Contents Research in International Business Finance · GCC equity markets in order to show the size of these Gulf equity markets. From an intraregional perspective, in 2011 the stock

Research in International Business and Finance 33 (2015) 221–246

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Research in International Businessand Finance

journal homepage: www.elsevier.com/locate/r ibaf

Are the regional Gulf stock markets weak-formefficient as single stock markets and as aregional stock market?

Fouad Jamaania,∗, Eduardo Rocab

a Department of Investment and Finance, College of Finance and Administration, Taif University, SaudiArabiab Department of Accounting, Finance and Economics, Griffith Business School, Griffith University, Australia

a r t i c l e i n f o

Article history:Received 30 November 2013Received in revised form 23 July 2014Accepted 3 September 2014Available online 7 October 2014

Keywords:EfficiencyStock marketGCCCointegrationRandom walkInformation asymmetry

a b s t r a c t

The purpose of this article is to examine the efficiency of theGulf Cooperation Council (GCC) stock markets of Saudi Arabia,the United Arab Emirates, Kuwait, Oman, Qatar, and Bahrain. Weattempt to answer whether GCC stock markets are weak-form effi-cient individually or as a group by applying a battery of parametric,nonparametric, unit root, and Johansen cointegration tests to dailyindex prices denominated in local currencies covering the periodDecember 2003 to January 2013. The findings of our study show thatGCC stock markets are not individually weak-form efficient. That isto say, current prices of each GCC stock markets can be predictedfrom past price changes in that market. The study also finds thatcollectively, GCC stock markets are not weak-form efficient, as themovements of past prices of one GCC market can be used to predictthe current price movement of another GCC stock market. This inef-ficiency could be due to the weak degree of foreign participation andthe high concentration in the banking and financial sectors. Finally,the study suggests a number of policy implications for academics,policy makers and investors, and directions for future research.

© 2014 Elsevier B.V. All rights reserved.

∗ Corresponding author. Tel.: +614 11758527.E-mail address: [email protected] (F. Jamaani).

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dx.doi.org/10.1016/j.ribaf.2014.09.001

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222 F. Jamaani, E. Roca / Research in International Business and Finance 33 (2015) 221–246

1. Introduction

Over the last few decades, the economies of developing markets have experienced progressivegrowth, where the business environment has shown many signs of enhancement, resulting in thelisting of new, productive and profitable companies. As a result, the investing in these emerging stockmarkets has attracted a significant amount of offshore, regional, and local migrant investment capital toconsider emerging stock markets as new, lucrative and diversifiable alternative investment channels.However, this rising interest in investment opportunities in developing equity markets, includingin the GCC1 stock markets, has raised questions in relation to the efficiency of these stock markets.An empirical investigation of the efficiency of a stock market begins with identifying the economicenvironment where this market operates. The identification of this economic environment assists inunderstanding how a characteristic of a particular economy or equity market may impact on its marketefficiency. The concept of market efficiency becomes very important when it begins to influence theeconomic growth of an economy or investment decisions of investors. From an economic viewpoint,lack of market efficiency of a stock market may influence economic growth through affecting factorssuch as flow of investment capital, cost of capital, and market returns. As an example, the weak-form of stock market efficiency implies that prices paid for stocks should reflect past prices, andaccordingly reflect the underlying value of stocks. This results in efficient investment decisions andoptimal allocation of financial resources, hence leading to more productive economic activities andinvestment choices. Efficiency of stock markets also works as a protective mechanism that preventsstock markets from distortions and arbitrage opportunities resulting from the presence of asymmetricinformation among market participants (Jensen, 1978). From an investment viewpoint, the presenceof asymmetric information among market participants can allow informed investors to strategicallyutilise fundamental information of particular asset groups in a stock market to identify mispricedassets, leading to enhancing their risk-adjusted returns (Fama, 1970). Thus, the concept of marketefficiency may be seen as both a tool to measure the degree of a country’s economic development andas a forecasting tool to make excessive returns by utilising stocks’ historical information to identifyunderpriced stocks.

Previous empirical studies concerned with investigating the efficiency of emerging equity marketsshowed that stock markets in the developing markets are to a significant extent weak-form efficient(Harvey, 1995; Kim and Shamsuddin, 2008). Up to the present time, research concerned with theexamination of the efficiency of the GCC stock markets has been limited, produced fragmented results,and only examined the efficiency of GCC stock market from a single market perspective (Ariss et al.,2011; Elango and Hussein, 2008). For this reason, we aim to examine the efficiency of the GCC stockmarkets, and whether these markets are weak-form efficient either as single markets or as a regionalmarket. Thus, the research question that we attempt to answer is, are GCC stock markets weak-formefficient as single stock markets and as a regional stock market?

This study is organised as follows. Section 2 discusses a number of important economic and stockmarket characteristics for GCC markets and their implications on the efficiency of these markets; inother words, it shows how these characteristics make GCC equity markets an attractive regional envi-ronment to examine the efficiency of emerging stock markets. Section 3 briefly reviews the empiricalliterature. Section 4 presents the methodologies applied, and the data that is used is described in Sec-tion 5. Empirical findings are presented in Section 6, followed by discussion of the results in Section7. A number of policy implications are discussed in Section 8, and the conclusion is made in Section 9.

2. GCC economies and equity markets: brief review

We have chosen Gulf stock markets as an attractive laboratory to examine the efficiency of regionalemerging equity markets because these markets share common and distinct economic and stock mar-ket features that influence their efficiency. GCC state members share similar economic, geographical,

1 Gulf Corporation Council (GCC), namely Saudi Arabia, the United Arab Emirates (UAE), Kuwait, Oman, Qatar, and Bahrain.

F. Jamaani, E. Roca / Research in International Business and Finance 33 (2015) 221–246 223

demographic, social, and religious features.2 Gulf member states are rich in natural hydrocarbonresources including oil and gas resources, where in 2011 the combined GCC regional reserves of oiland gas was 33.5% and 21.3% respectively of the proven world reserves.3 These large reserves of oil andgas resources have led the Gulf economies to depend heavily on hydrocarbon industries as a majorsource of local and regional economic growth. In 2008 the percentage of hydrocarbon products toGCC Gross Domestic Product (GDP), exports, and government revenues was 51.3%, 85.2%, and 88.2%respectively.4 This substantial reliance on these non-renewable natural resources constitutes the firstcommon and distinct feature related to the GCC economies, and may make for serious economic chal-lenges for the GCC region when the values of these natural resources fluctuate or when they becomedepleted.

The problem is that prices of energy, including hydrocarbon products, are by nature quite volatileand do not provide stable and sustainable local and regional economic growth to GCC member statesacross the long horizon (Bhattacharyya, 2011). GCC policy makers have become aware of these seri-ous economic challenges, and consequently have been aiming for the last two decades to diversifythe sources of their economic growth by initiating remedial plans to reduce reliance on oil and gasexports. One of these remedial plans is to effectively reduce over-reliance on oil and gas revenues. GCCgovernments aim to reduce the contribution of the hydrocarbon industries to GCC nominal GDP from49% in 2008 to 31% in 2020.5 The supplementary and practical plan set forth by GCC governments isto expand the growth of the GCC stock markets and simultaneously to enhance the efficiency of GCCequity markets, in order to enhance market sentiment leading to the attraction of more investment.This plan aims to provide alternative and sustainable sources for local and regional economic growth(AlKhazali, 2011).

The lack of efficiency of GCC stock markets may hinder these long-term growth plans set forthby GCC policy makers, and GCC leaders are interested in enhancing market liquidity, efficiency, andcompetitiveness within the regional stock markets (Al-Khazali et al., 2006). That is, it is argued thatthe improvement of stock market liquidity, efficiency, and competitiveness results in lowering thecost of capital, optimising market returns, and increasing the attractions of cross-border capital flowsrespectively in the GCC markets (Boughanmi, 2008; Neaime, 2002). These benefits may establish anattractive regional investment environment that works as a supplemental boost to reduce the relianceof the GCC region on oil revenues. The inefficiency among GCC markets may act as a disincentive forgenuine regional, migrant, and offshore investment capital to flow into GCC markets (Al Janabi et al.,2010). We discuss two distinct stock market characteristics that may indeed play a major role ininfluencing the efficiency of these regional equity markets. These features include the weak degreeof foreign participation in GCC equity markets and the high concentration in banking and financialsectors. Analytically, the former feature incorporates two challenging aspects including the presenceof regulatory restrictions on foreign ownership, and the existence of signs of information asymme-try resulting from weak financial market developments in the GCC region. We further analyse thedegree of financial market development using three proxies, including the ease of doing business, thetransparency of private and public sectors, and the adequacy of digital financial disclosure capacitymeasures as exhibited in Fig. 1.

Before moving to the presentation of these important stock market characteristics of the GCC region,we present distinct intraregional, interregional, and global market capitalisation comparative analysesof GCC equity markets in order to show the size of these Gulf equity markets. From an intraregionalperspective, in 2011 the stock market of Saudi Arabia was the dominant one in size, with 49% market

2 On the 25th of May 1981, leaders of the Kingdom of Saudi Arabia, the United Arab Emirates, Kuwait, Qatar, Bahrain, andOman announced the birth of the Gulf Cooperation Council (GCC). The objective of this union is to effectively establish bridges ofco-operation, integration, and inter-connections amongst member states in political affairs, and military, legal, media, security,and economic matters.

3 Oil and Gas figures are obtained from Annual Statistical Bulletin 2012 published by OPEC.4 GDP, exports and government revenues figures are obtained from report entitled ‘The GCC hydrocarbon sector: big and

getting bigger’, published by National Bank of Kuwait in 2011.5 Figures are obtained from a report entitled ‘The GCC in 2020: Broadening the economy’, published by The Economist

Intelligence Unit Limited, 2010.


Fig. 1. Features of GCC stock markets resulting in inefficiency.

capitalisation of the combined GCC equity market, followed by 18%, 15%, and 13% to the Qatar, Kuwait,and UAE equity markets respectively. The Oman and Bahrain equity markets were the smallest marketsin the region, with a percentage of the combined GCC equity market of 3% and 2% respectively in 2011.6

From an interregional viewpoint, GCC stock market capitalisation in 2011 was equivalent to almost570%, 67%, 44%, and 31% of the market capitalisation size of the North African,7 Scandinavian,8 ASEAN9

and Latin American10 equity markets. Whilst from a global standpoint, combined GCC equity marketsaccounted only for 4.31% and 1.45% of the U.S and the world equity market capitalisation in 2011.11

The first distinct stock market feature that GCC markets share, that is likely to have a negativeinfluence on the degree of efficiency of these markets, is related to the presence of the weak level offoreign participation in GCC equity markets. In this context, it is presumed that the level of foreignownership in stock markets has an adverse relationship with the degree of information symmetry.Previous studies argued that foreign investors play an important role in reducing the presence of infor-mation asymmetry in equity markets, as they frequently demand more information disclosure, robustaccounting and auditing standards, incentive alignments, and better market monitoring mechanisms(Choi et al., 2013; Jiang and Kim, 2004). Hence, it can be said that greater access for foreign investorsto local or regional equity markets in the emerging markets, particularly in the GCC market, wouldlead presumably to greater information symmetry, resulting in more efficient market characteristics.

