Efficient Market Hypothesis :
Evidence from Indonesia Stock Exchange (IDX)
Siti Rahmi Utami
Mahasiswa Program Doktor
Trisakti International Business School – Maastricht School of Management
Abstract
In this research we test market efficiency in Indonesia Stock Exchange (IDX) by
examining whether stock price changes is independent of past changes or not, and
examining do profitability, liquidity, debt, and market value information of the firms
have influence on stock returns, hence these information can be used to estimate the
future stock returns. We use stock price and return as dependent variable, and financial
ratios as independent variables. We have applied Ljung-Box Q-Test and regression to
analyze the data, and collected the data of companies from the Indonesia Stock Exchange
from the year 1994 to 2005. The sample size consists of 77 companies from all sector
companies of LQ 45 Index.
Ljung-Box Q-Test result has shown that the significant amount of lag are 16,
therefore there is autocorrelation between current price and previous price, and we
conclude that market is not efficient in weak-form. Meanwhile, regression results have
shown that profitability information (ROA) has negative significant effect on stock
returns, while ROE has positive significant effect on stock returns. As one of market
value information, dividend has positive significant influence on stock returns. However,
sales to asset (SALASS), all debt information (TLTE, LTLTE, and TLTA), liquidity
information (current ratio), and price to earning ratio (PER), have insignificant effect on
stock returns.
Key words
Efficiency
: Efficient Market Hypothesis (EMH), Stock Return, Weak-Form Market
1. Introduction
Market efficiency means that the market price of a security represents the market’s
consensus estimate of the value of that security. An efficient financial market exists when
security prices reflect all available public information about the economy, about financial
markets, and about the specific company involved. The implication is that market prices
of individual securities adjust very rapidly to new information (Van Horne, 1998).
All investors who hold security expect to increase the return in the future. Many
investors, including investment managers, believe that they can select securities that will
outperform the market. Therefore, they need to use available public information to lead
them in their investment decisions, as Anderson et al (2005) states that in investing the
1
investors could use financial statements as their primary decision-making tool. If the
market is efficient, investor will purchase the security at least at its current market price
based on all available public information. Therefore, investors who purchase the stock or
any other security interpret that their information as a higher appraisal (Van Horne,
1998).
In the literature, such as in Corrado and Jordan (2000), the question of whether a
market is efficient is meaningful only relative to some type of information. Three general
types of information are particularly interesting to define three forms of market efficiency
are weak-form efficient market, semi-strong-form efficient market, and strong-form
efficient market. In Indonesia Stock Exchange, we assume that the stock market is
efficient in weak-form as investors can not use a time series of past stock price to discern
a pattern of price changes in predicting future stock return. Therefore, we are interested to
examine whether stock price changes is independent of past changes or not, and to
examine the extent to which market reacted with signal on releasing public information in
Indonesia Stock Exchange.
To answer these questions, we test the serial correlation between previous and
current stock price, and examine whether profitability, liquidity, debt, and market value
information have any impact on stock returns, so that all these information can be used to
estimate the future stock returns. By using Ljung-Box Q-Test and regression analysis,
and collecting the data of firms in IDX over the period 1994-2005, Ljung-Box Q-Test
result has shown that the significant amount of lag are 16, therefore there is
autocorrelation between current price and previous price. The interpretation of the result
is that the market is not efficient in weak-form, and it indicates that investor could use
price from one period to predict returns in later periods and make higher profits.
Regression result of testing the extent to which market reacted with signal on releasing
public information, have shown that ROA, ROE, and dividend can predict the future
movement of stock returns, while SALASS, TLTE, LTLTE, TLTA, current ratio, and
PER, can not be used to estimate the stock returns.
The rest of the paper is divided into 5 sections. Section 2 explains the theoretical
framework and also covers some results from earlier studies. Section 3 discusses the
hypotheses of the research. Section 4 presents the methodology used for the study.
Section 5 interprets the empirical results and the statistic results. Section 6 presents the
conclusions respectively.
2. Literature Review
The term "efficient market" is introduced for the first time by Eugene Fama (1965), in his
paper titled “Random Walks in Stock Market Prices”. He defined an efficient market as :
“a market where there are large numbers of rational profit maximizers actively
competing, with each trying to predict future market values of individual securities, and
where important current information is almost freely available to all participants”. It
implies that investors trying to use information to predict future market values of
securities.
