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CAPITAL 

nd BURSA MALAYSIA 

MARKETS 

REVIEW 

Volume 25, Number 1, 2017 

VOl 

ARTICLES 

ABDOLHOSSEIN ZAMENI and OTHMAN YONG 

Substantial Shareholders and Their Trading Behaviour around Lock-Up Expiry: Evidence 

from Emerging Markets 

WAI-MUN HAR, AI-LIAN TAN, CHONG-HENG LIM and CHAI- 

THING TAN 

Does Interest Rate Still Matter in Determining Exchange Rate? 

SHANGKARI V. ANUSAKUMAR and RUHANI ALI 

Momentum and Investor Sentiment: Evidence from Asian Stock Markets 

YOU-HOW GO and WEE-YEAP LAU 

The Relationship of Crude Palm Oil Spot-Futures under Inflationary Expectation in Gold 

Market 

The Journal of the MALAYSIAN FINANCE ASSOCIATION and 

BURSA MALAYSIA

MALAYSIAN FINANCE ASSOCIATION 

Founded in 1998 

EXECUTIVE COMMITTEE 2017/2018 

President .......................................................... Catherine Soke-Fun Ho, Universiti Teknologi MARA 

Vice President .................................................. Chee-Wooi Hooy, Universiti Sains Malaysia 

Secretary .......................................................... Wee-Yeap Lau, University of Malaya 

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PRESIDENTS 

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......................................................................... Education in Islamic Finance (INCEIF) 

2017/2018 – Present......................................... Catherine Soke-Fun Ho, Universiti Teknologi MARA 

ABOUT MFA 

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Capital Markets Review 


Chief Editor 

Catherine Soke-Fun Ho 

Universiti Teknologi MARA, Malaysia 

Managing Editor 

Chee-Wooi Hooy 

Universiti Sains Malaysia 

Chee-Keong Choong 

Universiti Tunku Abdul Rahman 

Hooi Hooi, Lean 


Kian-Ping, Lim 

University of Malaya 

Associate Editors 

Evan Lau 

Universiti Malaysia Sarawak 

Irwan Adi Ekaputra 

Universitas Indonesia 

Kym Brown 

Monash University 

Mansor H. Ibrahim 

INCEIF 

Ruzita Abdul Rahim 

Universiti Kebangsaan Malaysia 

Tai-Leung Terence Chong 

Chinese University of Hong Kong 

Razali Haron 

International Islamic University of Malaysia 

Siong-Hook, Law 

Universiti Putra Malaysia 

Yusnidah Bt Ibrahim 

Universiti Utara Malaysia 

A. Mansur M. Masih 

INCEIF 

Editorial Advisers 

Ali M. Kutan 

Southern Illinois University, USA 

Allaudeen Hameed 

National University of Singapore, Singapore 

Fauzias Mat Nor 

Universiti Sains Islam Malaysia 

Kim-Leng, Goh 


M. Shahid Ebrahim 

Durham University, UK 

Obiyathulla Ismath Bacha 

INCEIF 

Ruhani Ali 


Wing-Keung Wong 

Asia University, Taiwan 

Annuar Md Nassir 

Universiti Putra Malaysia 

Ferdinand A. Gul 

Deakin University, Australia 

Mansor Md Isa 


Nur Adiana Hiau Abdullah 

Universiti Utara Malaysia 

Robert Faff 

University of Queensland, Australia 

S. Ghon Rhee 

University of Hawaii, USA 

Editorial Assistant 

Shu Fen, Chuah

Capital Markets Review 


CONTENTS 

Substantial Shareholders and Their Trading Behaviour around Lock-Up Expiry: Evidence 

from Emerging Markets 

ABDOLHOSSEIN ZAMENI and OTHMAN YONG ................................................................ 01-18 


WAI-MUN HAR, AI-LIAN TAN, CHONG-HENG LIM and CHAI-THING TAN .................. 19-25 

Momentum and Investor Sentiment: Evidence from Asian Stock Markets 

SHANGKARI V. ANUSAKUMAR and RUHANI ALI ............................................................ 26-42 

The Relationship of Crude Palm Oil Spot-Futures under Inflationary Expectation in Gold 

Market 

YOU-HOW GO and WEE-YEAP LAU ..................................................................................... 43-62

Capital Markets Review Vol. 25, No. 1, pp. 1-18 (2017) ISSN 1823-4445 

Substantial Shareholders and Their Trading 

Behaviour around Lock-Up Expiry: Evidence from 

Emerging Markets 

Abdolhossein Zameni 1* & Othman Yong 2 

1 Henley Business School, University of Reading Malaysia, Malaysia. 

2 

Graduate School of Business, University of Kebangsaan, Malaysia. 

Abstract: This paper examines the effects of substantial shareholders’ trading 

behaviour on share prices, trading volume and bid–ask spread in relation to the 

efficient market hypothesis (EMH) around the lock-up expiry for a sample of 

379 Malaysian IPOs, between 2001-2011. Our analysis shows that the number 

of companies with substantial institutional and individual shareholders has 

increased after the IPO. This indicates that individual and substantial investors 

are optimistic about the future of the IPO companies in general. In addition, 

the number of existing substantial individual and institutional shareholders that 

sold their shares is greater than the existing substantial individual and 

institutional shareholders that bought shares. That is the reason why we witness 

an abnormal trading volume and abnormal bid–ask spread, which leads to 

abnormal returns. The two other categories, ‘new individual investors that 

came in as substantial shareholders after lock-up expiry’ and ‘new institutional 

investors that came in as substantial shareholders after lock-up expiry’, show 

that some investors are still optimistic about the future of these IPO companies. 

Our analysis shows an increase in trading volume before the lock-up expiry 

date by substantial shareholders, which is an indicator of illegal insider trading. 

Consequently, market makers to protect themselves would increase the spread, 

which results in a price drop. Significant cumulative average abnormal returns 

show inconsistency about the EMH. The results are vital to provide input into 

the enforcement of laws to regulate insider trading. This is to strengthen the 

legal regimen to prevent the influences of insider trading. 

Keywords: Lock-up provision, Malaysian IPOs, abnormal return, bid–ask 

spread, trading volume, Efficient Market Hypothesis. 

JEL classification: G02, G10, G14, G18, G38 

1. Introduction 

Initial public offerings (IPOs) universally and domestically (in Malaysia) have recorded high 

initial returns for institutional and individual investors over the decades (Bradley and Jordan, 

2002; Low and Yong, 2011; Yatim, 2011). There are several studies related to the 

performance of IPOs, e.g., the influence of the lock-up provisions on IPO’s initial return or 

influence of lock-up provision on flipping activity. The missing piece of the IPO puzzle is the 

trading behaviour of shareholders around one of the most crucial events of the IPO market, 

which is called the lock-up expiry date. Hence lock-up is one of the main variables that mainly 

impacts on IPOs’ performance (Mohan and Chen, 2002). Therefore the study of IPOs without 

study of the performance of IPOs around lock-up expiry is incomplete. Lock-up provision, 

commonly known as share moratorium in Malaysia or lock-in in the United Kingdom (UK), 

* 

Corresponding author: Abdolhossein Zameni. Tel.: 607-2686244. Email: a.zameni@henley.edu.my 

1

Abdolhossein Zameni & Othman Yong 

is the period of time that is imposed on controlling shareholders, which prevents them from 

selling their shares after the issuance of the IPO. 

The reason behind lock-up provision is to mitigate the moral hazards and asymmetric 

information among IPO participants (Yung and Zender, 2010). Brav and Gompers (2003) and 

Mohan and Chen (2002) cite that the form of the lock-up contract shows the amount of the 

adverse selection and moral hazard phenomenon among IPO participants. In some countries 

(e.g. United States, UK) the form of the lock-up agreement is negotiable between 

underwriter(s) and issuer, but this is not the case in Malaysia. The form of the lock-up 

provision in Malaysia, in terms of its duration and locked-up shares by insiders, is fixed and 

mandated by Security Commissions (SC). Hence the duration of the lock-up provision in 

Malaysia cannot be a significant indicator of moral hazards and information asymmetries 

among the IPO participants as we cannot predict their trading behaviour around lock-up 

expiry. Despite this drawback, significant trading by substantial shareholders around IPO and 

lock-up expiry would be a substantial indicator of the true value and future prospect of the 

company, as they have access to the insider information that affects their trading behaviour. 

In Malaysia, since the most general IPO pricing mechanism is a fixed-price offering (as 

opposed to book building and auction), the level of information asymmetries among IPO 

investors are arguably high and investors cannot perceive the true value of the IPO, as 

suggested by Ma (2007). This also means that the divergence in opinion among IPO 

substantial shareholders (insiders) regarding the prospects of the company is likely to be high. 

Although these opinions are not observable, they are important because shareholders’ 

aftermarket behaviours are essentially driven by their opinions or expectations about the new 

issue. Shareholders (investors) with heterogeneous opinions and beliefs will show different 

behavioural trends when the IPO issue begins trading. This in turn will influence the 

performance of IPO equities in the short and long-runs, which would encourage flipping 

activity, illegal insider trading and significant trading around lock-up expiry. 

A recent study by Che-Yahya et al. (2014a) shows a negative relationship between 

institutional investors’ participation and the flipping activity of Malaysian IPOs. Institutional 

investors are normally assumed to be long-term investors and less likely to flip their allocated 

IPOs in the immediate aftermarket (Che-Yahya et al., 2014a). If flipping activity is done 

excessively it could produce a damaging effect on the aftermarket performance of the new 

shares (Che-Yahya et al., 2014b). This means that institutional shareholders in terms of 

keeping their shares for a longer period of time are more loyal than individual shareholders 

to the company. While stopping and controlling investors from flipping their shares is not a 

choice legally, the decision by the SC of Malaysia to impose a mandatory lock-up provision 

is seen as an effort that could control the flipping activities to a certain extent. Hence 

controlling shareholders would keep their shares after the IPO until the first subsequent sale 

opportunity, which is the lock-up expiry date. 

On the other hand, a study by Goergen et al. (2010) relating to behaviours of shareholders 

around lock-up expiry shows a significant increase in trading volume and bid–ask spread and 

no significant change of share price. The significant trading volume and bid–ask spread 

around lock-up expiry were not strong enough to move the Hong Kong IPO price from its 

efficiency. The absence of significant abnormal returns around the lock-up expiry event 

confirms the semi-strong form of the efficient market hypothesis (EMH). The reason given 

by Goergen et al. (2010) for the absence of a price reaction around lock-up expiry is that most 

of the Hong Kong IPO firms are controlled by one or two non-institutional shareholders 

(individual) who choose not to sell their shares after the lock-in expiry. 

Based on a study by Che-Yahya et al. (2014a), institutional shareholders are more loyal 

to a company in the longer term; in contrast, a study by Goergen et al. (2010) mentions that 

individual shareholders are more loyal to a company and will keep their shares for a longer 

2

Substantial Shareholders and Their Trading Behaviour around Lock-Up Expiry 

time. Putting this contradiction along with a fixed-price mechanism, fixed duration and fixed 

percentage of shares under the lock-up provision, we intend to analyse the behaviour of an 

individual and of institutional shareholders around the lock-up expiry of Malaysian IPOs. 

Our study will narrow down the sample to substantial shareholders: as they are 

considered insiders and have access to insider information that will affect their trading 

behaviour (Brau et al., 2004) and eventually the performance of the IPO. Their trading will 

have an effect on price, trading volume and bid–ask spread of the IPO shares around the lockup 

expiry, which would allow us to test the EMH as we expect all the related information has 

already been incorporated into the share price. This study attempts to find the distribution of 

companies with substantial institutional and individual shareholders before and after the IPO 

in order to compare it with their trading pattern activity around lock-up expiry. In addition, 

we intend to find a number of new individuals and institutional shareholders that came in as 

substantial shareholders around the lock-up expiry. 

The main driver of doing this research is that, according to Malaysia’s Vision 2020, it 

should achieve the status of a developed country, in terms of financial and social 

developments, by the year 2020. As a consequence of the Vision 2020 plan, Malaysia’s 

financial market should become a developed financial market with regards to information 

availability and transparency. 

According to studies by Brau and McQueen (2000), Brav and Gompers (2000), Ofek and 

Richardson (2000), Bradley et al. (2001) and Field and Hanka (2001) for the US IPOs market 

due to insider selling, the price of shares drops and the trading volume increases around lockup 

expiry. Price drop around lock-up expiry is inconsistent with the semi-strong form of the 

EMH, as information related to the lock-up expiry has already been published in the 

prospectus of a company and is considered as accessible public information. Hence, based on 

the EMH, lock-up expiry should not have a severe impact on share price (Fama, 1991). One 

of the main reasons for negative abnormal returns around lock-up expiry is an increase in bid– 

ask spread induced by market makers to protect themselves against informed insider traders 

(Field and Hanka, 2001; Cao et al., 2004). In addition to the price drop there is a trading 

volume increase around lock-up expiry in the United States, European countries and Malaysia 

(Ofek and Richardson, 2000; Espenlaub et al., 2001; Zameni and Yong 2016), as lock-up 

expiry is the first exit opportunity for insiders to free up their tied up capital. 

A recent study by Zameni and Yong (2016) shows a significant positive abnormal trading 

volume around lock-up expiry, related to the Malaysian IPO market. High trading volumes at 

and around the lock-up expiry date is compatible with shareholders’ selling due to 

diversification reasons and wealth recognition: these high trading volumes could be an 

indication of insiders’ lack of confidence about a company’s future prospect. 

The present study is motivated by the unique structures of the Malaysian IPO market, as 

opposed to those of the developed markets where empirical evidence on lock-up expiry is 

mostly established. Unlike those developed markets, e.g. the United States, UK, Australia and 

Finland, where lock-up provisions are voluntary, the SC imposes compulsory lock-up 

provision on Malaysian IPOs. In addition, most Malaysian IPOs are issued through a fixedoffer 

price mechanism. In short, these differences provide valid reasons to believe that the 

behaviour of lock-up expiry in Malaysia is different than that found in the developed countries 

and so are the factors that influence the trading behaviour of the substantial shareholders in 

this market. Furthermore, Islam and Munira (2004) cite that the securities markets in 

developing countries differ from those of developed countries with respect to investors’ 

behaviour, size of market and, particularly, the regulatory framework. Another motivation of 

our study relies on the conclusion of the study by Leland and Pyle (1977) and Brau et al. 

(2004). Leland and Pyle (1977) cite that the level of insiders’ participation in financial 

activities of the company carries information. Moreover, Brau et al. (2004) assert that when 

3


insiders (substantial shareholders) sell large amounts of their personal stocks, this sends a 

negative signal to outsiders. 

To the best of our knowledge our study is different than other IPO studies in Malaysia – 

as other researchers by utilising the Malaysian data have concentrated on the trading volume 

behaviour of the IPO market after lock-up expiry (Zameni and Yong, 2016) or on the 

performance of an IPO itself (e.g. Abdul-Rahim et al., 2013; Sapian et al., 2013; Che-Yahya 

and Abdul-Rahim, 2015). Other studies, such as Wan-Hussin (2005), concentrate on owners’ 

participation and level of under-pricing. 

Our main contribution is to find the trading behaviours of the substantial shareholders 

around lock-up expiry, as they are considered insiders and their significant trading would have 

an impact on the share price and performance of the IPO. For fulfilling this objective we 

narrow down the sample to substantial shareholders’ trade around lock-up expiry. The reason 

for narrowing down the sample is based on a study by Chemmanur (1993). According to 

Chemmanur’s (1993) information production model, when insiders perceive value in the 

company, they decide to sell equities both in the IPO and in the seasoned equity offering 

(SEO). Substantial shareholders’ selling around lock-up expiry is a signal to the IPO market 

regarding the future of the company – as shareholders have access to crucial (insider) 

information. As there is no database to record the insider and substantial shareholder activity 

in Malaysia, we have gone through the companies’ prospectuses and annual reports and 

manually collected the related data. We expect a bid–ask spread increase, consequently price 

decrease and trading volume increase around lock-up expiry – as this is the main event 

through which substantial shareholders can reveal their real evaluation of a company. 

The significance of our study is based on the results of research by Benveniste and Spindt 

(1989), who presume that certain investors, such as institutional investors, are more informed 

than the company and underwriter, and therefore suggest that book building causes the 

institutional investors to declare their information. But in Malaysia the common IPO pricing 

mechanism is a fixed-price mechanism, which causes a high level of uncertainty and 

information asymmetry among investors. This is the main reason why we segregated our 

sample into individual and institutional investors. Many other studies have segregated their 

samples into venture capital (VC) and non VC-backed companies. Venture capital companies 

are considered as insiders to the company. Venture capital- backed industry is quite new in 

Malaysia and there is a paucity of data related to it (Ajagbe and Ismail, 2014). 