The weak participation of foreign investors in GCC stock markets can be attributed to the presenceof regulatory restrictions on foreign ownership. As an illustration of the current regulatory foreignownership restrictions imposed in GCC markets, for example, foreign investors are only allowed todirectly own 49% of listed companies in the UAE, Kuwait, and Bahrain stock markets. While they canown up to 25% and 70% in Qatari and Omani listed equities respectively, only 25% indirect ownership isallowed in the largest GCC stock market of Saudi Arabia via mutual funds, equity swaps, and ExchangeTraded Funds (ETFs).12 To some extent these ownership figures may not seem too bad for some GCCmarkets, but in linking the impact of the presence of information asymmetry with actual ownershipfigures, a different picture arises. In 2012, for example, foreign investors participated in only 6%, 3.3%,and 28% of listed companies in Kuwait, Saudi Arabia, and Oman, whereas according to equity market

6 Figures for market capitalisation of listed companies in GCC region are obtained from The World Bank Group.7 North African region includes Egypt, Morocco, and Tunisia only, due to data unavailability of Libya, Algeria, and Mauritania.8 Scandinavian region includes Finland, Iceland, Norway, Sweden, and Denmark.9 Association of Southeast Asian Nations (ASEAN) includes Indonesia, Malaysia, The Philippines, Singapore, and Thailand.

10 Latin American markets include Argentina, Brazil, Chile, Colombia, Mexico, and Venezuela.11 Figures for equity market capitalisation of North African, Scandinavian, ASEAN, Latin American, US markets are obtained

from The World Bank Group.12 Figures are obtained from a report entitled ‘GCC financial markets: Long-term prospects for finance in the Gulf region’,

published by Deutsche Bank in 2012.


1.02.03.04.05.06.07.0

Oman Saudi Arabia Kuwait Qatar Bahrain United Arab Emi rates GCC Develop ing Asia

Ease of access to loans 1-7 (best)

Venture capital avail abi lity 1-7 (best)

Enforcment of securities exchange regulations 1-7 (best)

Fig. 2. Financial market developments in the GCC region. Note: Data are hand collected and constructed by authors, and sourcedfrom the annual Global Competitiveness Report published by The World Economic Forum from 2006 to 2013. Global Compet-itiveness Reports published prior to 2006 do not contain data related to ease of access to loans, venture capital availability,and enforcement of securities exchange regulations, as these proxies were only introduced to the report since 2006 (WorldEconomic Forum, 2014). The ease of access to loans measure assesses to what extent it is easy to obtain a bank loan with only agood business plan and no collateral, and it is scaled from 1 to 7 where 1 = extremely difficult, 7 = extremely easy. The venturecapital availability measure assesses to what extent it is easy for entrepreneurs with innovative but risky projects to find venturecapital, and it is scaled from 1 to 7 where 1 = extremely difficult, 7 = extremely easy. Ease of access to loans and venture capitalavailability data are average data from 2006 to 2013 for all GCC economies, excluding Oman and Saudi Arabia where data areonly available from 2007 to 2013. For developing Asia economies (34 economies), ease of access to loans and venture capitalavailability data are average data from 2006 to 2013. The enforcement of securities exchange regulations measure assesses theeffectiveness of the regulation and supervision of securities exchanges, and it is scaled from 1 to 7 where 1 = not at all effective,7 = extremely effective. Data related to enforcement of securities exchange regulations measure are average data from 2007 to2013 for both GCC economies and the 34 developing Asia economies, as this proxy was introduced in the Global CompetitivenessReport in 2007. Developing Asia data are included for comparability purposes.

rules they are able to participate in 49%, 25%, and 70% of these three GCC stock markets respectively.13

The thin foreign ownership is a crucial equity market characteristic that resulted from the regulatoryrestrictions imposed on foreign ownership that distinguish GCC markets. Thus, we expect a negativerelationship between the presence of the weak foreign participation characteristic and the prevailingstatus of inefficiency among GCC stock markets.

The weak participation of foreign investors in GCC stock markets might also be attributed to therelatively low degree of financial market development in the GCC. We provide a descriptive assessmentof the degree of financial market development in GCC stock markets by using various proxies related tothe ease of doing business, the transparency of private and public sectors, and the adequacy of digitalfinancial disclosure capacity. As exhibited in Fig. 2, we employ two measures to describe the level ofGCC financial market development based on ease of access to loans and venture capital availability.14

As well, we discuss the trustworthiness of GCC financial systems using the degree of enforcement ofsecurities exchange regulations.15

On average, GCC financial markets seem to score only moderately in terms of ease of access toloans and venture capital availability: 4.4 and 3.9 out of 7 respectively, with slight variations acrossGCC countries. As a result of this, it may be quite difficult for entrepreneurs with risky but innovativeprojects to access venture capital in the region. Despite this feature of the GCC financial system, itnevertheless seems to be more competitive compared to financial systems in developing Asia, wherethe latter’s ease of access to loans and venture capital availability measures is less than the former byapproximately 32% and 26% respectively. The trustworthiness of the GCC financial system, as mea-sured by the extent that securities regulations are enforced, illustrates a better picture comparedto developing Asia on average, as it seems that GCC market participants place some trust in the

13 Figures are obtained from a report entitled ‘GCC financial markets: Long-term prospects for finance in the Gulf region’,published by Deutsche Bank in 2012.

14 Data related to ease of access to loans and venture capital availability sourced from Global Competitiveness Report publishedby The World Economic Forum. The Global Competitiveness Network has published reports measuring country competitivenesssince 1979, reaching global coverage to 148 economies by 2013. The data used in the report are sourced from leading interna-tional sources as well as from the World Economic Forum’s annual Executive Opinion Survey, a unique source that captures theperspectives of more than 13,000 thousand business leaders on topics related to national competitiveness (World EconomicForum, 2014).

15 Data related to enforcement of securities exchange regulations sourced from Global Competitiveness Report published byThe World Economic Forum (World Economic Forum, 2014).


0.001.002.003.004.005.006.007.008.009.00

10.00

Oman Saudi Arabia Kuwait Qatar Bahrain United Arab Emirates GCC Developing Asia

Transparency of government policymaking 1 -7 (best)

Favoritism in decisions of government officials1 -7 (best)

Strength of investor protection 1 -10(best)

Fig. 3. Transparency Environments of GCC public and private sectors. Note: Data are hand collected and constructed by theauthors, and sourced from the annual Global Competitiveness Report published by The World Economic Forum from 2006to 2013. Global Competitiveness Reports published prior to 2006 do not contain data related to transparency of governmentpolicymaking, favouritism in decisions of government officials, and strength of investor protection, as these proxies were onlyintroduced to the report since 2006 (World Economic Forum, 2014). The transparency of government policymaking measureassesses to what extent it is easy for businesses to obtain information about changes in government policies and regulationsaffecting their activities, and it is scaled from 1 to 7 where 1 = extremely difficult, 7 = extremely easy. The favouritism in decisionsof government officials measure assesses the extent government officials show favouritism to well-connected firms and indi-viduals when deciding upon policies and contracts, and it scaled from 1 = always show favouritism, 7 = never show favouritism.Data related to transparency of government policymaking and favouritism in decisions of government officials measures forKuwait, Oman, and Saudi Arabia economies are only average data from 2007 to 2013, as 2006 data are missing from the report.For developing Asia economies (34 economies), transparency of government policymaking and favouritism in decisions of gov-ernment officials data are average data from 2006 to 2013. The strength of investor protection index assesses the strengthof minority shareholder protections against directors’ misuse of corporate assets for personal gain. The index incorporatesthree dimensions of investor protections: transparency of related party transactions (extent of disclosure index), liability forself-dealing (extent of director liability index) and shareholders’ ability to sue officers and directors for misconduct (ease ofshareholder suits index). The strength of investor protection index is the average of the extent of disclosure index, the extentof director liability index and the ease of shareholder suits index. The index ranges from 0 to 10, with higher values indicatingmore investor protection. Data related to the strength of investor protection index are reported as average values from 2006 to2013 for both all GCC economies and the 34 developing Asia economies. Developing Asia data are included for comparabilitypurposes.

enforcement mechanism of their securities regulations while Asian investors display 20% less con-fidence in theirs. Thus, it can be said that, on average, while there are some difficulties in the ease ofconducting business in the GCC region, it is slightly easier than in developing Asia.

Apart from the slightly difficult environment for doing business that foreign investors may expectto experience in the GCC region, the transparency environment of GCC public and private sectorsseems to be at question and possibly to be the source of information asymmetry, as shown in Fig. 3.We employ two indices to measure the GCC public sector’s transparency which relate to the trans-parency of government policymaking and favouritism in decisions of government officials,16 whilewe use an index on the strength investor protection17 to gauge the transparency of the GCC privatesector. On average, we obtained a score of 4.76 out of 7 with regards to transparency of the governmentpolicymaking and 4.30 out of 7 in relation to favouritism. Based on these scores, it can be said that it isto some extent not difficult for well-connected individuals and businesses to obtain information aboutrelated government policy changes and to jump the line when it comes to policies and contracts. Asevere transparency problem is notable in the Kuwait public sector, which is almost similar to what isexpected in developing Asia compared to other GCC public sectors. As a result, the weak transparencyof the GCC public sector seems to spill over to the GCC private sector, as indicated by the moderatelyweak result of 5.4 out of 10, on average, in the strength of the investor protection index. This impliesthat it is quite expected that minority shareholders’ rights will be to some extent misused for personalgain, the ability to file lawsuits against directors’ misconduct to be slightly inferior, and financial dis-closure rights to be violated. The investor protection climate is fragile for both the GCC and developingAsian regions, where the presence of information asymmetry is a prevalent market feature in devel-oping stock markets (Abdmoulah, 2010; Klapper and Love, 2004). Therefore, the existence of weak

16 Data related to transparency of government policymaking and favouritism in decisions of government officials sourcedfrom Global Competitiveness Report published by The World Economic Forum (World Economic Forum, 2014).

17 Data related to strength of investor protection index obtained from Global Competitiveness Report published by The WorldEconomic (World Economic Forum, 2014).


transparency to some extent in both the public and private sectors in the GCC markets can facilitatethe presence of weak financial market development resulting from the presence of asymmetric infor-mation between individuals and businesses. The weak financial market development is also a crucialequity market characteristic that results from the presence of a partly difficult business environmentand weak transparent market mechanisms in both private and public sectors that distinguish the GCCmarkets. These non-friendly investment characteristics would deter foreign investors from exercisingactive participation in GCC stock markets. Therefore, we expect a negative relationship between thepresence of this weak foreign participation characteristic and the information efficiency among GCCstock markets.

The third financial market development measure that is likely to have an influence on the degree offoreign participation in these markets, is related to the presence of information asymmetry caused bythe inadequate digital financial disclosure capacity of GCC equity markets. Equity markets in the Gulfregion have a short history compared to the global financial markets. Most of the GCC stock marketswere established and regulated in the 1980s and 1990s,18 and only recently applied electronic tradingplatforms. The electronic trading incorporates the setting up of an official trading website for everystock market or every listed company that provides frequent release of importantly relevant historicaland fundamental information. As electronic trading has been implemented in most GCC equity marketsfor little more than a decade, it is not unreasonable to assume a linkage between the unavailability ofdigital financial information and the presence of information asymmetry in GCC markets. This linkageis already established and empirically examined in the literature, in the sense that more use of digitalmeans for information disclosure of financial information of listed firms is expected to have a negativeimpact on the presence of information asymmetry in equity markets (Healy and Palepu, 2001). To putthis linkage to the test from a GCC stock market perspective, Ismail (2002) empirically investigated theuse by 128 GCC listed companies of Internet-based information disclosure, and found that almost 61%of these firms do not have websites to publish their financial information to investors, leading to theconstitution of information asymmetry. Similar findings are emphasised by Joshi and Al-Modhahki(2003) and Hussainey and Al-Nodel (2009). It is repeatedly argued in the literature that the presenceof information asymmetry in equity markets has negative implications on the weak-form efficiencystatus of markets, so that inefficiency of such markets is expected (Peress, 2010). Since it is known thatdigital means of communicating financial information to GCC investors is a specific feature of GCC stockmarkets, we expect the presence of inefficient market characteristics in GCC equity markets, drivenby the presence of information asymmetry, in turn caused by the lack of effective dissemination ofdigital financial information.

