financial liberalization and emerging stock market efficiency: an empirical analysis of structural...

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Financial liberalization and emergingstock market efficiency: an empiricalanalysis of structural changesAymen Ben Rejebab & Adel Boughrarac

a Faculty of Economics and Management of Mahdia, University ofMonastir, Tunisiab Laboratory of Management of Innovation and SustainableDevelopment (LAMIDED), University of Sousse, Tunisiac Research Laboratory for Economy, Management and QuantitativeFinance (LaREMFiQ), University of Sousse, TunisiaPublished online: 03 Mar 2014.

To cite this article: Aymen Ben Rejeb & Adel Boughrara (2014): Financial liberalization andemerging stock market efficiency: an empirical analysis of structural changes, Macroeconomics andFinance in Emerging Market Economies, DOI: 10.1080/17520843.2014.889186

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Financial liberalization and emerging stock market efficiency: anempirical analysis of structural changes

Aymen Ben Rejeba,b* and Adel Boughrarac

aFaculty of Economics and Management of Mahdia, University of Monastir, Tunisia; bLaboratory ofManagement of Innovation and Sustainable Development (LAMIDED), University of Sousse,

Tunisia; cResearch Laboratory for Economy, Management and Quantitative Finance (LaREMFiQ),University of Sousse, Tunisia

(Received 8 March 2013; accepted 26 January 2014)

This article aims to determine the impact of financial liberalization on the informa-tional efficiency in emerging stock markets. For this purpose, we estimate a time-varying parameter model combined with structural change technique for 13 emergingeconomies from January 1986 to December 2008. Empirical results show a greaterefficiency in recent years. They also show that the structural breaks detected in theemerging market predictability indices coincide with the official liberalization dates,and with their alternative events. These findings corroborate those of the relatedliterature regarding how emerging markets react to the adoption of the financialliberalization process.

Keywords: informational efficiency; financial liberalization; emerging markets;Kalman filter; structural breakpoint

JEL classification: G14; G15; G18

1. Introduction

According to Fama (1970, 1991), an efficient market is a market where prices fully reflectall available information. This has strict implications on stock market analysis andportfolio management. In efficient markets, abnormal profits are non-existent; however,investors are able to easily determine the risk and the return of their investments becausethere are no overvalued and/or undervalued assets (Fontaine and Nguyen 2006). Inaddition, it is believed that in an efficient market, stock’s current prices accurately reflectwhat investors know about the stock. Therefore, an efficient market helps allocatingefficiently the most profitable investments, and thereby enhances economic growth.

It should be noted, however, that emerging markets are characterized by a low qualityof information disclosure, a weak trading volume and an inadequate accounting regula-tions, which result in a weak price time dependency and lead to the rejection of the weak-form efficiency hypothesis. For these reasons, financial literature focuses on testing theweak-form efficiency in emerging markets. Moreover, it is important to verify whetherfuture price movements of financial assets can be predicted from past price movements.However, so far there is no consensus on the validity of the weak-form efficiencyhypothesis in emerging markets. Some studies conclude that emerging market returnsare not autocorrelated, which reject the weak-form efficiency hypothesis (see among

*Corresponding author. Email: [email protected]

Macroeconomics and Finance in Emerging Market Economies, 2014http://dx.doi.org/10.1080/17520843.2014.889186

© 2014 Taylor & Francis

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others, Lo and MacKinlay 1988; Kim and Singal 2000; Füss 2005), while others empha-size the invalidity of the weak-form efficiency hypothesis (see for instance, Dockery andVergari 1997; Emerson et al. 1997; Zalewska-Mitura and Hall 1999; Rockinger and Urga2001; Harrison and Paton 2005).

Since emerging stock market liberalization in the mid-1980s, these markets havebecome more integrated into the global markets. They tried to attract internationalinvestors and benefit from their experiences to diversify the portfolio risk, to increasethe level of liquidity, to enhance informational transparency and consequently the degreeof efficiency. However, despite their increased integration, previous attempts to test therelationship between emerging counties’ stock market liberalization and the informationalefficiency have remained still highly debatable, with no consensus given the empiricalresults divergence. Such divergence may be due to the fact that the existing studies haveexamined the effects of financial liberalization on the informational efficiency by compar-ing the measurements of the market efficiency over two sub-periods (pre-liberalizationand post-liberalization). We believe that this methodology is inappropriate and generallyleads to spurious results. Furthermore, we believe that the econometric methods used inprevious studies in order to determine the degree of efficiency are not adapted to emergingmarket specificities. For instance, the use of econometric models assuming parameterstability does not allow capturing the degree of efficiency given that the latter may varydepending on the structural changes affecting the prerequisites of efficiency.

This article proposes an original empirical framework that allows determining moreaccurately the potential impact of financial liberalization on the informational efficiency.In line with Jefferis and Smith (2005); Fontaine and Nguyen (2006) and Arouri andNguyen (2010), we take into consideration the evolutionary characteristics over time ofemerging markets. To test the impact of financial liberalization on informational effi-ciency, the Bai and Perron (1998, 2003) technique based on structural break identificationis used. More specifically, the empirical strategy this article carries out is based onidentifying whether, or not, structural breaks are present at the time of, or near, the initialliberalization date and its alternative events. If so, structural break presence could beinterpreted as a significant impact of market reforms on the return variability.

The remainder of the article is organized as follows. The second section provides aliterature review of the linkage between financial liberalization and informational effi-ciency. The third section presents the econometric methodology carried out to determinestock return predictability indices as well as the empirical model that identifies thepotential effects of financial liberalization on the informational efficiency. The fourthsection describes the data used. The fifth section summarizes and discuses the mainempirical results. Finally, the sixth section concludes.