The sample of the study comprises 379 Malaysian IPOs, issued from January 2001 to 

December 2011. Thus the EMH is investigated in relation to lock-up provision by using the 

standard event study methodology. Our analysis shows that the number of companies with 

substantial institutional and individual shareholders has increased after the IPO. This indicates 

that individual and substantial investors are optimistic about the future of the IPO companies 

and economics in general. In addition, the number of existing substantial individual and 

institutional shareholders that sold their shares is greater than the existing substantial 

individual and institutional shareholders who bought shares. That is the reason why we 

witness an abnormal trading volume and abnormal bid–ask spread that leads to an abnormal 

return around lock-up expiry. The two other categories as the name of “New individual and 

New institutional investors that came in as substantial shareholders after the lock-up expiry” 

show that some investors are still optimistic about the future of these IPO companies. Our 

analysis shows increase in trading volume before lock-up expiry by substantial shareholders, 

which is an indicator of illegal insider trading. Consequently, market makers in order to 

protect themselves would increase the spread, which results in price drop. Significant 

cumulative average abnormal returns (CAARs) show inconsistency about the EMH. 

4


2. Literature Review 

Brav and Gompers (2003) propose that insiders can signal the quality of a company using 

three tools: under-pricing, the portion of stocks locked-up and the duration of the lock-up. A 

high quality issuer mostly under-prices more, locks-up for a longer duration or locks-up a 

bigger portion of outstanding stocks. 

Many researchers argue that lock-up agreements mitigate the information asymmetry 

between the outside shareholders and the insider managers (Brau et al., 2004). Furthermore, 

Ibbotson and Ritter (1995) cite that investors are ready to pay a higher price for a company 

with a lock-up contract due to the following two reasons: firstly, confidential negative 

information is likely to be revealed prior to the selling of retained stocks, thus mitigating the 

advantage of confidential information; secondly, as long as insiders keep huge amounts of 

shares, their motives are in lieu of outsiders’ motives. Consistent with these results, an 

analytical study reports that retained ownership by insiders at the date of the IPO is positively 

related to company value (Downs and Heinkel, 1982; Ritter, 1984a). Analytically, many 

researchers argue that insiders refrain from selling stocks during the lock-up period for fear 

of transferring negative signals to the share market (Brau and Fawcett, 2006). Since 

substantial selling activity happens prior to the lock-up duration (Brav and Gompers, 2003), 

insiders wait until the lock-up contract expires to mitigate the kept shares in their IPO. 

Brav and Gompers (2003) examine 2,794 US IPOs between 1988 and 1996, and find that 

under-pricing is higher for companies with a larger fraction of the shares outstanding subject 

to liquidity restrictions. They also show that opaque or less transparent companies, which are 

associated with greater informational asymmetries, have longer lock-ups. 

The reaction of the share price at and around lock-up expiry has been examined recently. 

Some research regarding lock-up expiry on the US IPO market shows a market reaction at 

and around the expiry (Brau and McQueen, 2000; Brav and Gompers, 2000; Ofek and 

Richardson, 2000; Bradley et al., 2001; Field and Hanka, 2001). These researchers report 

significant abnormal returns (ARs) of between –1 and –3% surrounding the lock-up expiry 

for the US IPO market. Since the information about the attributes and characteristics of the 

lock-up is public information at the time of the IPO, the significant price movement at and 

around lock-up expiry is not consistent with the semi-strong version of the EMH (Fama, 

1991). In line with the above argument we hypothesise: 

H1: The abnormal returns are not significantly different from zero for substantial 

shareholders’ trade around lock-up expiry. 

Aggarwal et al. (2002) cite that under-pricing of IPOs is positively associated with insider 

retaining of equities at the IPOs. Moreover, their model argues that under-pricing of IPOs is 

positively associated, through information drive, with insider sale of stocks at lock-up expiry. 

However, concentrating only on the lock-up expiry date is ambiguous. Brav and Gompers 

(2003) report that 60% of companies have insider sales before the lock-up expiry date; and 

Lee (1997) shows substantial insider sales during seasoned equity offering. Aggarwal et al. 

(2002) propose several plausible interpretations for these outcomes. Firstly, manager/owner 

risk aversion is a probable interpretation for why companies like to under-price in ensuring 

that the IPO is beneficial. Furthermore, there are managers/owners who like to sell more at 

lock-up expiry in order to diversify their holdings. Secondly, the asymmetric information of 

Welch (1989) states that high quality companies under-price IPOs for the sake of earning 

greater prices in the SEO. Moreover, this model also applies to insider sales where quality 

companies under-price the IPO to get better prices at lock-up expiry. Thirdly, Chemmanur 

(1993) asserts that the manager/owner of a high quality company under-prices the IPOs to 

compensate investors for gathering information about the company. A main finding of 

5


Chemmanur’s (1993) model is that higher under-pricing is linked to reduced gross proceeds 

from the IPOs. From the behavioural financial perspective, Goldberg and Nitzsch (2001) posit 

that asset price and its fluctuation show the behaviour of market players, and this behaviour 

is a reflection of investors’ understanding of information earned and opinions formed 

following such understanding. Actually, in the IPO context, investors’ knowledge and 

information about the IPO will influence their behaviour and, as a result, differences in 

investors’ opinions or expectations will influence the performance of IPO equities. 

Consequently, in line with these arguments, we intend to find the distribution of companies 

with one, two, three and more than three substantial individual and institutional shareholders 

before and after the IPO and analyse their trading patterns and also hypothesise: 

H2: The abnormal returns at the lock-up expiry date are significantly negative for substantial 


Mostly, investing IPOs may show abnormal trading activities after lock-up expiry as an 

index of insider confidence. Hence, heavy sale by insiders instantly after lock-up expiry is 

understood as a signal of low insider confidence. This is explained to be a bad signal related 

to the prospects of companies. On the other hand, if there are no abnormal changes in insider 

trading volume subsequent to lock-up expiry, this is seen by investors as a signal of high 

insider confidence and thus a positive index of future company value. In line with this line of 

argument we hypothesise: 

H3: The abnormal volume around the unlock day is significantly positive for substantial 


Since insiders possess information regarding the firm’s future prospects, their information 

pushes them forward for trading, based on possessed information or for liquidity. Information 

traders can benefit from the possession of important information around the unlock day. The 

reason for the sale of the shares by liquidity traders is to disperse their invested wealth. 

Recognising the information and liquidity of traders around the unlock day is impossible. As 

a consequence, market makers intensify the information rent to prevent losses at the moment 

of trade with informed traders. On the other hand, the unlock day is supposed to be related to 

the supply of shares by insiders. This result is earned by the test of the effect of the unlocking 

day on the bid–ask spread of locked shares (Field and Hanka, 2001; Cao et al., 2004). Bid– 

ask spread has the following elements: asymmetric information, inventory and order 

processing. The asymmetric information element can help to forecast the proposed 

hypotheses of the study. Stoll (1978) explains the reason for changes on the inventory element 

of the bid–ask spread. He cites that market makers are forced to separate their inventory 

positions from their optimum target for holding equilibrium in inequalities of order. In 

addition, high trading volume after the expiry date gives market makers the chance for early 

substitution of their inventory, thus creating a negative association between bid–ask spread 

and trading volume (Demsetz, 1968). The last component of the bid–ask spread is order 

processing cost, which has several elements. These elements are exchange and clearing fees; 

book-keeping and back office costs; market makers’ time and effort, etc. Basically, because 

of some fixed costs of these elements and also heavy trades around unlock day, the order 

processing cost must be decreased. Stock spreads reflect, among other things, the degree of 

information heterogeneity among traders (Fedenia and Grammatikos, 1992). Goergen et al., 

(2010) show that the bid–ask spread increases significantly around lock-up expiry. We argue 

that a wider spread is likely to be caused by potential sales by insiders, and the risk for market 

6


makers to end up trading with better informed insiders. Putting all the arguments together, we 

hypothesise: 

H4: The abnormal bid–ask spread surrounding the unlock day for substantial shareholders’ 

trade around lock-up expiry is significantly positive or negative. 

3. Research Methodology 

3.1 Sample and Procedures 

The sample used in this study comprises 379 Malaysian IPOs listed on the Main Board, 

Second Board, MESDAQ, Main Market and the ACE Market with the lock-up provision, 

covering a period from January 2001 to December 2011. January 2001 is chosen as the start 

date since the after-effects of the 1997 Asian financial crisis had faded by this time. In 

addition, this sample period allows us to study the microstructure effect of the lock-up 

provision of Malaysian IPOs when the mandatory lock-up provision began to be imposed on 

3 May 1999. In a similar way to Abdul-Rahim and Yong (2008, 2010) and Yong (2007a), the 

present study selects a sample of IPOs that are offered as: a public issue; offer for sale; private 

placement; and a hybrid of the aforementioned forms. The selection criteria essentially 

excluded special purpose IPOs, such as restricted offers for sale; restricted public issues; 

restricted offers for sale to eligible employees; restricted offers for sale to Bumiputra 

investors; special and restricted issues to Bumiputra investors; tender offers; and special 

issues. Special purposes IPOs are rare and including them may lead to a less meaningful 

outcome (Abdul-Rahim and Yong, 2008; Yong, 2007a). IPOs issued under the real estate 

investment trust (REITS) category are excluded due to the different formats of presentation 

of their financial statements. The reason to exclude these companies with uncommon types 

of offer, is due to the fact that the number of companies with these issues is very small, leading 

to less meaningful outcomes as suggested in Abdul-Rahim and Yong (2010) and Yong 

(2007b). This study defines the event horizon as the (−20, +20 days) time period surrounding 

the lock-up expiry date, in line with the event horizon used in studies such as Goergen et al. 

(2010) and Ofek and Richardson (2000) and to better capture lock-up expiry effects. We chose 

this definition in preference to longer windows to avoid possible confounding events. For 

presenting the performance of the Malaysian equity market, the EMAS index has been 

chosen. The data employed in this research were collected from the Bursa Malaysia website 

(www.bursamalaysia.com), the SC website (www.SC.com.my), the Star Online website 

(http://biz.thestar.com.my/marketwatch/ipo), the www.klse.info website and Datastream. In 

addition, data related to the distribution of substantial shareholders before and after IPO 

issuance from 2001 to 2011 and the distribution of substantial shareholders after first lock-up 

expiry from 2001 to 2011 have been collected manually from prospectus and annual reports. 

3.2 Method 

This section explains the methodology that is used to calculate and test the effects of price 

around lock-up expiry date. Lock-up expiry date is considered as an event date. 

3.2.1 Analysing Abnormal Returns 

An event study analysis is used to assess the stock price reactions to firm-specific events 

(Binder, 1998; Fama, 1998). The market model is a standard model used in event studies 

when it comes to calculating abnormal returns (Dimson, 1979; Field and Hanka, 2001). 

The market model coefficients are obtained from the regression of the security returns 

against the corresponding market index. The ARs of each stock are calculated as the residuals 

of the model: 

7


R it = α i + β i R mt + ε it (1) 

where R it is the return of firm i on day t; R mt is the return on the market portfolio at day t, ɛ it 

ε it is a residual term and the event period is equal to (–20, +20). In analysing ARs, it is normal 

to mark the event date as t = 0, and AR i.0 represents the ARs on the event date. By using the 

daily average abnormal returns ( AAR t ) and the cumulative average abnormal returns 

( CAAR (t1,t2) ) over the (t1, t2) period, we can measure the price effect of the expiry date. 

AAR t = 1 N ∑ 

CAAR = 1 N ∑ 

N 

AR it 

i=1 

N 

i=1 

CAR i 

(2) 

(3) 

AAR t = daily average abnormal returns; AR it = abnormal return of firm i on day t; N = sample 

size. The significant deviation of the AARs from zero shows abnormal performance. 

t2 

CAR i = ∑ AR it 

t=t1 

(4) 

CAR i = Cumulative abnormal return of firm i. 

Goergen et al. (2010) used the following statistical test to evaluate the significance of 

AAR t and CAAR (t1,t2) in their study: 

test 1 = AAR t 

⁄ √N 

S t 

test 2 = CAAR( t1,t2) 

⁄ √N 

S t1,t2 

(5) 

(6) 

where 

S t = √ 1 N 

N − 1 ∑ (AR it − AAR t ) 2 

i=1 

S t1,t2 = √ 1 N 

N − 1 ∑ [CAR 1(t1, t2) − CAR(t1, t2)] 2 

i=1 

(7) 

(8) 

The statistics, test1 and test2 are Student’s t-distributions with N − 1 degrees of freedom. 

3.2.2 Analysing Abnormal Volume 

Goergen et al. (2010), in their paper on the Hong Kong IPO market, applied the following 

equations for measuring abnormal daily trading volume around the unlock day: 

VR it = 

V it 

V mt 

1 

52 [∑ −49 

( V it 

) 

V mt 

t=−100 ] 

(9) 

8


This equation is used to calculate the market adjusted volume ratio, VR it of firm i on day t. 

In this formula, V it is the trading volume of firm i and V mt is the market index on day t. The 

V mt can be downloaded from the Datastream database. AVR t is a daily average abnormal 

volume across N companies: 

N 

AVR t = 1 N ∑ VR it 

i=1 

(10) 

and, MAVR s is the average abnormal volume for N companies in the event window (t1, t2), 

where S is: 

S = t 2 − t 1 (11) 

MAVR s = 1 s ∑ 

T2 

t=T1 

AVR t 

respectively. 

For testing the AVR t and MAVR s , the standard t-test is applied. If the AVR t and MAVR s 

are greater than one, the trading volume on day t over the event window is abnormal. 

3.2.3 Analysing the Bid–Ask Spread Effect 

For understanding whether the ARs around the expiry day are because of variations in the 

trading costs, we employ the methodology that has been used by Goergen et al. (2010) and 

Fedenia and Grammatikos (1992). This methodology is able to capture the asymmetric 

component of the bid–ask spread, which we are looking for. The formula below measures the 

abnormal relative market-adjusted spread ratio of each firm i: 

(12) 

RS it 

RS 

ARS it = 

mt 

1 

52 ∑ −49 

( RS 

t=−100 

it 

) 

RS mt 

(13) 

RS it is the symbol for the relative spread of company i on day t, and, RS mt is the spread of 

the market portfolio (where m represents the number of companies in each different Board, 

sector and IPO market). For measuring the RS it , we employ the following equation: 

RS it = 

PA it − PB it 

(PA it + PB it )/2 

(14) 

and for calculating the RS mt , the following equation is used: 

RS mt = 1 M ∑ 

M 

RS it 

i=1 

(15) 

In Equation (14), PA it and PB it are the closing ask and bid prices of company i on day t, 

respectively. The formula for daily average market-adjusted abnormal spread (AARS t ) 

across N firms is (Goergen et al., 2010): 

9


N 

AARS t = 1 N ∑ ARS it 

i=1 

(16) 

Also, the average market adjusted relative spread (MAARS s ) across N companies in the event 

window of (t 1, t 2) with the length of S (S = t 2 − t 1), is calculated as: 

T2 

MAARS S = 1 S ∑ AARS t 

t=T1 

(17) 

The standard t-test is used to test the AARS t and MAARS s over day t and window (t1, t2). 

If they significantly differ from one over the expiry day and event window, it means there is 

an abnormal relative spread. 

4. Data Analysis 

The preliminary results highlight the characteristics of variable, basic profiles and the 

descriptive statistics. The highlighted characteristics are related to the Malaysian IPO market. 

4.1 Profiles of the IPO Sample 

Table 1 shows the number of companies in each Board between January 2001 and December 

2011. Total number of companies for Main, Second, MESDAQ Boards and Main and ACE 

Markets is 379 between the years 2001 and 2011. 