Further scrutiny into the composition of individual and collective GCC equity markets provides thesecond distinct shared characteristic of these regional equity markets that may have implications fortheir degree of information efficiency. In 2012, banking and financial services dominated almost 50%of the indices of the Qatar, Kuwait, Bahrain, Oman, and the Abu Dhabi Securities Market.19 By contrast,banking and financial services accounted for 13% and 23.9% in the Saudi Stock Exchange and DubaiFinancial Market respectively.20 From the combined GCC perspective, banking and financial servicesconstituted almost 41% of the composite of the combined Gulf equity markets in 2012. GCC banking andfinancial sectors have well-established regional and global banking and financial networks (Al-Khazaliet al., 2006; Neaime, 2012, 2002). These global and regional banking and financial inter-linkages mayhave implications on the efficiency status of GCC stock markets. It is claimed that financial markets thatare tight in international, interregional, and intraregional financial linkages are more crisis-prone thanother unconnected markets (Bekaert et al., 2005; Neaime, 2005). These global and regional financial

18 The Kuwait Stock Exchange (KSE) was the first regulated GCC market in 1983, and the UAE’s was the last regulated GCCequity market in 2000. The Saudi and Kuwaiti stock markets are the first two markets that traded shares electronically, wherethe latter commenced electronic trading in 1995 and the former in 1990. The Qatar, UAE, Bahrain, and Oman stock marketsintroduced electronic trading in 2002, 2000, 1999, and 1998 respectively. Figures are obtained from a report entitled ‘GCCEconomic Outlook’ published by Global Investment House in 2011.

19 Figures are obtained from Abu Dhabi Securities Exchange, Bahrain Bourse, Kuwait Stock Exchange, Muscat Securities Market,and The Qatar Exchange official websites.

20 Figures are obtained from Dubai Financial Market and Saudi Stock Exchange official websites.


linkages can work as facilitating vehicles to ease the transfer of external financial shocks across regionalbanking (Guyot et al., 2014). The argument is that a banking crisis in one country gets transmittedto other countries via cross-country banking linkages (Lagoarde-Segot and Lucey, 2006; SandovalJunior and Franca, 2012). In this context, banks play a significant role in the development of financialcrises, and previous empirical studies have shown that a financial crisis has an adverse effect on theinformational efficiency of stock markets (Azad, 2009; Risso, 2008). As GCC stock markets have a highconcentration in banking and financial sectors, and experienced two financial crises in 2006 and 2008,we expect the current efficiency status of these equity markets to be inefficient.

The analysis of these GCC stock market characteristics is very important, as it shows that the over-reliance of GCC economies on oil and gas revenues constitutes a serious economic challenge, as theprices of these resources are volatile and have limited reserves. GCC stock markets have only one pathto follow forward, namely enhancing market efficiency, if they are keen to attract regional and globalinvestment capital to their equity markets. The problem is that our analysis of the regional stockmarkets shows that these markets possess characteristics that are likely to lead to informationallyinefficient markets. First, there is thin foreign participation in GCC equity markets, disincentivised bythe presence of regulatory restrictions on foreign ownership and weak financial market developments.Second, there is high concentration of GCC equity markets in banking and financial business that isregionally and globally well connected which may serve as channels for the spill-over of excessiveintraregional, interregional, and global volatility into these markets during times of financial criseswhich will then have adverse effects on market efficiency. Hence, given the presence of these charac-teristics among GCC stock markets, we are more inclined to anticipate that these markets are likely tofail weak-form efficiency tests, meaning that present stock price changes are not independent of pastprice movements.

3. Literature review

Although the empirical literature of the weak-form efficiency of the GCC stock markets is stillat its infancy stage, what exists has produced fragmented results and highlighted a number of issuesrelated to problematic methodological limitations of previous studies that aimed to test the behaviourof GCC stock market returns. One of the earliest empirical studies that examined the efficiency ofGCC stock markets used daily index data for the Kuwaiti stock market from 1975 to 1987. The studyrejected the normality hypothesis and employed parametric autocorrelation and nonparametric Runstests to examine the random behaviour of the Kuwaiti stock returns and concluded the presence ofsignificant dependency of current prices changes to past price changes (Gandhi et al., 1980). Anotherstudy extended the sample of the previous research to include the Saudi and Kuwaiti stock markets fora similarly short data frame from 1985 to 1988. The study found that stock returns are not normallydistributed and used autocorrelation and Runs tests. In a similar conclusion to the previous study, theweak-form efficiency is rejected due to the presence of significant predictability of the behaviour ofcurrent prices movements to past price movements in these markets (Butler and Malaikah, 1992).Further evidence arguing for the rejection of the random behaviour of GCC stock market returns isprovided by a study that used index prices of Saudi Arabia, UAE, Kuwait, Oman, Qatar, and Bahrainfrom 2001 to 2006 using the Runs test. On the other hand, the randomness of Bahraini and UAE stockmarket returns was suggested using autocorrelation and Runs tests for daily index prices from 1996 to2000 and from 2001 to 2003 for the first and second study respectively. Both studies found that bothBahraini and UAE stock returns are not normally distributed (Moustafa, 2004; Rao and Shankaraiah,2003).

Other GCC studies employed the parametric variance ratio (VR) test suggested by Lo and MacKinlay(1988) and produced fragmented results. On the one hand, the random walk behaviour of Saudi,Kuwaiti, Omani, and Bahraini stock market returns was examined using weekly index prices from1994 to 1998 through using a number of tests including the parametric autocorrelation and VR test(Dahel and Laabas, 1999). The study found the market returns of these markets are not normallydistributed and the results of the autocorrelation and VR tests supported the random walk behaviour.On the other hand, the efficiency of the Saudi stock markets was investigated using Runs and VRtests for daily index prices from 2003 to 2004 and provided statistically significant rejection of the


weak-form efficiency of the Saudi market returns (Onour, 2004). Another study used VR and Runstests to examine the randomness of daily sectorial indices data between 2000 and 2005 for the DubaiFinancial Market (DFM) and the Abu Dhabi Securities Market (ADSM) (Squalli, 2006). The study foundthat UAE stock markets are not weak-form efficient except for the banking sector in the DFM and theinsurance sector in the ADSM.

In regard to the different results achieved by these previous empirical studies about the statusof the weak-form efficiency of GCC stock markets, they seem to share two methodological problemsthat may have led to these conflicting results. First, a critical limitation to parametric tests includingautocorrelation and VR tests is that they require time series to be normally distributed. This normal-ity requirement implies that employing autocorrelation tests to investigate the dependency of stockprices, in the case that normality of stock prices is rejected, may constitute statistical bias to the results(Guidi and Gupta, 2013). This statistical bias may result in a high likelihood of misjudgement of thenull hypothesis of random walk. Second, it has been argued in the literature that although the non-parametric Runs test does not have a normality requirement, it does suffer from different types ofdeparture from random behaviour, consequently rejecting the random walk hypothesis due to theuse of shorter sampling periods (Elango and Hussein, 2008). As the abovementioned empirical GCCstudies employed short sampling periods, on average less than four years, some inaccuracy in theirresults may be expected. However, it is argued that the randomness of stock prices may not be testedby parametric and nonparametric dependency tests alone, as randomness of stock prices requires alsothe condition of non-stationarity (Guidi and Gupta, 2013). As randomness of stock markets requiresthe existence of non-stationarity amongst time series, previous GCC studies used unit root tests toexamine the condition of non-stationarity among GCC stock market returns. The concept behind unitroot tests is to examine whether or not a time series has a unit root. Accepting the null hypothesis of aunit root implies the non-stationarity of the time series that rejects the possibility of the presence of apredictable component in time series. Similarly to the parametric and nonparametric tests, the resultsof the empirical studies that aimed to test the weak-form efficiency of GCC stock returns using the unitroot application yielded fragmented results. For example, early studies concerned with examining theweak-form efficiency of the Gulf stock markets used unit root tests for weekly index prices for theBahrain, Kuwait, Oman, and Saudi Arabia stock markets from 1994 to 1998. The study showed thatunit root tests accepted the randomness of the Gulf stock markets (Dahel and Laabas, 1999).

On the other hand, the vast majority of the studies that used unit root tests to investigate thedependency of successive price changes of the GCC stock prices rejected the null hypothesis of inde-pendency. For instance, the randomness of the Saudi Arabia stock prices was examined using unit roottests and daily stock prices from March 2003 to June 2006. The results of the unit root tests showedthat the Saudi stock market is not weak-form efficient (Onour, 2009). Other studies used unit roottests for studying the weak-form efficiency of the GCC stock markets using different frequency ofdata, and concluded with supportive evidence rejecting the randomness hypothesis of the GCC stockmarket returns (Al Ashikh, 2012; Bley, 2011; Lee et al., 2010). Apart from these fragmentary results ofunit root tests, it has been argued in the literature that unit root cannot stand by itself to provide anadequate judgement of the random walk, as the process of unit root can have predictable components(Azad et al., 2014). This implies that unit root can only examine the stationarity status of time series,without further ability to show if these stationarity time series are serially correlated or not. The pro-cess of random walk implies that equity prices must not be correlated, for which unit root tests cannotprovide any information.

The empirical investigation of the efficiency of the GCC stock markets encountered macroeco-nomic challenges to account for the impact of financial crises, including the GCC price bubble crisis in2006, and the global financial crisis in 2008, that are likely to cause structural breaks in stock marketreturns behaviour. Lee et al. (2010) investigated whether the EMH holds in a group of developed anddeveloping stock markets, including the Saudi Arabia stock market, under various economic develop-ments. The study accounted for the impact of the presence of two structural breaks that resulted fromthe twin hits of the 1998 Asian crisis and the 2006 GCC bubble crisis. The results of the study con-cluded that the Saudi stock market does not follow random walk behaviour. In addition, Bley (2011)examined the weak-form efficiency of the GCC stock markets taking into consideration the five-yearsub-periods from 2000 to 2009, in which he accounted for the impact of the GCC price bubble crisis in


2006 and the Global Financial Crisis in 2008. Consistent with the results of Lee et al. (2010), the dailyand weekly results of Bley (2011) rejected the weak-form efficiency of GCC stock markets. Furtherchallenging issues relating to the characteristics of GCC stock market prices are identified in the liter-ature, including the impact of thin trading on the accuracy of the results of the weak-form efficiencytests.

