𝒔𝒔(𝜸𝜸�𝟎𝟎) 0.020 0.007 0.033 0.014 0.012 𝒕𝒕(𝜸𝜸�𝟎𝟎) 3.625 -0.825 -0.822 -0.468 1.867

𝜸𝜸�𝟏𝟏 -0.023 0.013 -0.004 0.004 -0.002

𝒔𝒔(𝜸𝜸�𝟏𝟏) 0.019 0.006 0.030 0.012 0.011 𝜸𝜸𝟏𝟏 = 𝑹𝑹�𝑴𝑴 -0.700% 3.466% 0.889% 0.067% 0.145%

15

𝒕𝒕(𝜸𝜸𝟏𝟏 − 𝜸𝜸�𝟏𝟏) 0.826 3.436 0.450 -0.306 0.338 Hypothesis Rejected Rejected Not rejected Not rejected Not rejected

The hypotheses are rejected only for the intercept in the first and slope in the

second sub period. They are not rejected in the other sub periods as well as for the entire

period. In general, the hypotheses that the intercept equals to zero and that average risk

premium exists are not rejected in this test, which suggests that CAPM is rather in-line

with the empirical evidence.

If we compare these results with the previous findings, Johnson and Soenen (1996)

found that alpha estimates are not significantly different from zero. In this test, most

intercepts are also equal to zero, which suggests the same thing. In addition, they found

that most beta coefficients are positive, which means that average risk premium exists.

4.4 Test of Non-Linearity

The next hypothesis is that the relationship between portfolio’s return and its

systematic risk is linear. Therefore we need to conduct a test on possibility of non-linearity

according to equation (6). Adding a new hypothesis to the ones on the previous test, in

case of a linear relationship, 𝛾𝛾𝟐𝟐 should also be equal to zero. The result of the test is

presented in the table below.

Table 5

Statistics for Non-Linearity Tests

Item

Time Period Sub Periods Total

Period 1 2 3 4 𝜸𝜸�𝟎𝟎 0.065 0.008 -0.105 -0.034 0.079

𝒔𝒔(𝜸𝜸�𝟎𝟎) 0.032 0.033 0.148 0.059 0.058 𝒕𝒕(𝜸𝜸�𝟎𝟎) 2.057 0.242 -0.711 -0.580 1.359

𝜸𝜸�𝟏𝟏 0.010 -0.013 0.285 0.058 -0.110

𝒔𝒔(𝜸𝜸�𝟏𝟏) 0.081 0.064 0.316 0.112 0.109 𝜸𝜸𝟏𝟏 = 𝑹𝑹�𝑴𝑴 -0.700% 3.466% 0.889% 0.067% 0.145% 𝒕𝒕(𝜸𝜸𝟏𝟏 − 𝜸𝜸�𝟏𝟏) -0.210 0.752 -0.873 -0.512 1.022

𝜸𝜸�𝟐𝟐 -0.020 0.012 -0.146 -0.024 0.050 𝒔𝒔(𝜸𝜸�𝟐𝟐) 0.049 0.030 0.159 0.049 0.050

16

𝒕𝒕(𝜸𝜸�𝟐𝟐) -0.415 0.419 -0.920 -0.484 0.993 Hypothesis Rejected Not rejected Not rejected Not rejected Not rejected

The result of this test is even less significant than the previous one. Only the

intercept in sub period one significantly differs from zero. The hypothesis is not rejected in

the rest of the sub periods as well as the total period. This indicates that there is actually a

linear relationship between the risk and return from the evidence, as predicted by the

model.

There was no tests on the non-linearity of the model found to be conducted

previously in Indonesia, particularly by Johnson and Soenen (1996). However, in most

cases the risk and return relationship is found to be linear, even including the emerging

stock markets. This means the quadratic version of the model as in equation (6) is not

relevant enough to be applied.

4.5 Test of Non-Systematic Risk

According to the Capital Asset Pricing Model, investors are only compensated for

bearing the systematic risk (i.e. not for the idiosyncratic risk). This implies that coefficient

𝛾𝛾𝟑𝟑 in equation (7) should equal to zero. The summary of the test conducted for the non-

systematic risk is presented below.