2. Literature review

By and large, the empirical literature on weak-form efficiency can be split into twostrands. The first strand tends to reject the weak-form efficiency hypothesis in emergingmarkets after financial liberalization. For instance, Groenewold and Ariff (1998) test theweak-form efficiency hypothesis using a sample of both developed and emerging marketsusing regression/autocorrelation techniques and unit root tests. They explain changes inthe degree of efficiency by financial deregulation. They come to the conclusion thatemerging markets have not become more efficient after liberalization. In the same vein,Kawakatsu and Morey (1999) examine the weak-form efficiency in stock markets of nineemerging economies before and after liberalization. More specifically, the authors check

2 A. Ben Rejeb and A. Boughrara

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whether past returns help predicting future returns. They use a first-order autoregressivemodel and perform a unit root test in the price process. They come to the conclusion thatliberalization does not contribute to improve significantly the stock market efficiency.Their conclusion suggests that many markets are efficient before the effective liberal-ization. Likewise, Basu et al. (2000) assess the predictability performances of returnsbefore and after financial liberalization in a sample of emerging countries using Ljung andBox (1978) and Lo and MacKinlay (1989) tests. Their results provide little support tomore efficiency in open markets. Laopodis (2003, 2004) investigate whether financialliberalization in emerging economies has impacted stock market indices evolution. Theauthor uses data on the Athens Stock Exchange and performs structural change tests aswell as efficiency tests in order to find out whether the Greek stock market was weaklyefficient before liberalization or not. Laopodis (2003, 2004)’s findings corroborate thoseof Maghyereh and Omet (2002) who conclude, in the case of Amman Stock Exchange,that financial liberalization has no significant effect on market efficiency.

The second strand of the empirical literature tends to support the weak-form efficiencyhypothesis in emerging markets. Examples are Kim and Singal (2000) and Füss (2005). Forinstance, Kim and Singal (2000) use the Lo andMacKinlay (1988) variance ratio test to assesswhether or not asset prices, from 14 emerging markets, follow a random walk after theireffective liberalization. They conclude that stock prices are less dependent after liberalization,which reflects an efficiency improvement. Recently, Füss (2005) tests the random walk andthe efficiency hypotheses in the presence of an increase in the integration degree of sevenAsian countries. He uses tests such as the Lo and MacKinlay (1988) variance ratio, themultiple variance tests and the Chow and Denning (1993) ratio. The weak-form of efficiencyis also checked directly, using a non-parametric test. The author reaches the same conclusionas Kim and Singal (2000).

To sum up, there is no consensus on how best to test the effects of financial liberal-ization on the efficiency in emerging markets. However, the empirical studies that dealwith this issue test two types of hypotheses, namely the serial dependence of returns andthe random walk over two sub-periods: pre-financial liberalization period and post-financial liberalization period. The effect of financial liberalization is therefore appraisedby comparing the empirical results over the two sub-periods, which often leads toinconclusive or contradictory results.

3. Empirical methodology

3.1. A state-space model for time-varying predictability

The weak-form efficiency hypothesis necessitates the instantaneous incorporation infinancial asset prices of the available information contained in past prices, which impliesthat past returns should have no predictive power in the dynamics of future returns. Inpractice, the weak-form efficiency can be tested using an autoregressive stochastic processof order one, linking the current return to the past one.1 Researchers check whether futurereturns cannot be predicted from past returns, which is accomplished by testing whetherthe autoregressive coefficient is statistically significant or not. If so, this would indicatethat the weak-form efficiency hypothesis is valid.

Unlike traditional methods, this article focuses on the evolution of the degree ofefficiency through time. The idea behind this intuitive approach is based on the notionthat the rapid maturation of emerging markets subsequent to the liberalization of stockmarkets includes major changes in the structure of the markets, an increasing

Macroeconomics and Finance in Emerging Market Economies 3

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sophistication of market participants and a greater availability of the information. Thesechanges likely induce the level of market efficiency to vary through time (Fontaine andNguyen 2006; Arouri and Nguyen 2010). Such feature (changes over time), if it exists,cannot be accounted for only by dynamic modelling of returns. We rather use the time-varying technique proposed by Zalewska-Mitura and Hall (1999) and extended byFontaine and Nguyen (2003) in which the stock returns is allowed to vary over timedepending on market conditions. The weak-form efficiency can therefore be tested byusing the following state-space four-equation model:

Ri;t ¼ βð0Þi;t þ βð1Þi;t Ri;t�1 þ Ui;t Ui;t , Nð0; hi;tÞ (1)

Ui;t ¼ hi;t zi;t (2)

hi;t ¼ αð0Þi þ αð1Þi U2i;t�1 þ αð2Þi hi;t�1 (3)

βðkÞi;t ¼ βðkÞi;t�1 þ ηðkÞi;t ; k ¼ 0; 1 ηðkÞi;t , N 0; σ2ðkÞi

� �(4)

where Ri,t represents the stock market returns observed at time t and computed asRi;t ¼ ln Pi;t=Pi;t�1

� �and the parameters βð0Þi;t and βð1Þi;t measure, respectively, for country i,

the long-term trend and the potential serial dependency of stock market returns at time t. htrepresents the conditional variance of the measurement equation residuals ðUi;tÞ, which issupposed to be generated by the GARCH(1,1) specification suggested by Bollerslev

(1986). zi;t and ηðkÞi;t are random noises normally distributed with a mean of 0 and respec-

tive variances of 1 and V ðkÞi . Equation (1) is the space equation whereas Equations (3) and

(4) are the state equations. Equation (3) describes the conditional variance residualbehaviour, and Equation (4) describes the behaviour of βðkÞi;t . They are assumed to varyover time as described by the state vector. The state-space specification is appealing sinceit assumes that hidden factors are function of the underlying market fundamentals thatgovern the stock market price formation process (Arouri and Nguyen 2010).