Previous studies argued that GCC stock markets, like most of the emerging markets, are char-acterised by infrequent trading behaviour (Bekaert et al., 2005; Harvey, 1995; Moustafa, 2004). Thintrading happens when equities do not trade at every consecutive interval, consequently the infrequenttrading effect can yield statistical biases in the time series of stock prices, leading to induced serial cor-relations (AlKhazali, 2011). Some of the previous GCC empirical studies concerned with examining theefficiency of GCC stock markets recorded different results before and after correcting for the effect ofthin trading. For example, VR and Runs tests were used to examine the null hypothesis of random walkof the GCC stock markets using weekly index returns from 1992 to 1998. The study showed differentresults before and after the consideration of the effect of thin trading (Abraham et al., 2002). The studyrevealed that GCC markets tested as not efficient before correcting for infrequent trading, yet showedthe opposite results after accounting for infrequent trading effects. These results are consistent withthe results of some previous GCC studies (Al-Khazali et al., 2007; AlKhazali, 2011). A recent study, onthe other hand, investigated the efficiency of the Gulf stock markets using different parametric andnonparametric tests on daily and weekly market returns from 1999 to 2010 (Al-Ajmi and Kim, 2012).The study found no differences in results before and after correcting for infrequent trading, althoughthe study argued that less signs of random walk were recorded after correction for thin trading withweekly data.

This view of the marginal difference of results before and after correcting for the infrequent tradingeffect argued that differences in the dataset is the cause of previous studies observing an influentialdifference after correcting for the thin trading effect. For example, GCC studies that showed differentresults before and after correcting for thin trading used stock index prices data ranged from 1992 to2007, in which trading volumes in the Gulf stock markets were indeed very thin. The argument thatthese authors identified is that during the 1990s and the first half of the 2000s, the trading volumeof most of the GCC stock markets was drastically low, but since the second half of the 2000s tradingvolume in the Gulf stock markets has multiplied dramatically. Thus, once trading volumes are nolonger thin, correction for infrequent trading becomes obsolete, as empirically proven by Al-Ajmi andKim (2012).

To this point in the GCC empirical literature, the weak-form efficiency of the GCC stock marketswas only investigated from a single perspective. The investigation of the weak-form efficiency of theemerging stock markets has been extended to consider testing the collective weak-form efficiency ofa group of stock markets using the application of cointegration testing (Guidi and Gupta, 2013). Theexistence of cointegration has implications on the efficiency of equity markets. It has been debatedby previous studies that if the prices of two stock markets are cointegrated, then the movement ofone market can be predicted from the past prices movements of the other (Granger, 1986; Lenceand Falk, 2005). In this sense, these two markets cannot be weak-form efficient as investors can usepast price changes in one market to predict the current price movement in another market. Recently,the weak-form efficiency of the ASEAN stock markets, including those of Indonesia, Malaysia, thePhilippines, Singapore, Thailand and Vietnam, was examined using daily index prices from 2000 to2011. The study used a number of unit root, parametric, and nonparametric tests to investigate theweak-form efficiency of the ASEAN stock markets from an individual perspective. The researchers alsoused the Johansen cointegration test to examine the weak-form efficiency of these Asian stock marketsfrom a collective perspective (Guidi and Gupta, 2013). The study showed that Indonesia, Malaysia, thePhilippines and Vietnam are not weak-form efficient, whereas the Singapore and Thailand stock mar-kets are weak-form efficient individually. From a collective perspective, the results of the cointegrationtest rejected the null hypothesis of cointegration among the ASEAN stock markets. This indicates thatalthough these markets are not weak-form efficient individually, they are efficient collectively. Froman economic perspective, the results imply that there are no common shared trends among the ASEANstock prices that can be exploited to predict the movement of other ASEAN stock market prices.The results of this study may impose challenges related to the individual and collective efficiency


characteristics of regional stock markets as previously suggested by Lim et al. (2008), to the extentthat regionally close economies may in fact not share similar stock market efficiency characteristics,as initially theorised.

The presence of cointegration amongst individual stock markets in the GCC region would implythat Gulf stock markets are not efficient as a group, as the movement of prices in an individualGCC market can be used to predict the movement of other GCC stock market prices. To the best ofour knowledge, using the cointegration test to examine the collective efficiency of the GCC stockmarkets has not been applied by previous studies on the efficiency of the Gulf equity markets.Thus, we aim to fill this gap in the empirical literature by using the application of Johansen coin-tegration in order to add a new perspective to the testing methods of the efficiency of the GCCequity markets. This leads us to the second part of our research question, are GCC stock marketsweak-form efficient as a regional stock market?21 It is evident from the review of the previousempirical literature related to the examination of the weak-form efficiency of the GCC stock mar-kets that mixed results have been achieved through using different methods and different datasets.

Our study aims to extend the empirical literature of the market efficiency in emerging markets infour ways. First, we focus on the regional GCC stock markets that have similar economic reliance on oiland gas revenues. The lack of stock market efficiency among GCC markets is likely to be a disincentivefor regional, migrant and offshore investment capital to invest in GCC markets, consequently leading tocontinuing reliance on the non-renewable oil resources to boost local and regional economic growth.An empirical investigation of the current efficiency status of the GCC stock markets would inform GCCleaders about the current efficiency condition of their equity markets, so that they can implementthe appropriate strategy based on their agenda. Second, regional and international investors wouldbenefit from having current empirical results about the returns profitability and efficiency statusof GCC stock markets. For example, identifying a GCC stock market that has positive market returnrates and simultaneously is inefficient, may imply the presence of a valuable investment opportunityto generate consistently excessive returns by using technical and fundamental information of thatstock market. This possibility of such investment opportunities may require retesting of the currentefficiency condition of GCC stock markets. Third, unlike previous GCC studies, our study considers theuse of current and longer daily index prices data of the Gulf equity markets from the end of December2003 to the end of January 2013, in order to provide a broader and more accurate view about thecurrent expected market returns and the current status of the efficiency of these markets. It has beenidentified in the literature that high frequency data such as daily data produces more robust results(Boehmer and Wu, 2013; Lo and MacKinlay, 1988). Fourth, our review of the empirical literatureshowed that a single testing method seems to be inadequate to draw accurate conclusions aboutthe status of weak-form efficiency of a stock market, due to the inherent limitations suffered by everymodel. We acknowledge this limitation and consider the employment of a wide range of independencytests, including a number of unit root, parametric, nonparametric, and Johansen cointegration tests,in order to maximise the robustness of our results. The application of the cointegration test has neverbeen used previously in the examination of the weak-form efficiency in the Gulf stock markets froma collective perspective. Hence, the methodology that will be employed by our study extends thehorizon of the ways previous studies have focused on investigating the weak-form efficiency of theGulf equity markets from a regional perspective.

4. Methodology

The procedure applied to test the random walk behaviour of the GCC stock markets is chosenon the basis of the implications of EMH suggested by Fama (1970). This procedure implies that ifall relevantly available information is fully reflected in equity prices, then: Successive price changeswill be normally distributed, hence stock returns are normally distributed (Condition 1). Also,successive price changes will be independent, thus no serial correlations will exist between stock

21 Our research question is, are GCC stock markets weak-form efficient as single stock markets and as a regional stock market?


returns (Condition 2). To test the normality of the GCC stock market returns, Condition (1), the nullhypothesis is as follows:

Ho: GCC stock markets’ returns are normally distributed.The reason we first test for normality is that if time series are not normally distributed, then the

results of parametric tests such as VR and multiple variance ratio (MVR) tests should be used withcaution, as these tests are designed to test dependency of time series for normally distributed timeseries. The results of tests that employ parametric tests in the cases when time series are not normallydistributed may result in statistical errors (Kim and Shamsuddin, 2008). We acknowledge this poten-tial limitation of the parametric tests; so, if time series tested to be non-normally distributed, thenwe use non-parametric tests such as Wright and Runs tests, as the structural properties of these non-parametric tests do not require time series to be normally distributed. Therefore, we employ two typesof commonly applied normality tests, including tests of skewness and kurtosis, and the Jarque–Beratest to examine the normality of the GCC stock market returns. In order to investigate and test forrandomness, Condition (2), the study examines null and alternative hypotheses to test the serial depen-dence of the returns behaviour of the GCC stock markets. Accordingly, the null hypothesis is as follows:

Ho: GCC stock markets’ returns are independent as single stock markets, thus are weak-formefficient individually.

To test the randomness of returns in the GCC stock markets, we follow similar testing techniquesto those applied by Granger (1986), Fifield and Jetty (2008), Guidi and Gupta (2013), and Mobarekand Fiorante (2014). Hence, we apply a range of tests, including Augmented Dickey–Fuller (ADF),Phillips–Perron (PP), VR, MVR, Wright, Runs, and Johansen cointegration, in order to examine whetherGCC stock markets are weak-form efficient as individuals or as a group.

4.1. Augmented Dickey–Fuller (ADF) test

The unit root test is designed to test for stationarity of time series, as the presence of non-stationarity indicates the existence of randomness that supports the weak-form efficiency hypothesis(Azad, 2009). We employ three unit root tests including ADF and PP tests. The ADF test is carriedout to find out if the time series being analysed is stationary. When using ADF, one should considerwhether to include exogenous variables in the test regression. For example, there is the choice ofchoosing among two models, including a constant (Eq. (1)), and a constant and a linear time trend(Eq. (2)), as shown below.

Model two : �yt−1 = c0 + ıyt−1 + ˇ

p∑i−1

�yt−1 + �t. (1)

Model three : �yt = c0 + c1t + ıyt−1 + ˇ

p∑i−1

�yt−1 + �t. (2)

yt is a series that follows an autoregressive (AR) process. c0 and c1 are optional exogenous regressors,ı and are parameters to be estimated. �t is assumed to be white noise. The null hypothesis is thepresence of a unit root, so not rejecting that hypothesis means the series follows a random walk. Thehypothesis is evaluated using the conventional t-ratio for ı:

ta = ı

se(ı). (3)

where ı is the estimate of ı, and se (ı) is the coefficient standard error.

4.2. Phillips–Perron (PP) test

The stationarity of GCC market returns can be tested using the procedure developed by Phillipsand Perron (1988) as a non-parametric alternative and controlling test for serial correlations while


testing the unit root. Phillips and Perron (1988) proposed to estimate the long-run variance by theNewey and West estimator. The difference between the PP and the ADF tests is the manner in whichheteroskedasticity and serial correlation in errors are dealt with. The PP test’s idea is to use nonAugmented Dickey–Fuller regression, then make adjustment for bias which may exist because ofcorrelation in innovation terms. The PP test is non-parametric and its specifications are as follows:

Pt = � + ıPt−1 + εt. (4)

Pt = � + ˇ(

t − 12

T)

+ ˛Pt−1 + εt. (5)

in the equations, Pt is natural price index logarithm during time t while � represents a constant. Also and are parameters which need to be estimated, and εt happens to be an error term. Eq. (5) has

the constant term only, while Eq. (4) has both a constant and linear trend terms, � and ˇ(

t − 12 T)

.The PP tests hypotheses may be stated as H0 if the series has unit root and H1 if it is stationary or doesnot have unit root.