Table 1: Number of companies in each Board 

Panel A Main Board Second Board MESDAQ Total 

2001 6 14 - 20 

2002 19 16 8 43 

2003 17 18 14 49 

2004 14 23 26 63 

2005 10 16 41 67 

2006 3 7 22 32 

2007 10 8 2 20 

2008 7 8 8 23 

Total 86 110 121 317 

Panel B † Main Market ACE Market 

2009 11 2 13 

2010 21 6 27 

2011 12 10 22 

Total 44 18 379 

4.2 Distribution of Substantial Shareholders Before and After the IPO from 2001 to 2011 

According to Section 69 of the Malaysia Companies Act 1965, a substantial shareholder is 

described as ‘a person that has a stake in one or more voting stocks in a firm, where the 

nominal volume of that share (or the aggregate of the nominal amounts of those stocks) is not 

less than five percent of the aggregate of the nominal amounts of all the voting stocks in the 

firm.’ 

† After 3 August 2009, the structure of Bursa Malaysia changed from three Boards: Main, Second and MESDAQ 

Boards to the Main and ACE Markets, respectively. The number of companies before 3 August 2009 for Main and 

Second Boards is 10 and 1, respectively. 

10


Table 2: Distribution of substantial shareholders before and after IPO issuance from 2001 to 2011 (from 

prospectuses) 

Number Percentage 

Panel A 

Companies with one substantial shareholder 26 6.9 

Companies with two substantial shareholders 59 15.6 

Companies with three substantial shareholders 44 11.6 

Companies with more than three substantial shareholders 250 65.9 

Panel B 

Companies with institutional shareholders before the IPO 284 74.6 

Companies without institutional shareholders before IPO 95 25.4 

Panel C 

Companies with institutional shareholders after the IPO 287 75.7 

Companies without institutional shareholders after IPO 92 24.3 

Panel D 

Number of institutional shareholders (one) before the IPO 114 30.1 

Number of institutional shareholders (one) after the IPO 105 27.7 

Number of institutional shareholders (two) before the IPO 81 21.4 

Number of institutional shareholders (two) after the IPO 88 23.1 

Number of institution more than two shareholders before the IPO 184 48.6 

Number of institution more than two shareholders after the IPO 186 49.1 

Panel E 

Number of companies with individual shareholders before the IPO 269 71.1 

Number of companies with individual shareholders after the IPO 298 78.6 

Number of companies with individual shareholders (one) before the IPO 39 10.4 

Number of companies with individual shareholders (one) after the IPO 29 7.5 

Number of companies with individual shareholders (two) before the IPO 68 17.9 

Number of companies with individual shareholders (two) after the IPO 80 20.8 

Number of companies with individual shareholders (more than two) 162 42.8 

before the IPO 

Number of companies with individual shareholders (more than two) after 

the IPO 

189 49.7 

Table 2, which consists of Panels A, B, C, D and E, shows the distribution of substantial 

shareholders before and after the IPO, from January 2001 until December 2011, in Malaysia’s 

equity market. As can be seen in Panel A, 250 companies have more than three substantial 

shareholders, (65.9%). The companies with one substantial shareholder total 26 (6.9%). 

Panel B shows there are 284 companies (74.6%) with institutional shareholders before the 

IPO. Panel C shows there are 287 companies (75.7%) with institutional shareholders after the 

IPO. 

Panel D shows the number (one, two and more than two) of institutional shareholders 

before and after the IPO. Actually the number of (one) institutional shareholders decreases 

from 114 to 105 (30.1 to 27.7%) after the IPO. In contrast, the number of (two) institutional 

shareholders increases from 81 to 88 (21.4 to 23.1%), after the IPO. In addition, there is not 

much difference between the number of institutional (more than two) shareholders before and 

after the IPO. The number of institutional (more than two) shareholders increased slightly 

from 184 before the IPO (48.6%) to 186 after the IPO (49.1%). 

Panel E shows the number of individual shareholders before and after the IPO. The 

number of companies having only individual shareholders increased from 269 to 298 (71.1 to 

78.6%) after the IPO. The number of companies that had one individual shareholder reduced 

from 39 to 29 (10.4 to 7.5%); in contrast, the number of companies that had two individual 

shareholders increased from 68 to 80 (17.9% to 20.8%) after the IPO. In addition, the number 

11


of companies with individual shareholders (more than two) rose from 162 to 189 (42.8 to 

49.7%) after the IPO. 

Collectively, there is an increase in the number of substantial individual and institutional 

shareholders after the IPO. An analytical study reports that retained ownership by insiders at 

the date of the IPO is positively related to company value (Downs and Heinkel, 1982; Ritter, 

1984a). 

4.3 Distribution of Substantial Shareholders After Lock-up Expiry From 2001 to 2011 

Table 3 highlights the distribution of substantial shareholders after lock-up expiry from 

January 2001 to December 2011. On comparing Panels A and B, we can conclude that the 

number of existing individual shareholders that sold their shares (197; 56.1%) is more than 

the number of existing institutional shareholders that sold their shares (180; 48.0%). 

Moreover, the number of existing individual shareholders that bought new shares (153; 

30.6%) is more than the number of existing institutional shareholders that bought new shares 

(125; 16.8%). In contrast, the number of new individual shareholders that came in as 

substantial shareholders (153; 30.6%) is less than the number of new institutional 

shareholders (169; 39.9%). 

Table 3: Distribution of substantial shareholders after first lock-up expiry from 2001 to 2011 (from 

annual reports) 

Number Percentage 

Panel A 

Number of existing institutional shareholders that sold their shares 180 48.0 

Number of existing institutional shareholders that bought new shares 125 16.8 

Number of new institutional shareholders that came in as substantial 169 39.9 

shareholders 

Panel B 

Number of existing individual shareholders that sold their shares 197 56.1 

Number of existing individual shareholders that bought new shares 153 30.6 

Number of new individual shareholders that came in as substantial 

shareholders 

153 30.6 

4.4 Empirical Results of Substantial Shareholders’ Buy and Sell around Lock-up Expiry 

In this section, we report the empirical results of substantial shareholders’ buy and sell around 

lock-up expiry. In addition, we analyse the movement results of price, trading volume and 

bid–ask spread around lock-up expiry and discuss whether any abnormalities seen are because 

of insiders’ trading or due to other factors. 

The results show that there is no significant price movement at and around the lock-up 

expiry day for any category. In addition, the CAARs over the (–7, +7), (–20, +20) and (–2, 

+2) event windows mostly are different from zero for all categories (Table 4) except existing 

institutional investors that have bought new shares (Table 4), but the sale of institutional 

investors shows the most significant ARs at the 1% significance level. 

The AVR is not significantly bigger than one on the first expiry day for most categories 

except existing individual shareholders that bought shares and individual shareholders that 

sold their shares. The MAVR over the (–20, +20), (–2, +2) and (–7, +7) windows around the 

first lock-up expiry is significantly greater than one for all categories (Table 5), which is an 

indication of significant sales by the substantial shareholders. 

In contrast to Hypothesis 4, the AARs ratio does not improve significantly on the day 

before, the day after, or on the expiry day for all categories. The mean average abnormal 

returns (MAARs) of all categories increases significantly over the (–20, +20), (–2, +2) and (– 

7, +7) windows (Table 6). 

12


Table 4: Abnormal returns of buy and sell of substantial shareholders (individual and institutional) before and after lock-up expiry 

AARt (%) 

CAARs 

Days N –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7 (-7, 

+7) 

(–2, 

+2) 

(–20, 

+20) 

Abnormal 125 0.00 –0.60 0.30 –0.70 –0.20 0.30 0.00 0.20 0.10 –0.00 –0.80 1.07 –0.10 –0.20 0.20 0.00 0.00 0.01 

returns of 

existing 

institutional 

investors 

that bought 

new shares 

p-value 1.00 0.10 0.40 0.00 0.27 0.20 0.99 0.50 0.70 0.94 0.10 0.14 0.60 0.43 0.50 0.75 0.40 0.73 

Abnormal 169 –0.40 –0.00 –0.10 –0.10 0.04 –0.00 –0.45 –0.00 –0.30 –0.00 –0.10 –0.70 0.20 –0.08 –0.30 –0.03 –0.00 –0.13 

returns of 

new 

institutional 

investors 

came in 

p-value 0.20 0.80 0.60 0.50 0.93 0.70 0.05* 0.90 0.20 0.90 0.70 0.08* 0.50 0.76 0.39 0.02** 0.10 0.00*** 

Abnormal 153 0.40 –1.00 –0.00 –0.30 0.31 0.40 –0.20 –0.00 –0.00 –0.10 –0.20 –0.70 –0.00 0.39 0.20 –0.01 0.00 –0.04 

returns of 

existing 

individual 

bought 

p-value 0.20 0.10 0.90 0.30 0.50 0.30 0.30 0.70 0.90 0.52 0.60 0.15 0.90 0.20 0.70 0.35 0.80 0.01** 

Abnormal 153 1.00 –0.20 –0.30 0.10 0.34 0.10 0.93 –0.10 0.40 –0.00 –0.20 0.12 0.10 1.06 –0.10 1.05 –0.20 0.03 

returns of 

new 

individual 

investors 

came in 

p-value 0.09* 0.50 0.50 0.70 0.42 0.60 0.15 0.60 0.20 0.70 0.40 0.83 0.70 0.12 0.74 0.03** 0.10 0.24 

Abnormal 180 –0.20 –0.50 –0.50 –0.50 –0.20 0.00 –0.12 –0.00 –0.60 –0.30 –0.00 –0.50 –0.10 0.35 –0.50 –0.04 –0.00 –0.13 

returns of 

institutional 

investors 

that sold 

p-value 0.30 0.01*** 0.07* 0.07* 0.39 0.90 0.65 0.80 0.03** 0.14 0.80 0.09* 0.50 0.42 0.03** 0.00*** 0.02** 0.00*** 

Abnormal 

returns of 

individual 

investors 

that sold 

197 0.20 0.20 –0.10 –0.20 0.58 0.30 0.28 0.20 –0.20 0.20 0.50 –0.30 –0.10 0.89 –0.10 0.02 0.00 0.03 

p-value 0.30 0.20 0.30 0.20 0.01** 0.20 0.32 0.40 0.40 0.30 0.10 0.15 0.50 0.01*** 0.48 0.01*** 0.30 0.02** 

Notes: ***, ** and * indicate significance at the 1, 5 and 10% levels (two-tailed test), respectively. 

13


Table 5: Abnormal volume of buy and sell of substantial shareholders (individual and institutional) before and after lock-up expiry 

Abnormal volume 

Days N –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7 (–20, 

+20) 

(–2, 

+2) 

(–7, 

+7) 

Abnormal 125 14.00 3.20 2.80 2.80 4.80 3.00 3.50 2.20 2.70 3.00 2.20 1.80 12.70 2.60 1.10 3.20 2.80 4.20 

volume of 

existing 

institutional 

investors that 

bought new 

shares 

Test Value = 1 0.16 0.22 0.10 0.09* 0.10 0.20 0.10 0.10 0.10 0.14 0.14 0.24 0.30 0.20 0.60 0.00*** 0.00*** 0.01*** 

Abnormal 169 8.12 8.15 2.00 2.00 2.00 5.00 1.10 2.30 2.30 2.05 15.00 3.53 5.74 12.00 8.60 6.10 2.50 5.40 

volume of new 

institutional 


came in 

Test Value = 1 0.25 0.22 0.10 0.20 0.10 0.20 0.50 0.10 0.10 0.17 0.28 0.25 0.28 0.10 0.20 0.00*** 0.07** 0.00*** 

Abnormal 153 15.30 9.91 3.00 3.00 4.00 6.00 2.40 3.30 3.60 4.79 20.20 5.49 13.80 9.50 2.80 6.49 4.10 7.30 

volume of 

existing 

individual 


bought 

Test Value = 1 0.12 0.24 0.03** 0.10 0.05* 0.20 0.10 0.05* 0.07* 0.03** 0.25 0.13 0.11 0.10 0.04** 0.00*** 0.01** 0.00*** 

Abnormal 153 11.70 10.00 2.90 3.70 2.80 6.00 1.70 2.70 2.70 4.35 20.00 5.72 8.18 9.10 2.80 6.73 3.50 6.30 

volume of new 

individual 


came in 

Test Value = 1 0.17 0.23 0.10 0.10 0.06* 0.20 0.06* 0.10 0.10 0.05** 0.26 0.11 0.21 0.10 0.04** 0.00*** 0.02** 0.00*** 

Abnormal 180 8.50 6.74 2.00 2.54 2.40 4.70 1.70 2.10 2.10 2.79 13.40 3.61 5.17 11.00 7.50 5.52 2.70 5.10 

volume of 

institutional 


sold 

Test Value = 1 0.15 0.23 0.07* 0.10 0.06* 0.10 0.10 0.10 0.10 0.08* 0.24 0.15 0.24 0.10 0.20 0.00*** 0.03** 0.00*** 

Abnormal 197 3.48 2.47 1.90 1.30 1.60 1.80 1.40 1.80 2.90 2.32 2.64 2.07 1.84 1.60 1.80 2.67 2.00 2.00 

volume of 

individual 


sold 

Test Value = 1 0.08* 0.02** 0.04** 0.20 0.20 0.10 0.39 0.06* 0.06* 0.11 0.07* 0.06* 0.04** 0.17 0.09* 0.00*** 0.01** 0.00*** 

Notes: ***, ** and * indicate significance at the 1, 5 and 10 percent levels (two-tailed test), respectively. 

14


Table 6: Abnormal bid–ask spread of buy and sell of substantial shareholders (individual and institutional) before and after lock-up expiry 

Abnormal spread 

Days N –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7 (–20, (–2, (–7, 

+20) +2) +7) 

Abnormal spread of 125 2.15 2.25 1.81 1.84 1.71 1.84 1.79 1.77 1.64 1.53 1.23 1.01 1.11 1.06 0.90 1.31 1.71 1.58 

existing 

institutional 


bought new shares 

Test Value = 1 0.39 0.38 0.42 0.38 0.43 0.27 0.36 0.38 0.35 0.31 0.43 0.97 0.54 0.71 0.57 0.00*** 0.00*** 0.00*** 


new institutional 

investors that came 

in 

Test Value = 1 0.41 0.35 0.18 0.46 0.35 0.34 0.75 0.42 0.24 0.26 0.61 0.63 0.13 0.27 0.23 0.00*** 0.01*** 0.00*** 


existing individual 


bought new shares 

Test Value = 1 0.59 0.39 0.81 0.46 0.39 0.86 0.98 0.99 0.87 0.76 0.51 0.51 0.27 0.37 0.41 0.00*** 0.15 0.00*** 


new individual 

investors that came 

in 

Test Value = 1 0.15 0.33 0.63 0.91 0.36 0.32 0.64 0.53 0.37 0.46 0.62 0.40 0.53 0.37 0.58 0.00*** 0.00*** 0.00*** 


institutional 

investors that sold 

Test Value = 1 0.27 0.15 0.16 0.38 0.30 0.22 0.39 0.31 0.12 0.15 0.15 0.25 0.27 0.27 0.72 0.00*** 0.00*** 0.00*** 


individual investors 

that sold 

Test Value = 1 0.25 0.21 0.19 0.16 0.16 0.15 0.38 0.27 0.16 0.13 0.25 0.23 0.05* 0.20 0.24 0.00*** 0.00*** 0.00*** 

Notes: ***, ** and * indicate significance at the 1, 5 and 10% levels (two-tailed test), respectively. 

15


5. Conclusion 

This paper examines the effects of substantial shareholders’ trading behaviour on share prices, 

trading volume and bid–ask spread around the lock-up expiry date for a sample of 379 

Malaysian IPOs, during the period January 2001 to December 2011. In line with this objective, 

we find a number of existing individual and institutional shareholders that traded their shares 

around lock-up expiry and also a number of new individual and institutional shareholders that 

came in as substantial shareholders around lock-up expiry. In addition, we intend to find the 

distribution of companies with one, two, three and more than three substantial individual and 

institutional shareholders before and after the IPO and interpret their trading pattern. 

Our analysis shows that the number of companies with substantial institutional and 

individual shareholders has increased after the IPO. This indicates that individual and 

substantial investors are optimistic about the future of the IPO companies and economics in 

general. A model by Leland and Pyle (1997) declares that the portion of shares kept by 

insiders at the IPO time can be accepted as a signal of quality. 

Our findings show that the number of existing substantial individual and institutional 

shareholders that sold their shares was greater than the number of existing substantial 

individual and institutional shareholders that bought shares. That is the reason why we witness 

an abnormal trading volume and abnormal returns around the lock-up expiry date. The two 

other categories, ‘New individual investors that came in as substantial shareholders’ and ‘New 

institutional investors that came in as substantial shareholders’ show that some investors are 

still optimistic about the future of these IPO companies. The number of new individual 

shareholders that came in as substantial shareholders is less than the number of new 

institutional shareholders. In conclusion, the reason for a price drop could be increase in 

trading cost/bid–ask spread by market makers and also a downward sloping demand curve. 