Table 5

Statistics for Non-Systematic Risk Tests

Item

Time Period Sub Periods Total

Period 1 2 3 4 𝜸𝜸�𝟎𝟎 -0.178 -0.066 -0.043 -0.054 0.023

𝒔𝒔(𝜸𝜸�𝟎𝟎) 0.113 0.030 0.119 0.067 0.094 𝒕𝒕(𝜸𝜸�𝟎𝟎) -1.578 -2.205 -0.364 -0.810 0.245

𝜸𝜸�𝟏𝟏 0.361 0.127 0.141 0.086 -0.018

𝒔𝒔(𝜸𝜸�𝟏𝟏) 0.169 0.057 0.256 0.123 0.165 𝜸𝜸𝟏𝟏 = 𝑹𝑹�𝑴𝑴 -0.700% 3.466% 0.889% 0.067% 0.145% 𝒕𝒕(𝜸𝜸𝟏𝟏 − 𝜸𝜸�𝟏𝟏) -2.178 -1.611 -0.515 -0.694 0.119

𝜸𝜸�𝟐𝟐 -0.175 -0.056 -0.080 -0.038 0.007 𝒔𝒔(𝜸𝜸�𝟐𝟐) 0.078 0.028 0.128 0.055 0.076 𝒕𝒕(𝜸𝜸�𝟐𝟐) -2.252 -2.042 -0.628 -0.705 0.096

17

𝜸𝜸�𝟑𝟑 2.051 0.476 0.542 0.335 0.066

𝒔𝒔(𝜸𝜸�𝟑𝟑) 0.936 0.158 0.309 0.406 0.084 𝒕𝒕(𝜸𝜸�𝟑𝟑) 2.192 3.004 1.753 0.825 0.786

Hypothesis Rejected Rejected Not rejected Not rejected Not rejected

The result shows that 𝛾𝛾�𝟑𝟑 is insignificant in sub period 3 and 4, as well as the entire

period. More or less similar results are also obtained for the intercept and other slopes.

When the explanatory variable unsystematic/idiosyncratic risk is introduced, the result

suggests no significant relationship between these measures of risk and average portfolio

returns.

The study by Johnson and Soenen (1996) also suggests a similar result in which

only systematic risks have a considerable effect on Indonesian stock movements. This is in

accordance with the theory, as the investor is only rewarded for taking systematic risk

because non-systematic risk can be diversified away.

18

5. Conclusion

This study provides the investigation on the validity of CAPM when applied to the

Indonesian stock market using the testing method by Black, Jensen, and Scholes (1972).

Using monthly returns data of 38 companies registered in the LQ45 index for the estimation

period of 2008-2012, there are four hypotheses associated with CAPM to be tested: intercept

equals zero, average risk premium exists, the relation between risk and return is linear, and

beta is the only risk variable.

The results provide no significant evidence to reject and rather supportive of these

CAPM’s prediction, apart from the sub period 1 and most of the sub period 2 results. These

are contrary to many other tests conducted in the emerging market, and suggest that CAPM

actually holds in the Indonesian stock market.

5.1 Remarks on the Effect of 2008 Crisis

In general, each of the tests has shown insignificant results for the hypotheses

proposed. However, it is obvious that all of these tests are in fact rejected in sub period 1,

also for the Security Market Line and Non-Systematic Risk tests in sub period 2. As

mentioned previously in the methodology section, the sub period 1 is using beta estimation

from 2008-2009 and the sub period 2 from 2009-2010.

Just as we acknowledge, the crisis in 2008 that hit the global financial market may

also affect the Indonesian stock market down to the following years after. While no

separated tests results on CAPM performance are provided between crisis and non-crisis

periods in this study, there are several previous empirical findings that may support this

argument.

In their study, Black et. al. (1972) obtained first sub period results that mainly

deviate from the latter sub periods. The first sub period of their study was using the excess

returns from 1926-1930 for the beta estimation period and excess returns from 1930 for

the testing period. During the 1930s, a major crisis also occured in the U.S. which may

have derived those contradictory results. Thus, there may as well be an effect of the 2008

financial crisis to these Indonesian stock market results.