4.3. Variance ratio (VR) test

Lo and MacKinlay (1988) provide two different test statistics for the random walk properties of aseries with different sets of null hypotheses. First, they make a strong assumption that the residualsare independently and identically distributed (IID) like Gaussian with variance �2, while normalityassumption is considered as not strictly necessary, called a homoskedastic random walk hypothesis.Alternatively, they outline a heteroskedastic random walk hypothesis to allow for more general formsof conditional heteroskedasticity and dependence, termed as the martingale hypothesis, offering a setof sufficient conditions for the errors to be a martingale difference sequence. The test is based on theassumption of linearity that the variance of a random-walk term is a linear function over time, whichhas made the test the most commonly applied procedure to test the hypothesis of random walk. TheVR statistic is given as follows:

VR(q) = �2(q)�2(1)

. (7)

where

�2(q) = 1Tq

T∑t=1

(YT − YT−q − q�)2. (8)

and the estimator of variance should be adjusted for bias when T is replaced with (T − q + 1) or(T − q + 1)(1 − q/T). Thus the variance ratio statistic is given as:

z(q) = VR(q) − 1

[s2(q)]−1/2. (9)

the statistic is normally distributed for an appropriate choice of variance at q. The variance is givenunder the normality assumption by:

s2(q) = 2(2q − 1)(q − 1)3q1

. (10)

and under the martingale difference sequence it is given by:

s2(q) =q−1∑j−1

(2(q − j)

q

)2

· ıj. (11)


where the ıj is given by:

ıj =

{∑Tt=J+1(yt−j − �)2(yt − �)2

}{∑T

t=J+1(yt−j − �)2} . (12)

Hence, the variance ratio statistic is based on the underlying distribution to be normal if the seriesand errors of the series to be IID Gaussian. z(q) statistic under the homoskedastic and heteroskedasticapproaches to examine the null hypothesis that VR(q) = 1. If the results of the test show that:

VR(q) > 1 or VR(q) < 1. (13)

then understudy stock market returns are said to be positively and negatively serially correlatedrespectively.

4.4. Multiple variance ratio (MVR) test

The main difference between the VR and MVR tests is that the latter overcomes the limitation ofsingle interval testing of the former, to include multiple interval testing (Chiang et al., 2010). Due tocommonly applied statistics at differently selected values of q, the VR conditionality is valid for eachdifference in q > 1. To hold down the size of the joint test, Chow and Denning (1993) test providesa statistic examining the maximum absolute value of multiple variance ratio statistics. The p-valuefor the Chow–Denning statistic is bounded from the VR test by the probability for the studentizedmaximum modulus (SMM) distribution for a set of m given parameters and T degrees-of-freedom.MVR test provides a procedure for the multiple comparison of the set of variance ratio estimates withunity. The null hypothesis of the VR is as follows:

VR(q) = 1 thus MVR(q) = VR(q) − 1 = 0. (14)

under the random walk null hypothesis, MVR can be tested as follows:

H0i : MVR(qi) = 0 or H1i : MVR(qi) /= 0 for any i = 1, 2, . . ..m. (15)

4.5. Runs test

The Runs test is a nonparametric test used to examine the independence of successive price changes.A run is defined as the successive occurrences of the same pattern of changes; positive changes inconsecutives runs, negative changes in consecutives runs, and so forth (Mobarek and Fiorante, 2014).The Runs test applies for the randomness of the runs of series of stock market prices or returns. For ameasure of random stock market returns or prices, the R runs should be nearly equal to the expectednumber of m runs. The null hypothesis of Runs test is thus the weak-form efficiency of GCC stockmarket returns. The calculation of the expected number of runs can be achieved by applying Eq. (16),m as:

m = N(N + 1) −∑3

i=1n2i

N. (16)

where N is the total number of runs, i is the number of positive and negative changes in series and nis the total changes in category of change. The expected number of runs is approximately normallydistributed for n > 30 for standard deviation equal to �m (ibid). The runs are calculated by followingequation:

�m =[∑3

i=1

[n2

i+ N(N + 1)

]− 2N

∑3i=1n3

i− N3

N2(N − 1)

]1/2

. (17)


where the standard normal distribution for conducting Runs test can be determined from the followingequation:

Z = R − m ± 0.5�m

∼N(0, 1). (18)

the number 0.5 in the above specification determines the continuity adjustment being negative whenR ≥ m and positive for R < m. Butler and Malaikah (1992) have indicated if the total number of runsis exceeding (falling behind) the expected number of runs, the occurrences of positive (negative)Z statistic is obtained. It is also to be noted that a negative Z value determines the positive serialcorrelation, while a positive Z value highlights the negative serial correlation. If the value of Z statisticsis above 1.96 at 5% level of significance, the non-randomness is violated.

4.6. Wright test

Wright (2000) provides nonparametric VR tests based on ranks (R1 and R2) that can be morepowerful than the tests suggested by Lo and MacKinlay (1988) in conditions where the distribution ofthe given series is not normal. The rank statistic is given by:

rit = r(�Yt) − T+12√

(T−1)(T+1)12

. (19)

r2t = �−1(

r(�Yt)T + 1

). (20)

in case where the r is tied, the denominators can be modified to account for the ties. Based on theabove the test statistics can be derived as:

R1 (k) =(

1Tk

∑Tt=k(r1t + . . . + r1t−k+1)2

1T

∑Tt=1r2

1t

)× ∅(k)−1/2. (21)

R2(k) =(

1Tk

∑Tt=k(r2t + · · · + r2t−k+1)2

1T

∑Tt=1r2

2t

)× ∅(k)−1/2. (22)

r2t = F − 1(r(DYt)(T + 1). (23)

where T shows the number of observations of first differences of variables Y, (stock prices), ∅ is theasymptotic variance, r is the rank of Y among at 1, T, and ˚−1 is the inverse of the standard normalcumulative distribution function.

4.7. Johansen cointegration test

To this point, all previously mentioned tests have the ability to examine only the random walkbehaviour or the weak-form efficiency of GCC stock markets from the individual perspective. As theaim of this paper is to also investigate the weak-form efficiency of the GCC stock markets from acollective viewpoint, we use the application of cointegration testing. Thus, the null hypothesis is asfollows:

Ho: GCC stock markets are weak-form efficient as a group; thus, there is no long-term cointegrationbetween GCC stock prices.

The presence of cointegration among GCC stock market prices has implications for the EMH. Previ-ous studies debated that if two stock market prices are cointegrated, then past movements of one setof market prices can be used to predict the movement of other market prices (Granger, 1986; Guidiand Gupta, 2013). The basic concept behind cointegration testing is that for two or more nonstation-ary time series, there may be a long-term relationship existing among them. This concept implies


that although the two variables might move randomly from each other, they might converge in thelong run, resulting in what is called a cointegrating relationship (Guidi and Gupta, 2013). The pres-ence of cointegration among stock market returns would imply that the movement of one marketcould be used to predict the movement of the other. Thus, the presence of cointegration among equitymarkets would produce supporting evidence leading to the refutation of the weak-form efficiency.The cointegration test is based on maximum likelihood estimation that proposes two distinct testsfor determining likelihood ratio, including the trace and maximum Eigenvalue tests. The trace test:determines r cointegrating vectors’ null hypothesis alongside the substitute n cointegrating vectors’hypothesis. If the value of r is 0, it therefore implies that a relationship does not exist among thenonstationary variables, hence no cointegration exists. Maximum Eigenvalue test: determines r coin-tegrating vectors’ null hypothesis alongside alternative hypothesis of (r + 1) cointegrating vectors. TheJohansen test adopts a starting point from a vector autoregression of the order p represented as:

yt = � + A1yt−1 + · · · + Apyt−p + εt. (24)

where yt represents n × 1 integrated variables’ vector generally represented as I(1) while εt representsan n × 1 innovations vector. The two likelihood ratio tests include trace test and maximum Eigenvaluetest, and are shown in the following two equations respectively.

Jtrace = −T

n∑t−r+1

ln(l − �i). (25)

JMaxim = −T ln(l − �r+1). (26)

In the equations, T shows the size of the sample while �i shows the ith biggest canonical correlation.The advantage associated with the model is that it can be used in estimation of several cointegrationrelationships.

5. Data and descriptive statistics

Our study comprises daily stock market index prices of Saudi Arabia, Dubai, Abu Dhabi, Kuwait,Oman, Qatar, and Bahrain stock markets in local currencies obtained from Thomson Datastream fromthe end of December 2003 to the end of January 2013. The rationale behind choosing daily data fre-quency has been identified in the literature to the effect that different or inferior results may beexpected when employing low frequency data such as yearly, quarterly, monthly and weekly data,as compared to high frequency data such as daily data (Lo and MacKinlay, 1988). One interpretationof this situation is that as the lag length augments in daily stock prices data, correlation becomessmaller. Hence, this phenomenon clearly argues the notion that the larger the interval of the observa-tions of equity prices, the less significant is the lag price in determining the future stock price changes(Boehmer and Wu, 2013). The selection of our data range from December 2003 to the end of January2013 has two motivations. As we identified previously in the GCC stock market section, two financialhits struck GCC markets in 2006 and 2008. Firstly, investigating the current efficiency status of theGCC stock markets using a current GCC index data to capture the pre-crisis and post-crisis effect on theefficiency of GCC equity markets is necessary. In this paper, we are not going to examine the changesof GCC stock market efficiency pre- and post-crisis, rather examining the entire independence of suc-cessive price changes of GCC stock returns from 2003 to 2013. Secondly, this data range is the longestand most current dataset that we can obtain from Thomson Datastream. The testing process of dailyraw GCC stock prices data are transformed into log returns22 data using the following formula:

(Rt) = Ln(Pt)(Pt − 1)

(27)

22 The aim of using GCC log returns instead of index prices is to convert the index price data into continuously compoundedrates, which is the more common applied practice than employing discrete compounding.


Table 1Summary statistics for daily stock market returns.

No ofobservations

Mean Maximum Minimum SD Skewness Kurtosis Jacque–Bera

P-values

Oman 2397 0.000329 0.080388 −0.086990 0.011044 −0.887619 18.18 23,343 0.0000Saudi Arabia 2397 0.000191 0.163995 −0.116816 0.017647 −0.566263 13.38 10,882 0.0000Kuwait 2397 0.000021 0.074561 −0.075200 0.010485 −0.272469 10.44 55,540 0.0000Qatar 2397 0.000321 0.094220 −0.093592 0.015499 −0.373149 09.56 43,530 0.0000Bahrain 2397 −0.000079 0.036132 −0.049200 0.006064 −0.431974 09.22 39,333 0.0000Abu Dhabi 2397 0.000221 0.398183 −0.364920 0.016963 1.156443 220.11 47,084 0.0000Dubai 2397 0.000270 0.102199 −0.121573 0.018312 −0.126897 08.66 32,080 0.0000

where Rt = GCC market returns for period, Ln = natural log of GCC stock prices, Pt = GCC index price atperiod t, and Pt-1 = GCC index price at period t − 1.

On average, daily market returns for all GCC stock markets are positive with the exception ofthe Bahraini stock market. The highest and lowest daily positive market returns were recorded inOman (.000326%) and Kuwait (.000021%) respectively, as shown in Table 1. The lowest and highestobserved daily standard deviations (SDs) were recorded in Bahrain (.00606%) and in Dubai (.018312%)respectively. On average, risk-return tradeoff seems to not hold very well in GCC stock market returns,for example, as the highest daily SD of (.018312%) in Dubai is not achieved by the highest recordeddaily mean return of (.000326%) in Oman. Daily kurtosis values of all GCC index returns are higherthan three, suggesting that returns distributions are with fatter tails. The skewness values for alldaily GCC index returns are negative with the exception of Abu Dhabi index returns. These negativevalues indicate that asymmetric tail extends further towards negative returns than positive returns,suggesting early signs of non-normality of GCC stock returns. This non-normality suggestion is furtherconfirmed by all daily GCC stock returns, as all Jarque–Bera test statistic values exceed the value of5.99 of which the normality hypothesis of GCC stock returns is rejected at 5% level of significance.