Our analysis shows an increase in trading volume before the expiry, which is an indicator of 

illegal insider trading. Market makers, to protect themselves, would increase the spread, 

which results in a price drop. Significant CAARs show inconsistency about the EMH. 

We assert that abnormal trading (extremely heavy or thin) following the expiry of the 

lock-up duration signals the amount of insider confidence regarding future prosperity. 

Apparently, trading volume signals the degree of insider confidence as they cannot sell shares 

prior to the expiry, but can buy unlimited shares. Heavy quantity exactly following the expiry 

may be understood and explained by investors as insider selling, and hence is a signal of a 

company of less quality. From another view, thin trading following the expiry date may signal 

insiders’ confidence, and investors may explain this as good news. 

The results are vital to provide input into the enforcement of laws and regulations to 

regulate insider trading and market manipulation. This is to strengthen the legal regimen to 

prevent the corrupt influences of insider trading and to provide a cure for insider trading. 

Insider trading and market manipulation are not beneficial and can have adverse effects on 

Bursa Malaysia. 

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18


Does Interest Rate Still Matter in Determining Exchange 

Rate? 

Wai-Mun Har 1 , Ai-Lian Tan 2 , Chong-Heng Lim 2 & Chai-Thing Tan 2 

1 Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, 

Malaysia. 

2 Faculty of Business and Finance, Universiti Tunku Abdul Rahman, 

Malaysia. 

Abstract: Interest rate plays important roles in exchange rate determination in 

various economics theories. However, this has been challenged conceptually 

or practically. Rapid development of global financial linkages makes available 

many attractive non-interest-bearing investments which dwarf profit 

opportunity from interest-bearing assets. Sensitivity of exchange rate 

fluctuations and institutional factors also doubt the role of interest rate in 

determining exchange rate. This research used nine Asian countries, which five 

from ASEAN (Thailand, Indonesia, Malaysia, Singapore and Philippine) and 

South Korea, Japan, India and China. Sample period varied between 1994 and 

2015. Result from Pool Mean Group method shows that real interest rate and 

real effective exchange rate have long run negative relationship. This implies 

that interest rate does matter in determining exchange rate. 

Keywords: Interest rate, exchange rate, pool mean group, Asia. 

JEL classification: E43, F31, N15 


Interest rate has long been associated with exchange rate determination either directly or 

indirectly. Interest rate parity directly linking exchange rate changes with expected interestbased 

returned through active arbitrage activities. Conventional economics theories preach on 

increase interest rate to support the currency through inducing inflow of capital. Hence, any 

changes in economics policy and fundamental may affect exchange rate, especially monetary 

policy. Theoretically, increase of money supply (expansionary policy) reduce interest rate 

which in return causing capital outflow. However, these theories are challenged practically 

and conceptually. Practically during Asian Crisis 1997/98, countries that subscribed to 

increase interest rate to halt currency depreciation failed. Perhaps, this has prompted Malaysia 

as the only countries succeed in quick recovery by decreasing interest rate to stimulate 

consumption while imposing exchange rate control. Conceptually, Ohmae (1996) claimed 

that there are many attractive non-interest-bearing opportunities such as forex market, stocks 

and real estate investment. Profit-making opportunities for those could reach 50%, which 

dwarf any interest-bearing instrument, hence reducing its impact to exchange rate. This 

thought is also consistent with asset market theory, where inflow of in-demand currency not 

only limited to interest-bearing bond instruments but equities too. Ohmae (1996) also claimed 

that exchange rate is more sensitive to announcement (sentiment) rather than economic 

fundamental. A recent example is Donald Trump’s threat of recalling overseas American 

investment back to Unites States through imposing punitive tax. The announcement itself has 

resulted in immediate appreciation of the dollar. Institutional factor also play more impactful 

 

Corresponding author: Wai-Mun Har. Tel.: 603-90860288. Fax: 603-90197062. 

Email: mun_hwm@hotmail.com 

19

Wai-Mun Har, Ai-Lian Tan, Chong-Heng Lim & Chai-Thing Tan 

roles. Reaganomics policy of “strong dollar, strong America” while China’s and Japan’s 

intention to keep suppressing their currency value are stronger determinant than interest rate 

or any economic fundamentals like trade and debt. These anomalies role of interest rate in 

determining exchange rate motivated this research. 

The objective of this research is to determine the significant of interest rate in determining 

exchange rate. This research used nine Asian countries, which five from ASEAN (Thailand, 

Indonesia, Malaysia, Singapore and Philippine) and South Korea, Japan, India and China. 

Sample period varied between 1994 and 2015 in an unbalanced panel data. Pool Mean Group 

(PMG) with error correction method is applied to capture the dynamic effect between interest 

rate and exchange rate with trade balance and government debt as control variable. 

2. Literature Review 

Real exchange rate and interest rate relationship is often studies with a combination of various 

economic variables like gross domestic products, money supply (Ibrahim, 2016), trade 

balance, government expenditure and debt (Yu, 2010) and commodities prices (Kandil and 

Bahmani-Oskooee, 2007). Insignificant long run relationship between interest rate and 

exchange rate is found in Kia (2013) and Bouraoui and Phisuthtiwatcharavong (2015). Kia 

(2013) found that interest rate only has negative short run relationship with exchange rate, 

using Canada quarterly data from 1972 to 2010. In fact, real factors such as productivity shock 

gave higher impact than monetary shock like interest rate to the volatility of exchange rate 

(Meese and Rogoff, 1988). Interest rate has different impact to different exchange rate 

regimes. Bensaid and Jeanne (1997) claimed that using interest rate hike to defend fixed 

exchange rate is ineffective, thus costly and making it prone to speculation attack. Interest 

rate changes may be due to actual or perceived political risk (Dooley and Isard 1980) that 

may not be simultaneously affecting exchange rate movement. Edwards (1988) claimed that 

macroeconomics shock only give short term effect to real exchange rate, which long run 

equilibrium depends on fundamentals like term of trade, government consumption, 

technology progress, capital inflow and investment. Other significant factors affecting 

exchange rate movement, either in long or short run include economies linkage and cointegration 

(De Truchis et al., 2007), structural adjustment, economy openness (Ibrahim 

2016), productivity shocks (Meese and Rogoff, 1988), quality of institution and financial 

development (Nouira and Sekkat 2015) and inflow of bank loans (Comunale, 2017). 

Additionally, exchange rate has been studied with trade balance, especially under 

Marshall Learner hypothesis and J-curve (Ng et al., 2008; Sek and Har, 2014; Bahmani- 

Oskooee, 1991; Arize 1994). Interest Rate Parity (IRP) also has major theoretical link 

between expected exchange rate and interest rate. Over decades of debate, there are literatures 

which supported it, thus implying long run relationship between exchange rate and interest 

rate (Bahmani-Oskooee et al., 2016). Those who found no significant evidence are aplenty 

too, such as (Baharumshah et al., 2005; De Los Rios and Sentana, 2011). Liquidity risk also 

plays a role in determining exchange rate. Fukuda and Tanaka (2017) found money market 

risk and policy rates have significant effect on covered interest parity condition for currencies 

of European Union, United Kingdom, Canada, Japan, Australia and New Zealand using 

United States dollar as benchmark. 

Regression specifications do matter in determining significant of relationship between 

interest rate differential and exchange rate. Through uncovered interest parity theory, Herger 

(2016) favoured that time-specific fixed effect panel data testing. Sarantis (1999) claimed 

non-linearity in exchange rate in eight of G-10 developed countries tested using Smooth 

Transition Autoregressive (STAR) model. Other models applied included structural vector 

autoregressive (SVAR), generalized autoregressive conditional heteroscedasticity (GARCH) 

(Fukuda and Tanaka, 2017), exponential GARCH (EGARCH) (Meng and Huang, 2016) and 

20


Autoregressive Distributed Lag (Bahmani-Oskooee et al., 2016). Nonetheless, dynamic panel 

analysis is rarely used to test on determinants of exchange rate but offer better analysis 

methods especially when dynamic heterogeneous problem exist (Pesaran et al., 1999). 

3. Research Methodology 

3.1 Model Specification and Estimation Approach 

The nexus between exchange rate and interest rate is not only drawn a considerable concerned 

by researchers but also policymakers. As the exchange rate plays a more significant role in 

developing countries than in developed countries, and central banks typically use interest rate 

as a policy instrument to affect and stabilize currency values, then a good knowledge in the 

relationship between these variables becomes important for a clearer understanding of 

monetary transmission mechanism. In exploring the effect of interest rate on exchange rate, 

the model is specified as follows: 

REER it = δ i + θ 1itINTRATE it + θ 2itTOT it + θ 3itGOVDEBT it + υ it (1) 

where REER is the real exchange rate (domestic currency in relative to foreign currency) and 

INTRATE is the real interest rate. In this study, control variables consist of TOT and 

GOVDEBT, which refer to the terms of trade and government debt, respectively; while δit is 

the country-specific effect, θ’s are long-run parameters, and υ refers to residual terms. Several 

studies have shown that interest rate has an impact on exchange rate (Eichanbaum and Evans, 

1995; Hnatkovska, Lahiri, and Vegh, 2013; Andries, Capraru, Ihnatov, and Towari, 2017). In 

the economics theory, a higher interest rate induces an appreciation of currency due to higher 

expected return from the investment and vice-versa (Basurto and Ghosh, 2001). Based on 

portfolio hypothesis, changes in interest rate will reposition portfolio allocation since a rising 

in interest rate makes the interest-bearing assets of a country become more attractive, results 

in an appreciation of the currency value (Branson, Halttunen, and Masson , 1977). Therefore, 

the expected sign of interest rate coefficient is to be negative on real exchange rate. The 

impacts of the terms of trade and real exchange rate are ambiguous as it depends on the income 

and substitution effects. When the TOT increases, the REER can appreciate if the income 

effect dominants the substitution effect and vice-versa (Ibarra, 2011; Combes, Kinda, and 

Plane, 2012). For government debt, it has a deleterious effect on the real exchange rate since 

countries with a higher debt level tend to experience a depreciation of its exchange rate 

(Galstyan and Velic, 2017). Hence, the sign of government debt is expected to be negative. 

This study investigates the factor of interest rate on exchange rate by applying panel 

econometric estimation. For estimation, there are several techniques prevalently utilize in the 

panel model. Firstly, the conventional pooled method (OLS) restricts homogeneous of 

intercepts and all slope coefficients across units. At one extreme, Fixed effect (FE), Random 

effect (RE), and Generalized Method of Moments (GMM) impose homogeneity on all longrun 

parameters but allows intercepts freely independent. Nevertheless, Pesaran and Smith 

(1995) stress that under slope heterogeneity, GMM will be potentially affected by 

heterogeneity bias and leads to an inconsistent of slope coefficients. 

On the other extreme, Pesaran and Smith (1995) introduce Mean Group (MG) estimate to 

produce more consistent average parameters since it is averaging coefficients in ARDL 

regressions and has no constrains on intercepts and all long-run parameters. Nonetheless, this 

method is being criticized by Pesaran et al. (1999) where MG does not consider some 

parameters can be homogenous across units in the long run. Hence, in alternative, author 

propose Pooled Mean Group (PMG) estimate that allows intercepts, speeds of adjustments, 

and short-run parameters are to be varied but a common long-run coefficient. Therefore, PMG 

estimate will be more efficient and consistent than MG estimate under the hypothesis of 

21


homogeneity. In this regard, Hausman test will be utilized to examine the homogeneity of 

long-run parameters. 

Based on PMG approach, the ARDL (p,q,q,q) dynamic panel regression for Eq.(1) is 

specified as below: 

p q q q 

(2) 

REER REER INTRATE TOT GOVDEBT 

it i 0 i, j i, t j 1 i, j i, t j 2 i, j i, t j 3 i, j i, 

t 

j it 

j1 j0 j0 j0 

where p and q refer to the lags of the dependent and explanatory variables, respectively. The 

re-parameterized of Eq.(2) as an error correction model can be specified as follows: 

REER ( REER INTRATE TOT 

GOVDEBT ) 

it i i, t1 1i 1i it 2i it 3i it 

p1 q1 q1 

* * * 

a0 i, j REERi , t j a1 i, j INTRATEi, t j a2 i, j TOTi , t 

j 

j1 j0 j0 

 

q1 

 

j0 

* 

3 i, j GOVDEBTi , t 

j i it 

a 

(3) 

 

 

 

 

 

 

where 

i 1 a0i, 

j , 

i 

i 

p 

j1 

, i 

a1, 

q 

q 

q 

i j , 

2i 

a2i, 

j , and 3 i 

 

j0 

j0 

j0 

based on Eq.(3).π i refers to the parameter of error correction terms which measures the speed 

of adjustment of exchange rate towards is long-run equilibrium. θ’s are the long-run 

coefficients for explanatory variables while a’s imply the short-run coefficients. Finally, δ i 

defines country-specific effect while ν it signifies residual terms. 

3.2 Data Description 

The data in this study consists of an unbalanced panel annual data for nine ASEAN countries 

and the countries’ list is shown in Table A1 (see Appendix A). The data of real exchange rate, 

interest rate, terms of trade and government debt are retrieved from three sources: (a) World 

Economic Outlook, IMF; (b) World Development Indicator (WDI) and (c) Bank for 

International Settlements (BIS). The definition of variables and data sources are summarized 

in Table A2 (see Appendix A). 

4. Data Analysis 

The results of panel estimations are summarized in Table 1. Based on PMG estimate, both 

interest rate and terms of trade are important in influencing the real exchange rate, while 

government debt is statistically insignificant in the model. In light of the results, an 

improvement in terms of trade results in an increase of quantity in exports but a decrease of 

quantity in imports, hence a depreciation of exchange rate. In addition, a higher interest rate 

of a country would lead to an appreciation of country’s currency (decrease exchange rate) by 

virtue of a more attractive returns from the investment. 

In the view of long-run perspective, PMG estimate reveals a more consistent findings 

with the literature, compared to MG estimate, since most of the coefficients of PMG 

estimate are significant in the model. Moreover, the p-value of Hausman test proposes that 

the null hypothesis of homogeneity in long-run coefficients cannot be rejected in the model. 

Hence, PMG estimate is in favour to the MG estimation. Furthermore, for the convergence 

a 

3i, 

j 

22


coefficient, a negative and significant parameter implies that the real exchange rate, on 

average, has a correction speed of 31.11% towards its equilibrium in the long run during 

each period. 

Table 1: Panel estimations 

Equation PMG MG DFE 

Long run coefficients 

INTRATE -0.0184*** -0.0015 -0.0216 

TOT 0.4117** 0.4149 1.130* 

GOVDEBT -0.0478 -0.1983 -0.0220 

Error Correction ( πi) -0.3111*** -0.4281** -0.1747*** 

Short run coefficients 

ΔINTRATE 0.0216*** 0.0113* 0.0125*** 

ΔTOT -0.3420** -0.3953** -0.2632** 

ΔGOVDEBT 0.1197 0.0809 -0.2381*** 

Δ 2 INTRATE -0.0102** -0.0067*** -0.0048*** 

Δ 2 TOT 0.1335 0.1754* -0.0265 

Δ 2 GOVDEBT -0.0867 -0.0472 0.0395 

Hausman p-value 0.6409 

Observation 170 

Notes: 1. ***, ** and * indicate significant at 1%, 5% and 10% significance levels, respectively. 

2. The appropriate lag order for ARDL (p,q,q,q) in each equation is selected based on Akaike information 

criterion. 

5. Discussion of Result 

Results show positive relationship between trade balance (TOT) and real exchange rate, 

which is consistent with Marshall-Lerner theorem. Ignoring the causality aspect, the former 

advocates higher trade balance associated with depreciation of currency. Practically in almost 

all selected countries, and perhaps other developed and developing Asian countries, export 

competitiveness comes from price competitiveness which attained from lower domestic 

currency. This is contrast to Western developed countries where export competitiveness 

comes from innovation, better technology and higher labour productivity. Hence, pressure to 

sustainable higher export could cause depreciation of domestic exchange rate, especially 

through managed float system. In export oriented Asian countries, export revenues in foreign 

currency (usually in US dollar) are not converted (or just partially) to domestic currency. For 

example, this can be seen in fierce objection to recent direction from Bank Negara Malaysia 

to force conversion of export revenue up to a certain percentage. Hence, increase of export 

revenue did not actually equal capital or foreign reserve inflow into domestic economy. 

