Analysis Weak Form Efficiency in Indonesia Stock Exchange Period
2011-2016
Evida Rahimah
1
, Indra Maipita
1
and Sri Fajar Ayu
2
1
Department of Economic Science, State University of Medan, Medan, Indonesia
2
Department of Agribusiness, University of Sumatera Utara, Medan, Indonesia
Keywords: LQ 45, random walk, weak form efficiency
Abstract: Stock market efficiency is a very important study, because an inefficient market allows the
market authorities to consistently obtain an abnormal return indicated by stock returns showing
predictable behavior or not following a random walk pattern. The purpose of this study is to find
out whether stock returns on the Indonesia Stock Exchange are random walk evidenced from
non-parametric tests (runs test) and parametric tests (unit root test). This study uses 34 samples,
namely the issuer in the LQ45 index, with the study period from January 2011 to December
2016. The data used in this study is the LQ45 index weekly stock closing price from January
2011 to December 2016 obtained from the publication report Indonesia stock exchange. This
study using a significance level of 5%. The analytical method used is non-parametric test (run
test and phillips perron test) and parametric test (unit root test and autocorrelation function test).
The result of the research shows that stock return of Indonesia Stock Exchange are not random
walk or inefficient in weak form.
1 INTRODUCTION
Disclosure of information is a reflection of an
efficient capital market. Where the efficient market
theory proposed by fama defines the efficiency of
the capital market as a market where prices fully
reflect all available information (Fama, 1970). The
faster the new information is reflected in the price,
the more efficient the capital market. Thus the
presence of information has an important role in
stock trading in capital markets conducted by
investors. This information is needed in making
decisions related to the selection of investment
portfolios that provide the highest level of profit
with a certain level of risk (Setiawati, 2013).
If the equity markets work efficiently, the price
would indicate the intrinsic value of the shares and
in exchange, limited savings will be allocated to
productive investment sector in an optimal way in a
way that will provide a stream of benefits for
individual investors and the national economy as a
whole (Copeland and Weston, 1988). Thus there is
no opportunity to obtain information that allows
market authorities to consistently gain an abnormal
return, because market returns show unpredictable
behavior (Khairunnisa, 2015). Conversely, if an
inefficient capital market can complicate various
parties (Rahman, 1991), ie issuers difficulty in
measuring the maximum shareholder wealth.
Whereas for investors, of course, many will suffer
because inefficient market conditions make a lot of
manipulation that can be done to increase stock
prices. Lastly, with this can prompt investors to
reduce their investment in the stock market because
they would have had difficulty detecting the return,
risk and liquidity of the company's stock is traded.
Therefore it becomes very important to make
efficient capital markets, efficient capital market can
be created with a lot of competition among
investment analysis for investment analysis leads to
a situation where at any time, the stock price
indicates that the actual value. The more the number
of financial analysts and the competition between
them will make the price of the securities fair and
reflect all the relevant information in which the
analyst will attempt where the analyst will attempt to
obtain as comprehensive information as possible
compared to other analysts with the closest possible
analysis that will make the price of the securities fair
or in other words, the stock prices reflect all
442
Rahimah, E., Maipita, I. and Ayu, S.
Analysis Weak Form Efficiency in Indonesia Stock Exchange Period 2011-2016.
DOI: 10.5220/0009500704420446
In Proceedings of the 1st Unimed International Conference on Economics Education and Social Science (UNICEES 2018), pages 442-446
ISBN: 978-989-758-432-9
Copyright
c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
available information and make adjustments to fully
and rapidly to new information (Husnand, 2005).
The idea of testing the efficiency of the market
as the information contained in efficient market
hypothesis. Fama divides the efficient market
hypothesis into three categories: first, the strong
form market efficiency hypothesis is to answer the
question of whether investors have private
information that is not reflected in the price of
securities. Second, the semi strong form efficiency
market hypothesis is how quickly the price of a
security reflects the published information. Third,
the weak form efficiency market hypothesis is how
strongly historical information can predict future
returns. This hypothesis is known as Random Walk
Hypothesis (RWH) states that the current price of
securities fully reflects the information contained in
the historical price. Therefore, the best predictor of
future prices is the current price. It is not possible for
investors to design profitable strategies based on the
prices of securities in the past. The capital market
will be more efficient in a weak form if the
prediction rate is lower, so the current stock market
price is independent of the stock market prices in the
past. In other words, the efficient market forms weak
if the stock price follows a random walk process. To
test the efficiency of weak form, it is necessary to do
random walk hypothesis (RWH) test considering the
relation between current and past stock price
(Fawson. et.al, 1996; Ananzeh, 2016; Arora, 2013;
Okpara, 2010; Borges, 2010; Shaker, 2013)
From three forms of testing efficiency in the
information market, the discussion in this study
focused on the weak form market efficiency testing
or return predictability test, because most of the
research in the market efficiency hypothesis (EMH)
focuses on the weak form level, because if the
research results do not support weak form market
efficiency, testing at the next level is useless
(Gimba, 2010; Ikechukwu, 2015; Phan & Zhou,
2014). The Indonesian capital market is a capital
market that was established since the Dutch
occupation in Indonesia under the name Vereniging
Voor de Effekteenhundel in 1912 in Batavia with the
aim of raising funds to support the expansion of the
Dutch Colonial plantation business. But it stopped
when World War I and II happened and was
reactivated in 1977 and a few years later the capital
market experienced growth. Indonesian capital
market over the last 5 years have improved
performance. This is reflected in the JCI, which is
shown in Figure 1.