19

5.2 Limitations to This Study

Several limitations to this paper may exist and should be considered before

drawing a clear-cut conclusion to these results. First of all, the time period of estimation is

rather short, which only covers 5 years returns (2008-2012). Even though longer time span

may help reducing the distortion from random factors that can arise in shorter time span,

there are impediments in collecting the complete data set for the whole longer period.

Some historical prices are not published anywhere, even some of the companies were not

established or have not launched their stock to public before 2008.

Second, the sample stocks used in this test are not randomly selected. They are

taken from an index which includes only the largest and most liquid companies in the

market. The purpose of using this index is that it is likely to represent the whole market.

However, since the number of stocks included here is relatively small, this can lead to

inefficient and/or biased tests results.

Moreover, it may be important to put emphasize on analyzing the effect of crisis to

CAPM performance since the tests conducted during the period of financial crisis have

shown anomalies in the results. There are possibilities that the tests yield in different

conclusion when the impact of crisis is taken into account. Therefore, further investigation

on this matter may be essential to conduct in another extensive study.

Despite these limitations, we can still conclude from the study that so far the results

from the Indonesian market in general are consistent with the hypotheses proposed in order to

test the validity of CAPM. In other words, the returns on the Indonesian stock market are

relatively predictable by using the Capital Asset Pricing Model. These empirical findings,

especially the distinctive results compared to other emerging markets, may be interesting for

further studies and useful to the financial analysts or investors in their consideration

regarding the Indonesian stock market.

20

References

Aljinović, Z. and Džaja, J. (2013). “Testing CAPM Model on the Emerging Markets of the Central and Southeastern Europe”. Croatian Operational Research Review 4: 164-175.

Black, F., Jensen, M. C., Scholes, M. (1972). “The Capital Asset Pricing Model: Some Empirical Tests”. Studies in the Theory of Capital Markets. New York: Praeger. p. 79-121.

Brooks, R., Galagadera, D. U. A., Iqbal, J. (2010). “Testing Conditional Asset Pricing Models: An Emerging Market Perspective”. Journal of International Money and Finance 29: 897-918.

Cavusgil, S. T. (1997). “Measuring the Potential of Emerging Markets: An Indexing Approach”. Business Horizons 40: 87-91.

Chan, L. and Lakonishok, J. (1993). “Are the Report of Beta’s Death Premature?”. Journal of Portfolio Management 19: 51-62.

Cohen, K. Hawanini, G. and Maier, S. (1983). “Estimating and Adjusting for the Intervalling Effect Bias in Beta”. Management Science 29: 135-148.

Dimson, E. “Risk Measurement when Shares are Subject to Infrequent Trading”. Journal of Financial Economics 7: 197-226.

Drobetz, W., Stürmer, S., and Zimmermann, H, (2002). “Conditional Asset Pricing in Emerging Stock Markets”. The Swiss Journal of Economics and Statistics 138: 507-526.

Fama, E. F. and French, K. R. (2004). “The Capital Asset Pricing Model: Theory and Evidence”. Journal of Economic Perspectives 18: 25-46.

Fama, E. F. and Macbeth, J. (1973). “Risk, return and equilibrium: Empirical tests”. Journal of Political Economy 81: 607-636.

Garg, V. (1998). The Capital Asset Pricing Model: A Test on the Stock Exchange on Singapore. Unpublished master’s thesis, University of Western Sydney Nepean, Sydney, Australia.

Graham, J. and Harvey, C. (2001). “The Theory and Practice of Corporate Finance: Evidence from the Field”. Journal of Financial Economics 60: 187-243.

Hartmann, M. A. and Khambata, D. (1993). “Emerging Stock Markets: Investment Strategies of the Future”. The Columbia Journal of World Business 28: 82-104.

Harvey, C. R. (2000). Asset Pricing in Emerging Markets (Working Paper C20). Retrieved from:

21

https://faculty.fuqua.duke.edu/~charvey/Research/Working_Papers/W54_Asset_ pricing_in.pdf

Ho, Y. W., Strange, R., Piesse, J. (2000). “CAPM anomalies and the pricing of equity: evidence from the Hong Kong market”. Applied Economics 32: 1629-1636.

Johnson. R. and Soenen, L. A. (1996). “The Jakarta Stock Exchange: Risk/Return Characteristics”. The Journal of Investing 5: 37-46.