2
Corrado and Jordan (2000) noted three general types of information are
particularly interesting to define three forms of market efficiency are the following : (1)
A weak-form efficient market is one in which the information reflected in past prices and
volume figures is of no value in beating the market. If past prices and volume are of no
use, then technical analysis is of no use whatsoever. (2) In a semi-strong-form efficient
market publicly available information of any and all kinds is of no use in beating the
market. If a market is semi-strong-form efficient, then the fundamental analysis
techniques are use less. Past prices and volume data are also publicly available
information, so if a market is semi-strong-form efficient, it is also weak-form efficient.
(3) Finally, in a strong-form efficient market no information of any kind, public or
private, is useful in beating the market. If a market is strong-form efficient, it is clear that
nonpublic inside information of many types would enable us to earn essentially unlimited
returns.
According to Fama (1965), weak-form market efficiency means that the
unanticipated return is not correlated with previous unanticipated returns. Semi strongform market efficiency means it is not correlated with any publicly available information.
Strong-form market efficiency, indicates the unanticipated return is not correlated with
any information, be it publicly available or insider.
Based on the view of Spiegel and Stanton (2000), market efficiency refers to the
extent that market efficiency prices reflect all available information. In their article, three
primary forms of market efficiency are (1) Weak-form efficiency exists if market prices
incorporate all past price information. (2) Semi-strong form efficiency exists if market
prices incorporate all publicly available information. (3) Strong-form efficiency exists if
market prices incorporate all information (both public and private).
Many researchers have tested the weak-form market efficiency hypothesis.
Rindisbacher (2002) presented the evidence that in “weak-form”, US market that
contrarian strategies achieve abnormal returns in the long run, while US market that
momentum strategies achieve abnormal returns in the short run. Brock, Lakonishok, and
LeBaron (1992) find that relatively simple technical trading rules would have been
successful in predicting changes in the Dow Jones Industrial Average.
Rosenberg and Rudd (1982) found that the first order serial correlation of daily
return residual from the market model is small but significantly negative. Gibbons and
Hess (1981) reported “the Monday Effect” of stock prices tended to go down on
Mondays. This finding was clearly inconsistent with the weak-form market efficiency.
They also noticed that the Monday Effect seemed to decrease over time.
Meanwhile, many researchers tested the semi-strong-form market efficiency
hypothesis. The study of Rindisbacher (2002) presented the evidence that in semi-strongform, public information is reflected in the stock prices. The evidence is that markets
react quickly. Survey of market efficiency of Fama (1991) has focused on testing
informational efficiency. He reports a stronger evidence a predictability in returns based
both on lagged values of returns and publicly available information.
The study of Roncati (2005) which has the objective to determine whether a
group of investors, is able to forecast the future security price, using all information it
wishes to employ, to increase returns on the portfolio and consequently to beat the
market. The sample consists of the returns on the portfolios of 80 actively managed Swiss
equity funds and of 14 passively managed Swiss equity funds for which monthly
3
performance (or rate of return) information and detailed information were available at
Reuters and Lipper for the period 2001-2005. The result of his work is that on average
the active funds managers are not able to predict the security prices well enough to
outperform the market and consequently the passive funds and consequently the market
seems to be efficient.
Researchers who have tested EMH in the strong-form are the following. Penman
(1982) found that insiders can achieve high return by buying shares before the
announcement and selling their shares after the announcement. Rindisbacher (2002)
found that corporate insiders earn positive abnormal returns, and prices do not seem to
reflect all information. Morse (1980) found a grater trading than normal a day before
public announcement. Keown and Pinkerton (1981) observed high abnormal return and
trading volume prior to merger announcement. Abdel-Khalik and Ajinkya (1982)
discover high return a week before analyst earning announcement.
Some of study results are inconsistent with EMH, such as the study result of
Ozmen (1997). He finds that future prices are predictable by using past price movements,
and because of this ISE is not weak-form efficient. It is possible for professional
investors who carefully observed this trend to make extraordinarily high profit. Sullivan,
Timmermann, and White (1999) find that the apparent historical ability of technical
trading rules to generate excess returns. Fama and French (1992) found that after
controlling for firm size and the variance of portfolio returns, stocks with low priceearnings ratios outperform the market.
Several previous research review the issue of whether financial ratios have the
influence on stock return or not. For instance, in the study of Martel and Padrón (2006),
the methodology of event study has been used for testing empirically if the share price in
the Spanish Stock Market reacts to the straight debt issue by companies that have quoted
in the Stock Exchange of Madrid over the period 1989-1998. The obtained results show
that the Spanish Stock Market reacts positively and significantly to debt issue
announcements. The market response to the debt issue announcements has been positive
and significant for the companies with a policy of low dividends. On the other hand, this
relation has been positive but non-significant for companies with high dividend payouts.
