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This study aims at examining the efficiency of stock returns of BRICS markets. Here we consider the daily data from 25th September 1997 to 31st March 2018. This study employs variance ratio tests for linear dependencies and BDSL test for nonlinear dependence. Further, the entire period of study is divided into sub-periods such as pre-crisis, crisis and post-crisis periods to understand the level of efficiency in different time periods. The results of variance ratio tests show that Brazil and China markets are weak-form efficient in all time periods while Russia and South Africa are a weak form efficient in the full period, crisis and post-crisis periods but not in pre-crisis period. With regard to Indian stock markets, the markets are found to be weakly efficient during pre-crisis and crisis period while market inefficiency is observed in full period and post-crisis period. However, the results of the nonlinear test show that all the BRICS markets are rejecting the random walk hypothesis due to the nonlinear dependence in all time periods of study.
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60 The Romanian Economic Journal
Year XXII no. 72 June 2019
Analysis of Stock Market Efficiency
in Emerging Markets: Evidence
from BRICS
Siva Kiran1
Prabhakar Rao.R2
Abstract
This study aims at examining the efficiency of stock returns of BRICS markets. Here we consider the
daily data from 25th September 1997 to 31st March 2018. This study employs variance ratio tests for
linear dependencies and BDSL test for nonlinear dependence. Further, the entire period of study is divided
into sub-periods such as pre-crisis, crisis and post-crisis periods to understand the level of efficiency in
different time periods. The results of variance ratio tests show that Brazil and China markets are weak-
form efficient in all time periods while Russia and South Africa are a weak form efficient in the full
period, crisis and post-crisis periods but not in pre-crisis period. With regard to Indian stock markets, the
markets are found to be weakly efficient during pre-crisis and crisis period while market inefficiency is
observed in full period and post-crisis period. However, the results of the nonlinear test show that all the
BRICS markets are rejecting the random walk hypothesis due to the nonlinear dependence in all time
periods of study.
Keywords: Weak form Efficiency, Variance ratio tests, BDSL test, BRICS Stock Markets
JEL Classifications: G10, G14, G15
1. Introduction
In recent times, the behavior of stock returns has become an interesting topic for
discussion among researchers, investors, and regulators. The researchers want to
know the movements of stock indices for prediction purposes, the investors take
advantage of the imperfections present in the market in order to gain from
arbitrage opportunities. On the other hand, regulators frame policies to increase
the efficiency of the markets. The Efficient Market Hypothesis (EMH) formulated
by Fama (1965, 1970) states that the current stock prices reflect all available
1Sri Sathya Sai Institute of Higher Learning, Puttaparthi-515134, AP, India, e-mail:
kiran.akshaya@gmail.com
2Sri Sathya Sai Institute of Higher Learning, Puttaparthi-515134, AP, India, e-mail:
rprabhakarrao@gmail.com
The Romanian Economic Journal 61
Year XXII no. 72 June 2019
information at any given point of time. Hence, investors can earn only normal
profits or zero economic profits on their investments, i.e. no investor can earn
abnormal profits. Therefore, the historical information is not useful for predicting
the prices (ineffective technical analysis3). This phenomenon is called the Random
Walk Hypothesis wherein the successive price changes are independent or
unrelated. In an inefficient market, investors can predict security's price
movements thereby outperform the markets. The concept of the efficient market
was first theorized by Bachelier (1900). Later on, the studies by Samuelson (1965),
Fama (1965,1970), Ayadi and Pyun (1994), Areal and Armada (2002), Lock (2007),
and Aymen and Adel (2013) have found that the markets are efficient. While few
studies have found that the markets are inefficient as the dependence of
successive day’s market returns helps in predicting stock returns (see Poterba and
Summers (1987), Richards A. J. (1995), Abraham et al (2002), M. R. Borges (2010),
and Said and Harper (2015)). This inconsistency in the findings denotes that there
is no consensus on the findings of studies in understanding the behavior of stock
returns. The prime reason for the divergent findings is the use of several statistical
tests with restrictive assumptions employed on the different frequencies of data
(Gourishankar and Kamaiah (2010)).
In testing the market efficiency several statistical tests have evolved over a period
of time. In early studies, researchers used the conventional serial correlation and
run tests. As these techniques suffer from more restrictive assumptions they tend
to be less efficient in identifying the patterns in the returns. To overcome this
issue Lo and Mackinlay (1988) (LMVR) proposed individual variance ratio test.
However, the major limitation of this variance ratio test is the problem of sample
size distortions. Chow and Denning (1993) suggested a multiple variance ratio
tests to address this size distortion problem of the individual variance ratio test.
These tests can validate only linear dependencies in the return series. Granger and
Anderson (1978) argued that the rejection of linearity in the series alone does not
validate market efficiency as non-linearity might enable to predict future prices.
Furthermore, the observational effect of Black Monday4 in the early 1980s ignited
the interest in capturing non-linear dependencies in the series. Brock, Dechert,
Scheinkman, and LeBaron (BDSL, 1996) developed a test to examine the non-
linear dependencies in the series.
The globalization and liberalization in the late '80s have helped emerging
economies in attracting more capital flows from developed and other emerging
economies. The economic and financial integration with the world economies
coupled with the reforms are taken up by the emerging economies have brought a
3 Using historical prices data to forecast the direction of prices
4 The stock market crash of 1987
62 The Romanian Economic Journal
Year XXII no. 72 June 2019
sea change in these economies and especially in their financial markets. Jim O Neil
(2001) of Goldman Sachs carried out a study to understand the growth patterns of
some developed and emerging economies. He observed that four countries,
namely Brazil, Russia, India, and China together has the potential to grow at a
faster rate. Thus the acronym BRIC was coined in 2001. Later in 2010 with South
Africa joining this group - the new emerging economic order - BRICS was
established. These five economies together contribute about 32% of global GDP
(PPP). Not only in terms of its economy size, but also with regard to the financial
sector the implementation of reforms has helped capital markets to experience a
steady increase in size and volume. Capital market development indicators have
shown significant improvement with a combined market capitalization of US$13
trillion and the value of stocks traded US$19 trillion, which accounts for 17% of
world's market capitalization and 25% of the value of stocks traded as on 2017.