Kester, G. W., Chang, R. P., Echanis, E. S., Haikal, S., Isa, M. M., Skully, M. T., Kai-Chong, T., and Chi-Jeng, W. (1999). “Capital Budgeting Practices in the Asia-Pacific Region: Australia, Hong Kong, Indonesia, Malaysia, Philippines, and Singapore”. Financial Practice and Education 9, 25-33.

Lumby, S. and Jones, M. C. (2003). Corporate Finance: Theory and Practice (7th ed). London: Thomson Learning.

McLaney, E., Pointon, J., Thomas, M., and Tucker, J. (2010). “Practitioners’ Perspectives on the UK Cost of Capital”. The European Journal of Finance 10: 123-138.

Merton, R.C. (1980). “On Estimating the Expected Return On the Market”. Journal of Financial Economics 8: 323-361.

Michailidis, G., Tsopoglou, S., Papanastasiou, D., and Mariola, E. (2006). “Testing the Capital Asset Pricing Model (CAPM): The Case of the Emerging Greek Securities Market”. International Research Journal of Finance and Economics 4: 78-91.

Miller, R. R. (1998). Selling to Newly Emerging Markets. New York: Quorum Books.

Modigliani, F., Pogue, G. A., and Solnik, B. H. (1973). A Test of the Capital Asset Pricing Model on European Stock Markets. Doctoral dissertation, School of Business, Stanford University.

22

Appendix

Appendix 1: List of Sample Companies

Company Names Ticker Symbol Sector Astra Agro Lestari, Tbk. AALI Agriculture Adhi Karya, Tbk. ADHI Infrastructure and Transportations Adaro Energy, Tbk. ADRO Industrials AKR Corporindo, Tbk. AKRA Trade, Service & Investment Astra International, Tbk. ASII Industrials Alam Sutera Realty, Tbk. ASRI Property and Real Estate Bank Central Asia, Tbk. BBCA Finance Bank Negara Indonesia, Tbk. BBNI Finance Bank Rakyat Indonesia, Tbk. BBRI Finance Bank Danamon Indonesia, Tbk. BDMN Finance Sentul City, Tbk. BKSL Property and Real Estate Bank Mandiri, Tbk. BMRI Finance Global Mediacom, Tbk. BMTR Infrastructure and Transportations Bumi Serpong Damai, Tbk. BSDE Property and Real Estate Charoen Pokphand Indonesia, Tbk. CPIN Basic Industry and Chemicals Ciputra Development, Tbk. CTRA Property and Real Estate XL Axiata, Tbk. EXCL Infrastructure and Transportation Gudang Garam, Tbk. GGRM Consumer Goods Indofood Sukses Makmur, Tbk. INDF Consumer Goods Indocement Tunggal Prakasa, Tbk. INTP Basic Industry and Chemicals Indo Tambangraya Megah, Tbk. ITMG Mining Jasa Marga Persero, Tbk. JSMR Infrastructure and Transportation Kalbe Farma, Tbk. KLBF Consumer Goods Lippo Karawaci, Tbk. LPKR Property and Real Estate PP London Sumatra Indonesia, Tbk LSIP Agriculture Malindo Feedmill, Tbk. MAIN Basic Industry and Chemicals Multipolar, Tbk. MLPL Trade, Services & Investment Media Nusantara Citra, Tbk. MNCN Trade, Services & Investment Perusahaan Gas Negara, Tbk. PGAS Infrastructure and Transportation Tambang Baturbara Bukit Asam, Tbk. PTBA Mining Pakuwon Jati, Tbk. PWON Property and Real Estate Semen Gresik, Tbk. SMGR Basic Industry and Chemicals Summarecon Agung, Tbk. SMRA Property and Real Estate Surya Semestra Internusa, Tbk. SSIA Property and Real Estate Telekomunikasi Indonesia, Tbk. TLKM Infrastructure and Transportation United Tractors, Tbk. UNTR Trade, Services & Investment Unilever Indonesia, Tbk. UNVR Consumer Goods Wijaya Karya Persero, Tbk. WIKA Infrastructure and Transportation