Figure 1: JCI Developments for the Last 5 Years
In theory emerging countries tend to be inefficient.
Claessens et al believe that there are several
motivations behind attempting to test efficiency in
emerging countries (Claessens et al, 1993). First,
domestic and foreign investors do not really like to
invest in the stock market in emerging countries
because there are inefficiencies. For example, the
thin market in Africa is often considered the subject
of insider manipulation and consequently makes
foreign investors lose [Magnusson and Wydick,
2002). If the inefficiency of the market continues to
the stage only individuals or certain companies are
entitled to exclusive information or insider trading,
certainly not encouraging domestic and foreign
investors to approach the market. Second, the
efficiency test is trying to give an assessment of the
effectiveness of the role played by the market, as an
example of a role in asset allocation.
2 METHODS
The type of research used in this study is
explanatory research. In this study the researchers
tested a theory that has been tested empirically by
previous researchers. In this context, the variables
tested related to the weekly stock price movement of
the period January 1, 2011 to December 31, 2016.
The data used in this study is the weekly data from
the stock exchange LQ45 index from January 2011
until December 2016. The sampling technique was
conducted by purposive sampling method are taken
based on certain criteria, that are:
1. Number of issuers listed in the LQ 45 Index.
2. Issuers are not consistently listed in the LQ 45
Index during the year 2011-2016
3. Issuers incomplete publish weekly stock price
LQ 45 Index during the year 2011-2016
4316.69
4274.18
5226.95
4593.01
5296.71
5949.7
0
1000
2000
3000
4000
5000
6000
7000
2012 2013 2014 2015 2016 2017
IH…
Analysis Weak Form Efficiency in Indonesia Stock Exchange Period 2011-2016
443
Based on the criteria established, then obtained 34
sample data with the number of observations is 8160
obtained from 34 x 240 (multiplication of the
number of samples with the study period ie weekly
during the year 2011-2016). The main variable in
this study is a weekly return of stocks from 34
companies listed in the LQ-45 for the period January
2011 to December 2016, by formula:
!
"
#
$
%
&$
%'(
$
%'(
)*++
,
(1)
Description:
Z
t
= return
P
t
= current close price
P
t-1
= previous stock closing price
2.1 Data analysis method
Data analysis method used in this research non
parametric test (runs test) and parametric test (unit
root test and autocorrelation function test).
2.2.1 Non parametric test
1. Runs Test
Runs test is non parametrik test for serial
dependence in the stock returns, which designed to
examine whether or not an observed sequences is
random (campbell et al,1997; Gujarati, 2003). With
the following equation:
- #
.
/
.01
2
&
3
4
5
6
7
58(
.
(2)
9 #
:
;<=>?&@
A
B
C
(3)
Description:
N = total number of observations
n
i
= the number of price changes (returns) in each
category
Z= standard normal Z-statistics
r = number of actual runs;
µ = number expectations of runs,
Hypothesis testing criteria:
H
0
: market is weak form efficiency
H
1
: market is not weak form efficiency
If the Z-statistic is less than 1% and ρ value also
less than 5% level of significance, then we reject the
null hypothesis which mean market is not weak form
efficiency.
2. Phillips perron (PP) test
PP test is a non parametric test from the unit root test
conception. The PP test forces a non paremetric
correction to the t-test statistic and corrects for any
serial correlation and heterocedasticity in the error
term (εt) of the regression test by directly modifying
the tests statistic (Hasan, 2015).
DE
F
=
GHI
"
HJK
"&1
H
L
F
(4)
Where, α = constants, β = coefficient of time trend,
and Y is parameter and ε_t= error term.
Hypothesis testing criteria:
H0: stock returns are not stationary (random)
H1: stock returns are stationary (not random)
If the value of Phillips perron test statistic (tα)
Statistics greater than 1%, 5% & 10% of critical
value and ρ value greater than 5% then we accept
the null hypothesis which mean stock returns are
random (market is weak form efficiency).