Hadi (2006) examines the market reaction to accounting numbers release by using
stock returns as the dependent variable and dividends, net income on sale, return of
equity, return on asset, debt ratio, interest coverage, current ratio, and price/earning ratio
as independent variables. He used ordinary least square method (OLS) to solve the
regression equations, and all analysis is performed at pool 2000-2003. His study result
shows that dividend, net income on sale and ROA have impact on security returns,
current ratio can be used an estimate for future return, debt ratio coefficient is negative
which carry negative information to the market, and interest coverage can be used to
estimate for future returns.
An empirical study conducted by Karan (1996) using the Istanbul Stock Exchange
data, showed that there is PER effect on stock return. Bhana (2002) observes a sample of
100 companies listed in Johannesburg Stock Exchange (JSE), announcing special
dividends over the period 1975 – 1994. The result shows that share price reactions are
negatively related to dividend declaration frequency. Woolridge (1983) has argued that
one cannot infer that dividend increases convey positive information about the firm by
examining share prices alone.
4
According to Beechey, Gruen, and Vickery (2000), in an efficient market,
publicly available information should already be reflected in the asset price. In the stock
market, such as, public information on price-earnings ratios, cash flows or other measures
of value should not have implications for future share returns (unless these variables are
revealing information about the riskiness of the asset). Abeyratana et al. (1993) found a
significant abnormal return in relationship with firms announcing cash dividends. Hadi
(2005) found evidences from Kuwait that market reactions of the release dividedness
information.
3. Hypothesis
Based on the efficient market hypothesis and previous research findings reviewed above,
we hypothesize that :
H1 : There is a weak-form of market efficiency in IDX, as weak-form of market
efficiency hypothesis asserts that the current price fully incorporates information
contained in the past history of prices only.
H2 : Profitability information, liquidity information, debt information, and market value
information have influence on stock return.
4. Research Methodology
4.1 Hypotheses Testing and Measurement of Variables
To test the hypothesis 1, we examine the serial correlation between the current stock
price and the stock price over a previous period, following research methodology used by
Rosenberg and Rudd (1982). Meanwhile, for testing the hypothesis 2, we examine the
effect of profitability, liquidity, debt, and market-value information on stock returns, by
applying the methodology of research used by Hadi (2006), but with appropriate
modification.
The selection of variables and definitions are following Van Horne (1998). The
regression model of hypothesis 2 and definition of variables used in the research are as
follow :
STRET = a + b1*ROA + b2*ROE + b3*SALASS + b4*CURRAT+ b5*TLTE +
b6*LTLTE + b7*TLTA + b8*PER + b9*DIV + ε
Where :
a. Profitability Information
Stock Return or STRET is return for stockholders. Return on assets, or the ROA is
measured as net profits after taxes to total assets. Return on Equity or ROE is measured
as net profits after taxes minus preferred stock dividend divided by shareholders’ equity.
This ratio tells us the earning power on shareholders’ book investment. Asset turnover
5
ratio is measured as total sales to total assets (SALASS). This ratio tells us the relative
efficiency with which the firm utilizes its resources in order to generate output.
b. Liquidity Information
Liquidity ratio is used to judge a firm’s ability to meet short-term obligations. From them,
much insight can be obtained into the present cash solvency of a company and its ability
to remain solvent in the event of adversities. One of the most general and most frequently
used of liquidity ratios is the current ratio (CURRAT) as measured by current assets to
current liabilities.
c. Debt Information
The debt-to-equity ratio is computed by simply dividing the total debt of the firm
(including current liabilities) by its shareholders’ equity (TLTE). The long-term debt-toequity ratio (LTLTE) is computed by simply dividing the long-term debt of the firm by
its shareholders’ equity. Total debt to assets ratio (TLTA) is computed by dividing total
liabilities by its total Assets. Debt information is used to measure the relative obligations
of a company.
d. Market-Value Information
The price to earnings ratio (PER) of a company is simply share price divided to earnings
per share. This ratio is described as one measure of relative value. The higher this ratio,
the more the value of the stock that is being ascribed to future earnings as opposed to
present earnings. The dividend (DIV) for a stock relates the annual dividend per share.