The burgeoning BRICS market development indicators triggered interest in
researchers to examine the behavior and informational efficiency of markets.
Some researchers have carried out the studies to verify the informational
efficiency of markets in each of these countries (see, for example, Regis Augusto
Ely (2012) for Brazil, Said and Harper (2015) for Russia, Gupta and Sankalp
(2017) for India, Andrea et al (2016) for China, Lumengo (2012) for South Africa).
Against this background, this study attempts to examine the market efficiency of
BRICS stock markets. The present study contributes to the existing literature on
the following aspects. First, the study extends to the most recent period, i.e. from
25th September 1997 to 31st March 2018. Second, to investigate the level of
efficiency in varying time periods, the sample period is subdivided into Pre-crisis,
during a crisis, and Post-crisis periods. Third, this study employs variance ratio
tests Lo and MacKinlay (1988) and Chow-Denning (1993) for linear dependence
and BDSL (1996) test for nonlinear dependence to examine the efficiency in
BRICS stock markets.
The rest of the paper is organized as follows: A literature review is presented in
section 2, followed by a description of the data and methodology in the third
section. In section 4 we present the findings of this study. Section 5 ends the
paper with a summary and conclusions.
2. Literature Review
In this section, we present a brief overview of the literature available in this area of
research. Several empirical studies have been carried out to study the behavior of
stock markets by employing the conventional techniques, namely autocorrelation
test, and runs test. Fama (1965) used these techniques on the Dow Jones daily
returns and found that the market is efficient. Later on, some studies have been
The Romanian Economic Journal 63
Year XXII no. 72 June 2019
conducted to verify the market efficiency by using conventional tests such as
correlation and run tests. However, Lo and Mackinlay (1988) showed that these
tests are less efficient and proposed the most powerful variance ratio test and is
widely known as Lo and Mackinlay variance ratio test (hereafter LMVR, 1988).
They tested the random walk hypothesis for US stock indices and found that the
markets are inefficient due to mean reversion in returns5. Using LMVR test Ayadi
and Pyun (1994) observed that the Korean Stock Exchange (KSE) is a random
walk market. Abraham et al (2002) studied the random walk and weak form
efficiency of Gulf stock indices for the period of 1992-2008 and found that they
are inefficient. By using the traditional parametric and nonparametric tests Areal
and Armada (2002) showed that the Portuguese stock markets are weak form
efficient. Buguk and Brorsen (2003) tested the efficient market hypothesis for the
Istanbul Stock Exchange (ISE) using weekly returns and showed that it follows a
random walk. To test the random walk on weekly returns of Taiwan stock market
from 1990 to 2006, Lock (2007) applied the variance test and found that the
markets move in a random walk. Even though the individual LMVR test is widely
used for testing the random walk hypothesis Chow and Denning (1993) developed
the multiple variance ratio tests to overcome the issue of sample size distortions
from LMVR tests. Huber (1995) used the multiple variance ratio tests for the
Austrian Stock Exchange and found that the markets are inefficient or don't have
a random walk. Using multiple variance ratio and autoregressive fractionally
integrated moving average (ARFIMA) test Ojah and Karemera (1999) showed that
Latin American equity markets indices follow a random walk. To test the random
walk hypothesis of stock returns in the Middle East markets, Smith (2007) by
employing the multiple variance ratio tests found that Israeli, Jordanian and
Lebanese markets follow a random walk while other markets showed no random
walk. By applying the nonparametric variance ratio test in the Middle East and
North African (MENA) markets Al-Khzali et al (2007) found that the MENA
markets are weak-form efficient. In order to test a random walk phenomenon for
8 Asian emerging markets, Hoque et al (2007) employed two new VR tests,
namely Wright's rank and Whang-Kim subsampling tests along with the variance
ratio tests and found that among the Asian markets only Taiwan and Korea
markets follow a random walk whereas other markets do not follow a random
walk. Kim and Shamsuddin (2008) studied the behavior of advanced and
secondary emerging markets for the period from 1990 to 2005. By employing
multiple variance ratio tests they found that advanced emerging markets are
efficient while the secondary emerging markets are inefficient. M. R. Borges
(2010) used variance ratio tests and showed that the European equity markets are
5 Laura Spierdijk et al(2012)
64 The Romanian Economic Journal
Year XXII no. 72 June 2019
inefficient. Aymen and Adel (2013) studied the impact of financial liberalization
on informational efficiency in 13 emerging markets and found that the financial
liberalization improved the efficiency of these markets. Yang et al (2015)
examined the efficiency of Mexico, Indonesia, South Korea and Turkey (MIST)
utilizing Fourier transformation and showed that the markets are efficient.
Geoffrey Ngene et al (2017) examined whether stock prices in 18 emerging
markets follow random-walk in the presence of single and multiple structural
breaks employing Phillips-Perron test. The results found that in single break test
16 markets rejected the random walk hypothesis and in multiple breaks 14 markets
showed the random walk process. Assaf and Charif (2017) used the variance ratio
test to investigate the random walk hypothesis in the MENA equity markets and
showed that MENA markets are weak-form efficient.