23

Appendix 2: Bank Indonesia’s Risk-Free Rate

Date Yearly Rate Adjusted for Monthly Dec 01, 2013 7,50% 0,6045% Nov 01, 2013 7,50% 0,6045% Oct 01, 2013 7,25% 0,5850% Sep 01, 2013 7,25% 0,5850% Aug 01, 2013 6,50% 0,5262% Jul 01, 2013 6,50% 0,5262% Jun 01, 2013 6,00% 0,4868% May 01, 2013 5,75% 0,4670% Apr 01, 2013 5,75% 0,4670% Mar 01, 2013 5,75% 0,4670% Feb 01, 2013 5,75% 0,4670% Jan 01, 2013 5,75% 0,4670% Dec 01, 2012 5,75% 0,4670% Nov 01, 2012 5,75% 0,4670% Oct 01, 2012 5,75% 0,4670% Sep 01, 2012 5,75% 0,4670% Aug 01, 2012 5,75% 0,4670% Jul 01, 2012 5,75% 0,4670% Jun 01, 2012 5,75% 0,4670% May 01, 2012 5,75% 0,4670% Apr 01, 2012 5,75% 0,4670% Mar 01, 2012 5,75% 0,4670% Feb 01, 2012 5,75% 0,4670% Jan 01, 2012 6,00% 0,4868% Dec 01, 2011 6,00% 0,4868% Nov 01, 2011 6,00% 0,4868% Oct 01, 2011 6,50% 0,5262% Sep 01, 2011 6,75% 0,5458% Aug 01, 2011 6,75% 0,5458% Jul 01, 2011 6,75% 0,5458% Jun 01, 2011 6,75% 0,5458% May 01, 2011 6,75% 0,5458% Apr 01, 2011 6,75% 0,5458% Mar 01, 2011 6,75% 0,5458% Feb 01, 2011 6,75% 0,5458% Jan 01, 2011 6,50% 0,5262% Dec 01, 2010 6,50% 0,5262% Nov 01, 2010 6,50% 0,5262% Oct 01, 2010 6,50% 0,5262% Sep 01, 2010 6,50% 0,5262% Aug 01, 2010 6,50% 0,5262% Jul 01, 2010 6,50% 0,5262% Jun 01, 2010 6,50% 0,5262% May 01, 2010 6,50% 0,5262% Apr 01, 2010 6,50% 0,5262% Mar 01, 2010 6,50% 0,5262% Feb 01, 2010 6,50% 0,5262% Jan 01, 2010 6,50% 0,5262% Dec 01, 2009 6,50% 0,5262% Nov 01, 2009 6,50% 0,5262% Oct 01, 2009 6,50% 0,5262% Sep 01, 2009 6,50% 0,5262% Aug 01, 2009 6,50% 0,5262% Jul 01, 2009 6,75% 0,5458% Jun 01, 2009 7,00% 0,5654% May 01, 2009 7,25% 0,5850%

24

Apr 01, 2009 7,50% 0,6045% Mar 01, 2009 7,75% 0,6240% Feb 01, 2009 8,25% 0,6628% Jan 01, 2009 8,75% 0,7015% Dec 01, 2008 9,25% 0,7400% Nov 01, 2008 9,50% 0,7592% Oct 01, 2008 9,50% 0,7592% Sep 01, 2008 9,25% 0,7400% Aug 01, 2008 9,00% 0,7207% Jul 01, 2008 8,75% 0,7015% Jun 01, 2008 8,50% 0,6821% May 01, 2008 8,25% 0,6628% Apr 01, 2008 8,00% 0,6434% Mar 01, 2008 8,00% 0,6434% Feb 01, 2008 8,00% 0,6434% Jan 01, 2008 8,00% 0,6434%