Indeed, the capital flow theory is observed in the result of negative relationship between 

interest rate and real exchange rate. Higher interest rate induces inflow of capital, assumable 

into mostly interest bearing instruments. This also implies that selected countries as a whole 

have not yet fallen into liquidity trap where interest rate no longer instrumental or effective 

in affecting exchange rate or other economic fundamentals. 

6. Conclusion 

Theoretically, interest rate and trade balance play an important role in determining real 

exchange rate. However, globalization, financial innovation and variety of factors have 

encouraged research to revaluate the determinant of exchange rate. This research aims to 

determine the significant of interest rate in determining exchange rate with trade balance as 

control variable. Nine Asian countries, which five from ASEAN (Thailand, Indonesia, 

Malaysia, Singapore and Philippine) and South Korea, Japan, India and China were tested 

empirically using Pool Mean Group (PMG) with error correction method. Sample period 

23


varied between 1994 and 2015 in an unbalanced panel data. Results revealed negative 

relationship between interest rate and real exchange rate, which reaffirmed the validity of 

capital flow theory. Positive relationship between trade balance (TOT) and real effective 

exchange rate implies the importance of price competitiveness from lower exchange rate on 

export competitiveness. 

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Appendix A 

Table A1: List of sample countries 

Country Income Level Sample Period 

Thailand Middle 1996-2015 

Indonesia Middle 2000-2015 

Malaysia Upper middle 1994-2015 

Singapore High 1994-2015 

Korea High 1994-2015 

Japan High 1994-2015 

India Lower middle 1994-2015 

China Middle 1994-2015 

Philippine Lower middle 1994-2015 

Notes: All data are collected based on the availability of data for each country. 

Table A2: Sources of data 

Variables Variable definition Data Source 

REER Real effective exchange rate (2010=100) BIS 

(Billions of dollars) 

INTRATE Real interest rate (%) WDI 

TOT Terms of trade WDI 

Government Debt Government Gross Debt (% of GDP) IMF 

Notes: All variables are transformed into the logarithm, except for interest rate. 

25


Momentum and Investor Sentiment: Evidence from 

Asian Stock Markets 

Shangkari V. Anusakumar 1 & Ruhani Ali 2 

1 School of Management, Universiti Sains Malaysia, Malaysia. 

2 Graduate School of Business, Universiti Sains Malaysia, Malaysia. 

Abstract: We investigate whether investor sentiment affects momentum 

profitability using a sample of 13 Asian countries: Bangladesh, China, Hong 

Kong, India, Indonesia, Japan, Malaysia, Pakistan, Philippines, Singapore, 

South Korea, Taiwan and Thailand. We find that momentum arises only during 

optimistic and mild periods. Momentum is absent for periods of pessimism. 

This suggests that investors are detail oriented during pessimistic periods and 

thereby hinder the occurrence of momentum in the stock market. We also find 

that global sentiment affects momentum which affirms the contagious nature 

of sentiment. In addition, the findings indicate that holding period sentiment 

also affects momentum. The results are robust to changes in sentiment period 

classification and the use of alternative proxies for investor sentiment. 

Keywords: Momentum, investor sentiment, global sentiment, Asia, optimism. 

JEL classification: G11, G12, G14, G15 


Within the large body of literature that documents return predictability, the momentum effect 

is arguably one of the most intriguing. It is one of the few anomalies that have yet to be 

explained in its entirety. Stocks which performed poorly (well) in the past continue to perform 

poorly (well) in the future. The basic concept of momentum strategy is to buy ‘winners’ 

(stocks that performed well in the past) and sell ‘losers’ (stocks that performed poorly in the 

past). The momentum effect was first documented by Jegadeesh and Titman (1993). Decades 

of subsequent research provided support for existence of momentum in international markets 

and in varying time periods (Cakici et al., 2016; Dhouib and Abaoub, 2007; Fama and French, 

2012; Griffin et al., 2003; Khosroazad and Chitsazan, 2016). 

Recently, Antoniou et al. (2013) found that momentum was influenced by investor 

sentiment in the US. Higher momentum was reported during periods of high investor 

sentiment compared to low sentiment. However, this relationship may or may not hold in 

Asia. Manifestation of momentum in Asia drastically differs from other regions around the 

world (Griffin et al., 2003). So much so that Hameed and Kusnadi (2002) argued that the 

factors that drive momentum in Asia may not be the same as those in the US. More 

importantly, the psychology of Asians is notably distinct from Westerners including 

reasoning (Buchtel and Norenzayan, 2008; Hedden et al., 2008). As highlighted by Schmeling 

(2009), the effect of sentiment varies from country to country, and as such the relationship 

between momentum and sentiment needs to be reexamined in Asian markets. 

To the best of our understanding, the first study specifically on sentiment and momentum 

was conducted by Antoniou et al. (2013) for the US market. Subsequently, Stambaugh et al. 

(2012) tested the effect of sentiment on anomalies (including momentum) in the US stock 

market and reported that the anomalies were stronger for periods following high sentiment 

 

Corresponding author: Ruhani Ali. Tel.: 604-6533954. Fax: 604-6532792. Email: ruhani@usm.my 

Acknowledgements: The authors gratefully acknowledge the support of Universiti Sains Malaysia 

Research University grant: 1001.PPAMC.816192. 

26

Momentum and Investor Sentiment 

(i.e. optimism). Another related study is Hao et al. (2016), who demonstrated that Real Estate 

Investment Trust (REIT) momentum returns are significantly positive (negative) following 

optimistic (pessimistic) periods. Needless to say, the literature on momentum and sentiment 

is sparse. International evidence in support or against the existing results would provide much 

needed insight into the issue. 

Thus, we investigate whether investor sentiment affects momentum profitability in Asian 

countries. Not only does this study provide out-of-sample evidence, it also expands the study 

of sentiment to incorporate other facets of sentiment: global investor sentiment and investor 

sentiment during the momentum portfolio holding period (i.e. holding period sentiment). 

Notably, this study is the first to explore the effect of holding period sentiment on momentum. 

There is a plethora of studies on momentum in the US and other developed markets, 

investigating not only the magnitude of momentum but also the source of momentum. 

However, the studies on Asian markets are not as extensive and lack depth. The studies on 

investor sentiment have also predominantly focused on the US market. In spite of the 

increased attention paid to investor sentiment in recent years, the studies on international 

markets are limited. Thus, our study fills this critical gap by contributing to the literature on 

investor sentiment and momentum. The interest in exploring momentum in Asia is not a 

purely academic pursuit but is of interest to investors worldwide. The results of this study 

could be used to streamline the momentum strategy. Resources can be concentrated on periods 

where momentum is more likely to occur. As we examine each market individually, a detailed 

and market specific information is available compared to an overall study of the Asian region 

(e.g. Brown et al., 2008). In short, insight is provided on improving the practical 

implementation of momentum strategy and into the underlying cause of momentum through 

the investigation of investor sentiment. 

Sentiment represents the state of mind of the investors. A variety of studies have linked 

sentiment and other financial aspects from IPO prices to feedback trading (e.g. Chau et al., 

2011; Clarke et al., 2016; Danbolt et al., 2015; Hung, 2016; Liang, 2016; Liston, 2016). In a 

positive state, individuals are more likely to stick to their normal routine but negative state 

elicits a more severe response whereby processing is more detail oriented (Schwarz, 2002). 

Ali and Gurun (2009) also echoed the view that optimism decreases the attentiveness of 

investors. To surmise, individuals are more alert during pessimistic periods and less attentive 

at optimistic times. Inattentiveness causes a delayed reaction to the arrival of new information 

supporting behavioural theories of underreaction (Dellavigna and Pollet, 2009). As for 

pessimistic periods, the increased awareness and processing of information could reduce or 

even remove the cognitive bias that drives momentum. Thus, it could be conjectured that 

optimism leads to a high level of momentum whereas pessimism is associated with lower or 

even absence of momentum. 

In addition to sentiment in the local stock market, cognitive biases and correspondingly 

momentum returns could be swayed by sentiment on an international scale. Baker et al. (2012) 

also advocated the view and demonstrated the influence of global sentiments on stock prices. 

Analysis showed that US investor sentiment had the greatest bearing on global sentiment. 

Thus, we postulate that global investor sentiment, apart from local sentiment, would affect 

stock momentum in Asia. 

Apart from formation period sentiment, it is anticipated that the investor sentiment during 

the holding period would also be of consequence. Though a mispricing has occurred in the 

previous period, the extent of correction may be affected by the investor sentiment in the 

current period. The state of mind of the investor could have a bearing on the extent of trading 

conducted to rectify the earlier mispricing. This implies that holding period sentiment would 

provide an indication of the extent of return continuation for the said holding period. 

27

Shangkari V. Anusakumar & Ruhani Ali 

We find that momentum arises only during optimistic and mild periods. Notably 

momentum is absent for periods of pessimism. The results also indicated that global sentiment 

affects momentum, which affirms the contagious nature of sentiment. In addition, we find 

that holding period sentiment affects momentum. Overall, the findings suggest that investors 

seeking to implement momentum strategy should avoid pessimistic periods. Pessimistic 

periods do not yield any significant returns and in some cases may lead to substantial losses. 

On the other hand, optimistic periods are preferable as momentum strategy could provide 

investors with significant portfolio returns. As momentum is influenced by investor 

sentiment, investors should take into consideration the sentiment prevalent at the time, global 

sentiment and also the sentiment prevailing during the portfolio holding period prior to 

implementing the momentum strategy. 

Given the strong influence of sentiment on momentum, a behavioural explanation, in part 

or in whole, seems likely. In particular, our evidence suggests that an underreaction theory of 

momentum is a likely explanation. Hong and Stein (1999) explain momentum in terms of the 

actions of heterogeneous agents: news watchers and momentum traders. Due to a gradual 

diffusion of information, news watchers cause underreaction (momentum) to occur. 

Subsequently, momentum traders would detect the underreaction and engage in trading in 

order to eliminate the mispricing. Momentum was generally found to be absent during 

pessimistic periods. The absence of momentum is likely due to the increased alertness and 

detail oriented information-processing capabilities of investors during the negative state. 

During pessimistic periods, information would be incorporated and disseminated rapidly; 

therefore, news watchers would cause less underreaction compared to the other sentiment 

states. Moreover, momentum is detected rapidly during pessimistic holding periods due to the 

increased alertness of momentum traders. Thus, momentum traders would quickly exploit the 

mispricing thereby eliminating any momentum. On the other hand, newswatchers would be 

less alert during optimistic periods and this would lead to momentum. Moreover, optimistic 

holding periods cause a delayed detection and correction of the mispricing as momentum 

traders are relatively not as alert and maintain their status quo with regards to information 

processing. 

2. Data 

The study encompasses a 12-year period from 1 January 2000 to 31 December 2011. This 

particular period is examined in order to avoid the Asian financial crisis. As noted by 

Abdelhédi-Zouch et al. (2015), the effect of sentiment may be amplified during times of crisis; 

the inclusion of crisis periods could distort the results. Based on data availability, we test 

stock exchanges from 13 Asian countries: Bangladesh, China, Hong Kong, India, Indonesia, 

Japan, Malaysia, Pakistan, Philippines, Singapore, South Korea, Taiwan and Thailand. Stock 

return index, trading volume and other data are obtained from Datastream. Table 1 (Panel A) 

states the number of stocks for each country. This figure includes active and delisted stocks. 

Sentiment measures derived from stock market related data could be compounded by a 

multitude of factors, thus a proxy independent of the stock market is needed. As demonstrated 

by Sibley et al. (2016), the popular market derived sentiment proxy developed by Baker and 

Wurgler (2006) may not fully reflect investor sentiment. Sibley et al. (2016) showed that 

roughly 63% of the total variations in the Baker and Wurgler (2006) index may be explained 

by economic variables; this was suggested to be source of the index’s predictive power. In 

contrast, consumer confidence index provides a measure based on direct survey of individual 

consumers. As stated by Schmeling (2009), consumer confidence index “seems to be the only 

consistent way to obtain a sentiment proxy that is largely comparable across countries” (p. 

397). Table 1 (Panel B) details the source of the sentiment proxies. Consumer confidence 

index available for each country is obtained from Datastream. As the local sentiment data for 

28


Malaysia and India are not available through Datastream, the information is procured from 

Malaysian Institute of Economic Research (MIER) and BluFin respectively. Consumer 

sentiment index obtained from MIER was shown to be a possible direct measure of investor 

sentiment in Malaysia (Tuyon et al., 2016). 

Table 1: Sample description 

Panel A: Stock Data 

Country Stock Exchange Abbr. No. of Stocks 

Bangladesh Dhaka Stock Exchange DSE 351 

China Shanghai Stock Exchange SSE 948 

Hong Kong Hong Kong Stock Exchange HKEX 1505 

India Bombay Stock Exchange BSE 3101 

Indonesia Indonesia Stock Exchange IDX 498 

Japan Tokyo Stock Exchange TSE 2913 

Malaysia Bursa Malaysia MYX 1052 

Pakistan Karachi Stock Exchange KSE 447 

Philippines Philippine Stock Exchange PSE 274 

Singapore Singapore Exchange SGX 775 

South Korea Korea Exchange KRX 995 

Taiwan Taiwan Stock Exchange TSEC 926 

Thailand Stock Exchange of Thailand SET 675 

Total Sample 14,460 

Panel B: Sentiment Data 

Sentiment Country/Index Years Source 

Local Sentiment Bangladesh N/A N/A 

China 2000-2011 Datastream 

Hong Kong 2000-2011 Datastream 

India 2008-2010 BluFin 

Indonesia 2000-2011 Datastream 

Japan 2000-2011 Datastream 

Malaysia 2000-2011 MIER 

Pakistan N/A N/A 

Philippines 2004-2011 Datastream 

Singapore N/A N/A 

South Korea 2000-2011 Datastream 

Taiwan 2009-2011 Datastream 

Thailand 2000-2011 Datastream 

Global 

Conference Board Consumer 

2000-2011 Datastream 

Sentiment Confidence Index 

University of Michigan consumer 2000-2011 Datastream 

sentiment index 

Baker and Wurgler (2006) composite 

index 

2000-2010 http://pages.stern. 

nyu.edu/~jwurgler/ 

A US based consumer confidence index is used to gauge the global investor sentiments. 

The choice is appropriate given the US market’s standing as one of the most prominent and 

influential market in the world and its movements are purported to have wide and often global 

reach. Baker et al. (2012) further confirm this as analysis showed that US investor sentiment 

had the greatest bearing on global sentiment. As duly noted by Baker et al. (2012), “the United 

States is widely considered the world’s bellwether market. Consistent with this position, the 

United States’ total sentiment index exhibits a high degree of commonality with other 

countries’ indices and receives the highest loading in the global index” (p.278). The 

Conference Board Consumer Confidence Index has been used to measure investor sentiment 

29


in several US market based studies (e.g. Ho and Hung, 2009; Tang and Yan, 2010). Moreover, 

Qiu and Welch (2004) and Lemmon and Portniaguina (2006) noted that the consumer 

confidence index is an appropriate measure of investor sentiment. 

3. Methodology 

Stocks are ranked based on cumulative returns from t-2 to t-7. Equally weighted winner and 

loser portfolios are formed using the top (winner stocks) and bottom (loser stocks) 10% of the 

stocks. A month is skipped after portfolio formation in order to mitigate microstructure biases. 

The constituents of the winner and loser portfolio are maintained for 6 months. The monthly 

returns for the winner, loser and momentum portfolio are computed for each month from t to 

t+5. At the end of formation period t, the weighted rolling average consumer confidence index 

of the previous 3 months is calculated with the weight of 3, 2 and 1 for month t, t-1 and t-2 

respectively. 

1 

AvgSent 

6 

Sent 

2 

6 

t2 

Sentt 

1 

 

3 

Sent 

6 

t 

(1) 

where AvgSent is the weighted average sentiment used to classify formation periods as 

pessimistic or optimistic. Sent t-2, Sent t-1 and Sent t represent confidence index value at month 

t-2, t-1 and t respectively. 