The estimation of the model Equation (1)–Equation (4) requires the application of anoptimal algorithm, namely the Kalman filter, which recursively delivers the optimal

estimator of the system’s current states βðkÞi;t ; k ¼ 0; 1n o

depending on the available

information at that time by having recourse to a two-step procedure. First, the expectationsof the unobserved state vector are calculated based on the previously available informa-tion. Second, the state vector is updated when a new observation becomes available. Theimplementation of the Kalman filter assumes that innovations in Equation (1) are ortho-

gonal to those in Equation (4), i.e. Cov ðUi;t; ηðkÞi;t Þ ¼ 0. In order for the weak-form

efficiency hypothesis to be corroborated, the estimated values of βð1Þi;t should be eitherequal to ‘zero’ or statistically insignificant. The estimation of the other unknown para-

meters Vki;t; αðjÞi ; j ¼ 0; 1; 2

n orequires the construction of a log-likelihood function

based on the Kalman gain under the normality assumption (Harvey 1995). Finally, theestimation of the model is carried out using the quasi-maximum likelihood method2

introduced by Bollerslev and Wooldridge (1992), which provides asymptotic and robustestimates even though the conditional returns are not normally distributed. It should be


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noted that this model has been used in many studies to evaluate the weak-form informa-tional efficiency in emerging markets; examples are Zalewska-Mitura and Hall (1999);Jefferis and Smith (2005); Fontaine and Nguyen (2006) and Arouri and Nguyen (2010).

3.2. Test of structural change: Bai–Perron’s test

To test the effect of financial liberalization on the evolution of a weak informationalefficiency, we adopt the technique of Bai and Perron (1998, 2003) which is based ondetermining the dates of structural breaks. Our empirical strategy is based on comparingthe occurrence dates of financial liberalization with the structural breakpoints identified inthe time-varying predictability indices. Using Monte Carlo experiments, Bai and Perron(2006) find that the Bai and Perron (1998)’s methods are powerful enough to detectstructural breaks. We consider the following regression model with m breaks and m + 1regimes.

βð1Þi;t ¼ λ0 þ λ1 βð1Þi;t�1 þ εi;t (5)

βð1Þi;t is the estimated return predictability index in period t. If there are m multiple

structural breaks (T1, …, Tm) in the time path of βð1Þi;t , the problem of dating structuralbreaks consists of finding the breakpoints for various regimes (by convention T0 = 0 andTm+1 = T). Bai and Perron (1998, 2003) explicitly treat these structural breakpoints asunknown, and estimates of the breakpoints are computed using the ordinary least-squaresmethod (OLS). Indeed, Equation (5) is estimated by OLS for each Tm. The breakpointestimations are generated by minimizing the sum of squared residuals and are given by:

ðT̂1; :::; T̂mÞ ¼ arg minT1;:::;TM ST ðT1; :::; TmÞ (6)

In Equation (6), ST is the sum of squared residuals issued from the estimation of mregressions. The selection procedure of structural breaks is based on the BayesianInformation Criteria (BIC). To conduct this analysis, Bai and Perron (2006) imposesome restrictions on the possible values of break dates. In particular, each break datemust be asymptotically distinct and bounded by the borders of the sample. To thispurpose, they assume different thresholds (trimming parameters) for the estimation oftheir model [ε ¼ ð0:25; 0:15; 0:10; 0:05Þ], with ε ¼ h=T , where T is the sample size and his the minimal permissible length of a segment. We retain the threshold of 5% in thisarticle following Bai and Perron (2006) who recommend not using a trimming parameterbelow 5% when taking into account the heteroscedasticity and the serial correlation.

4. Data

Throughout this article, we use monthly data of a sample of 13 emerging countries. Thechoice of these countries is guided by data availability. Market data are extracted from theDatastream database. They are expressed in US dollars and they cover the period fromJanuary 1986 to December 2008. They include the S&P/IFCG index for 13 emergingcountries, namely Argentina, Brazil, Chile, Colombia, South Korea, India, Jordan,Pakistan, the Philippines, Malaysia, Mexico, Thailand and Venezuela.

Table 1 reports the descriptive statistics of monthly returns. We note that they areglobally similar to the findings of previous studies. First, market returns are significantly


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Table

1.Basic

statisticsof

stockmarketmon

thly

returns.

Mean

Stand

arddeviation

Skewness

Kurtosis

Jarque–B

era

ADFstatistics

Q(6)

Q(12)

ARCH

(6)

ARCH

(12)