2.2.2 Parametric test
1. Unit root test
Unit root test is used to see whether the data random
or not. With the following equation:
DE
F
= α
H
β
"
H
γ
K
"&1
H
M
F
DE
F&1
+
M
N
DE
F&N0
L
F
(5)
Description:
G
= constants
I # OPQRRSOSQTUVPRVUSWQVUXQTY
M # Z[X[WQUQX
\
= lag order of the autoregressive process
DK
= First Difference series of Y
]
= error term
Hypothesis testing criteria:
H
0
: stock returns have a unit root (random)
H
1
: stock returns have not a unit root (not random)
If the value of Augmented Dickey Fuller test
statistic (t
α
) greater than 1%, 5% & 10% critical
value and
\
value greater than 5% then we accept
the null hypothesis which mean stock returns are
random (market is weak form efficiency).
1. Autocorrelation function test (ACF).
Auto autocorrelation function test is examine to
identify the degree of autocorrelation in a time
series. With the following equation:
\
^
#
_
`
_
a
V
(6)
Where
\
^
is autocorrelation function,
b
^
is
covariane on laq k and
b
=
is variance.
Hypothesis testing criteria:
UNICEES 2018 - Unimed International Conference on Economics Education and Social Science
444
H
0
: no autocorrelation for stock returns of LQ45
index (market is weak form efficiency)
H
1
: there is autocorrelation for stock returns of
LQ45 index (market is inefficient in weak form)
If the autocorrelation function (AC) value heading to
zero and ρ value is less than 5% level of
significance, then the null hypothesis is rejected.
Therefore the historical returns can be used to
predict future returns and this element indicates that
the weak form of market efficiency does not hold.
3 EMPERICAL RESULT
In data analysis method used in this research are
testing the weak form efficiency on the Indonesia
Stock Exchange, this study uses non-parametric test
that is runs test & phillips perron test and parametric
test that is unit root test and autocorrelation function
test.
1. Runs test
Runs test is a non parametric test for serial
dependence in stock return, designed to test whether
the sequence observations are random or not.
Table 1: Results of runs test LQ45index
Result
(average
)
Number of
Observation
10469
Cases < Test Value
153.2059
Cases >= Test Value
154.7059
Number of Runs
642
Z
-15.5346
\ value
0,00
Based on table 1 it shows that the output result show
that Z-statistic less than 1% and ρ value also less
than 5% level of significance, then we reject the null
hypothesis which mean the market is not weak form
efficiency.
2. Phillips perron (PP) test
PP test is a non parametric test from the unit root test
conception.
Table 2: Results of phillips peron test LQ45index
Result
(Average)
Level 1
st
Difference
The ADF
statistic
Critical
value at 1%
Critical
value at 5%
Critical
value at 10%
Based on table 2 that the output result show the
Phillips perron test statistic (tα) less than critical
value at the 1%, 5% & 10% level of significance and
ρ value also less than 5% level of significance, then
we reject the null hypothesis which mean stock
returns are stationary (market is not weak form
efficiency).
3. Unit Root Test
This test is used to see whether the time series data
being analyzed is stationary (not random) or not
stationary (random). The results of unit root test
show in table 2.
Table 3: Results of unit root test LQ45ind
Result
(Average)
Level 1
st
Difference
The ADF
statistic
-19.77776
Critical
value at 1%
-3,451561
Critical
value at 5%
-2,870774
Critical
value at
10%
-2,571761
Based on table 3 that the output result show the
ADF-statistic (tα) less than critical value at the 1%,
5% & 10% level of significance and ρ value also
less than 5% level of significance, then we reject the
null hypothesis, this gives the same conclusion to the
previous test is the market is not weak form
efficiency.
4. Autocorrelation function test (ACF)
Auto autocorrelation function test is parametric test
examine to identify the degree of autocorrelation in
a time series.
Table 4: Results of autocorrelation test LQ45index
laq
AC
Q-Stat
Prob
1
0.941
275.66
0.000
2
0.895
525.61
0.000
3
0.856
755.19
0.000
4
0.824
968.61
0.000
5
0.804
1172.4
0.000
6
0.786
1367.5
0.000
7
0.762
1551.8
0.000
8
0.743
1727.4
0.000
9
0.725
1895.3
0.000
10
0.710
2056.8
0.000
Based on table 4 it shows that AC value towards
zero and ρ value less than 5%, then we reject the
null hypothesis which is mean the market is not
weak form efficiency or the historical returns can be
used to predict future returns.
Analysis Weak Form Efficiency in Indonesia Stock Exchange Period 2011-2016
445
4 CONCLUSIONS
The result of this study shows that the testing of
weak form efficiency market on Indonesia Stock
Exchange (BEI) during the period of January 2011
to December 2016 by using non parametric test is
runs test & phillips perron test and parametric test is
unit root test and autocorrelation function test,
jointly reject the null hypothesis or in other word
Indonesia Stock Exchange is inefficient in weak
form, this indicates that investors who use technical
analysis can exceed the market returns because
future return can be predicted by historical return.
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