With regard to each of the BRICS stock markets, several studies were conducted
to examine the efficiency of markets using the traditional parametric,
nonparametric tests. In the case of stock market efficiency in Brazil, using
variance ratio tests on Latin American equity markets Urrutia (1995) showed that
the markets do not follow a random walk while the runs test showed that markets
are efficient. Grieb and Reyes (1999) employed variance ratio tests on Brazil and
Mexico markets and found that Brazil markets are efficient. Karemera et al (1999)
tested for random walk in emerging equity markets using multiple variance ratio
tests and found that Brazil market follows a random walk. Using conventional
serial correlation test on Brazil equity markets Capobianco et al (2002) found that
the markets follow a random walk. Regis Augusto Ely (2012) investigated the
market efficiency in five sectoral indices of Brazil markets. The results of variance
ratio tests indicate that except industrial sector all other sectors considered for the
study showed a random walk in their returns. With reference to the Russian stock
market, Natalia Abrosimova et al (2002) examined weak form efficiency using the
autocorrelation and variance ratio tests and found that the markets are efficient.
MC Gowan (2011) by employing a serial correlation test found that the Russian
stock market is a weak form efficient. Said and Harper (2015) employing
autocorrelation and the variance ratio test showed that the Russian market is not
efficient. With reference to Indian stock markets, there exist several studies such
as Sharma and Kennedy (1977) confirmed that the Indian market follows a
random walk. A similar result was found by Barua (1981, 1994), Amanulla and
Kamaiah (1996,1998), Mitra (2000), Chawla et al (2006), Gupta (2014).
Conversely, studies by Poshakwale (2002), Chaudhuri and Wu (2003), Ahmed et al
(2006), Rakesh Gupta and Parikshit (2007), Anil k Sharma and Neha (2011),
Hiremath and Kamaiah (2010, 2012) Hiremath and Jyoti (2014), Gupta and
Sankalp (2017) found markets are not efficient. As for stock market efficiency in
China, studies are conducted by Liu et al (1997), Darrat and Zhong (2000), Lee et
The Romanian Economic Journal 65
Year XXII no. 72 June 2019
al (2001), Lock (2007), Charles and Darne (2008), Fifield and Jetty (2008), Kian-
Ping Lim et al (2009), Lim and Brooks (2009) supported weak form efficiency of
Chinese markets. On the other hand, Lima and Tabak (2004), Seddighi and Nian
(2004), Andrea and Marianna (2016) observed the dependence in a return series of
Chinese markets which implied that markets are inefficient. In the case of South
Africa, Mc Millan and Thupayagale (2008) found that the African markets do not
follow a random walk while Smith et al (2002) and Lumengo (2012) showed that
South African markets are efficient.
Very few studies are conducted on BRICS markets to study market efficiency.
Camelia (2012) tested weak-form market efficiency for five U.E emerging markets
and BRIC emerging markets and found that all the markets are not efficient
during the study period. Karamchandani et al (2014) employed Hurst exponent to
study the efficiency of BRIC stock markets and found that all the four markets
have more predictability indicating these markets are inefficient. Tiwari and
Kyophilavong (2014) used Wavelet-based unit root tests to check whether the
BRICS stock markets follow the random walk and observed that except for
Russian market, all the other markets do not follow random walk during the study
period. Conversely, Mobarek and Fiorante (2014) using individual and multiple
variance ratio tests found that BRIC stock markets are efficient. Robert (2016)
study found that the BRIC stock markets exhibit weak-form market efficiency.
It is evident from the above literature that the efficiency of markets is time-
dependent. Also, the efficiency results heavily rely on the type of tests that have
been used for testing the market efficiency of the respective stock markets. Hence,
there is a need to test the efficiency for different periods by employing appropriate
tests.
3. Data and Methodology
This study uses the daily returns of BRICS stock markets calculated from closing
prices for the period 25th September 1997 to 31st March 2018. The stock indices
selected are BOVESPA (Brazil), MICEX (Russia), SENSEX (India), SSE (China)
and JSE (South Africa). It is also perceived that the markets are inherently volatile
and sensitive to the information available domestically and externally at a given
time period. It is also evident from the literature that the behavior of markets is
highly time-dependent. Therefore, to analyze the behavior of these emerging
markets before, during and after the US financial crisis of 2008, the entire time
period is divided into three sub-periods. So, the study considers; Sub-period I as
Pre Crisis period from 25th September 1997 to 29th June 2007; Sub-period II as
Crisis period from 2nd July 2007 to 29th May 2009 and Sub period III as Post Crisis
66 The Romanian Economic Journal
Year XXII no. 72 June 2019
period from 1st June 2009 to 30th March 2018. The closing prices data for Brazil,
Russia, India, and China are obtained from the Yahoo finance and South Africa
prices are obtained from the Wallstreet Journal site. The asset returns are
calculated from the closing prices using formula =log
×100
Where and  are the closing prices at period (t) and (t-1) respectively.
4. Methodology
Here we present some testing procedures of weak form efficiency of stock
markets. In this study, we employ parametric tests, namely Lo and Mackinlay
(1988) individual variance ratio test, Chow-Denning (1993) multiple variance ratio
test for testing the linear dependence and BDSL (1996) which is a non-parametric
test for nonlinear dependence in the return series of BRICS stock markets.