Argentin

a0.93

616

.526

−0.03

816

.081

1968

.041

**−1

8.61

0**

14.489

19.876

43.117

**50

.943

**Brazil

0.61

615

.828

−0.67

56.47

215

9.67

9**

−16.99

9**

3.56

011.756

7.60

432

.744

**Chile

1.33

77.22

3−0.26

84.26

121

.596

**−1

3.00

5**

16.865

*23

.866

8.27

818

.58

Colom

bia

1.32

88.76

70.18

44.68

334

.172

**−11.565

**32

.788

**38

.162

**25

.910

**27

.252

**India

0.56

98.91

0−0.07

03.25

10.95

8−1

4.99

6**

8.32

110

.785

15.294

*19

.746

Jordan

0.47

35.96

01.12

010

.198

653.70

3**

−6.46

8**

19.254

*23

.329

32.711**

+35

.360

**Malaysia

0.26

29.05

4−0.25

47.30

921

6.51

5**

−9.31

5**

19.099

**40

.857

**51

.081

**69

.993

**Mexico

1.38

211.706

−2.46

318

.641

3092

.773

**−11.418

**33

.778

**38

.458

**62

.181

**62

.150

**Pakistan

0.38

69.63

6−0.21

75.88

898

.147

**−1

5.38

3**

5.50

510

.792

28.240

**35

.815

**Philip

pines

0.89

39.71

50.09

55.45

869

.898

**−1

2.27

1**

26.144

**36

.564

**12

.227

21.617

*Sou

thKorea

0.64

910

.667

0.18

65.81

892

.929

**−1

5.65

6**

6.05

59.44

453

.687

**65

.521

**Thailand

0.43

011.176

−0.47

75.10

461

.411**

−15.36

5**

13.636

36.357

**36

.052

**43

.047

**Venezuela

0.35

613

.644

−0.96

77.72

029

9.26

9**

−17.66

9**

4.61

99.19

36.86

98.74

7

Notes:T

hetablepresentsbasicstatisticsof

monthly

returns.Colum

ns1–5arereserved

tothemean(%

),thestandard

deviation(%

),theskew

ness,the

kurtosisandtheJarque

andBera

norm

ality

teststatistics.Q(6)andQ(12)

arestatisticsof

theLjung–B

oxautocorrelationtestappliedon

returnswith

lags

between6and12

.ARCH(6)andARCH(12)

arethestatistics

oftheconditionalheteroscedasticity

testproposed

byEngle(198

2),u

sing

theresidualsof

thefirst-orderautoregressive

model.A

DFisthestatisticsof

theADFunitroot

testproposed

byDickeyandFuller(198

1).The

ADF

test

iscond

uctedwith

outtim

etrendor

constant.*and**

denote

that

thenu

llhy

pothesis

oftests(normality,no

n-stationarity,no

n-autocorrelation,

homogeneity)arerejected

at,respectiv

ely,

5%and1%

levels.The

studyperiod

isfrom

January1986

toDecem

ber2008.


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departed from the normality hypothesis according to the Jarque–Bera test. Second, theanalysis of stationarity using the Augmented Dickey–Fuller (ADF) unit root test clearlyshows that the distribution of market returns is stationary at the 1% level, since thecalculated ADF values are strictly below the critical threshold. Finally, the Engle’s (1982)test for conditional heteroscedasticity rejects the null hypothesis of no ARCH effect inmonthly returns which lends support to the use of GARCH specification.

5. Empirical results

5.1. Evolution of the weak-form efficiency

The estimation results of the state-space model (time-varying coefficient model) arereported in Table 2. They show that the mean of coefficients βi;t is generally very closeto zero, which means that past returns do not much contribute to predict future returns.Consequently, one may conclude to the independence between past prices and futureprices.

A glance at the coefficients βð0Þi;t , which represents the constant term in Equation(1), reveals that the average values of these coefficients, for all countries in the sample,are near to zero (or statistically insignificant) and fall in the confidence interval[0.377,1.945]. This finding suggests a low level of return predictability related toother potentials, such as macroeconomic effects, political events and external shocks(Arouri and Nguyen 2010). Then, a close inspection of the coefficients βð1Þi;t , whosevariations inform about the time-varying predictability (autocorrelation) levels in stockreturns, indicate that their averages are not very different across markets and stand, onaverage, around the 11% level. This finding lends support to the hypothesis of serialindependence between past and future returns for all countries, except for Chile,Colombia and the Philippines whose recorded coefficients remain very high, indicatingthereby that past returns predict about 17%, 39% and 22% of the current evolution ofreturns, respectively.

Finally, regarding the global significance of the two coefficients βð0Þi;t and βð1Þi;t , onenotice a relative stability over time, given the low estimated values of the innovationsvariance issued from the state vector. Moreover, the GARCH(1,1) model seems to beperforming to explain the variations of emerging stock market returns seeing its ability todetect the leptokurtic behaviour and the conditional heteroscedasticity in the returns,except for Venezuela. Indeed, the parameters of the conditional variance equation arepositive and statistically significant at 1% level; they also satisfy the theoretical stabilityconditions, namely the coefficients associated with the conditional state equation are non-

negative. Furthermore, since the risk premium as measured by αð1Þi þ αð2Þi

� �is superior

to 0.9, the persistence of the conditional volatility is fulfilled for the majority of stockmarkets, except for Argentina, Brazil, Chile and Malaysia.

To test the weak-form efficiency hypothesis before and after financial liberaliza-tion, it seems important to depict the dynamics of the time-varying predictabilityindices along with 95% confidence intervals, while taking into account the presenceof the official dates of financial liberalization provided by Bekaert and Harvey(2000).3 This permits to test the immediate impact of financial liberalization onstock return predictability. Simultaneously, we draw with the official dates an areaaround to capture the impact of other reforms4 which have been implemented before orafter the official dates. Then, we take a year before the official date of liberalizationand a year after.


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Table

2.Estim

ationresults

from

thestate-spacemod

elwith

GARCH

effects.