6. Results of the study

6.1. Results of unit root tests

We examine the presence of a unit root in GCC stock returns using ADF and PP tests with bothconstant, and constant and trend. The examination of the presence of a unit root in the GCC stockmarkets implies the non-stationarity behaviour of the individual GCC stock returns, thus proving therandomness of GCC stock returns. We can observe that the values of the test statistics for the ADF andPP under both models are considerably higher than the critical values of Mackinnon equivalent criticalvalues, as shown in Table 2. The results of the daily ADF and PP tests show that all GCC stock market

Table 2Unit root tests for daily stock market returns.

ADF PP

Constant Constant and trend Constant Constant and trend

Oman −40.44612* −40.50217* −40.06483* −40.12973*

Saudi Arabia −45.92687* −45.94655* −40.06483* −45.95849*

Kuwait −42.66640* −42.71700* −42.6628* −42.69838*

Qatar −39.13006* −39.14652* −39.14043* −39.13724*

Bahrain −41.96402* −42.20579* −43.32189* −43.01435*

Abu Dhabi −53.60351* −53.63229* −53.48788* −53.55762*

Dubai −31.81637* −31.91387* −49.16496* −49.06730*

* Indicates rejection of the null hypothesis of random walk at (−3.432) 1% and (−3.961) 1% level of significance for modelswith constant and for the model with constant and trend respectively.


Table 3VR tests for daily GCC stock market returns.

Number of days, Q, in holding period Q = 2 Q = 4 Q = 8 Q = 16

OmanVR (q) 0.60982 0.330076 0.152722 0.07155Homoscedasticity (q) −19.0989* −17.52811* −14.02057* −10.32478*

Heteroscedasticity-robust (q) −5.466594* −5.480285* −4.907736* −4.174539*

Saudi ArabiaVR (q) 0.549661 0.254851 0.129994 0.065177Homoscedasticity (q) −22.0436* −19.49631* −14.39666* −10.39566*


KuwaitVR (q) 0.573773 0.294258 0.143619 0.071982Homoscedasticity (q) −20.86337* −18.46526* −14.17118* −10.31998*


QatarVR (q) 0.617088 0.336404 0.154484 0.078615Homoscedasticity (q) −18.74316* −17.36253* −13.9914* −10.24623*


BahrainVR (q) 0.570458 0.29278 0.142272 0.074332Homoscedasticity (q) −21.02563* −18.50393* −14.19348* −10.29385*


Abu DhabiVR(q) 0.452586 0.233901 0.113952 0.056999Homoscedasticity (q) −26.79534* −20.04446* −14.66212* −10.4866*

Heteroscedasticity-robust (q) −2.062029** −1.873771** −1.811281** −1.744885**

DubaiVR (q) 0.482882 0.24705 0.121099 0.065832Homoscedasticity (q) −25.31236* −19.70042* −14.54386* −10.38838*


*, and ** Indicate rejection of the null hypothesis of random walk at (2.59) 1% and (1.69) 5% level of significance.

returns reject the null hypothesis of the presence of a unit root, at 1% significance level for constant(−3.432) and constant and trend (−3.961).

6.2. Results of variance ration (VR) test

The null hypothesis of independence of successive price changes of the GCC stock market returnsis further investigated by VR tests. In order to accept the null hypothesis of random walk of the GCCstock markets, consequently accepting the weak-form efficiency, the VR test should equal a value ofone in order to reject the presence of autocorrelation between lags. If the results of VR test go below orabove one, then positive or negative autocorrelations imply the possibility of predicting future pricechanges. The presence of such potential for exploitation would provide evidence towards rejectingthe random walk hypothesis of the GCC stock markets, leading to the rejection of the weak-formefficiency of these Gulf markets. Table 3 displays the results of VR tests for daily GCC stock marketreturns at different time lags, including 2, 4, 8, and 16 lags, with the consideration of homoscedasticand heteroscedastic growth of GCC stock returns’ residuals. Results of the daily VR tests for individ-ual GCC stock markets are quite similar, as all VR results for all lags are above the critical value of2.59 with negative signs indicating the rejection of random walk at 1% level of significance. Underthe homoscedasticity assumption, all GCC stock market returns illustrate significantly negative serialcorrelations in all lags at 1% level of significance (indicated with * signs). However, even after het-eroscedastic growth is assumed, signs of significant autocorrelations are evident for all lags and forall GCC stock market returns at 1% level of significance (indicated with * signs). Hence, we show thatthe null hypothesis of independent successive price changes of all daily GCC stock market returns issignificantly rejected at 1% level of significance, indicating the rejection to the null hypothesis of theweak-form efficiency of the GCC stock markets.


Table 4MVR Tests for the GCC stock markets returns.

Daily

MVR1 MVR2

Oman 19.09890* 5.480285*

Saudi Arabia 22.0436* 9.422655*

Kuwait 20.86337* 9.515027*

Qatar 18.74316* 8.330987*

Bahrain 21.02563* 11.44112*

Abu Dhabi 26.79534* 2.062029Dubai 25.31236* 11.95115*

MV1 is the homoscedastic and MV2 is the heteroscedastic-robust version of the Chow–Denning test.* Indicates rejection of the null hypothesis of random walk at (3.089) 1% level of significance.

6.3. Results of multiple variance ratio (MVR) test

Table 4 presents the results of the MVR tests for daily stock returns for the GCC stock markets,under homoscedastic and heteroscedastic growth assumptions of GCC stock returns. According toTable 4, signs of significant autocorrelation coefficient prevail for all GCC stock market returns andcritical values of Cow–Denning of 2.68 (5%) are exceeded for all GCC stock returns. For example,under the homoscedastic and heteroscedastic growth assumptions for daily GCC stock returns, thenull hypothesis is significantly rejected at 1% level of significance for all markets due to the presenceof significant positive autocorrelation effect (indicated with * signs). Although the MVR test overcamethe individual testing problems of the VR test, both the VR and MVR are prone to statistical bias that canbe caused by the violation of the normality prerequisites. However, in the literature review section, wehighlighted that parametric tests, including VR, and MVR tests, are likely to cause a statistical bias fromthe use of non-normally distributed time series, as they all have normality prerequisites. To overcomethe problems of non-normality while testing for dependency of the GCC stock market returns, we usenonparametric tests, including Wright and Runs tests.

6.4. Results of Wright test

The results of R1 and R2 for Wright test, including 2, 5, 10, and 10 time lags for daily GCC stockreturns, are reported in Table 5. The results of R1 and R2 for the GCC stock returns for all lags exhibitsignificant serial correlation coefficient values indicating the relation between past and current stockprice movements. The possible predictability, as illustrated by the significant R1 and R2 results, sup-ports the rejection of the random walk hypothesis of the GCC stock markets at 5% statistical level ofsignificance. Thus, we can state that GCC daily stock market returns cannot be weak-form efficient.

6.5. Results of Runs test

The guideline of accepting the null hypothesis of the independence of successive price changes ofthe GCC stock returns requires a close match between the expected and actual number of runs. Thesignificant presence of large or low numbers of actual runs compared to the number of expected runswould provide rejection evidence against the random walk hypothesis. From a statistical viewpoint,the null hypothesis of random walk of the GCC stock market returns is rejected at 5% level of signifi-cance when Z value of the Runs test is higher than the critical value of 1.96. Negative Z values indicatethe presence of significant autocorrelation among stock returns. Table 6 displays the daily results ofRuns tests for the GCC stock market returns using the median (Z) and mean (Z*) of returns as a base forestimating the number of runs. It should be noted that we use Z* just for results comparability ratherthan for concluding results. All reported values of Runs tests for both Z and Z* results are higher thanthe critical value of 1.96. This indicates a significant rejection of the randomness of the daily GCC stockreturns caused by the presence of statistically significant serial correlation.


Table 5Wright’s nonparametric VR tests using ranks for daily GCC stock market returns.

Number of K lags K = 2 K = 5 K = 10 K = 30

OmanR1 −17.61628** −15.28699** −11.33996** −6.683448**

R2 −18.45808** −16.19739** −12.03307** −7.092166**

Saudi ArabiaR1 −22.33245** −16.46014** −11.74368** −6.907769**

R2 −22.98358** −17.18421** −12.3987** −7.254416**

KuwaitR1 −19.34092** −15.70807** −11.54924** −6.792431**

R2 −20.26477** −16.68921** −12.3429** −7.22876**

QatarR1 −15.19351** −14.2316** −10.45506** −6.077322**

R2 −16.78736** −15.63567** −11.67984** −6.89871**

BahrainR1 −20.01641** −15.81231** −11.78683** −7.028656**

R2 −20.94608** −16.82346** −12.58638** −7.437216**

Abu DhabiR1 −17.16343** −14.87896** −10.96826** −6.453498**

R2 −18.50601** −15.99237** −11.7555** −6.915291**

DubaiR1 −23.12857** −16.66604** −11.99045** −6.849078**

R2 −24.74983** −17.64975** −12.70467** −7.355725**

** Indicates the rejection at 5% level of significance.

6.6. Results of Johansen cointegration test

As all previously applied tests are only able to examine the weak-form efficiency of the GCC stockmarkets from an individual perspective, we implement the Johansen cointegration test in order toexamine the possibility of the presence of long-term relationships among GCC stock prices. TheJohansen cointegration test requires stock prices to be integrated at the same order to be able toexamine the likelihood of observing stationary cointegrating relationship among non-stationary stockprices. In order to satisfy this essential requirement, Table 7 presents the results of daily ADF and PPtests for the GCC stock prices. As most of the GCC economies and stock markets have witnessed pro-gressive growth over the last few years, intercept and trend are included in the testing equation forADF and PP tests at level and at first difference in order to account for this growth. The results ofboth ADF and PP tests collectively show that all GCC stock prices are non-stationary at level, whileall are stationary at first difference (log returns) for daily stock prices. The non-stationarity of GCCstock prices at level for all ADF and PP values of all GCC stock market prices are below −3.961 (1%), asdisplayed in Table 7. These results imply that all GCC stock market prices are integrated at the same

Table 6Runs test for daily GCC stock market returns.

Observations(N)

n (+) n (−) n (0) Expectedruns (m)

Actual runs(R)

Z Z*

Oman 2397 1461 936 0 1142 929 −9.14** −6.87**

Saudi Arabia 2397 1199 1198 0 1199 1112 −3.58** −4.00**

Kuwait 2397 1424 973 0 1157 1039 −5.00** −5.99**

Qatar 2397 1244 1198 0 1199 1002 −8.07** −7.81**

Bahrain 2397 1265 1153 0 1198 1061 −5.60** −5.03**

Abu Dhabi 2397 1307 1090 0 1190 975 −8.84** −7.44**

Dubai 2397 1291 1106 0 1192 1105 −3.59** −2.30**

** Indicates the rejection at (1.96) 5% level of significance.


Table 7ADF and PP tests for testing the order of integration of the daily GCC stock market prices.

ADF test PP test

Daily Level First difference Level First difference

Oman −1.512118 −40.50217* −1.47592 −40.12973*

Saudi Arabia −2.350622 −45.94655* −2.33794 −45.95849*

Kuwait −1.598868 −42.717* −1.62154 −42.69838*

Qatar −2.613338 −39.14652* −2.4807 −39.13724*

Bahrain −2.316542 −42.20579* −2.22617 −43.01435*

Abu Dhabi −2.331444 −53.63229* −2.32265 −53.55762*

Dubai −2.172475 −31.91387* −2.21434 −49.0673*

* Indicates rejection of the null hypothesis of random walk at (−3.961) 1% level of significance for the model with constantand trend.

Table 8Results of Johansen cointegration test for GCC stock prices.