Lo and MacKinlay (1988) individual variance ratio test:
In order to test the weak form efficiency, Lo and MacKinlay (1988) proposed a
variance ratio test under the assumption of homoscedasticity i.e. constant
variance. (see, Campbell et al (1997)). The test statistic is given by
()= 
()()()
 
(0,1)(1)
Similarly, Z-statistic under heteroscedasticity assumption is computed as:
()=
()[()]
(0,1).(2)
Chow and Denning (1993) Multiple Variance Ratio Test:
The random walk hypothesis necessitates the variance ratios of all investment
horizons are equal to one and the test has to be done jointly over the time
horizons. This procedure further leads to size distortions. To overcome this issue,
Chow and Denning (1993) proposed a multiple variance ratio tests. The decision
regarding the null hypothesis is according to the maximum absolute values of the
individual variance ratio statistics of Lo and MacKinlay (1988). The variance ratio
estimates are the maximum test statistic of the Lo and MacKinlay (1988)
individual variance ratio test. Unlike the individual variance ratio test where the
standard normal critical values are used for significance, multiple variance ratio
tests uses Studentized Maximum Modulus (SMM) critical values. The test statistics
are defined as
()=|()|.(3)
()=|()|.(4)
The Romanian Economic Journal 67
Year XXII no. 72 June 2019
(),() are computed as in (1) and (2)
In which () are the different aggregation intervals for
=1,2,,. The
rejection of the null hypothesis is based on the maximum absolute value of the
individual variance ratio test statistic.
Test for Non-Linearity: BDSL (1996) test
For a random walk hypothesis, one of the required assumptions is the
(in)dependence among the return series. In order to check the linear or non-linear
independence of the stock returns, we employ Brock-Dechert-Scheinkman-
LeBaron(BDSL)test. It is a nonparametric test with the null hypothesis that the
series is independently and identically distributed against an unspecified alternative
Brock et al (1991).
The BDSL statistics, W, is given by
(,)= |(.) (,)| × 
(,)..(5)
where SN (e, T) is the standard deviation of the correlation integrals. The test is
able to locate many types of nonlinearity, nonstationarity, and deterministic chaos.
If the null hypothesis is rejected, one can say that the time series is nonlinearly
dependent.
5. Empirical Analysis
In order to examine the weak-form of market efficiency for each of the markets at
various investment horizons like 2, 5, 10, we have carried out Lo and Mackinlay
(1988) individual variance ratio tests for each return series at all time periods.
The individual variance test results are presented in Table 1.
Table 1 Results of individual variance ratio tests
Full Period
Test Statistic
Holding
Periods
BOVESPA
MICEX
SENSEX
SSE JSE
Lo-Mac
Z1 2 1.333 6.403* 5.342* 1.595 4.312*
5 1.158 3.400* 2.200* 1.713 2.179*
10 2.522* 1.340 0.929 1.837 0.327
Z2 2 0.654 2.457* 3.194* 1.098 2.458*
5 0.574 1.383 1.346 1.180 1.289
10 1.303 0.588 0.581 1.278 0.219
68 The Romanian Economic Journal
Year XXII no. 72 June 2019
Pre-Crisis Period
Test Statistic
Holding
Periods
BOVESPA
MICEX
SENSEX
SSE JSE
Lo-Mac
Z1 2 2.061* 6.329* 3.258* 0.834 4.951*
5 0.204 4.105* 1.941 1.037 4.834*
10 1.053 1.992 1.379 0.915 3.223*
Z2 2 0.973 2.528* 1.987* 0.629 2.490*
5 0.100 1.793 1.276 0.771 2.665*
10 0.548 0.967 0.965 0.693 1.964
Crisis Period
Test Statistic
Holding
Periods
BOVESPA
MICEX
SENSEX SSE JSE
Lo-Mac
Z1 2 0.471 0.086 2.145* 0.077 1.064
5 1.791 0.177 0.206 0.136 0.566
10 1.946 0.282 0.316 0.210 1.132
Z2 2 0.367 0.070 1.845 0.068 0.914
5 1.231 0.152 0.170 0.127 0.462
10 1.307 0.251 0.257 0.197 0.900
Post Crisis Period
Test Statistic
Holding
Periods
BOVESPA
MICEX
SENSEX SSE JSE
Lo-Mac
Z1 2 0.587 0.021 2.811* 1.902 0.493
5 0.938 0.472 1.403 1.460 2.130*
10 1.488 1.028 0.119 1.590 2.885*
Z2 2 0.499 0.014 2.491* 1.194 0.420
5 0.779 0.336 1.203 0.889 1.781
10 1.266 0.770 0.101 0.981 2.423*
*denotes the level of significance at 5% level
The empirical results under the assumption of homoscedasticity (Z1) show that in
the full period Brazil at lag 10, Russia at 2 and 5 day lag, India and South Africa at
lag 2 reject the weak form efficiency. In the pre-crisis period Brazil at 2 days, Russia
and South Africa in 2, 5 and 10 day lag, India in 2 days lag show that the markets are
not efficient. During the crisis period, India in 2 days lag, in post-crisis period India
at 2 lags and South Africa at 5 and 10 days lag show that the markets are inefficient.
Results of heteroscedasticity (Z2) show that Russia, India, and South Africa markets
at 2 lags in full period while in the pre-crisis period, Russia, Indian markets at 2 days
The Romanian Economic Journal 69
Year XXII no. 72 June 2019
lag, the South African markets at all lags reject the weak form efficiency. During the
pre-crisis period Russia and India in 2 days, South Africa at all lags while in post-
crisis period India at two lags and South Africa in 10 lags show that the markets are
inefficient. Both homoscedasticity (Z1) and heteroscedasticity (Z2) results confirm
that China markets are efficient in all time periods of the study. From this, we
observe that the results are mixed at various holding periods which may be due to
the limitation of size distortions. To overcome this we employed a multiple variance
ratio tests proposed by Chow and Denning (1993). The results of the test are
presented in Table 2. Here we give the maximum homoscedastic (Z1) and
heteroscedastic (Z2) robust test statistics of LMVR test.