4.2. Data Analysis
A. Ljung-Box Q-Test
Following the methodology in the study of Rosenberg and Rudd (1982), we use serial
correlation test to measure the association between two elements of time series separated
by a constant number of time periods. The objective of this test is to examine the
autocorrelation between the current stock price and the stock price over previous period.
Hence, we employ Ljung-Box Q-Test to detect serial correlation of the data 16th lag
(Ghozali, 2002).
B. Regression Analysis
We employ regression technique and equation to measure the degree of relationship
between two or more variables in two different but related ways. The model of the
relationship has hypothesized, and the objective of the analysis is to test whether or not
profit information, liquidity information, debt information, and market value information,
have influence on stock returns.
6
4.3. Data Description
We have collected the data of companies from the Indonesia Stock Exchange 1(IDX),
from the year 1994 to 2005. The sample size consists of 77 companies and includes all
sector companies of LQ 45 Index as sample. The Index is one of Indonesia’s Stock
Exchange Index, which consists of 45 firms from all sectors.
5. Hypotheses Test Results
5.1. Ljung-Box Q-Test Result
For testing hypothesis 1, we examine whether IDX is efficient in weak-form market
efficiency or not. Therefore, we test the serial correlation between current price and
previous price of the stocks, by employing the Ljung-Box Q-Test for analyzing serial
correlation between variables. The result of the test is presented in table 1 as follows.
Table 1 : Ljung-Box Q-Test Result
Autocorre lations
Series : Current _price
Lag
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Autocorrel
ation
.275
.231
.193
.088
.048
.045
.006
-.029
-.051
-.050
-.046
-.021
-.012
-.013
-.013
.037
a
St d.Error
.049
.049
.049
.049
.049
.049
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
Box-Ljung Stat istic
b
Value
df
Sig.
31.753
1
.000
54.150
2
.000
69.876
3
.000
73.142
4
.000
74.112
5
.000
74.988
6
.000
75.002
7
.000
75.351
8
.000
76.483
9
.000
77.536
10
.000
78.450
11
.000
78.637
12
.000
78.695
13
.000
78.772
14
.000
78.847
15
.000
79.428
16
.000
a. The underlying proc ess ass umed is independence (white
noise).
b. Based on t he asym ptotic c hi-square approx imation.
1
www.idx.co.id
7
Ljung-Box Q-Test is one method that can be used to detect autocorrelation.
Autocorrelation arises if significant amount of lag is more than two, whereas if
significant amount of lag is less than two, then there is no autocorrelation between
variables (Ghozali, 2002). Table 1 shows that sixteen lag is significant. Therefore, we
conclude that there is autocorrelation between current price and previous price.
The interpretation of the result is that the market is not efficient in weak-form,
and it indicates that investor could use price from one period to predict returns in later
periods and make higher profits.
5.2. Regression Results
The objective of testing hypothesis 2 is to examine whether profitability, liquidity, debt,
and market value information have significant effect on stock return.
A. Regression Assumptions
Before analyzing regression coefficients of variables, we must first make several
assumptions about the population of the research. They represent an idealization of
reality, and as such, they are never likely to be entirely satisfied for the population in any
real study (Van Horne, 1998). A good regression model should not has the following
assumptions.
1. Multicollinearity
Multicollinearity implies that for some set of explanatory variables, there is an exact
linear relationship in the population between the means of the response variable and the
values of the explanatory variables (Van Horne, 1998). The goal of the multicollinearity
test is to analyze whether there is correlation between independent variables.
Multicollinearity in the regression model can be detected such as by testing the R2 value
and/or analyzing the correlation matrix (Ghozali, 2002).
Table 2 : Model Summary
Model Summ aryb
Model
1
R
R Square
.439a
.193
Adjust ed
R Square
.149
St d. Error of
the Es timate
10414. 00632
DurbinW atson
2.482
a. Predic tors: (Constant), DIV, SALASS, PER, TLTE, ROE, CURRAT, LTLTE,
TLTA, ROA
b. Dependent Variable: STRET
8
Table 2 shows the value of R2 that has generated by an empirical regression model
estimation. It values is 0.193 (less than 0.90).
We also see from table 2 that the Adjusted R-squared value of profitability
information, liquidity information, debt information, and market value information as
predictors for stock return is 0.149. These provide one evidence that only 14.9% of the
movement of the stock return could be explained by the existence of these information.
Therefore there is no multicolinearity in the regression model.