Daily GCC stockprices

Model 1:Intercept without trend Model 2: Intercept with trend

Number ofcointegratingvector

Tracestatistic

5%criticalvalues

Max-Eigenstatistic

5%criticalvalues

Tracestatistic

5%criticalvalues

Max-Eigenstatistic

5%criticalvalues

None 170.2 125.6** 61.1 46.2** 201 150.5** 62.6 50.5**

At most 1 109.1 95.7** 42.3 40.0** 138 117.7** 52.6 44.4**

At most 2 66.7 69.8 29.8 33.8 85.5 88.8 35.6 38At most 3 36.8 47.8 20.5 27.5 49.9 63.8 22 32

** Indicates the rejection at 5% level of significance.

level (first difference) at 1% level of significance, meeting the prerequisite requirement of the Johansencointegration test.

Before choosing the type of the deterministic trend assumptions required for the Johansen cointe-gration test, the number of lags is determined to be 4 lags in order to allow for longer testing windows.Among the five available models23 that the Johansen cointegration test provides, we assume the pres-ence of a linear deterministic trend in the GCC stock prices. The logic behind this assumption is thatas progressive economic and stock market growth is observed in the GCC region, linear growth isexpected. However, under this deterministic trend assumption, there are two options, including theconsideration of including trend, or trend and intercept, in the cointegrating equation. In order toobserve whether the inclusion or exclusion of time trend in the cointegrating equation may cause dif-ferences in the results, Table 8 presents the results of the Johansen cointegration test for both interceptwithout and with trend. All daily GCC stock price data for both models with and without trend, bothTrace and Max-Eigen test statistics rejected the null hypothesis of the presence of no cointegrationbetween GCC stock prices. Two cointegrating vectors are present for daily GCC stock prices at 5% levelof significance, (indicated with ** signs) in Table 8.

These results imply the presence of strong long-term common trends among GCC stock prices,indicating that GCC stock markets are not weak-form efficient as a group. From an investment view-point, GCC investors may be able to predict, for example, future movement of Dubai stocks from thepast movement patterns of the Saudi or Qatari stock markets. The proven inefficient stock marketscharacteristics would imply that other regionally close economies like the GCC may indeed share sim-ilar efficiency characteristics. The presence of long-term relationships of the GCC stock market pricesusing Johansen cointegration tests was acknowledged by previous scholars, including Abraham and

23 The first model has not intercept and trend VAR, while the second model permits intercept without trend in the CointegratingEquation (CE) and no intercept in VAR. The third model permits intercept in the CE and VAR, while the fourth model permitsintercept in CE and VAR along with linear trend in CE but not for VAR. The fifth model permits intercept and quartic trend in CEand VAR.


Seyyed (2006), Al-Khazali et al. (2006), Hammoudeh and Choi (2006), and Neaime (2002). However,to the best of our knowledge, none of these authors or other studies has used the application of thecointegration analysis of stock market prices to examine the collective weak-form efficiency in theGCC region. Thus, the results of this study extend the horizon of the literature of GCC stock marketefficiency, in which the long-term relationship of stock prices can be considered as a tool for testingthe weak-form efficiency of a group of stock markets in the Middle East, and elsewhere.

7. Discussion of the results

The prevailing significant serial correlation among GCC stock market returns and the long-termprice co-movements may be caused by the presence of excessive stock market volatility and infor-mation asymmetry. In fact, the results are in line and are a consequent response to the stock marketefficiency characteristics that GCC markets share, and were outlined in the economic and stock marketsection. This excessive market volatility found its way through GCC stock markets due to a specificstock market characteristic that GCC markets together share. The high concentration of GCC equitymarkets in banking and financial services, coupled with well-developed global and interregional finan-cial linkages, may be acting as a mobiliser of this volatility. Previous studies argued that global andinterregional connected banking systems play a crucial role in the development of financial crisesas financial intermediaries which contribute to the efficient transfer of funds from the cash surplusto the cash deficit (Lagoarde-Segot and Lucey, 2006; Neaime, 2012). It is also well documented inthe literature that excessive volatility normally results from financial crisis shocks, and these criseseventually result in reducing the efficiency of stock markets through the augmentation of serial corre-lation trends within and amongst market returns (Azad, 2009; Risso, 2008). The previous two studiesempirically identified the negative relationship of stock market efficiency and financial crises. As GCCstock markets received two financial hits in 2006 and 2008, and their indices are heavily weighted inbanking and financial sectors, we can say that the characteristics of the GCC stock markets, coupledwith the impact of these two major events that occurred to Gulf equity markets, helped in constitutingthe current weak-form inefficiency of single and collective GCC stock markets.

We believe that the results of our study are very important for four reasons. First, the comprehensiveanalysis we carried forward from the economic and stock market overview of GCC countries allowed usto reveal some distinct features of these regional equity markets. The rapid growth in GCC stock markettrading volumes from the early to late 2000s highlighted the presence and absence of thin tradingeffects during the early and late 2000s respectively. The results of our study constitute a challengeto the results of Dahel and Laabas (1999), Moustafa (2004), and Rao and Shankaraiah (2003). Thesestudies accepted the efficiency of GCC stock markets using a short range of data, less than four yearson average, and their data covered periods where GCC stock markets were characterised as thinlytrading markets as empirically proven by Al-Khazali et al. (2007) and Elango and Hussein (2008). Incontrast, our study employed a longer and current range of data from 2003 to 2013, the first GCC studythat uses such a long and current dataset. The argument we have carried forward from the literaturereview is that the impact of thin trading becomes obsolete as GCC stock market trading volumesmultiplied many times since the mid-2000s. Bley (2011) used a GCC data range from 2000 to 2009,and Al-Ajmi and Kim (2012) employed GCC index prices from 1999 to 2010, and they both correctedfor the impact of thin trading. They both found no different results before and after correcting for theeffect of infrequent trading, an outcome that our own results reinforce as highly valid and reliable.Secondly, due to the use of current GCC stock market data that captured the full financial effect ofthe recent financial crises on GCC market returns, our results reveal that not all GCC stock marketsproduced positive returns as previously thought. The Bahrain stock market produced negative stockmarket mean returns. Acknowledging the significant effect of the recent credit crisis on equity marketreturns and particularly banking sector returns, it is no wonder that Bahrain stock market returnsproduced negative returns as, in 2012, almost 68% of its stock market was concentrated in bankingand financial services.

Thirdly, the analysis and argument carried forward in our study also establish an important basicunderstanding of the relationship between single and regional stock market characteristics of emerg-ing equity markets, particularly the GCC markets, and the implications of these features on the status of


the efficiency in these markets. Discounting the impact of thin trading because it has become obsolete,the two identified stock market features, including weak foreign participation and high concentrationin banking businesses, may continue to have a negative influence on the efficiency of GCC markets.Lastly, although the results of our study complement the empirical results of Al-Ajmi and Kim (2012)and Bley (2011) who applied a similar range of robust parametric and nonparametric tests to provethe inefficient status of GCC stock markets, we went the extra mile by being the first GCC study toemploy the technique of cointegration analysis to examine the collective weak-form efficiency of GCCstock markets.

8. Implications of the results

The implications of the results of our study are of importance to the academic body of knowledge,and to investors and policy makers in the GCC countries. For academics, the use of cointegration test-ing applications in order to show the presence of a long-term relationship of GCC stock market pricesprovides supporting evidence that GCC stock markets are inefficient as a regional stock market. To thebest of our knowledge, none of the previous studies have used the application of the cointegrationanalysis of stock market prices to examine the collective weak-form efficiency in the GCC stock mar-kets. Therefore, using the application of the cointegration testing to examine the efficiency of stockmarkets may extend the horizon of the literature of GCC stock market efficiency, in which a long-termrelationship of stock prices can be considered as a tool for testing the weak-form efficiency of a groupof stock markets in the Middle East, and elsewhere.

For investors, the conclusion drawn about the rejection of the weak-form efficiency of the GCCstock returns implies that future successive price changes can be predicted from past price changes,as not all past stock information is fully incorporated in current prices. Hence, investors may apply,for example, a “beat the market” strategy against a “hold the market” strategy to produce excessivereturns. GCC stock markets provide valuable investment opportunities for regional and global investorswho seek diversification opportunities in markets that have simultaneously positive mean returns andpredictable price movements, with the exception of the Bahraini stock market that has negative meanreturns. The documented presence of a long-term relationship among GCC stock prices implies thatinvestors in an ‘X’ GCC stock market may be able to predict the movement of ‘Y’ GCC stock marketfrom past movements of the former, due to the presence of a long-term price bond between stockmarkets X and Y.

For GCC policy makers, the results of our study are an alarming outcome that may hinder long-term growth plans set forth by GCC policy makers, that is, to reduce reliance on the non-renewable oilrevenues in order to promote local and regional economic growth. GCC leaders’ plans to expand the sizeof their markets along with enhancing the market efficiency may be at risk, as GCC stock markets sharecommon persistent stock market characteristics that would disable the presence of such efficiency forGCC stock markets. We have identified two persistent stock market features that GCC markets share.First, there is weak foreign participation caused by the presence of signals of information asymmetry, inturn caused by the presence of foreign ownership restriction and weak financial market development.The financial market development proxies provided, including measures of ease of doing business,transparency of public and private market sectors, and adequacy of digital financial disclosure, indicatethat the GCC financial market is informationally inefficient. Second, the high industry concentration incrisis-prone industries such as the banking business may negatively influence GCC markets’ efficiencystatus, for just as while banking and financial linkages promote the transfer of offshore financial crisesinto GCC markets, in turn a financial crisis causes a rapid establishment of increasing frequencies ofserial correlations among current and past price changes. The conclusions drawn by this study aboutthe rejection of the weak-form efficiency of the GCC stock markets imply the presence of asymmetricinformation among GCC investors, by which not all investors have the same access to all publiclyavailable information. Thus, the lack of stock market efficiency among GCC markets is likely to be adisincentive, and may work as a negative sentiment signal for serious regional, migrant, and offshoreinvestment capital to invest in GCC markets, consequently leading to continuing reliance on the non-renewable oil resources to boost local and regional economic growth.


9. Conclusion

With the growing interest of investors in identifying investment opportunities, of academics inunderstanding the working mechanism of equity markets, and of policy makers in building positivemarket sentiment, efficiency of developing stock markets matters. Efficiency of stock markets pro-vides a theoretical and predictive model imperative for the operation of equity markets that wouldassist local, regional, and global investors to identify mispriced assets, leading to enhancing their risk-adjusted returns. Ensuring market efficiency is also vital to regulators of equity markets, in the sensethat efficiency of stock markets works as a safeguard mechanism that protects markets from distort-ions resulting from the presence of information asymmetry amongst market participants, which inturn acts as a disincentive to serious regional and global investment capital. We employed daily indexprices denominated in local currencies from the end of December 2003 to the end of January 2013using a number of parametric and nonparametric, unit root, and cointegration tests. We aimed toanswer an important research question, whether GCC stock markets are weak-form efficient as singlestock markets or as a regional stock market. The results of our study showed that all GCC stock marketsare not weak-form efficient, in the sense that current stock price movement can be predicted frompast price movements. When we examined the weak-form efficiency of GCC stock markets from a col-lective perspective, we found that GCC stock market prices were cointegrated in the long run, meaningthat the movement of individual GCC stock market prices can be predicted by the past movements ofother GCC market prices.