Table 2 Results of Multiple variance ratio tests
Full Period
Test Statistic
Holding
Periods BOVESPA MICEX SENSEX
SSE JSE
Chow-
Denning
CD1 2,5,10 2.522* 6.403* 5.342* 1.837 4.312*
CD2 2,5,10 1.303 2.457 3.194* 1.278 2.458
Pre-Crisis
Test Statistic
Holding
Periods BOVESPA MICEX SENSEX
SSE JSE
Chow-
Denning
CD1 2,5,10 2.061 6.329* 3.258* 1.037 4.951*
CD2 2,5,10 0.973 2.528* 1.987 0.771 2.490*
Crisis
Test Statistic
Holding
Periods BOVESPA MICEX SENSEX
SSE JSE
Chow
Denning
CD1 2,5,10 1.946 0.580 2.114 0.210 1.132
CD2 2,5,10 1.307 0.251 1.845 0.197 0.914
Post Crisis
Test Statistic
Holding
Periods BOVESPA MICEX SENSEX
SSE JSE
Chow-
Denning
CD1 2,5,10 1.488 0.936 2.811* 1.902 2.885*
CD2 2,5,10 1.266 0.770 2.491* 1.194 2.423
*denotes the level of significance at 5% level
From table 2, we see that under the assumption of homoscedasticity (CD1) Brazil,
Russia, India, and South Africa markets are inefficient in full period that is from
25th September 1997 to 31st March 2018. Also, Russia, India, and South Africa
during the pre-crisis period, while the post-crisis period India and South Africa
70 The Romanian Economic Journal
Year XXII no. 72 June 2019
markets are not efficient. Under the heteroscedasticity (CD2) assumption, the only
Indian market is found to be inefficient. In the pre-crisis period, Russia and South
Africa, during the post-crisis period India rejects the weak form market efficiency.
However, in both tests (CD1 and CD2), China markets found to be efficient in all
time periods of the study as its calculated values are less than the critical value
(2.49). It is important to note that all the markets are found to be efficient during
the crisis period. Here we can notice that the individual variance ratio tests (Z1
and Z2) and heteroscedasticity (CD2) of multiple variance ratio test reject the
market efficiency for the same markets, this may be due to the robustness of the
Chow - Denning test under the assumption of heteroscedasticity (CD2). From
table 1 and table 2 it is also observed that during the crisis period all the markets
are efficient from Lo and MacKinlay and Chow-Denning tests.
As the variance ratio tests can only indicate whether the series is linearly
(in)dependent, thus it is necessary to test for nonlinear (in)dependence in the
series. In order to test the nonlinear (in) dependence in the series, we applied the
BDSL non-linearity tests at embedding dimensions (M = 2, 4, 8, 10) and at epsilon
values (E = 0.5, 1, 1.5, 2). The results of the BDSL test on the return series are
reported along with the p-values in parentheses are reported in table 3.
Table 3 Results of BDSL test
Full
Period Stock Return
M=2, E=0.5 M=4, E=1 M=8, E= 1.5 M=10, E= 2
BOVESPA 7.76(0.00) 16.44(0.00) 31.47(0.00) 38.78(0.00)
MICEX
19.46(0.00)
37.83(0.00)
93.17(0.00)
113.04(0.00)
SENSEX 15.7(0.00) 27.93(0.00) 50.27(0.00) 52.25(0.00)
SSE 9.19(0.00) 20.6(0.00) 37.39(0.00) 40.27(0.00)
JSE 12.59(0.00) 23.75(0.00) 37.34(0.00) 38.25(0.00)
Pre Crisis
BOVESPA 8.50(0.00) 13.12(0.00) 20.54(0.00) 24.45(0.00)
MICEX 13.27(0.00) 24.86(0.00) 64.16(0.00) 89.28(0.00)
SENSEX
11.42
(0.00)
18.00
(0.00)
29.53
(0.00)
32.08
(0.00)
SSE
4.88
(0.00)
11.05
(0.00)
18.19
(0.00)
20.20
(0.00)
JSE 6.77(0.00) 12.36(0.00) 19.19(0.00) 20.42(0.00)
Crisis BOVESPA 0.76(0.41) 3.31(0.00) 4.99(0.00) 6.83(0.00)
MICEX 5.18(0.00) 10.14(0.00) 34.69(0.00) 56.57(0.00)
SENSEX
3.25
(0.00)
7.24
(0.00)
38.20
(0.00)
38.09
(0.00)
SSE 0.86(0.38) 2.99(0.00) 8.88(0.00) 7.09(0.00)
JSE 2.74(0.00) 10.89(0.00) 21.47(0.00) 28.07(0.00)
Post
Crisis
BOVESPA 0.70(0.48) 5.08(0.00) 9.62(0.00) 11.76(0.00)
The Romanian Economic Journal 71
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Full
Period Stock Return
M=2, E=0.5 M=4, E=1 M=8, E= 1.5 M=10, E= 2
MICEX 5.87(0.00) 12.64(0.00) 21.18(0.00) 22.80(0.00)
SENSEX 3.55(0.00) 8.01(0.00) 13.87(0.00) 14.42(0.00)
SSE
4.86(0.00)
10.93(0.00)
18.52(0.00)
19.64(0.00)
JSE 5.73(0.00) 11.12(0.00) 16.15(0.00) 16.31(0.00)
(P values are in parenthesis)
The results of BDSL test show that the stock returns of all the BRICS markets
have nonlinear and chaotic behavior for all time periods of study except for Brazil
and China during crisis period at lag 2 and only in Brazil in post-crisis at lag 2.
From the above empirical analysis, the variance ratio tests, namely Lo and
MacKinlay and Chow-Denning test show that market efficiency is time-varying
and there is no uniformity in the results. However, BDSL test revealed that there
is a nonlinear dependence in all the periods of the BRICS markets. This confirms
that the markets are markets inefficient during the study period.