Table 3 : Correlations
Correlations
ROA
ROA
ROE
SALASS
CURRAT
TLTE
LTLTE
TLTA
PER
DIV
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
1
641
.317**
.000
640
-.321**
.000
623
.185**
.000
518
.048
.226
641
.101*
.010
641
.027
.490
641
-.091*
.046
483
.436**
.000
290
ROE
SALASS
CURRAT
.317**
-.321**
.185**
.000
.000
.000
640
623
518
1
-.137**
.020
.001
.652
643
622
520
-.137**
1
-.320**
.001
.000
622
623
509
.020
-.320**
1
.652
.000
520
509
521
.237**
-.006
-.008
.000
.872
.849
643
623
521
.254**
-.010
.008
.000
.810
.851
643
623
521
.148**
-.054
-.103*
.000
.177
.019
640
623
518
-.069
-.027
.001
.128
.560
.984
483
468
390
.341**
-.044
.186**
.000
.462
.004
290
282
244
TLTE
.048
.226
641
.237**
.000
643
-.006
.872
623
-.008
.849
521
1
644
.951**
.000
644
.097*
.014
641
-.017
.716
484
-.115
.051
290
LTLTE
.101*
.010
641
.254**
.000
643
-.010
.810
623
.008
.851
521
.951**
.000
644
1
644
.076
.054
641
-.016
.721
484
-.175**
.003
290
TLTA
.027
.490
641
.148**
.000
640
-.054
.177
623
-.103*
.019
518
.097*
.014
641
.076
.054
641
1
641
-.061
.181
483
-.214**
.000
290
PER
-.091*
.046
483
-.069
.128
483
-.027
.560
468
.001
.984
390
-.017
.716
484
-.016
.721
484
-.061
.181
483
1
505
-.120
.079
217
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
The correlations matrix above shows that there is only one quite high correlation value
(more than 0.90), that is the correlation between independent variables of TLTE and
LTLTE. Meanwhile, the other correlation values are less than 0.90. This is an indication
that multicollinearity is not exist in the regression model.
9
DIV
.436**
.000
290
.341**
.000
290
-.044
.462
282
.186**
.004
244
-.115
.051
290
-.175**
.003
290
-.214**
.000
290
-.120
.079
217
1
290
2. Autocorrelation
Autocorrelation requires probabilistic independence of the errors. This assumption means
that information on some of the errors provides no information on other errors. For time
series data this assumption is often violated. This is because of a property called
autocorrelation (Van Horne, 1998).
Test of autocorrelation aims to examine whether in a linear regression model has
correlation between gadfly errors in the period t with an error in the period t-1 (before).
One of the method that can be used to detect autocorrelation is the Durbin Watson (DW).
Table 2 shows that the DW value of 2.482 which means that there is no autocorrelation in
regression model.
3. Heteroscedasticity
This assumption concerns variation around the population regression line. Specifically, it
states that the variation of the Y’s about the regression line is the same, regardless of the
values of the X’s (Van Horne, 1998).
Test of heteroscedasticity aims to interpret whether the regression model has the
differences residual variance from one observation to another observation (Ghozali,
2002). If the residual variance from one observation to another observation is the same, it
is called homoscedasticity.
Figure 1 : Scatterplot
Scatterplot
Regression Studentized Residual
Dependent Variable: STRET
5
0
-5
-10
-4
-2
0
2
4
6
8
Regression Standardized Predicted Value
10
The graphic of scatterplot (figure 1) shows that the dots have not established a specific
pattern. Some of the dots located adjacent but some other dots spread above and below
the numbers of 0 at the axis Y. Thus, that the data in the graphic exhibit
homoscedasticity.
4. Normally Distributed
The assumption states that the errors are normally distributed. We can check this
by forming a histogram of the residuals. If the assumption holds, then the histogram
should be approximately symmetric and bell-shaped. But if there is an obvious skewness,
too many residuals more than, say, two standard deviations from the mean, or some other
non-normal property, then this indicates a violation of the assumption (Van Horne, 1998).
Figure 2 : Histogram
Histogram
Dependent Variable: STRET
100
Frequency
80
60
40
20
Mean = -4.99E-17
Std. Dev. = 0.974
N = 177
0
-10
-5
0
5
10
Regression Standardized Residual
11
Figure 3 : Normal P-P Plot
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: STRET
1.0
Expected Cum Prob
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
From the graphics of histogram (figure 2) and normal P-P plot (figure 3), we concluded
that the histogram gives the normally pattern of distribution. Meanwhile, the graphic of
normal P-P plot shows that the dots spread around the diagonal line, and the spreading
follows the diagonal line. Both of graphics show that the data meets reasonable
assumption of normality.