Con

ditio

nalmean

equatio

nState

equatio

nsCon

ditio

nalvariance

equatio

n

βð0Þ i(%

)βð

1Þ i(%

)V

ð0Þ

iV

ð1Þ

iαð

0Þ iαð

1Þ iαð

2Þ iαð

1Þ iþαð

2Þ iLikelihoo

dvalue

Argentin

a1.82

711.112

0.00

00.00

00.00

3**

0.47

0**

0.52

0**

0.99

014

6.52

3(0.031

)(0.097

)(0.001

)(0.010

)(0.000

)(0.005

)(0.000

)Brazil

0.37

76.45

60.00

00.00

00.00

2**

0.44

4**

0.51

3**

0.95

723

8.25

9(0.018

)(0.135

)(0.000

)(0.006

)(0.000

)(0.030

)(0.000

)Chile

1.35

917

.170

0.00

00.02

6*0.00

1**

0.44

0**

0.50

0**

0.94

039

4.94

5(0.014

)(0.156

)(0.000

)(0.012

)(0.000

)(0.048

)(0.000

)Colom

bia

0.92

238

.891

0.00

00.00

00.00

2**

0.15

3**

0.56

0**

0.71

330

7.60

4(0.007

)(0.108

)(0.001

)(0.025

)(0.000

)(0.050

)(0.000

)India

1.25

97.53

50.00

00.00

70.00

1**

0.17

6**

0.50

3**

0.67

945

5.60

8(0.020

)(0.252

)(0.000

)(0.010

)(0.000

)(0.050

)(0.000

)Jordan

0.47

17.86

4−0.00

2*0.00

00.00

0**

0.33

7**

0.511*

*0.84

857

0.91

7(0.008

)(0.101

)(0.000

)(0.017

)(0.000

)(0.000

)(0.000

)Malaysia

0.76

66.96

40.00

00.00

00.00

1**

0.40

9**

0.50

2**

0.911

312.35

2(0.006

)(0.182

)(0.000

)(0.008

)(0.000

)(0.050

)(0.000

)Mexico

1.94

511.850

0.00

0−0.01

40.00

3**

0.29

5**

0.50

8**

0.80

331

4.62

4(0.010

)(0.151

)(0.001

)(0.011)

(0.000

)(0.067

)(0.000

)Pakistan

0.45

16.20

40.00

0−0.02

90.00

2**

0.23

9**

0.50

4**

0.74

327

2.29

3(0.003

)(0.183

)(0.001

)(0.027

)(0.000

)(0.072

)(0.000

)Philip

pines

1.34

822

.290

−0.00

20.00

40.00

2**

0.20

3**

0.51

7**

0.72

027

5.53

5(0.022

)(0.092

)(0.001

)(0.024

)(0.000

)(0.072

)(0.000

)Sou

thKorea

1.28

56.28

30.00

00.00

00.00

2**

0.25

9**

0.50

0**

0.75

939

6.113

(0.015

)(0.160

)(0.000

)(0.006

)(0.000

)(0.050

)(0.000

)Thailand

0.65

64.26

90.00

4*0.00

00.00

1**

0.32

9**

0.51

7**

0.84

641

2.41

8(0.025

)(0.111)

(0.001

)(0.006

)(0.000

)(0.000

)(0.000

)Venezuela

0.55

7−0.58

00.00

00.02

80.01

3**

0.16

40.07

70.24

116

3.16

4(0.013

)(0.438

)(0.001

)(0.025

)(0.004

)(0.110

)(0.240

)

Notes:T

hestandard

deviations

ofestim

ated

parametersaregivenin

parentheses.For

theestim

ated

parametersin

theconditionalmeanequatio

n,werepo

rttheiraverages

sincethey

are

allowed

tovary

over

time.The

significance

ofthesecoefficients(β

ð1Þ

iin

particular)in

each

timeperiod

isexam

ined

byusingastandard

t-testandshow

nin

thegraphof

time-varying

predictability(see

Figure1).*and**

indicate

that

coefficientsarestatistically

sign

ificantat

5%and1%

level,respectiv

ely.


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The rationale behind this line of reasoning is that the weak-form efficiency hypothesisis deemed valid if the time-varying predictability evaluation is not statistically significant.A positive financial liberalization effect on the efficiency is explained by the reduction ofthe return predictability subsequent to financial opening. Even though the market wasefficient before liberalization, the liberalization positive effect is deemed as an efficiencyimprovement during the period following official liberalization dates. Figure 1 displaysthe evolution of the time-varying predictability indices along with 95% confidenceintervals around the official dates of financial liberalization.5

From Figure 1, we can make some general remarks for all considered markets andspecific comments for market groups that are identified based on their efficiency degree:

● As noted by Zalewska-Mitura and Hall (1999), at the beginning of the period,observations arising from the application of the Kalman filter are too volatile.

● We distinguish three market groups. The first group includes eight markets, namelyArgentina, Brazil, Korea, India, Jordan, Malaysia, Pakistan and Thailand. It ischaracterized by efficiency over the whole period. Indeed, the zero line falls withinthe confidence interval, which would indicate that the null hypothesis of weak-formefficiency cannot be rejected. The second group contains two markets, Mexico andVenezuela. These markets are characterized by inefficiency over several sub-peri-ods at the beginning, and in the middle, of the period. However, such inefficiencyconverges gradually towards efficiency at the end since the associated autocorrela-tion coefficients decline steadily over time towards zero. The last group is morecontroversial than the previous groups, and it involves three countries, namelyChile, Colombia and the Philippines. These countries are characterized either by anabsolute inefficiency over the entire period (i.e. Colombia) or by an efficiency for ashort period while exhibiting an increasing degree of inefficiency.

● The degree of efficiency varies from one market to another, which leads to thinkthat specific characteristics of each market, including the liquidity and the devel-opment level, might explain the differences in the level of efficiency betweenmarkets. This finding is also highlighted by Arouri and Nguyen (2010) andFontaine and Nguyen (2006), which note that the lack of liquidity slows downthe incorporation of available information in stock price, and thereby hinders theconvergence process to efficiency.

● We note that several changes in the time-varying predictability trend for somecountries (i.e. Argentina, Jordan and especially Thailand) take place either at thetime of financial liberalization as approximated by the official dates of Bekaert andHarvey (2000) or in the periods around financial liberalization for other countries(i.e. Chile, Colombia, Malaysia, Pakistan, the Philippines and Venezuela). At thisstage, one may be inclined to conclude that financial liberalization has a significantimpact on return predictability, but it is still difficult to confirm this finding basedon a graphical analysis.