The implications of our results imply that investors can easily identify mispriced GCC stocks byobserving past price changes within an individual GCC stock market. Investors who wish to spot mis-pricing in one GCC market by looking at historical price changes of another GCC market can do so, asGCC stock market index prices are cointegrated in the long term. Institutional investors such as fundmanagers who seek to diversify their investment portfolios by considering GCC stock markets as analternative channel of investment can take advantage of the findings of our study. The implications ofour findings are also of importance for those academics who wish to understand the working mecha-nisms and the efficiency characteristics of these regional stock markets. The outcomes of our study arealso of great importance to GCC policy regulators. Better informed about the current efficiency statusof their equity markets, they can improve the flow of information among market participants in orderto increase the attractiveness of their equity markets to regional and international investment capital,leading to enhanced market sentiment and to a greater role for their equity markets in boosting localand regional economic growth. As our results established the linkage between the composition ofemerging equity markets on the status of their equity market efficiency, future research endeavoursmay consider an empirical examination of this linkage.

Acknowledgements

We thank (the Editor) and the anonymous referee for their comments and suggestions. We alsothank Associate Professor Robert Bianchi for his initial comments on initial draft. Thanks also to Dr.Philip Robertson for his proofreading assistance.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, athttp://dx.doi.org/10.1016/j.ribaf.2014.09.001.

References

Abdmoulah, W., 2010. Testing the evolving efficiency of Arab stock markets. Int. Rev. Financ. Anal. 19 (1), 25–34.Abraham, A., Seyyed, F., Alsakran, S., 2002. Testing the random walk behavior and efficiency of the Gulf stock markets. Financ.

Rev. 37 (3), 469–480.Abraham, A., Seyyed, F.J., 2006. Information transmission between the Gulf equity markets of Saudi Arabia and Bahrain. Res.

Int. Bus. Financ. 20 (3), 276–285.

http://dx.doi.org/10.1016/j.ribaf.2014.09.001

http://refhub.elsevier.com/S0275-5319(14)00045-2/sbref0005


























































Al-Ajmi, J., Kim, J.H., 2012. Are Gulf stock markets efficient? Evidence from new multiple variance ratio tests. Appl. Econ. 44(14), 1737–1747.

Al-Khazali, O., Darrat, A.F., Saad, M., 2006. Intra-regional integration of the GCC stock markets: the role of market liberalization.Appl. Financ. Econ. 16 (17), 1265–1272.

Al-Khazali, O.M., Ding, D.K., Pyun, C.S., 2007. A new variance ratio test of random walk in emerging markets: a revisit. Financ.Rev. 42 (2), 303–317.

Al Ashikh, A.I., 2012. Testing the weak-form of efficient market hypothesis and the day-of-the-week effect in Saudi stockexchange: linear approach. Int. Rev. Bus. Res. Pap. 8 (6).

Al Janabi, M.A., Hatemi-J, A., Irandoust, M., 2010. An empirical investigation of the informational efficiency of the GCC equitymarkets: evidence from bootstrap simulation. Int. Rev. Financ. Anal. 19 (1), 47–54.

AlKhazali, O., 2011. Does infrequent trading make a difference on stock market efficiency?: Evidence from the Gulf CooperationCouncil (GCC) countries. Stud. Econ. Financ. 28 (2), 96–110.

Ariss, R.T., Rezvanian, R., Mehdian, S.M., 2011. Calendar anomalies in the Gulf Cooperation Council stock markets. Emerg. Mark.Rev. 12 (3), 293–307.

Azad, A., 2009. Random walk and efficiency tests in the Asia-Pacific foreign exchange markets: evidence from the post-Asiancurrency crisis data. Res. Int. Bus. Financ. 23 (3), 322–338.

Azad, A., Azmat, S., Fang, V., Edirisuriya, P., 2014. Unchecked manipulations, price–volume relationship and market efficiency:evidence from emerging markets. Res. Int. Bus. Financ. 30, 51–71.

Bekaert, G., Harvey, C.R., Ng, A., 2005. Market integration and contagion. J. Bus. 78 (1), 39–69.Bhattacharyya, S.C., 2011. Impact of high energy prices. Energ. Econ., 441–462.Bley, J., 2011. Are GCC stock markets predictable? Emerg. Mark. Rev. 12 (3), 217–237.Boehmer, E., Wu, J.J., 2013. Short selling and the price discovery process. Rev. Financ. Stud. 26 (2), 287–322.Boughanmi, H., 2008. The trade potential of the Arab Gulf Cooperation Countries (GCC): a gravity model approach. J. Econ. Integr.

23 (1), 42–56.Butler, K.C., Malaikah, S.J., 1992. Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia. J. Bank.

Financ. 16 (1), 197–210.Chiang, S.-M., Lee, Y.-H., Su, H.-M., Tzou, Y.-P., 2010. Efficiency tests of foreign exchange markets for four Asian countries. Res.

Int. Bus. Financ. 24 (3), 284–294.Choi, J.J., Lam, K.C., Sami, H., Zhou, H., 2013. Foreign ownership and information asymmetry. Asia-Pacif. J. Financ. Stud. 42 (2),

141–166.Chow, K.V., Denning, K.C., 1993. A simple multiple variance ratio test. J. Econometr. 58 (3), 385–401.Dahel, R., Laabas, B., 1999. The behavior of stock prices in the GCC markets. In: Paper presented at the Economic Research Forum

Working Papers.Elango, D., Hussein, M.I., 2008. An empirical analysis on the weak-form efficiency of the GCC markets applying selected statistical

tests. Int. Rev. Bus. Res. Pap. 4 (1).Fifield, S.G., Jetty, J., 2008. Further evidence on the efficiency of the Chinese stock markets: a note. Res. Int. Bus. Financ. 22 (3),

351–361.Gandhi, D.K., Saunders, A., Woodward, R.S., 1980. Thin capital markets: a case study of the Kuwaiti stock market. Appl. Econ. 12

(3), 341–349.Granger, C.W., 1986. Developments in the study of cointegrated economic variables. Oxford Bull. Econ. Stat. 48 (3),

213–228.Guidi, F., Gupta, R., 2013. Market efficiency in the ASEAN region: evidence from multivariate and cointegration tests. Appl.

Financ. Econ. 23 (4), 265–274.Guyot, A., Lagoarde-Segot, T., Neaime, S., 2014. Foreign shocks and the international cost of equity. Evidence from the MENA

region. Emerg. Mark. Rev. 18, 101–122.Hammoudeh, S., Choi, K., 2006. Behavior of GCC stock markets and impacts of US oil and financial markets. Res. Int. Bus. Financ.

20 (1), 22–44.Harvey, C.R., 1995. Predictable risk and returns in emerging markets. Rev. Financ. Stud. 8 (3), 773–816.Healy, P.M., Palepu, K.G., 2001. Information asymmetry, corporate disclosure, and the capital markets: a review of the empirical

disclosure literature. J. Acc. Econ. 31 (1), 405–440.Hussainey, K., Al-Nodel, A., 2009. Corporate governance online reporting by Saudi listed companies. Res. Acc. Emerg. Econ. 8,

39–64.Ismail, T. H. (2002). An empirical investigation of factors influencing voluntary disclosure of financial information on the internet

in the GCC countries. Available at SSRN 420700.Jensen, M.C., 1978. Some anomalous evidence regarding market efficiency. J. Financ. Econ. 6 (2), 95–101.Jiang, L., Kim, J.B., 2004. Foreign equity ownership and information asymmetry: evidence from Japan. J. Int. Financ. Manage.

Account. 15 (3), 185–211.Joshi, P., Al-Modhahki, J., 2003. Financial reporting on the Internet: empirical evidence from Bahrain and Kuwait. Asian Rev.

Account. 11 (1), 88–101.Kim, J.H., Shamsuddin, A., 2008. Are Asian stock markets efficient? Evidence from new multiple variance ratio tests. J. Empir.

Financ. 15 (3), 518–532.Klapper, L.F., Love, I., 2004. Corporate governance, investor protection, and performance in emerging markets. J. Corporate

Financ. 10 (5), 703–728.Lagoarde-Segot, T., Lucey, B.M., 2006. Financial vulnerability in emerging markets. Evidence from the Middle East and North

Africa. The Institute for International Integration Studies Discussion Paper Series, iiisdp114.Lee, C.-C., Lee, J.-D., Lee, C.-C., 2010. Stock prices and the efficient market hypothesis: evidence from a panel stationary test with

structural breaks. Jpn. World Econ. 22 (1), 49–58.Lence, S., Falk, B., 2005. Cointegration, market integration, and market efficiency. J. Int. Money Financ. 24 (6), 873–890.Lim, K.-P., Brooks, R.D., Kim, J.H., 2008. Financial crisis and stock market efficiency: empirical evidence from Asian countries.

Int. Rev. Financ. Anal. 17 (3), 571–591.


















































































































































































































































































































































































































































































































































































































































































































































Lo, A.W., MacKinlay, A.C., 1988. Stock market prices do not follow random walks: evidence from a simple specification test. Rev.Financ. Stud. 1 (1), 41–66.

Fama, Eugene F., 1970. Efficient capital markets: A review of theory and empirical work. The journal of Finance 25 (2), 383–417.Mobarek, A., Fiorante, A., 2014. The prospects of BRIC countries: testing weak-form market efficiency. Res. Int. Bus. Financ. 30,

217–232.Moustafa, M.A., 2004. Testing the weak-form efficiency of the United Arab Emirates stock market. Int. J. Bus. 9 (3), 309–325.Neaime, S., 2002. Liberalization and financial integration of MENA stock markets. In: Paper presented at the A paper presented

at the ERF’s 9th annual conference on Finance and Banking.Neaime, S., 2005. Financial market integration and macroeconomic volatility in the MENA region: an empirical investigation 1.

Rev. Middle East Econ. Financ. 3 (3), 231–255.Neaime, S., 2012. The global financial crisis, financial linkages and correlations in returns and volatilities in emerging MENA

stock markets. Emerg. Mark. Rev. 13 (3), 268–282.Onour, I.A., 2004. Testing weak-form efficiency of Saudi stock exchange market (Available at SSRN 611209).Onour, I.A., 2009. Testing efficiency performance of Saudi stock market. J. King Abdulaziz Univ.: Econ. Adm. 23 (2), 15–27.Peress, J., 2010. Product market competition, insider trading, and stock market efficiency. J. Financ. 65 (1), 1–43.Phillips, P.C., Perron, P., 1988. Testing for a unit root in time series regression. Biometrika 75 (2), 335–346.Rao, D., Shankaraiah, K., 2003. Stock market efficiency and strategies for developing GCC financial markets: a case study of the

Bahrain stock market. Arab Bank Rev. 5 (2), 16–21.Risso, W.A., 2008. The informational efficiency and the financial crashes. Res. Int. Bus. Financ. 22 (3), 396–408.Sandoval Junior, L., Franca, I.D.P., 2012. Correlation of financial markets in times of crisis. Phys. A: Stat. Mech. Appl. 391 (1),

187–208.Squalli, J., 2006. A non-parametric assessment of weak-form efficiency in the UAE financial markets. Appl. Financ. Econ. 16 (18),

1365–1373.World Economic Forum, 2014. The Global Competitiveness Report, Available from: http://www.weforum.org/reports?

filter[type]=Competitiveness (accessed 10.05.14).Wright, J.H., 2000. Alternative variance-ratio tests using ranks and signs. J. Bus. Econ. Stat. 18 (1), 1–9.































































































































































































































































































































Contents Research in International Business Finance · GCC equity markets in order to show the size of these Gulf equity markets. From an intraregional perspective, in 2011 the stock

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