Based on the results of assumptions of population described above, the regression
model does not has the assumptions of heteroscedasticity, multicollinearity,
autocorrelation, and the data is normally distributed. Thus, our regression model is
appropriate to use for testing the hypothesis 2.
B. Regression Coefficients
Table 4 presents the regression results to analyze the influence of profitability,
liquidity, debt, and market value information on stock returns.
12
Table 4 : Regression Coefficients
Coefficientsa
Model
1
(Constant)
ROA
ROE
SALASS
CURRAT
TLTE
LTLTE
TLTA
PER
DIV
Unstandardized
Coeffic ients
B
Std. Error
-983.524 3527.500
-706.828
199.253
219.257
98.192
279.811 1933.777
.925
9.582
-23.667 1000.427
-1481.314 1715.732
2563.545 7145.386
44.006
56.414
18.146
3.255
Standardiz ed
Coeffic ients
Beta
-.726
.423
.013
.010
-.003
-.093
.043
.056
.448
t
-.279
-3.547
2.233
.145
.097
-.024
-.863
.359
.780
5.574
Sig.
.781
.001
.027
.885
.923
.981
.389
.720
.436
.000
Collinearity Statistics
Tolerance
VIF
.115
.135
.605
.487
.347
.415
.332
.946
.747
8.667
7.416
1.652
2.055
2.885
2.410
3.016
1.058
1.338
a. Dependent Variable: STRET
Table 4 shows the following results :
Profitability information on stock return
ROA as measured by the return on assets, has negative significant regression coefficient
on stock return, with 0.001 level of significance and -3.547 t-value. This result suggests
that firms with high ROA would tend to create low return of stock to their stockholders.
ROE has positive significant regression coefficient on stock return, with 0.027
significance level and 2.233 t-value. This result implies that ROE can be used for
estimating the future stock returns. SALASS has insignificant positive regression
coefficient on stock return, with 0.885 level of significance and 0.145 t-value. This result
describes that SALASS ratio can not be used to predict the future stock returns.
Liquidity information on stock return
Current ratio has insignificant positive influence on stock returns with 0.923 level of
significance and 0.097 t-value. This result implies that liquidity information do not affect
stock returns, hence it can not be used to predict the future returns.
Debt information on stock return
TLTE and LTLTE have negative but not significant effects on stock returns. However,
TLTA has positive but not significant influence on stock returns. These results suggest
that debt information can not be applied for estimating the future stock returns.
13
Market value information on stock return
PER as measured by price to earning ratio, has positive but not significant regression
coefficient on stock return, with 0.436 level of significance and 0.780 t-value. This result
suggests that firms with high PER would tend to create high return of stock even though
insignificantly. Dividend as defined dividend per share for shareholders, has positive
significant regression coefficient on stock return, with 0.000 level of significance and
5.574 t-value. This result describes that firms with high dividend payment would tend to
create high return of stock for their stockholders. Therefore, dividend can be applied to
predict the future stock returns.
6. Conclusion
After obtaining the Ljung-Box Q-Test and regression results for examining whether
Indonesia Stock Market is efficient in weak-form, by using the data of LQ 45 Index
within the period 1994-2005, we can draw the conclusion. Firstly, the significant amount
of lag are 16, therefore there is autocorrelation between current price and previous price.
It implies that market is not efficient in weak-form as one could use price from one
period to predict returns in later periods and earn extraordinarily high return by using
pattern of price change.
Secondly, regression result of testing the extent to which market reacted with
signal on releasing public information, have shown that profitability information (ROA)
has negative significant effect on stock returns, while ROE has positive significant effect
on stock returns. Meanwhile, as one of market value information, dividend has positive
significant influence on stock returns. However, profitability information (SALASS),
debt information (TLTE, LTLTE, and TLTA), liquidity information (CURRAT), and
market value information (PER), have not significant effect on stock returns. These
results suggest that debt information and liquidity information can not be used to estimate
the stock returns, while ROA, ROE, and dividend can predict the future movement of
stock returns.
Based on these results, there is an indication that Indonesia Stock Exchange is not
efficient in weak-form. Our results are inconsistent with Efficient Market Hypothesis, as
it states that, it is not possible to earn extraordinarily high return in any form efficient
market by using pattern of price change, and also to predict future stock returns. Efficient
market prevents investors to use information because prices have already adjusted to take
that information into account. The efficient markets hypothesis also suggests that
profiting from predicting price movements is very difficult and unlikely as security prices
adjust before an investor has time to trade on and profit from a new information.