To summarize, one may conclude that overall the weak-form efficiency hypothesis isverified in the emerging market countries; however, this conclusion varies from onemarket to another depending on the specific characteristics of each of them. As to theimpact of financial liberalization on the level of efficiency, it is hard to decide on theexistence of a clear-cut effect. It is more judicious to pursue an insightful empiricalanalysis, which will be the subject of the next section.


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–4

–2

0

2

4

86 88 90 92 94 96 98 00 02 04 06 08

ArgentinaBH

–1 Y +1 Y

–4

–2

0

2

86 88 90 92 94 96 98 00 02 04 06 08

BrazilBH

–1 Y +1 Y

–2

–1

0

1

86 88 90 92 94 96 98 00 02 04 06 08

ChileBH–1 Y +1 Y

–4

–2

0

2

4

86 88 90 92 94 96 98 00 02 04 06 08

ColombiaBH–1 Y +1 Y

–2

–1

0

1

2

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y +1 Y

India

Figure 1. Evolving efficiency in emerging stock markets, time-varying predictability index with95% confidence intervals.


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–2

–1

0

1

2

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y + 1Y

Jordan

–4

–2

0

2

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y +1 Y

Malaysia

–4

–2

0

2

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y +1 Y

Mexico

–4

0

4

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y +1 Y

Pakistan

–2

–1

0

1

2

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y +1 Y

Philippines

Figure 1. (Continued).


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5.2. The impact of financial liberalization on weak-form efficiency: explanation ofsudden changes

It stands out from Table 3 that the number of structural breaks in the time variations ofpredictability measures differs from one market to another. The Thailand’s market is in thefirst position with the largest number of structural breaks (three), followed by Chile,Colombia and India with a number of structural breaks equal to two. This confirms thegraphical intuition (see Figure 1) that makes note of the presence of several changes in thetrend of the time-varying predictability index.

Once the breaks are identified, it is therefore interesting to investigate the potentialeffect of financial liberalization underlying their occurrence. To this end, we report inTable 3 the financial liberalization dates and we compare the similarity between thesedates and the structural break dates.

It stands out from Table 3 that observed breaks in the majority of markets do notcoincide exactly with any particular date of financial liberalization, except for the case ofIndia where the date of the structural breakpoint is similar to the date of the introductionof the first Country Funds (1988 M06). It is worth noting that their 95% confidenceintervals cover several important events related to the financial liberalization reforms. Inaddition, it can be seen that official liberalization dates fall into the 95% confidence

–4

–2

0

2

4

86 88 90 92 94 96 98 00 02 04 06 08

BH–1Y +1Y

South Korea

–2

–1

0

1

2

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y +1 Y

Thailand

–10

–5

0

5

86 88 90 92 94 96 98 00 02 04 06 08

BH–1 Y +1 Y

Venezuela

Figure 1. (Continued).


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Table

3.Com

parativ

eanalysisof

structural

breakdateswith

financiallib

eralizationdates.

Num

berof

structural

breaks

Estim

ated

breakdates

95%

confidence

intervalsforbreak

dates

Officialdatesof

financial

liberalization

[BekaertandHarvey

(200

0)]

Introd

uctio

nof

thefirst

Cou

ntry

Fun

dsdates

Introd

uctio

nof

the

firstADRdates

Increase

innetUS

capitalflow

dates

Argentin

a1

1988

M08

[198

8M01

–198

9M09

]19

89M11

1991

M10

1991

M08

1993

M04

Brazil

119

88M12

[198

7M10

–198

9M12

]19

91M05

1987

M10

1992

M01

1986

M06

Chile

219

87M07

[198

6M09

–198

7M12

]19

92M01

1989

M09

1990

M03

1988

M01

1991

M03

[199

0M12

–199

2M05

]Colom

bia

219

86M08

[198

6M05

–198

7M02

]19

91M02

1992

M05

1992

M12

1993

M08

1992

M01

[199

1M09

–199

2M05

]India

219

88M06

[198

8M02

–198

9M12

]19

92M11

1986

M06

1992

M02

1993

M04

1992

M05

[199

2M02

–199

2M12

]Jordan

119

88M12

[198

8M05

–198

9M09

]19

95M12

na19

97M12

naMalaysia

119

87M10

[198

7M02

–198

8M12

]19

88M12

1987

M12

1992

M08

1992

M04

Mexico

119

87M06

[198

6M08

–198

8M03

]19

89M05

1981

M06

1989

M01

1990

M05

Pakistan

119

91M06

[199

0M02

–199

2M08

]19

91M02

1991

M07

1994

M09

1993

M04

Philip

pines

119

90M07

[199

0M04

–199

0M12

]19

91M06

1987

M05

1991

M03

1990

M01

Sou

thKorea

0—

—19

92M01

1984

M08

1990

M11

1993

M03

Thailand

319

86M08

[198

6M03

–198

7M05

]19

87M11

[198

7M08

–198

8M08

]19

87M09

1985

M07

1991

M01

1988

M07

1989

M01

[198

8M10

–198

9M03

]Venezuela

119

88M12

[198

7M09

–199

0M05

]19

90M01

na19

91M08

1994

M02

Notes:Thistablerepo

rtstheresults

oftheBai–P

erron’stest

forunknow

nmultip

lestructural

breaks

inalin

earregression

fram

ework,

theofficial

datesof

financiallib

eralization

(BekaertandHarvey20

00)andthedifferentdatesof

financiallib

eralizationreform

s.The

optim

alnumberof

breaks

correspondsto

theonehaving

thelowestBayesianInform

ation

Criterion

(BIC)score.

na,notavailable;

ADR,American

Depositary

Receipt.