6. Summary & Conclusions
Market efficiency is one of the key concepts of financial economics studies. In this
study, the efficiency was tested for the five important emerging market economies,
namely Brazil, Russia, India, China and South Africa (BRICS) by considering the
daily data from 25th September 1997 to 31st March 2018. Further, the data is
subdivided into three periods; pre-crisis, during a crisis and post-crisis periods to
understand the behavior of these markets in all these periods with respect to
efficiency. We employed the parametric (Lao and MacKinlay (1988) individual
variance ratio and Chow and Denning (1993) multiple variance ratio test) and
non-parametric (BDSL (1996)) tests to verify the linear and nonlinear (in)
dependence in the return series.
The empirical analysis of variance ratio tests, Lao and MacKinlay (1988) and
Chow and Denning (1993) showed that market efficiency is varying during
a choice of time periods of the study. However, the non-parametric BDSL (1996)
test revealed that markets are inefficient in all the time periods. Thus, we can
conclude that the BRICS stock markets are inefficient during this period
of the study.
Acknowledgments The authors are grateful to the editors and the anonymous
referee for their valuable suggestions in improving this paper.
72 The Romanian Economic Journal
Year XXII no. 72 June 2019
References
Abraham, A., Seyyed, F. J. & Aisakran, S.A. (2002). Testing the Random Walk
Behavior and Efficiency of the Gulf Stock Markets. The Financial Review,
37, 469-480.
Ahmed, K. M., Ashraf, A. & Ahmed, A. (2006). Testing Weak Form of Efficiency
for Indian Stock Markets. Economic and Political Weekly, 7, 46-56.
Al-Khazali, O.M., Ding, D. K. & Pyun, C.S. (2007). A New Variance Ratio Test of
Random Walk in Emerging Markets: A Revisit. The Financial Review, 42,
303-317.
Amanulla, S. & Kamaiah, B. (1996). Stock Market Efficiency: A Review of Indian
Evidence. Prajnan, 24(3), 257-280.
Amanulla, S. & Kamaiah B. (1998). Indian Stock Market: Is It Informationally
Efficient? Prajnan, 25, 473-485.
Anil, K.S. & Neha. (2011). Recent Financial Crisis and Market Efficiency: An
Empirical Analysis of Indian Stock Market. Indore Management Journal,
2(4), 27-39.
Andrea Beltratti & Marianna Caccavaio (2016). Stock market efficiency in China:
Evidence from the split-share reform. The Quarterly Review of Economics and
Finance, 60, 125-137.
Areal, N. & Armada, M. (2002). The Long-Horizon Returns Behavior of the
Portuguese Stock Market. European Journal of Finance, 8, 93-122.
Asma Mobarek & Angelo Fiorante (2014). The prospects of BRIC countries:
Testing weak-form market efficiency. Research in International Business and
Finance, 30, 217-232.
Assaf, A. & HusniCharif. (2017). Market Efficiency in the MENA Equity Markets:
Evidence from newly developed tests and regime change. Journal of
Reviews on Global Economics, 6, 15-32.
Aviral Kumar Tiwari, & Phouphet Kyophilavong. (2014). New Evidence from the
random walk hypothesis for BRICS stock indices: a Wavelet unit root
test approach. Economic Modelling, 43, 38-41.
Ayadi, O.F. & Pyun, C.S. (1994). An Application of Variance Ratio Test to the
Korean Securities Market. Journal of Banking and Finance, 18, 643-658.
Aymen Ben Rejeb & Adel Boughrara. (2014). Financial liberalization and
Emerging stock market efficiency: An empirical analysis of structural
changes, Macroeconomics and Finance in Emerging Market Economics, 7(2),
230-245.
Bachelier, L. (1900). Theory of Speculation, A Thesis submitted to the Faculty of
the Academy of Paris on March 29, 1900 and also In Cootner, P H
The Romanian Economic Journal 73
Year XXII no. 72 June 2019
(1967), (Eds), The random walk character of stock market prices, Cambridge:
Mass, MIT Press.
Barua, S.K. (1981). The Short Run Price Behaviour of Securities: Some Evidence
on Efficiency of Indian Capital Market. Vikalpa, 16(2), 93-100.
Barua, S.K., Raghunathan, V. & Jayanth, R.V. (1994). Research on the Indian
Capital Market: A Review, Vikalpa, 19(1), 15-31.
Brock, w.A., D.A. Hsiech & LeBaron,B.(1991). Nonlinear Dynamics, Chaos and
instability: Statistical Theory and economic Evidence, Cambridge, MA,
MIT Press.
Brock, W.A., Dechert, W., Scheinkman, J. A. & LeBaron, B. (1996). A Test For
Independence Based On The Correlation Dimension, Econometric Reviews,
15, 197-235.
Buguk, C., & Brorsen, W.D. (2003). Testing Weak-Form Market Efficiency:
Evidence from the Istanbul Stock Exchange. International Review of
Financial Analysis, 12(5), 579-590.
Camelia, O. (2012). Testing Informational Efficiency: The case of U.E. and BRIC
emergent markets. Studies in Business and Economics, 7(3), 94-112.
Campbell, Y.J., Lo, W.A. & MacKinlay A. C. (1997). The econometrics of
financial markets. New Jersey: Princeton University Press, (Chapter2).
Capobianco, H.M.P., Cister, A.M. & Maceio, B.F. (2002). Market Efficiency In
Brazilian Stock Market: A Weak Form Evidence, Data Mining III,
685-694.
Charles, A. & Dame, O. (2008). The Random Walk Hypothesis for Chinese Stock
Markets: Evidence from Variance Ratio Tests. Econ Systems, 33(2),
117-126.
Chaudhuri, K. & Wu, Y. (2004). Mean-Reversion in Stock Prices: Evidence from
Emerging Markets. Managerial Finance, 30, 22-37.