Therefore, there is no reason to believe that prices are too high or too low.
Our results are also inconsistent with Malkiel (2003) who defined that markets are
efficient when markets do not allow investors to earn above-average returns without
accepting above-average risks, and the study result of Roncati (2005) which states that on
average the active funds managers are not able to predict the security prices well enough
to outperform the market.
14
Thus, the results imply that even though the evidence suggests that stock returns
are predictable, in ways that conflict with the efficient market hypothesis, the degree of
predictability is generally small compared to the high variability of returns.
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16
Appendix
1. Ljung-Box Q-Test Results
Model De scription
Model Name
Series Name
MOD_1
Previous_price
1
2
Current_price
Transformation
Non-S easonal Differenc ing
None
0
Seasonal Differenc ing
Length of S eas onal Period
0
No periodic ity
Maximum Number of Lags
16
Proces s A ssumed for Calc ulating the S tandard
Errors of the A utoc orrelations
Display and Plot
a
Independence(whit e noise)
All lags
Applying the model specific ations from MOD_1
a. Not applicable for c alculating the st andard errors of the partial
autocorrelations.
Ca se Processi ng Sum mary
Series Length
Number of Miss ing
Values
Us er-Missing
Sy stem-Missing
Number of Valid Values
Number of Computable Firs t Lags
Previous_
price
423
0
a
Current_price
423
0
a
6
6
417
417
416
416
a. Some of the missing values are imbedded within the series.
17
Previous Stock Price
Autocorre lations
Series : Previous_price
Lag
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Autocorrel
ation
.402
.215
.217
.112
.041
.015
-.004
-.038
-.037
-.044
-.048
-.032
-.021
-.020
-.005
-.005
Box-Ljung Stat istic
b
Value
df
Sig.
67.807
1
.000
87.211
2
.000
107.107
3
.000
112.457
4
.000
113.181
5
.000
113.275
6
.000
113.281
7
.000
113.890
8
.000
114.467
9
.000
115.313
10
.000
116.315
11
.000
116.751
12
.000
116.947
13
.000
117.113
14
.000
117.124
15
.000
117.136
16
.000
a
St d.Error
.049
.049
.049
.049
.049
.049
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
a. The underlying proc ess ass umed is independence (white
noise).
b. Based on t he asym ptotic c hi-square approx imation.
Previous_price
Coefficient
1.0
Upper Confidence Limit
Lower Confidence
Limit
ACF
0.5
0.0
-0.5
-1.0
1
2
3
4
5
6
7
8
9
10 11 12
13 14 15 16
Lag Number
18
Partial Autocorrelations
Series : Previous_price
Lag
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Partial
Autocorrel
ation
.402
.063
.133
-.029
-.028
-.023
-.011
-.034
-.006
-.022
-.012
.004
.003
-.003
.009
-.007
Std.Error
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
Previous_price
Coefficient
1.0
Upper Confidence Limit
Lower Confidence
Limit
Partial ACF
0.5
0.0
-0.5
-1.0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Lag Number
19
Current Stock Price
Autocorre lations
Series : Current _price
Lag
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Autocorrel
ation
.275
.231
.193
.088
.048
.045
.006
-.029
-.051
-.050
-.046
-.021
-.012
-.013
-.013
.037
Box-Ljung Stat istic
b
Value
df
Sig.
31.753
1
.000
54.150
2
.000
69.876
3
.000
73.142
4
.000
74.112
5
.000
74.988
6
.000
75.002
7
.000
75.351
8
.000
76.483
9
.000
77.536
10
.000
78.450
11
.000
78.637
12
.000
78.695
13
.000
78.772
14
.000
78.847
15
.000
79.428
16
.000
a
St d.Error
.049
.049
.049
.049
.049
.049
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
a. The underlying proc ess ass umed is independence (white
noise).
b. Based on t he asym ptotic c hi-square approx imation.