A particular formation period's sentiment is optimistic (pessimistic) when it ranks in the 

top (bottom) 30% of the average sentiment values. The remaining portfolios are assumed to 

have been formed during a ‘mild’ period. The momentum returns for the portfolios formed 

during pessimistic, mild and optimistic periods are assessed. For local sentiment, the 

consumer confidence index for each country is used for the aforementioned analysis. For 

global sentiment, the procedure is performed using the Conference Board Consumer 

Confidence Index. For holding period sentiment, the procedure is similar except that the 

weighted average of consumer confidence index values (local and global sentiment proxies) 

is computed over the portfolio holding period instead of portfolio formation period. 

4. Empirical Results 

4.1 Momentum Returns for Asian Markets 

Table 2 presents the average monthly returns along with corresponding t-statistics for winner, 

loser and momentum portfolio for the 13 countries. The winner portfolio generates positive 

return for all of the countries. The returns are statistically significant for a majority of the 

countries, specifically for nine out of the thirteen countries. This provides evidence of 

significant return continuations for winner stocks. In other words, stocks that performed well 

in the past continue to perform well in the future. In contrast, loser portfolio returns are 

significant for only six countries. 

Returns to the momentum portfolio are generally positive. Out of the sample of 13 

countries, 11 countries have positive returns for the momentum portfolio while 2 countries 

have negative returns. The highest momentum can be observed for Bangladesh whilst 

Philippines have the lowest return. Roughly one third of the sample countries display 

statistically significant momentum. Certain countries exhibit a high degree of momentum 

comparable to those reported in the US market. In short, there is evidence of momentum 

profitability in selected Asian countries. Bangladesh, in particular, has markedly strong 

momentum in the stock market. The momentum portfolio earns 1.470% per month which is 

higher than the returns reported in the US (e.g. Jegadeesh and Titman, 1993). The findings of 

momentum in Bangladesh concur with the results of Chui et al. (2010). 

30


Table 2: Returns for the momentum strategy (%) 

Momentum Winner Loser Momentum 

Bangladesh 2.829*** 1.358* 1.470** 

(3.76) (1.72) (2.27) 

China 1.041 0.486 0.555 

(1.36) (0.56) (1.50) 

Hong Kong 1.301* 0.320 0.981** 

(1.82) (0.35) (2.06) 

India 2.666*** 3.226*** -0.560 

(2.97) (2.78) (-0.91) 

Indonesia 1.639*** 1.363* 0.276 

(3.03) (1.90) (0.55) 

Japan 0.156 0.014 0.142 

(0.32) (0.02) (0.43) 

Malaysia 0.493 -0.260 0.753* 

(1.12) (-0.37) (1.71) 

Pakistan 1.910*** 1.298* 0.611 

(3.21) (1.72) (1.08) 

Philippines 1.503** 2.442*** -0.939 

(2.39) (2.87) (-1.51) 

Singapore 0.964* 0.067 0.897 

(1.66) (0.08) (1.65) 

South Korea 1.682** 0.552 1.130** 

(2.32) (0.65) (2.40) 

Taiwan 0.573 0.460 0.113 

(0.74) (0.49) (0.24) 

Thailand 1.932*** 1.347* 0.585 

(3.65) (1.79) (1.13) 

Notes: *, **, *** represent statistical significance at 10, 5, and 1 percent level respectively. 

4.2 Momentum and Local Sentiment 

Table 3 reports the portfolio returns for the momentum strategy during three sentiment states: 

optimistic, mild and pessimistic. The average monthly returns, in percentages, are presented 

for the winner, loser and momentum portfolio with the associated t-statistics. Due to 

unavailability of local sentiment data, the effect of local sentiment on momentum could not 

be explored for Bangladesh, Singapore and Pakistan. 

As can be observed there is no momentum for pessimistic periods. Momentum exists 

exclusively in the optimistic and mild periods. Moreover, there are instances where returns 

for the pessimistic period are negative whereas optimistic periods have strong positive returns. 

For example, the strategy in the Japanese markets undergoes significant losses of 2.599% for 

pessimistic period. The momentum portfolio in the optimistic period garners a significant 

return of 1.280%. Another notable finding is the presence of momentum during optimistic 

and mild periods in countries where momentum could not be found for the overall strategy 

(Indonesia, Japan and Taiwan). Thus, it could be conjectured that sentiment state is one the 

factors causing the apparent lack of momentum or rather masking the presence of momentum 

in Asia. 

Overall, the evidence on local sentiment concurs with the findings of Antoniou et al. 

(2013); momentum is only present for high sentiment periods. The factor that differentiates 

high and low sentiment periods is largely the loser portfolio. Loser portfolio returns are higher 

during pessimistic periods than optimistic periods. Moreover, returns for loser portfolio are 

on par with or higher than winner portfolio for optimistic periods. This causes the absence of 

momentum during pessimistic periods. The evidence suggest that investors have a greater 

31


propensity to engage in detailed processing during low sentiment periods which causes the 

elimination of momentum during pessimistic periods (Schwarz, 2002). 

Table 3: Local sentiment and momentum strategy 

Optimistic Mild Pessimistic 

Country 

Winner Loser Mom Winner Loser Mom Winner Loser Mom 

China -0.074 -0.600 0.526 1.816 0.983 0.833 0.152 0.243 -0.091 

(-0.06) (-0.40) (1.09) (1.62) (0.75) (1.44) (0.10) (0.16) (-0.14) 

Hong Kong 3.172 * 3.066 * 0.106 -0.269 -1.997* 1.728 *** 3.116 ** 3.013 0.103 

(1.85) (1.95) (0.17) (-0.29) (-1.81) (3.64) (2.38) (1.22) (0.06) 

India -5.138 -5.439 0.301 0.460 3.162 -2.701 4.444 3.121 1.323 

(-1.13) (-1.38) (0.27) (0.27) (0.70) (-0.81) (1.88) (1.53) (0.79) 

Indonesia 2.728 ** 1.263 1.465 ** 1.619 ** 2.035 * -0.416 0.758 -0.101 0.859 

(2.49) (1.17) (2.46) (2.17) (1.98) (-0.56) (0.68) (-0.06) (0.82) 

Japan 1.182 -0.098 1.280 *** -0.648 -1.203* 0.555 * 1.125 3.725 * -2.599** 

(1.46) (-0.11) (2.74) (-0.90) (-1.69) (1.68) (1.11) (2.02) (-2.21) 

Malaysia -3.656* -5.830** 2.174 0.849 * -0.127 0.976 * 1.379 2.157 -0.779 

(-2.11) (-2.66) (1.43) (1.74) (-0.17) (1.97) (1.47) (1.20) (-0.70) 

Philippines 1.335 2.063 -0.728 2.762 ** 2.991 ** -0.229 2.100 1.742 0.358 

(0.86) (1.56) (-0.66) (2.33) (2.04) (-0.22) (1.47) (1.55) (0.44) 

South Korea 1.354 -1.245 2.598 *** 1.312 0.105 1.207 ** 4.234 ** 5.608 * -1.374 

(0.93) (-0.86) (3.36) (1.44) (0.10) (2.22) (2.26) (2.06) (-0.74) 

Taiwan -4.939 -5.373 0.434 0.667 -1.281 1.947 * 1.644 1.784 -0.140 

(-1.41) (-1.55) (0.26) (0.57) (-0.95) (1.94) (0.52) (0.65) (-0.22) 

Thailand 2.145 * 1.640 0.505 1.368 ** 0.392 0.976 4.918 *** 6.535 *** -1.618 

(1.77) (1.12) (0.55) (2.11) (0.41) (1.50) (4.42) (3.62) (-0.90) 


4.3 Momentum and Global Sentiment 

Table 4 reports the winner, loser and momentum portfolio returns, along with the t-statistics, 

during periods of varying global sentiment. The portfolio return figures are in percentage and 

represent the average monthly return. Momentum portfolio returns are positive for all 

countries during the optimistic period, out of which five countries have significant returns. 

For the mild period, six countries have significant returns whilst there is no significant returns 

for the pessimistic period. 

Overall, eight of the thirteen countries have significantly positive returns to the 

momentum portfolio during the optimistic and/or mild period. In other words, more than half 

of the Asian countries exhibit momentum. One of the apparent finding is that the pessimistic 

period is devoid of momentum. Furthermore, almost all of the countries have negative returns 

to the momentum portfolio. In contrast, momentum strategy fares better for the other 

sentiment states especially optimistic periods. 

Similar to local investor sentiment, there are countries (China, Singapore and Thailand) 

which display strong momentum for high sentiment states but do not have any momentum for 

the overall momentum strategy. Global sentiment appears to have a stronger effect on 

momentum than local sentiment. An extreme example of this is the Chinese stock market 

where there was no momentum when using local sentiment but significant returns emerge 

when global sentiment is used for the analysis. It is suggested that sentiment spreads rapidly 

through mass media. Baker et al. (2012) suggested that “capital flows are a key mechanism 

through which global sentiment develops and propagates, but there are surely others, 

including word-of-mouth and the media” (p. 104). Regardless of the means by which 

sentiment spreads, the fact that global sentiment affects the level of momentum profitability 

in Asian markets further confirms the contagious nature of sentiment. 

32


Table 4: Global sentiment and momentum strategy 


Country 


Bangladesh 2.603 ** 0.036 2.568 ** 2.560 *** 1.010 1.549 3.502 2.880 * 0.622 

(2.19) (0.03) (2.46) (3.33) (0.92) (1.57) (1.64) (1.73) (0.56) 

China 1.556 0.269 1.288 ** 0.375 -0.644 1.019 * 2.031 2.856 * -0.825 

(1.00) (0.16) (2.74) (0.34) (-0.51) (1.80) (1.43) (1.82) (-1.33) 

Hong Kong 2.132 -0.772 2.904 ** 0.872 -0.497 1.369 *** 1.622 2.620 -0.998 

(1.04) (-0.32) (2.55) (0.94) (-0.46) (2.98) (1.25) (1.31) (-0.82) 

India 1.833 * 1.583 0.249 3.359 ** 4.199 ** -0.840 1.826 ** 2.344 -0.518 

(1.94) (0.77) (0.15) (2.16) (2.30) (-1.13) (2.10) (1.38) (-0.39) 

Indonesia 1.073 -0.753 1.826 1.455 * 1.015 0.440 2.358 *** 3.384 ** -1.026 

(0.72) (-0.41) (1.59) (1.92) (1.16) (0.87) (2.79) (2.24) (-0.79) 

Japan -0.353 -1.390 1.037 0.407 -0.151 0.558 -0.018 1.228 -1.246 

(-0.35) (-1.06) (1.47) (0.57) (-0.19) (1.41) (-0.02) (1.04) (-1.66) 

Malaysia -0.566 -1.536 0.970 0.413 -0.855 1.269 *** 1.319 * 1.723 -0.403 

(-0.43) (-0.62) (0.55) (0.71) (-1.09) (3.06) (1.85) (1.48) (-0.51) 

Pakistan 2.433 * 0.697 1.736 * 2.944 *** 2.595 ** 0.349 -0.463 -0.881 0.418 

(1.97) (0.57) (2.02) (3.38) (2.55) (0.63) (-0.49) (-0.54) (0.25) 

Philippines 1.571 0.849 0.723 1.239 2.253 ** -1.013 1.980 ** 3.823 ** -1.843 

(0.87) (0.34) (0.49) (1.35) (2.09) (-1.25) (2.45) (2.41) (-1.43) 

Singapore 0.369 -1.537 1.906 * 1.269 -0.329 1.598 ** 0.736 1.861 -1.125 

(0.25) (-0.77) (1.98) (1.49) (-0.29) (2.47) (0.86) (1.05) (-0.85) 

South 3.436 1.702 1.734 1.161 -0.524 1.686 *** 1.601 1.949 -0.347 

Korea (1.58) (0.57) (1.24) (1.16) (-0.52) (2.93) (1.54) (1.46) (-0.39) 

Taiwan -0.933 -1.478 0.545 0.614 0.529 0.084 1.442 1.546 -0.104 

(-0.42) (-0.47) (0.33) (0.58) (0.45) (0.15) (1.22) (1.00) (-0.14) 

Thailand 2.807 ** 2.561 0.246 1.537 * 0.255 1.281 ** 2.161 ** 2.734 ** -0.574 

(2.53) (1.00) (0.11) (1.92) (0.28) (2.60) (2.60) (2.30) (-0.67) 


4.4 Momentum and Holding Period Sentiment 

Table 5 reports the winner, loser and momentum portfolio returns, along with the t-statistics, 

during periods of varying holding period sentiment (optimistic, mild and pessimistic). The 

portfolio return figures are in percentage and represent the average monthly return. As the 

consumer confidence index is unavailable for Bangladesh, Pakistan and Singapore, the local 

holding period sentiment (Panel A) could not be computed for these countries. Nevertheless, 

the global holding period sentiment is reported in Panel B. 

Panel A reports the holding period sentiment using local sentiment proxies. The evidence 

for local holding period sentiment is mixed. There are significant positive returns to the 

momentum portfolio for optimistic and mild periods. Although there are a few instances of 

negative returns, these returns are insignificant. Moreover, the finding for pessimistic holding 

period is ambiguous with an equal number of significant positive and negative returns. 

As can be observed in Panel B, the results are more prominent for holding period 

sentiment computed using global sentiment. During optimistic period, momentum portfolio 

returns are positive for all of the sample countries. Mild period has largely positive returns 

expect for two cases of negative albeit insignificant momentum portfolio returns. In total, 

optimistic and mild holding periods yield significant and high levels of momentum for a 

majority of the countries, specifically nine out of the thirteen countries have momentum. In a 

clear display of the lack of return continuation during pessimistic holding period, returns to 

the momentum portfolio are all negative (expect for Bangladesh). Loser portfolio performs 

33


well during pessimistic holding period, thus causing the negative returns to the momentum 

portfolio. 

Table 5: Holding period sentiment and momentum strategy 

Country 



Panel A : Local Sentiment 

China -0.336 -0.896 0.560 1.702 0.785 0.917 0.306 1.040 -0.734 

(-0.25) (-0.61) (1.14) (1.64) (0.64) (1.66) (0.18) (0.62) (-1.48) 

Hong Kong 3.309 ** 2.941 ** 0.368 0.498 -0.035 0.533 1.135 -1.873 3.008 *** 

(2.25) (2.07) (0.61) (0.49) (-0.03) (0.71) (0.91) (-1.14) (3.80) 

India -5.138 -5.439 0.301 0.460 3.162 -2.701 4.444 3.121 1.323 

(-1.13) (-1.38) (0.27) (0.27) (0.70) (-0.81) (1.88) (1.53) (0.79) 

Indonesia 2.216 * 0.949 1.268 * 1.781 ** 1.945 * -0.164 0.900 0.191 0.708 

(1.90) (0.80) (1.87) (2.44) (1.98) (-0.24) (0.82) (0.13) (0.68) 

Japan 0.923 -0.318 1.241 *** -0.174 -0.601 0.427 0.089 2.768 -2.679* 

(1.14) (-0.38) (3.38) (-0.25) (-0.86) (1.28) (0.07) (1.21) (-1.86) 

Malaysia -1.949 -3.791 1.841 * 0.687 -0.292 0.979 ** 0.873 1.427 -0.554 

(-0.83) (-1.50) (1.80) (1.45) (-0.40) (1.99) (0.89) (0.71) (-0.44) 

Philippines 0.722 2.133 -1.411 3.005 *** 3.369 ** -0.364 1.802 1.150 0.652 

(0.46) (1.45) (-1.17) (3.18) (2.54) (-0.36) (0.82) (0.59) (0.56) 

South Korea 0.249 -1.316 1.565 * 1.885 ** 0.620 1.265 ** 2.460 2.686 -0.225 

(0.16) (-0.98) (1.81) (2.24) (0.65) (2.39) (0.93) (0.73) (-0.11) 

Taiwan -2.338 -1.294 -1.044 -0.801 -2.643 1.842 ** 1.418 1.966 -0.548 

(-0.29) (-0.32) (-0.26) (-0.58) (-1.71) (2.16) (0.45) (0.79) (-0.62) 

Thailand 2.162 * 1.823 0.339 1.449 ** 0.258 1.191 * 3.770 *** 5.558 ** -1.788 

(1.78) (1.27) (0.38) (2.14) (0.27) (1.87) (3.73) (2.64) (-0.99) 

Panel B: Global Sentiment 

Bangladesh 2.242 * -0.581 2.823 *** 2.495 ** 1.060 1.435 4.157 ** 3.596 ** 0.561 

(1.93) (-0.55) (3.11) (2.40) (0.94) (1.49) (2.58) (2.32) (0.53) 