Chawla, D., Mohanty, P.K. & Bhardwaj, S. (2006). Random Walk Hypothesis and
Integration among the Indian Stock Market Vis-A-Vis Some Developed
Markets. Prajnan, 34(2), 113-127.
Chen, W.W. & Deo, R.S. (2006). The Variance Ratio Statistic at Large Horizons,
Econometric Theory, 22(2), 206-234.
Choi, I. (1999). Testing the Random Walk Hypothesis for Real Exchange Rates,
Journal of Applied Econometrics, 14(3), 293-308.
Chow, K.V. & Denning, K.C. (1993). A Simple Multiple Variance Ratio Test,
Journal of Econometrics, 58(3), 385-401.
Darrat, A.F. & Zhong, M. (2000). On Estimating the Random Walk Hypothesis:
A Model Comparison Approach. The Financial Review, 35, 105-124.
74 The Romanian Economic Journal
Year XXII no. 72 June 2019
Daniel O. Cajueiro & Benjamin M. Tabak. (2006). The Long-Range Dependence
Phenomena In Asset Returns: The Chinese Case, Applied Economics
Letters, 13(2), 131-133.
David McMillan, & Pako Thupayagale. (2008). Efficiency of the South African
Equity Market, Applied Financial Economics Letters, 4(5), 327-330.
Fama, E.F. (1965). The behavior of Stock market prices. Journal of Business, 38,
34-105.
Fama, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical
Work. The Journal of Finance, 25(2), 383-417.
Fifield, S.G.M. & Jetty, J. (2008). Further Evidence on the Efficiency of the
Chinese Stock Markets: A Note. Research in International Business and
Finance, 22,351-361.
Geoffrey Ngene, Kenneth A Tah & Ali F Darrat. (2017). The random-walk
hypothesis revisited: new evidence on multiple structural breaks in
emerging markets, Macroeconomics and Finance in Emerging Market Economics,
10(1), 88-106.
Gourishankar, S.H. & Kamaiah, B. (2010). Some Further Evidence On the
Behavior of Stock Returns in India, International Journal of Economics and
Finance, 2(2), 2010.
Gourishankar, S. H. & Kamaiah, B. (2012). Variance Ratios, Structural Breaks and
Non-Random Walk Behavior in the Indian Stock Returns. Journal of
Economics and Business studies, 18, 62-82.
Gourishankar, S. H. & Jyoti Kumari (2014). Stock Returns Predictability and the
Adaptive Market Hypothesis in Emerging Markets: Evidence from India,
SpringerPlus, 3:428.
Granger, C.W.J. & Anderson, A.P. (1978). An Introduction to Bilinear Time
Series Models. Vandenhoeck and Ruprecht, Gottingen.
Grieb, T. & Reyes, M.G. (1999). Random Walk Tests for Latin American Equity
Indices and Individual Firms. Journal of Financial Research, 4, 371-383.
Gupta, R. K. (2014). Weak form efficiency of Indian stock market with special
reference to BSE. International journal of research in business Management, 2,
14-18.
Gupta, J. & Sankalp, S. (2017). The Impact of Global Financial Crisis on Market
Efficiency: An empirical analyis of Indian Stock Market. International
Journal of Economics and Finance, 9(4), 225-252.
Hoque, H.A.A.B., Kim, J.H. & Pyun, C.S. (2007). A Comparison of Variance
Ratio Tests of Random Walk: A Case of Asian Emerging Stock Markets.
International Review of Economics and Finance, 16, 488-502.
The Romanian Economic Journal 75
Year XXII no. 72 June 2019
Huber, P. (1995). Random Walks in Stock Exchanges Prices and the Vienna Stock
Exchange, Working Paper, Retrieved from
http://www.ihs.ac.at/publications/eco/es-2.pdf
Islam, S., Watanapalachaikul, S. & Clark, C. (2007). Some Tests of the Efficiency
of The Emerging Financial Markets: An Analysis Of The Thai Stock
Market. Journal of Emerging Market Finance, 6(3), 291-302.
Jennergreen, L. P. & Korsvold, P.E. (1974). Price Formation of the Norwegian
and Swedish Stock Markets: Some Random Walk Tests. The Swedish
Journal of Economics, 76, 171-185.
Jim O Neil (2001). Building Better Global Economic BRICs. Goldman Sachs Global
Economics Paper No: 66, 1-16.
Karamchandani, M. M. Mohadikar, M. S. & Jain, M. S. (2014). Stock Indices of
BRIC economies: Explored for Non Linear Dynamics and Volatility.
IOSR Journal of Economics and Finance, 2(6), 53-65.
Karemera, D. Ojah, K. & Cole, J.A. (1999). Random Walks and Market Efficiency
Tests: Evidence from Emerging Equity Markets, Review of Quantitative
Finance and Accounting, 13(2), 171-188.
Khan, W., Baker, H. & Brabec, K. (2000). Stock Price Behavior on Prague Stock
Exchange. Journal of Emerging Markets, 5(2), 39-53.
Kian-Ping Lim, Muzafar Shah Habibullah & Melvin J. Hinich (2009). The weak
form efficiency of Chinese stock markets: Thin Trading, Nonlinearity
and Episodic Serial Dependencies. Journal of Emerging Market Finance, 8(2),
133-163.
Kim, J. & Shamsuddin, A. (2008). Are Asian Stock Markets Efficient? Evidence
from new multiple variance ratio tests. Journal of Empirical Finance, 15(3),
518-532.
Laura Spierdijk & Jacob, A. B. (2012). Mean Reversion in Stock Prices:
Implications for Long-Term Investors, Discussion Paper Series 12-07,
http:// www.uu.nl/rebo/economie/discussionpapers
Lee, C.F., Chen, G.M. & Rui, O.M. (2001). Stock Returns and Volatility on
China’s Stock Markets. Journal of Financial Research, 24, 523-543.