Current_price
Coefficient
1.0
Upper Confidence Limit
Lower Confidence
Limit
ACF
0.5
0.0
-0.5
-1.0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Lag Number
20
Partial Autocorrelations
Series : Current_price
Lag
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Partial
Autocorrel
ation
.275
.168
.105
-.017
-.022
.011
-.017
-.040
-.045
-.018
-.006
.018
.011
-.003
-.008
.048
Std.Error
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
Current_price
Coefficient
1.0
Upper Confidence Limit
Lower Confidence
Limit
Partial ACF
0.5
0.0
-0.5
-1.0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Lag Number
21
2. Regression Results
De scriptive S tatistics
STRET
ROA
ROE
SA LAS S
CURRAT
TLTE
LTLTE
TLTA
PE R
DIV
Mean
846.2160
8.2371
16.1564
.3710
98.8537
1.3092
.6115
.4801
14.8740
163.4476
St d. Deviation
11291.34482
11.59823
21.77084
.52175
117.44746
1.33274
.71032
.19080
14.30945
278.95534
N
177
177
177
177
177
177
177
177
177
177
Note: Stock return (STRET) is return for shareholders. Return on equity (ROE) is measured as net profits
after taxes minus preferred stock dividend divided by shareholders’ equity. Return on Assets (ROA) is
measured as net profits after taxes to total assets. Asset turnover ratio is measured as total sales to total
assets (SALASS). Current ratio (CURRAT) is measured as current assets to current liabilities. Debt-toequity ratio is computed by simply dividing the total debt of the firm (including current liabilities) by its
shareholders’ equity (TLTE). Long-term debt-to-equity ratio (LTLTE) is computed by dividing the longterm debt of the firm by its shareholders’ equity. Total debt to assets ratio (TLTA) is computed by dividing
total liabilities by its total assets. Price to earnings ratio (PER) of a company is simply share price divided
to earnings per share. Dividend (DIV) for a stock relates the annual dividend per share.
b
Va riables Entere d/Re moved
Model
1
Variables
Entered
DIV,
SA LAS S,
PE R,
TLTE,
ROE,
CURRAT,
LTLTE ,
a
TLTA, ROA
Variables
Removed
.
Method
Enter
a. All reques ted variables ent ered.
b. Dependent Variable: S TRE T
ANOVAb
Model
1
Regres sion
Residual
Total
Sum of
Squares
4E+009
2E+010
2E+010
df
9
167
176
Mean Square
480846800.2
108451527.7
F
4.434
Sig.
.000a
a. Predictors: (Constant), DIV, SALASS, PER, TLTE, ROE, CURRAT, LTLTE, TLTA,
ROA
b. Dependent Variable: STRET
22
Collinearity Diagnosticsa
Model Dimension Eigenvalue
1
1
5.507
2
1.915
3
.930
4
.606
5
.391
6
.308
7
.152
8
.121
9
.046
10
.024
Condition
Index
(Constant)
1.000
.00
1.696
.00
2.433
.00
3.015
.00
3.755
.00
4.227
.01
6.020
.00
6.744
.12
10.959
.04
15.053
.82
ROA
.00
.01
.00
.00
.02
.02
.00
.00
.80
.15
ROE
.00
.01
.00
.00
.02
.04
.00
.00
.76
.16
SALASS
.00
.03
.14
.01
.32
.02
.04
.32
.04
.07
Variance Proportions
CURRAT
TLTE
.00
.00
.02
.01
.01
.02
.06
.02
.00
.00
.48
.00
.00
.47
.23
.25
.09
.04
.11
.18
LTLTE
.00
.01
.05
.02
.01
.00
.82
.02
.05
.01
TLTA
.00
.00
.00
.00
.00
.00
.01
.10
.03
.86
PER
.01
.01
.03
.31
.21
.25
.02
.15
.01
.01
a. Dependent Variable: STRET
Re siduals Sta tisticsa
Minimum
Predic ted V alue
-15956.4
St d. P redic ted Value
-3. 389
St andard E rror of
1112.387
Predic ted V alue
Adjust ed P redicted Value -19525.6
Residual
-100927
St d. Residual
-9. 691
St ud. Residual
-10.267
Deleted Residual
-113276
St ud. Deleted Residual
-16.857
Mahal. Dis tanc e
1.014
Cook's Dis tanc e
.000
Centered Leverage Value
.006
Maximum
34740. 98
6.835
Mean
846.2160
.000
St d. Deviat ion
4958.70516
1.000
N
8576.386
2215.074
1107.967
177
18838. 61
625.7817
52759. 02
.00000
5.066
.000
6.942
.009
99065. 41 220.43423
8.206
-.021
118.373
8.949
4.230
.034
.673
.051
4674.23250
10144. 24521
.974
1.078
12751. 69307
1.514
13.651
.332
.078
177
177
177
177
177
177
177
177
177
177
177
a. Dependent Variable: STRET
23
DIV
.01
.02
.13
.29
.44
.01
.00
.02
.07
.01