China 3.725 * 3.450 0.275 -0.126 -1.246 1.119 ** 2.176 2.925 -0.750 

(1.82) (1.43) (0.38) (-0.14) (-1.27) (2.25) (1.21) (1.41) (-1.01) 

Hong Kong 1.991 0.109 1.883 0.729 -0.956 1.685 *** 2.320 3.891 -1.572 

(0.93) (0.04) (1.50) (0.87) (-1.09) (4.04) (1.45) (1.55) (-1.08) 

India -0.647 -2.507 1.861 * 3.910 *** 5.163 *** -1.253 1.795 2.295 -0.500 

(-0.59) (-1.54) (1.86) (2.84) (3.16) (-1.66) (1.62) (1.04) (-0.30) 

Indonesia 1.493 -0.523 2.016 1.593 ** 1.234 * 0.359 1.869 3.107 -1.238 

(0.92) (-0.28) (1.63) (2.40) (1.71) (0.82) (1.69) (1.50) (-0.76) 

Japan -0.929 -2.857** 1.928 *** 0.553 0.420 0.133 -0.100 1.060 -1.161 

(-0.95) (-2.36) (3.51) (0.87) (0.61) (0.36) (-0.09) (0.67) (-1.23) 

Malaysia -0.625 -1.988 1.364 0.321 -0.712 1.033 *** 1.785 ** 2.235 -0.451 

(-0.47) (-0.80) (0.75) (0.60) (-0.99) (2.70) (2.12) (1.50) (-0.46) 

Pakistan 1.679 0.590 1.089 2.829 *** 2.065 ** 0.764 -0.382 -0.229 -0.153 

(1.53) (0.49) (1.48) (3.39) (2.15) (1.40) (-0.36) (-0.12) (-0.08) 

Philippines 1.494 0.861 0.633 1.387 2.381 ** -0.994 1.820 * 3.780 * -1.959 

(0.85) (0.34) (0.43) (1.64) (2.55) (-1.34) (1.83) (1.76) (-1.23) 

Singapore 0.592 -2.531 3.122 *** 1.095 -0.158 1.254 ** 0.887 2.597 -1.710 

(0.36) (-1.24) (3.33) (1.51) (-0.16) (2.16) (0.75) (1.13) (-1.06) 

South Korea 2.697 0.686 2.011 1.568 * -0.075 1.643 *** 1.233 2.129 -0.895 

(1.20) (0.23) (1.45) (1.76) (-0.08) (2.92) (0.88) (1.14) (-0.93) 

Taiwan -1.698 -2.245 0.547 0.705 0.300 0.405 1.902 2.894 -0.992 

(-0.75) (-0.70) (0.32) (0.72) (0.28) (0.73) (1.35) (1.62) (-1.24) 

Thailand 2.063 * 1.913 0.149 1.928 *** 0.745 1.183 ** 1.846 2.538 -0.692 

(1.75) (0.72) (0.07) (2.79) (0.95) (2.61) (1.58) (1.51) (-0.66) 


34


4.5 Alternative Sentiment Classification and Proxies 

The investor sentiment investigations thus far have been conducted by classifying sentiment 

period based on a 30% cut off. A period is optimistic (pessimistic) if the index value is in the 

top 30% (bottom) of the time series of sentiment index values. In this section, an alternative 

sentiment cut-off of 40% is investigated to ensure the findings of this study are robust to 

changes in sentiment classification. For robustness, we also repeat the sentiment analysis 

using alternative sentiment measures: University of Michigan Sentiment Index (survey based 

sentiment measure) and Baker and Wurgler (2006) composite index (market based) 1 . 

For local sentiment, the findings are similar with the use of 40% cutoff instead of 30% cut 

off. Pessimistic periods are devoid of momentum. During optimistic and mild periods, there 

are countries that display high levels of profitability. Global sentiment also echoes the earlier 

findings. Returns to the momentum strategy are rampantly negative for pessimistic periods. 

High momentum profitability could be found in selected countries during optimistic and mild 

periods. For local holding period sentiment, the results are similar if not stronger with the use 

of 40% cut-off. Global holding period sentiment also retains its effect on momentum. 

The alternative survey based measure, University of Michigan sentiment index, produces 

similar results. Significant returns can be observed for optimistic and mild holding periods. 

The failure of momentum strategy during pessimistic periods is clearly visible. On the other 

hand, the analysis with Baker and Wurgler (2006) composite index yields sporadic 

momentum across the sentiment states with no obvious pattern. Significant returns could be 

detected in optimistic and pessimistic holding period sentiment. It is possible that Baker and 

Wurgler (2006) composite index poorly captures global sentiment. The indices from 

Conference Board and University of Michigan are survey based proxy of sentiment which 

may provide an edge over the composite index which is derived from market based variables. 

As suggested Sibley et al. (2016), information in the Baker and Wurgler (2006) index largely 

reflects business cycle variables rather than investor sentiment. 

4.6 Momentum, Sentiment and Size 

Baker and Wurgler (2006) documented the presence of size effect in the stock market only 

during optimistic periods. Moreover, results of the study indicated that small firms are 

affected to a greater extent by sentiment. Therefore, we test the potential influence of firm 

size on the relationship between momentum and sentiment. Stocks are based on firm size at 

the end of each formation period and allocated into ‘small’, ‘medium’ and ‘large’ categories. 

Within each size categories, stocks are sorted based on past return and the winner, loser and 

momentum portfolios are formed. Then, the periods are classified as optimistic, mild or 

pessimistic and momentum portfolio returns are computed for each sentiment state. 

The effect of local sentiment on momentum for the size categories is reported in Table 6. 

In each size category, a distinct pattern can be observed across the sentiment states; 

momentum is strong in optimistic and mild period whereas pessimistic periods have little or 

no momentum. In fact, the returns to the momentum portfolio are generally negative in the 

pessimistic period for the three size categories. 

The effect of global sentiment on momentum for the size categories is reported in Table 

7. Momentum returns for the large, medium and small stocks are all greatly affected during 

times of global pessimism. Small stocks suffer the most as evidenced by the significant 

negative returns (in two instances). Moreover, the presence of significant momentum is 

largely concentrated in large and medium stocks during optimistic and mild periods. 

1 

For brevity, tables are not reported. Our findings are also unaffected by market development and 

macroeconomic factors. Results are available upon request. 

35


Table 6: Size, local sentiment and momentum portfolio returns 


Country 

Large Medium Small Large Medium Small Large Medium Small 

China 0.759 0.500 0.321 0.833 0.594 0.111 0.158 0.089 -0.828** 

(1.38) (1.20) (0.62) (1.30) (1.24) (0.30) (0.16) (0.15) (-2.06) 

Hong Kong 1.623 * 1.527 ** -0.039 1.824 *** 1.970 *** 1.653 *** -1.514 2.183 -1.365 

(1.94) (2.21) (-0.04) (3.29) (3.75) (2.70) (-0.97) (1.34) (-0.77) 

India 0.743 0.627 1.567 -2.118 -2.449 -4.057 0.901 3.938 ** 0.199 

(0.64) (0.39) (1.18) (-0.56) (-0.70) (-1.48) (1.69) (3.59) (0.15) 

Indonesia 1.299 2.214 ** 1.246 0.144 0.168 -0.667 0.130 1.463 1.618 

(1.59) (2.34) (1.45) (0.14) (0.20) (-0.73) (0.07) (1.03) (0.99) 

Japan 0.850 1.020 ** 1.081 * 0.101 0.772 ** 0.981 *** -2.453** -2.351* -2.223* 

(1.55) (2.49) (1.79) (0.23) (2.22) (2.73) (-2.07) (-1.86) (-1.80) 

Malaysia 1.419 2.839 * 1.730 0.942 ** 1.037 ** 0.251 -0.870 -0.184 0.052 

(1.20) (1.98) (1.43) (2.08) (2.27) (0.40) (-0.56) (-0.14) (0.05) 

Philippines 2.021 -0.694 -1.411 0.504 -0.690 0.469 -0.258 2.010 -1.922 

(1.45) (-0.61) (-0.94) (0.48) (-0.50) (0.33) (-0.19) (1.51) (-1.49) 

South Korea 1.983 * 2.476 *** 1.573 1.199 1.229 ** 0.938 * -0.353 -1.906 -1.684 

(1.73) (2.89) (1.43) (1.55) (2.53) (1.83) (-0.17) (-1.15) (-0.79) 

Taiwan -0.832 1.812 1.091 2.565 * 1.538 * 1.557 -1.333 0.790 -0.378 

(-0.42) (0.74) (0.80) (2.04) (1.96) (1.68) (-1.40) (1.40) (-0.41) 

Thailand 1.753 -0.223 0.207 0.978 1.463 ** 1.103 * -2.431 0.160 -2.477 

(1.53) (-0.19) (0.18) (1.33) (2.18) (1.72) (-0.93) (0.10) (-1.55) 


Table 7: Size, global sentiment and momentum portfolio returns 


Country 


Bangladesh 1.471 1.102 0.812 1.739 ** 2.210 *** -0.539 1.263 0.656 -0.492 

(1.00) (1.32) (0.84) (2.42) (2.85) (-0.51) (1.10) (0.55) (-0.27) 

China 2.256 *** 0.977 ** -0.449 0.862 0.765 0.697 ** -0.747 -0.481 -1.328*** 

(3.23) (2.37) (-0.75) (1.50) (1.64) (2.03) (-0.78) (-0.83) (-3.16) 

Hong Kong 3.074 ** 4.335 *** 2.576 * 1.468 *** 2.583 *** 0.544 -1.155 -0.934 -0.628 

(2.36) (3.39) (2.03) (3.09) (5.36) (0.91) (-0.89) (-0.83) (-0.44) 

India 1.121 0.108 -0.011 1.188 * 0.310 -2.263** 0.339 0.085 -1.605 

(0.44) (0.06) (-0.01) (1.75) (0.40) (-2.61) (0.23) (0.06) (-1.34) 

Indonesia 2.046 4.165 ** -0.002 0.750 0.221 1.094 -1.416 0.172 -1.167 

(0.97) (2.68) (0.00) (0.96) (0.29) (1.50) (-0.84) (0.15) (-0.85) 

Japan 0.656 1.367 * 0.597 0.211 0.559 1.063 ** -1.525* -1.092 -1.039 

(0.80) (2.05) (0.75) (0.47) (1.34) (2.57) (-1.87) (-1.42) (-1.25) 

Malaysia 0.928 1.750 0.561 1.233 *** 1.345 *** 0.488 -0.759 -0.320 -0.087 

(0.61) (1.07) (0.24) (2.89) (3.32) (1.06) (-0.74) (-0.36) (-0.13) 

Pakistan 1.380 2.608 *** 0.611 0.624 0.937 * -0.890 -0.442 0.487 1.251 

(1.04) (2.96) (0.48) (0.70) (1.76) (-0.98) (-0.26) (0.26) (0.65) 

Philippines -0.977 1.475 1.406 -0.222 -1.397 -0.801 -0.161 -2.073 -2.788** 

(-0.42) (0.89) (0.65) (-0.22) (-1.30) (-0.77) (-0.13) (-1.27) (-2.32) 

Singapore 1.225 2.952 ** 2.554 * 1.482 *** 2.206 *** 1.557 * -1.073 -1.474 -1.116 

(1.09) (2.32) (2.03) (2.70) (4.14) (1.81) (-0.66) (-0.91) (-1.08) 

South Korea 3.209 1.009 -0.640 1.390 ** 1.690 *** 1.348 ** -0.648 -0.157 0.354 

(1.37) (0.76) (-0.50) (2.04) (3.41) (2.09) (-0.61) (-0.18) (0.39) 

Taiwan 1.156 0.851 -0.028 1.126 * 0.307 0.175 -0.100 0.085 0.017 

(0.72) (0.46) (-0.01) (1.81) (0.55) (0.31) (-0.12) (0.10) (0.02) 

Thailand 0.102 0.386 1.609 1.964 *** 1.349 ** 0.942 -1.128 0.478 -1.044 

(0.04) (0.18) (0.89) (3.22) (2.19) (1.51) (-0.98) (0.58) (-1.11) 


36


Table 8: Size, holding period sentiment and momentum portfolio returns 

Country 




China 0.667 0.410 0.477 1.006 0.727 0.138 -0.574 -0.444 -1.419*** 

(1.18) (0.97) (0.92) (1.58) (1.57) (0.41) (-0.71) (-0.90) (-2.92) 

Hong Kong 1.810 ** 1.737 ** 0.236 0.694 1.044 0.197 0.993 4.615 *** 2.067 *** 

(2.50) (2.59) (0.26) (0.90) (1.43) (0.22) (0.91) (5.00) (2.83) 

India 0.743 0.627 1.567 -2.118 -2.449 -4.057 0.901 3.938 ** 0.199 

(0.64) (0.39) (1.18) (-0.56) (-0.70) (-1.48) (1.69) (3.59) (0.15) 

Indonesia 1.793 * 1.769 * 1.014 0.308 0.517 -0.457 -0.398 1.264 1.601 

(1.90) (1.79) (1.24) (0.31) (0.65) (-0.53) (-0.23) (0.89) (0.98) 

Japan 0.755 1.015 *** 1.221 ** -0.025 0.623 * 0.747 * -2.342 -2.388 -2.200 

(1.57) (3.05) (2.60) (-0.06) (1.80) (1.96) (-1.66) (-1.56) (-1.47) 

Malaysia 1.419 2.839 * 1.730 0.942 ** 1.037 ** 0.251 -0.870 -0.184 0.052 

(1.20) (1.98) (1.43) (2.08) (2.27) (0.40) (-0.56) (-0.14) (0.05) 

Philippines 1.514 -0.553 -2.287 0.205 -0.549 -0.836 1.424 -0.057 2.383 

(1.01) (-0.40) (-1.22) (0.20) (-0.42) (-0.70) (0.93) (-0.03) (1.08) 

South Korea 0.566 1.568 * 2.023 ** 1.576 ** 1.212 ** 0.529 -0.541 -0.511 0.081 

(0.48) (1.77) (2.34) (2.07) (2.56) (1.02) (-0.25) (-0.27) (0.03) 

Taiwan 0.067 -1.957 0.069 1.766 2.096 ** 1.600 * -1.256 0.505 -0.781 

(0.01) (-0.48) (0.02) (1.53) (2.57) (2.03) (-1.29) (0.56) (-0.53) 

Thailand 1.600 -0.355 -0.027 1.182 1.815 *** 1.135 * -2.395 -0.962 -1.488 

(1.44) (-0.31) (-0.02) (1.57) (2.88) (1.77) (-1.09) (-0.53) (-0.92) 


Bangladesh 1.935 0.895 1.083 1.585 ** 2.221 *** -0.331 1.217 0.393 -1.196 

(1.31) (1.04) (1.20) (2.33) (2.84) (-0.30) (0.91) (0.36) (-0.74) 

China 1.256 -0.018 -0.288 1.014 * 0.919 ** 0.432 -0.730 -0.429 -1.230** 

(1.32) (-0.03) (-0.43) (1.96) (2.29) (1.38) (-0.64) (-0.62) (-2.37) 

Hong Kong 3.674 *** 3.702 ** 1.453 1.342 *** 2.731 *** 0.903 * -1.805 -1.598 -0.955 

(2.86) (2.81) (1.03) (2.95) (5.98) (1.68) (-1.17) (-1.20) (-0.54) 

India 3.351 * 2.473 ** 1.082 0.536 -0.451 -2.465*** 0.236 0.309 -1.653 

(2.03) (2.12) (1.27) (0.65) (-0.57) (-3.17) (0.13) (0.18) (-1.13) 

Indonesia 2.916 3.538 * 0.721 0.562 0.387 0.742 -2.005 0.307 -1.308 

(1.33) (2.07) (0.33) (0.81) (0.58) (1.11) (-0.97) (0.20) (-0.76) 

Japan 1.545 ** 1.976 *** 1.554 ** -0.265 0.209 0.603 -1.288 -0.953 -1.008 

(2.44) (3.63) (2.38) (-0.60) (0.55) (1.53) (-1.30) (-0.97) (-0.96) 

Malaysia 1.208 1.935 0.756 1.079 *** 1.135 *** 0.379 -1.014 -0.258 -0.068 

(0.77) (1.14) (0.32) (2.74) (3.12) (0.87) (-0.81) (-0.23) (-0.08) 

Pakistan 1.470 1.369 -0.055 0.702 1.477 *** -0.144 -0.935 -0.089 0.278 

(1.28) (1.57) (-0.05) (0.82) (2.86) (-0.16) (-0.46) (-0.04) (0.13) 

Philippines -1.591 1.820 1.398 0.092 -1.611* -0.794 -0.558 -1.816 -3.178** 

(-0.65) (1.11) (0.67) (0.10) (-1.68) (-0.81) (-0.39) (-0.87) (-2.22) 

Singapore 2.287 ** 4.436 *** 3.251 ** 1.330 ** 1.682 *** 1.140 -2.040 -1.981 -1.089 

(2.13) (3.59) (2.56) (2.58) (3.48) (1.50) (-1.05) (-0.99) (-0.80) 

South Korea 3.861 1.591 -0.219 1.262 * 1.518 *** 1.281 ** -1.190 -0.568 -0.067 

(1.68) (1.25) (-0.17) (1.86) (3.03) (2.13) (-1.01) (-0.59) (-0.06) 

Taiwan 1.619 0.934 0.081 1.259 ** 0.489 0.411 -1.075 -0.497 -0.738 

(1.01) (0.50) (0.04) (2.11) (0.90) (0.77) (-1.25) (-0.55) (-0.99) 

Thailand 0.450 0.310 0.846 1.485 ** 1.462 ** 1.063 * -0.862 0.004 -1.230 

(0.19) (0.14) (0.46) (2.61) (2.61) (1.79) (-0.60) (0.00) (-1.13) 


The effect of holding period sentiment on momentum for the size categories is reported in 

Table 8. In general, holding period sentiment also affects momentum across firm size 

37


categories. The effect is especially conspicuous for global holding period sentiment (Panel B) 

as there is no momentum for pessimistic holding periods. The evidence for local holding 

period sentiment (Panel A) is less unanimous. Momentum is largely concentrated in 

optimistic and mild holding periods but there is significant momentum for Hong Kong and 

India for the pessimistic holding period. Nevertheless, a majority of the returns is insignificant 

during pessimistic holding period and there is also a significant negative return for small 

stocks in China. We conclude that momentum generally does not exist during pessimistic 

holding periods. 