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intervals for the estimated break dates in six markets (i.e. Chile, India, Malaysia, Pakistan,Thailand and Venezuela). Such finding would indicate that official liberalization dateshave a great explanatory power regarding the changes in the time variations of predict-ability. It is worth noting also that for some of these markets, other financial liberalizationreforms coincide with the 95% confidence interval. For instance, in the case of India, the95% confidence interval covers the introduction of the first Country Funds and the firstADR dates. As for the other markets, results indicate that the different dates of financialliberalization reforms are located within the 95% confidence interval of the first breakdate. In Brazil and Colombia, the dates where the first ADR is introduced into theseemerging markets is bounded by the 95% confidence interval of the first break date. Thesame pattern is observed in the Philippines and Thailand for the dates of the increase innet US capital flows. In Argentina, Mexico and South Korea, none of the estimated breakdates is related to market liberalization events.

In sum, it must be noted that changes in the time-varying predictability indices mostoften coincide with the official dates of financial liberalization and with the financialinstruments, like Country Fund and ADR. This typically would indicate that emergingmarket performance responds to the financial liberalization process and to its alternativeevents. However, the change in the degree of efficiency around the dates of financialliberalization does not inform about the sign of the impact. Again, we cannot identifywhether financial liberalization contributes, or not, to enhancing the degree of informa-tional efficiency. But a close inspection of Figure 1 would indicate that there is animprovement in the informational efficiency during recent decades for the majority ofemerging countries, especially after financial openness. This improvement is probablybrought about by the adoption of the liberalization process.

6. Conclusion

The informational efficiency is a very important concept reflecting the effectiveness of themarket policy investment. In recent years, the financial literature has focused on determin-ing the degree of informational efficiency in emerging countries, which are considered asgood sites for investment, especially after the opening of their markets.

This article joins the literature to test the hypothesis of weak-form efficiency on asample of emerging countries and to determine the impact of financial liberalization on thedegree of efficiency over the past decades. The attention is mainly focused on modellingthe weak-form efficiency by taking into account the evolutionary characteristics ofemerging markets. More specifically, the argument that the weak-form efficiency evolvesover time is considered. Then, the attention is paid to determining the impact of financialliberalization on the informational efficiency. We have recourse to the technique devel-oped by Bai and Perron (1998, 2003) that permits to identify multiple structural breaks inthe time-varying predictability indices. Our empirical strategy is based on identifyingwhether structural breaks are present at the time of, or near, the initial liberalization dateand their alternative events.

The empirical findings this article puts forward show a greater efficiency during recentyears in emerging markets, which seems to be a good indicator for regulators, since greaterefficiency leads, ipso facto, to an increase in investment. They show also that structuralbreaks identified in emerging market predictability indices coincide with the official liberal-ization dates, and with their alternative events. These findings corroborate those of the relatedliterature regarding how emerging markets react to the financial liberalization process. By


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linking these two concluding remarks, we are inclined to conclude that financial liberal-ization enhances the informational efficiency in the emerging markets.

Notes1. According to Fontaine and Nguyen (2006), posing efficiency as a null hypothesis, the entire

information revealed by the periods t−2, t−3, …, 1 is assumed to be fully incorporated into thereturns observed in t−1. Therefore, taking into account a one-period lagged return in theequation generating stock returns appears to be sufficient to test the weak-form efficiency.

2. The optimization is carried out in GAUSS using the BFGS algorithm (Broyden, Fletcher,Goldfarb and Shanno).

3. We compared several financial liberalization dates including, in particular, those of Kim andSingal (2000) and Henry (2000) and we found strong similarities between these dates.

4. Regulatory reforms, the introduction of the first Country Funds and ADR and the increase innet US capital flows.

5. We use the following abbreviations in the presentation of the Figure 1: ‘BH’ for the official dateof financial liberalization provided by Bekaert and Harvey (2000). ‘–1 Y’ and ‘+1 Y’ for,respectively, 1 year before financial liberalization and 1 year after.

Notes on contributorsAymen Ben Rejeb is an assistant professor in Finance at the Faculty of Economics and Managementof Mahdia, University of Monastir, Tunisia. He is a member of the Laboratory of Management ofInnovation and Sustainable Development (LAMIDED). His area of research includes emergingmarkets finance, volatility, risk management and efficiency in international stock markets. He haspublished many research papers in refereed internationally reputed journals. He has also presentedmany research papers in various international conferences.

Adel Boughrara is a full professor of econometrics at the University of Sousse. He has been thedirector of the Doctoral school in Economics and Management of the University of Sousse (2008–2012). He has also been a visiting associate professor at the United Arab Emirates University (2005and 2007). He holds a PhD in Mathematical Economics and Econometrics from Aix-MarseilleUniversity. His academic research focuses on fiscal and monetary policies, with particular emphasison central banking issues. He has published in many academic journals and edited books. Hecoordinated many international research projects and has consulting experience with the FEMISE,the European commission and the Tunisian Government.

ReferencesArouri, M.H., and D.K. Nguyen. 2010. Oil prices, stock markets and portfolio investment: Evidence

from sector analysis in Europe over the last decade. Energy Policy 38: 4528–39.Bai, J., and P. Perron. 1998. Estimating and testing linear models with multiple structural changes.

Econometrica 66: 47–68. doi:10.2307/2998540Bai, J., and P. Perron. 2003. Computation and analysis of multiple structural change models. Journal

of Applied Econometrics 18: 1–22. doi:10.1002/jae.659Bai, J., and P. Perron. 2006. Multiple structural change models: A simulation analysis. In

Econometric theory and practice: Frontiers of analysis and applied research, ed. D. Corbae,S. Durlauf and B.E. Hansen, 212–37. Cambridge: Cambridge University Press.