Lim Kian Ping & Brooks, R.D. (2009). Are Chinese Stock Markets Efficient?
Further evidence from a battery of nonlinearity tests. Applied Financial
Economics, 19, 147-155.
Lima E.J.A. & Tabak, B.M. (2004). Tests of Random Walk Hypothesis for Equity
Markets: Evidences from China, Hong Kong and Singapore. Applied
Economic Letters, 11, 255-258.
Liu Xiaming, Haiyan Song & Peter Romilly. (1997). Are Chinese Stock Markets
Efficient? A Cointegration and Causality Analysis, Applied Economics
Letters, 4(8), 511-515.
76 The Romanian Economic Journal
Year XXII no. 72 June 2019
Lo, A. & Mackinlay, C. (1988). Stock Market Prices Do Not Follow Random
Walks: Evidence From A Simple Specification Test. Review of Financial
Studies, 1, 41-66.
Lock, D. (2007). The Taiwan Stock Market Does Follow A Random Walk,
Economics Bulletin, 7(3), 1-8.
Lumengo. (2012). The Evolving Efficiency of the South African Stock Exchange.
International Business and Economics Journal, 11 (9), 997-1002.
Mc Gowan, Jr., C. B. (2011). An Analysis of the Technical Efficiency of the
Russian Stock Market. International Business & Economics Research Journal
(IBER), 10(10), 31-44. https://doi.org/10.19030/iber.v10i10.5977
Mitra, S.K. (2000). Profitable Trading Opportunity in Indian Stock Market: An
Empirical Study with BSE-Sensex, Applied Finance, 6(3), 36-52.
Maria Rosa Borges (2010). Efficient Market Hypothesis in European Stock
Markets, The European Journal of Finance, 16(7), 711-726.
Mobarek, A. & Angelo. (2014). The prospects of BRIC countries:Testing weak-
form market efficiency. Research in International Business and Fincance, 30(C),
217-232.
Muskan Karamchandani, Shubhra Mohadikar & Savera Jain. (2014). Stock Indices
of BRIC economies: Explored for Non Linear Dynamics and Volatility,
IOSR Journal of Economics and Finance, 2(6), 53-65.
Natalia Abrosimova, Gishan Dissanaike & Dirk Linowsi (2002). Testing Weak-
Form Efficiency of the Russia Stock Market, EFA 2002 Berlin Meetings
Presented Paper, http://dx.doi.org/10.2139/ssrn.302287
Ojah, K. & Karemera, D. (1999). Random Walks and Market Efficiency Tests of
Latin American Emerging Equity Markets: A Revisit, The Financial Review,
34(2), 57-72.
Oprean Camelia. (2012). Testing Informational Efficiency: The case of U.E. and
BRIC Emergent Markets. Studies in Business and Economics, 7(3), 94-112.
Poshakwale, S. (2002). The Random Walk Hypothesis in the Emerging Indian
Stock Market. Journal of Business finance and Account, 29, 1275-1299.
Poterba, J. M. & summers, L.H. (1987). Mean Reversion in Stock Prices: Evidence
and Implications. NBER Working Paper Series 2343, Retrieved from
https://www.nber.org/papers/w2343.pdf
Rakesh Gupta & Parikshit, K. B. (2007). Weak Form Efficiency in Indian Stock
Markets, Journal of International Business Studies, 6(3), 57-64.
Regis Augusto Ely (2012). Returns Predictability and Stock Market Efficiency in
Brazil, Brazilian Review of Finance, 9(4), 571-584.
Richards, A. J. (1995). Comovements in National Stock Market Returns: Evidence
of Predictability but Not Cointegration. Journal of Monetary Economics, 36,
631-654.
The Romanian Economic Journal 77
Year XXII no. 72 June 2019
Robert D. Gay (2016). Effect of Macroeconomic Variables on Stock Market
Returns for four Emerging Economies: Brazil, Russia, India and China.
International Business & Economic Research Journal, 15(3), 119-126.
Said Ali & Alan H. H. (2015). The Efficiency of the Russian Stock Market: A
Revisit of the Random Walk Hypothesis, Academy of Accounting and
Financial Studies Journal, 19(1), 48-56.
Samuelson, P. (1965). Proof That Properly Anticipated Prices Fluctuate
Randomly, Industrial Management Review, 1, 41-49.
Seddighi, H. R. & Nian, W. (2004). The Chinese Stock Exchange Market:
Operations and Efficiency. Applied Financial Economics, 14(11), 785-797.
Sharma, J. L. & Kennedy, R.E. (1977). A Comparative Analysis of Stock Price
Behavior on the Bombay, London, and New York Stock Exchanges.
Journal Finance Quantitative Analysis, 12, 391-413.
Smith, G. (2007). Random Walks in Middle Eastern Stock Markets. Applied
Financial Economics. 17, 587-596.
Smith, G., Jefferis, K. & Ryoo, H. J. (2002). African Stock Markets: Multiple
Variance Ratio Tests of Random Walks. Applied Financial Economics,
12, 475-484.
Tiwari, Kumar, A., Kyophilavong & Phouphet. (2014). New evidence from the
random walk hypothesis for BRICS stock indices: a wavelet unit root test
approach. Economic Modelling, 43(c), 38-41.
Urrutia, J. L. (1995). Tests of Random Walk and Market Efficiency for Latin
American Emerging Equity Markets. Journal of Financial Restructure, 18(3),
299-309.
Yang, G. J. A., Lee, C. & Lee, C. H. (2015). Random walk in MIST. Journal of
Asia-Pacific Business, 16(2), 92-104.
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