25

Appendix 3: Portfolio Average Returns for the Entire Period

Date Portfolio 1 Portfolio 2 Portfolio 3 Portfolio 4 Portfolio 5 Portfolio 6 Dec 01, 2013 -11.141% -0.341% -1.333% 3.732% -3.293% -0.100% Nov 01, 2013 -9.723% -6.373% -7.323% -5.846% -10.302% -3.344% Oct 01, 2013 10.130% 7.730% 6.562% 7.872% 0.794% 3.732% Sep 01, 2013 5.873% 5.083% -4.445% -0.318% 4.101% -2.995% Aug 01, 2013 -12.628% -9.168% -4.413% -0.223% -16.662% -9.782% Jul 01, 2013 -20.940% -6.981% -5.518% -16.738% -3.292% -4.748% Jun 01, 2013 -6.420% -8.863% -13.563% -7.078% -7.690% -4.995% May 01, 2013 3.170% -8.254% 3.690% 11.303% 9.129% 3.200% Apr 01, 2013 1.256% 0.521% 5.556% 0.926% -0.578% 3.207% Mar 01, 2013 7.148% -2.650% 5.231% 3.135% 7.679% 1.027% Feb 01, 2013 11.478% 8.488% 14.583% 5.778% 19.080% 3.324% Jan 01, 2013 8.946% 9.173% 11.203% 1.576% 11.673% -1.573% Dec 01, 2012 -3.675% -4.580% -0.125% 7.960% 5.226% -0.590% Nov 01, 2012 12.565% 3.071% -3.454% -3.057% -7.331% 0.710% Oct 01, 2012 8.160% 4.085% -3.655% -0.885% 1.473% 3.497% Sep 01, 2012 24.836% 12.438% 9.953% 7.228% 3.516% 4.649% Aug 01, 2012 -7.555% -5.454% -5.755% -2.234% -1.174% -1.571% Jul 01, 2012 -1.022% 5.795% 6.015% 5.111% 7.549% 6.847% Jun 01, 2012 5.341% 4.471% 0.956% 0.793% 0.783% 11.386% May 01, 2012 -8.845% -12.079% -14.772% -14.277% -2.116% -6.141% Apr 01, 2012 2.295% 7.448% 5.205% -4.345% 0.043% 8.149% Mar 01, 2012 10.081% 11.003% 6.148% 3.498% 4.982% 4.229% Feb 01, 2012 3.828% 7.086% -0.389% 3.890% 5.804% 1.810% Jan 01, 2012 9.530% 4.993% 5.700% 1.008% 1.680% 3.402% Dec 01, 2011 7.022% 8.392% 8.210% 2.222% 2.702% 11.335% Nov 01, 2011 -10.475% -5.823% -1.038% 0.865% -0.651% -4.497% Oct 01, 2011 11.482% 8.887% 3.637% 6.597% 8.242% 5.280% Sep 01, 2011 -13.167% -10.356% -4.662% -9.389% -6.872% -10.649% Aug 01, 2011 2.046% -8.386% -10.199% -2.947% -5.504% -6.619% Jul 01, 2011 20.066% 8.644% 6.794% 2.656% 11.643% 5.803% Jun 01, 2011 -1.096% 4.899% -2.417% 0.653% 0.621% -2.827% May 01, 2011 -1.274% 0.033% 3.941% 0.279% 0.460% 2.574% Apr 01, 2011 4.693% 3.076% -2.352% 1.169% 3.527% 8.673% Mar 01, 2011 10.919% 8.886% 0.749% 9.018% 6.860% 5.280% Feb 01, 2011 2.948% 2.476% 10.476% -1.904% 1.716% 5.497% Jan 01, 2011 -14.418% -10.860% -5.068% -9.010% -12.696% -6.298% Dec 01, 2010 1.852% 1.157% 2.334% 0.814% 12.732% 13.727% Nov 01, 2010 4.289% -6.713% -6.438% 1.144% 0.012% 23.150% Oct 01, 2010 5.109% 5.942% 7.480% 1.067% 12.742% 13.182% Sep 01, 2010 14.625% 15.482% 9.684% 19.447% 16.434% 11.768% Aug 01, 2010 2.655% -6.388% -2.023% 7.705% 1.245% 4.201% Jul 01, 2010 9.670% 2.532% 5.114% 19.455% 0.381% 1.790% Jun 01, 2010 4.522% 7.985% 1.262% 14.465% 0.520% 2.127% May 01, 2010 -11.675% -13.030% -11.196% -4.633% -3.992% -2.409% Apr 01, 2010 15.150% 21.122% 5.817% 10.845% 9.472% -2.496% Mar 01, 2010 10.845% 11.680% 11.557% 10.355% 12.280% 9.730% Feb 01, 2010 -4.506% 4.194% -1.953% 0.734% 2.201% -8.590% Jan 01, 2010 -0.360% 0.580% 9.517% 11.062% 5.455% 12.267%