Basu, P., H. Kawakatsu, and M.R. Morey. 2000. Liberalization and stock prices in emergingmarkets. Emerging Markets Quarterly 4: 7–17.

Bekaert, G., and C.R. Harvey. 2000. Foreign speculators and emerging equity markets. The Journalof Finance 55: 565–613. doi:10.1111/0022-1082.00220

Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal ofEconometrics 31: 307–27. doi:10.1016/0304-4076(86)90063-1

Bollerslev, T., and J. Wooldridge. 1992. Quasi-maximum likelihood estimation and inference indynamic models with time-varying covariances. Econometric Reviews 11: 143–72. doi:10.1080/07474939208800229


Dow

nloa

ded

by [

T&

F In

tern

al U

sers

], [

Mr

Joel

Pet

ers]

at 1

2:25

16

Mar

ch 2

014

Chow, K.V., and K.C. Denning. 1993. A simple multiple variance ratio test. Journal ofEconometrics 58: 385–401. doi:10.1016/0304-4076(93)90051-6

Dickey, D.A., and W.A. Fuller. 1981. Likelihood ratio statistics for autoregressive time series with aunit root. Econometrica 49: 1057–72. doi:10.2307/1912517

Dockery, E., and F. Vergari. 1997. Testing the random walk hypothesis: Evidence for the Budapeststock exchange. Applied Economics letters 4: 627–9. doi:10.1080/758533288

Emerson, R., S.G. Hall, and A. Zalewska-Mitura. 1997. Evolving market efficiency with anapplication to some Bulgarian shares. Economics of Planning 30: 75–90. doi:10.1023/A:1003053807165

Engle, R.F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance ofUnited Kingdom inflation. Econometrica 50: 987–1007. doi:10.2307/1912773

Fama, E.F. 1970. Efficient capital markets: A review of theory and empirical work. The Journal ofFinance 25: 383–417. doi:10.2307/2325486

Fama, E.F. 1991. Efficient capital markets: II. The Journal of Finance 46: 1575–617. doi:10.1111/j.1540-6261.1991.tb04636.x

Fontaine, P., and D.K. Nguyen. 2003. The effect of capital market liberalization on weak formefficiency in emerging markets. Working Paper, University of Grenoble II, CERAG.

Fontaine, P., and D.K. Nguyen. 2006. Stock market liberalization and informational efficiency inemerging markets: New consideration and tests. Bankers, Markets and Investors 84: 6–17.

Füss, R. 2005. Financial liberalization and stock price behaviour in Asian emerging markets.Economic Change and Restructuring 38: 37–62. doi:10.1007/s10644-005-4522-6

Groenewold, N., and M. Ariff. 1998. The effects of deregulation on share-market efficiency in theAsia-Pacific. International Economic Journal 12: 23–47.

Harrison, B., and D. Paton. 2005. Transition, the evolution of stock market efficiency and entry intoEU: The case of Romania. Economics of Planning 37: 203–23.

Harvey, C.R. 1995. Predictable risk and returns in emerging markets. Review of Financial Studies 8:773–816. doi:10.1093/rfs/8.3.773

Henry, P.B. 2000. Stock market liberalization, economic reform, and emerging market equity prices.The Journal of Finance 55: 529–64. doi:10.1111/0022-1082.00219

Jefferis, K., and G. Smith. 2005. The changing efficiency of African stock markets. The SouthAfrican Journal of Economics 73: 54–67. doi:10.1111/j.1813-6982.2005.00004.x

Kawakatsu, H., and M.R. Morey. 1999. Financial liberalization and stock market efficiency: Anempirical examination of nine emerging market countries. Journal of Multinational FinancialManagement 9: 353–71. doi:10.1016/S1042-444X(99)00007-9

Kim, E.H., and V. Singal. 2000. The fear of globalizing capital markets. Emerging Markets Review1: 183–98. doi:10.1016/S1566-0141(00)00014-5

Laopodis, N.T. 2003. Financial market liberalization and stock market efficiency: The case ofGreece. Managerial Finance 29: 24–41. doi:10.1108/03074350310768274

Laopodis, N.T. 2004. Financial market liberalization and stock market efficiency: Evidence from theAthens stock exchange. Global Finance Journal 15: 103–23. doi:10.1016/j.gfj.2004.06.001

Ljung, G., and G. Box. 1978. On a measure of lack of fit in time series models. Biometrika 65: 297–303. doi:10.1093/biomet/65.2.297

Lo, A.W., and A.C. Mackinlay. 1988. Stock market prices do not follow random walks: Evidencefrom a simple specification test. Review of Financial Studies 1: 41–66. doi:10.1093/rfs/1.1.41

Lo, A.W., and A.C. Mackinlay. 1989. The size and power of the variance ratio test in finite samples:A Monte Carlo investigation. Journal of Econometrics 40: 203–38. doi:10.1016/0304-4076(89)90083-3

Maghyereh, A.I., and G. Omet. 2002. Financial liberalization and stock market efficiency: Empiricalevidence from an emerging market. African Finance Journal 4: 24–35.

Rockinger, M., and G. Urga. 2001. A time varying parameter model to test for predictability andintegration in the stock markets of transition economies. Journal of Business and EconomicStatistics 19: 73–84. doi:10.1198/07350010152472634

Zalewska-Mitura, A., and S.G. Hall. 1999. Examining the first stages of market performance: A testfor evolving market efficiency. Economics Letters 64: 1–12. doi:10.1016/S0165-1765(99)00074-9


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