26

Appendix 4: Constructed Portfolios and Returns per Sub Period

Portfolio Number

Sub Periods

1 2 3 4

Company Beta Average Return Company Beta Average

Return Company Beta Average Return Company Beta Average

Return

1

BSDE 1.883

4.348%

BKSL 2.313

1.562%

CPIN 2.196

4.628%

AKRA 2.146

-1.0708%

ITMG 1.651 BSDE 2.214 ADHI 2.064 SMRA 1.930 BBNI 1.571 SMRA 1.759 SMRA 1.867 CPIN 1.791 UNTR 1.451 BMRI 1.603 MLPL 1.805 SSIA 1.674 ADHI 1.394 BBNI 1.546 AKRA 1.646 BSDE 1.625 LSIP 1.381 ITMG 1.539 ASRI 1.633 PTBA 1.458

2

BKSL 1.298

3.712%

ASII 1.381

0.822%

MNCN 1.556

3.190%

BBRI 1.449

-0.9696%

INDF 1.276 BBRI 1.375 BMRI 1.550 ADRO 1.408 ASII 1.263 INDF 1.350 WIKA 1.494 SMGR 1.381 BMRI 1.235 ADHI 1.324 BSDE 1.445 UNTR 1.335 ASRI 1.213 ASRI 1.313 CTRA 1.415 BMRI 1.327 BDMN 1.198 UNTR 1.176 BBRI 1.286 ASRI 1.307

3

PTBA 1.180

2.596%

WIKA 1.128

0.672%

LPKR 1.279

0.486%

BBNI 1.245

0.8525%

AALI 1.151 MNCN 1.104 ASII 1.246 WIKA 1.205 SMRA 1.145 BDMN 1.091 BKSL 1.241 ITMG 1.171 INTP 1.134 CTRA 1.075 PTBA 1.192 MNCN 1.115 BBRI 1.125 BMTR 1.062 BBNI 1.183 MLPL 1.104 CTRA 1.078 PTBA 1.043 SSIA 1.163 ASII 1.104

4

WIKA 1.016

7.705%

BBCA 0.970

0.017%

INDF 1.127

0.391%

LSIP 1.079

0.3432%

KLBF 1.012 LSIP 0.951 ADRO 1.107 LPKR 1.062 CPIN 1.012 INTP 0.910 ITMG 1.036 INTP 1.042 AKRA 0.989 JSMR 0.837 SMGR 1.035 BBCA 0.994 GGRM 0.916 GGRM 0.826 BBCA 1.029 AALI 0.989 EXCL 0.907 MLPL 0.817 UNTR 0.980 CTRA 0.943

5 PGAS 0.906 5.790% SMGR 0.739 0.837% INTP 0.947 1.703% PWON 0.916 0.8865% ADRO 0.866 TLKM 0.721 KLBF 0.839 INDF 0.901

JSMR 0.816 AKRA 0.689 AALI 0.807 ADHI 0.886 PWON 0.729 PGAS 0.689 JSMR 0.760 KLBF 0.817 MNCN 0.717 CPIN 0.602 PGAS 0.755 PGAS 0.794 BMTR 0.699 KLBF 0.602 LSIP 0.725 BKSL 0.788 SMGR 0.691 UNVR 0.413 EXCL 0.698 MAIN 0.609

6

TLKM 0.675

6.537%

LPKR 0.376

1.129%

BDMN 0.575

3.031%

JSMR 0.561

-1.0872%

BBCA 0.581 AALI 0.296 BMTR 0.525 BDMN 0.523 MLPL 0.436 ADRO 0.236 GGRM 0.520 BMTR 0.489 LPKR 0.145 PWON 0.114 PWON 0.485 GGRM 0.410 UNVR 0.137 SSIA 0.091 TLKM 0.256 TLKM 0.272 SSIA 0.093 EXCL 0.004 UNVR 0.237 EXCL 0.199 MAIN 0.038 MAIN -1.911 MAIN -1.746 UNVR -0.083

28