4.7 Momentum, Sentiment and Volume 

Trading volume may contain an element of investor sentiment (Baker and Wugler, 2006). 

Optimistic investors are more likely to engage in trading activity in a market with short-sales 

constraints and this activity is reflected in trading volume and generally in liquidity (Baker 

and Stein, 2004). Trading volume may reflect investor sentiment but trading volume in itself 

is a simple and imperfect proxy of sentiment as it is confounded by factors unrelated to 

sentiment. Therefore, we test the robustness of the effect of sentiment on momentum by 

analyzing trading volume. At the end of each formation period, the stocks are segregated into 

three volume portfolios; high, medium and low. Winner, loser and momentum portfolios are 

formed within the three volume categories. Then, the periods are classified as optimistic, mild 

or pessimistic and the momentum returns for the respective states are computed. 

Table 9 reports the results of this robustness analysis for local sentiment. ‘High Vol.’, 

‘Med. Vol.’ and ‘Low Vol.’ refer to the high-, medium- and low-volume stock categories 

respectively. Pessimistic period is largely devoid of momentum while optimistic and mild 

period have instances of strong momentum. Momentum in all three volume categories appear 

to be affected by local sentiment. The earlier finding of momentum in Japan for high 

sentiment periods still holds and is perhaps stronger after taking into account trading volume. 

Table 9: Volume, local sentiment and momentum portfolio returns 


Country 

High Vol. Med. Vol. Low Vol. High Vol. Med. Vol. Low Vol. High Vol. Med.Vol. Low Vol. 

China 0.853 * 0.278 0.359 0.728 0.874 0.435 -0.018 -0.507 0.140 

(1.74) (0.52) (0.60) (1.11) (1.43) (0.74) (-0.04) (-0.85) (0.16) 

Hong Kong 1.078 -0.069 -0.493 1.289 ** 1.982 *** 2.228 *** -0.251 0.669 -0.747 

(1.48) (-0.11) (-0.57) (2.24) (3.94) (3.73) (-0.15) (0.41) (-0.59) 

India -0.495 1.630 -1.458 -1.491 -1.391 -1.343 0.373 -4.018 -7.297 

(-0.16) (0.66) (-0.95) (-0.33) (-0.58) (-1.51) (0.18) (-0.72) (-1.42) 

Indonesia 1.207 2.741 ** 1.171 * -1.054 0.511 -0.340 1.880 1.373 0.171 

(1.32) (2.70) (1.77) (-1.11) (0.57) (-0.42) (1.38) (0.95) (0.13) 

Japan 1.773 *** 1.086 ** 0.855 ** 0.394 0.764 ** 0.906 *** -2.112 -3.032** -2.049* 

(2.77) (2.33) (2.37) (0.98) (2.38) (3.13) (-1.69) (-2.56) (-1.87) 

Malaysia 0.982 1.715 2.060 * 1.314 ** 1.012 ** 0.966 * -1.483 -0.483 0.727 

(0.99) (1.50) (2.09) (2.62) (2.08) (1.83) (-1.00) (-0.38) (0.84) 

Philippines 0.332 -0.851 -0.114 -0.384 0.557 -0.547 0.272 0.452 -0.535 

(0.15) (-0.61) (-0.07) (-0.30) (0.46) (-0.41) (0.24) (0.44) (-0.38) 

South Korea 2.201 * 2.640 *** 3.318 *** 1.140 ** 1.351 ** 1.329 ** -0.561 -0.680 -2.221 

(2.03) (2.97) (3.90) (2.06) (2.29) (2.05) (-0.37) (-0.34) (-1.22) 

Taiwan -0.888 0.751 3.391 ** 2.787 * 2.776 ** 1.335 0.570 *** 0.805 -0.414 

(-0.33) (0.47) (3.16) (2.18) (2.38) (1.34) (5.64) (0.80) (-0.39) 

Thailand 0.138 1.066 -0.669 0.940 0.854 1.760 ** -2.179 -1.905 -1.190 

(0.11) (0.87) (-0.63) (1.27) (1.17) (2.12) (-1.22) (-0.98) (-0.58) 


38


Table 10 reports the results of the effect of global sentiment on momentum across volume 

categories. Momentum portfolio returns are all insignificant for global pessimistic periods for 

all of the volume categories, except for a marginally significant negative return in the medium 

volume category. On the other hand, there are momentum portfolio returns as high as 3.546% 

for global optimistic and mild periods. 

Table 10: Volume, global sentiment and momentum portfolio returns 


Country High 

Vol. 

Med. 

Vol. 

Low 

Vol. 

High 

Vol. 

Med. 

Vol. 

Low 

Vol. 

High 

Vol. 

Med. 

Vol. 

Low 

Vol. 

Bangladesh 1.930 3.038 *** 1.068 2.718 *** 1.411 -0.366 1.186 0.486 -1.107 

(1.61) (3.04) (0.85) (3.14) (1.62) (-0.32) (0.84) (0.48) (-0.89) 

China 0.568 1.292 ** 1.377 * 1.122 * 0.918 0.612 -0.483 -1.079* -0.813 

(0.81) (2.13) (2.07) (1.76) (1.52) (1.11) (-1.00) (-1.97) (-0.95) 

Hong Kong 1.232 2.917 ** 3.141 ** 1.774 *** 1.542 *** 0.552 -1.065 -0.540 0.240 

(0.83) (2.38) (2.37) (4.10) (3.00) (0.99) (-0.80) (-0.48) (0.22) 

India -0.240 0.937 -0.101 0.865 -0.419 -2.128** 0.540 0.382 -1.405 

(-0.08) (0.54) (-0.13) (0.95) (-0.59) (-2.52) (0.39) (0.36) (-1.25) 

Indonesia 1.885 3.546 * 2.161 0.242 0.937 0.092 -1.256 0.144 -1.215 

(1.10) (2.00) (1.34) (0.30) (1.33) (0.14) (-0.91) (0.10) (-1.08) 

Japan 0.946 1.274 * 0.874 0.881 ** 0.440 0.620 -1.358 -1.219 -0.577 

(0.98) (1.84) (1.65) (2.04) (1.06) (1.60) (-1.64) (-1.68) (-0.91) 

Malaysia 0.428 0.984 0.787 1.596 *** 1.113 ** 1.447 *** -0.775 -0.011 0.306 

(0.29) (0.60) (0.45) (3.25) (2.60) (3.07) (-0.75) (-0.01) (0.59) 

Pakistan 0.521 2.637 * 1.295 1.064 0.816 -1.149* 2.324 1.063 0.055 

(0.43) (1.80) (0.82) (1.39) (1.09) (-1.72) (1.46) (0.72) (0.03) 

Philippines 1.691 -0.756 1.466 -2.010 -0.911 -1.290 -0.995 -1.196 -1.929 

(1.04) (-0.50) (0.54) (-1.56) (-0.95) (-1.37) (-0.56) (-0.89) (-1.30) 

Singapore 1.520 3.048 ** 2.449 ** 2.369 *** 1.441 * 0.893 -0.557 -0.806 -0.399 

(1.41) (2.66) (2.42) (3.89) (1.90) (1.15) (-0.42) (-0.76) (-0.33) 

South Korea 1.059 2.120 1.685 1.495 ** 2.009 *** 1.610 ** 0.398 -0.528 0.217 

(0.77) (1.32) (0.85) (2.48) (3.25) (2.59) (0.46) (-0.60) (0.25) 

Taiwan 0.141 -0.233 1.076 0.539 0.006 -0.077 0.295 0.693 -0.111 

(0.08) (-0.13) (0.49) (0.91) (0.01) (-0.12) (0.33) (0.93) (-0.13) 

Thailand 0.059 -0.461 1.855 1.111 1.659 *** 1.324 ** -0.725 -0.820 -0.585 

(0.03) (-0.19) (0.68) (1.61) (2.64) (2.05) (-0.79) (-0.92) (-0.63) 


Table 11 reports the effect of holding period sentiment on momentum for the volume 

categories. As can be observed in Panel A, momentum portfolio returns during pessimistic 

holding periods are generally insignificant. In contrast to the pessimistic holding periods, 

momentum is prevalent during optimistic and mild holding periods. Panel B reports the results 

for global holding period sentiment. For the global pessimistic holding period, there is no 

momentum for all countries across the volume categories. Moreover, much of the returns are 

negative. In contrast, the momentum strategy garners significant profits during global 

optimistic and mild holding periods. 

39


Table 11: Volume, holding period sentiment and momentum portfolio returns 


Country High 

Vol. 

Med. 

Vol. 

Low 

Vol. 

High 

Vol. 

Med. 

Vol. 

Low 

Vol. 

High 

Vol. 

Med. 

Vol. 

Low 

Vol. 


China 0.666 0.455 0.492 0.841 0.844 0.536 -0.447 -1.068** -0.471 

(1.32) (0.85) (0.93) (1.41) (1.46) (0.92) (-0.83) (-2.33) (-0.58) 

Hong Kong 1.501 ** 0.205 -0.164 0.027 0.823 1.013 2.612 ** 3.520 *** 1.976 ** 

(2.15) (0.32) (-0.19) (0.03) (1.11) (1.38) (2.60) (4.50) (2.39) 

India 1.773 1.488 1.244 -1.902 -1.338 -4.220 1.831 * 0.958 0.434 

(1.36) (1.17) (1.14) (-0.57) (-0.52) (-1.59) (2.78) (0.56) (0.95) 

Indonesia 1.251 2.523 ** 0.895 -0.831 0.914 -0.066 1.707 0.920 -0.052 

(1.43) (2.21) (1.14) (-0.91) (1.07) (-0.08) (1.26) (0.64) (-0.04) 

Japan 1.653 *** 1.178 *** 0.825 ** 0.348 0.557 * 0.696 ** -2.268 -3.145** -1.781 

(3.25) (3.17) (2.67) (0.81) (1.77) (2.46) (-1.55) (-2.13) (-1.30) 

Malaysia 0.982 1.715 2.060 * 1.314 ** 1.012 ** 0.966 * -1.483 -0.483 0.727 

(0.99) (1.50) (2.09) (2.62) (2.08) (1.83) (-1.00) (-0.38) (0.84) 

Philippines -1.422 -2.287 0.304 -0.016 0.139 -0.997 -0.033 2.095 0.475 

(-0.73) (-1.33) (0.15) (-0.01) (0.13) (-0.85) (-0.03) (1.29) (0.24) 

South Korea 1.715 1.229 2.116 ** 1.248 ** 1.539 *** 1.208 * -0.462 0.236 0.209 

(1.70) (1.30) (2.44) (2.26) (2.70) (1.91) (-0.28) (0.11) (0.10) 

Taiwan -2.859 -1.364 1.893 2.315 * 2.653 ** 1.946 ** 0.430 0.486 -0.065 

(-0.41) (-0.52) (3.74) (2.09) (2.70) (2.18) (0.44) (0.50) (-0.08) 

Thailand -0.396 0.806 -0.575 1.160 1.041 1.748 ** -1.656 -1.759 -0.668 

(-0.33) (0.68) (-0.54) (1.59) (1.42) (2.07) (-0.87) (-0.94) (-0.36) 


Bangladesh 2.101 * 3.060 *** 1.670 2.433 ** 1.271 -0.369 1.451 0.687 -1.666 

(1.82) (3.49) (1.48) (2.63) (1.58) (-0.34) (1.23) (0.55) (-1.22) 

China -0.059 0.370 0.047 1.235 ** 0.979 * 0.755 -0.703 -0.998 -0.506 

(-0.07) (0.43) (0.05) (2.19) (1.84) (1.62) (-1.28) (-1.56) (-0.48) 

Hong Kong 1.577 2.080 2.109 1.652 *** 1.783 *** 1.068 ** -1.652 -0.990 -0.362 

(1.11) (1.47) (1.34) (3.63) (3.97) (2.18) (-1.06) (-0.73) (-0.29) 

India 2.602 2.331 ** 0.545 0.067 -0.787 -2.171*** 0.461 0.558 -1.541 

(1.44) (2.28) (0.75) (0.06) (-1.06) (-2.84) (0.28) (0.42) (-1.12) 

Indonesia 2.001 3.557 * 1.834 0.406 0.850 0.202 -2.066 0.273 -1.497 

(1.12) (2.00) (1.09) (0.55) (1.36) (0.33) (-1.27) (0.15) (-1.10) 

Japan 2.239 *** 2.080 *** 0.987 * 0.355 0.036 0.471 -1.412 -1.081 -0.524 

(2.94) (3.55) (1.94) (0.84) (0.09) (1.35) (-1.38) (-1.17) (-0.65) 

Malaysia 1.260 1.215 1.276 1.271 *** 1.045 *** 1.145 *** -1.095 -0.260 0.475 

(0.82) (0.72) (0.71) (2.75) (2.64) (2.66) (-0.88) (-0.24) (0.72) 

Pakistan -0.668 2.861 * 0.691 1.684 ** 0.974 -0.720 1.812 0.590 -0.293 

(-0.62) (2.06) (0.45) (2.27) (1.32) (-1.07) (0.97) (0.35) (-0.14) 

Philippines 1.569 -0.872 1.307 -1.906 -0.820 -1.322 -0.836 -1.411 -1.782 

(1.00) (-0.59) (0.47) (-1.59) (-0.93) (-1.56) (-0.40) (-0.85) (-0.94) 

Singapore 2.945 ** 4.036 *** 3.251 *** 1.866 *** 1.245 * 0.881 -0.956 -1.469 -1.205 

(2.59) (3.45) (3.21) (3.41) (1.84) (1.24) (-0.59) (-1.14) (-0.85) 

South Korea 0.738 2.749 * 2.171 1.619 *** 1.857 *** 1.599 *** 0.044 -1.156 -0.426 

(0.60) (1.77) (1.08) (2.64) (3.07) (2.71) (0.05) (-1.22) (-0.43) 

Taiwan 1.034 0.212 1.402 0.547 0.296 0.245 -0.458 -0.266 -1.186 

(0.64) (0.12) (0.62) (0.90) (0.53) (0.40) (-0.51) (-0.35) (-1.24) 

Thailand 0.478 -0.460 1.903 0.780 1.449 ** 1.224 ** -0.600 -0.886 -0.767 

(0.21) (-0.19) (0.67) (1.22) (2.48) (2.07) (-0.53) (-0.81) (-0.68) 


40


5. Conclusion 

The central finding of this study is that sentiment affects momentum profitability in Asia. 

Momentum is present only during optimistic and mild periods. Pessimistic periods are fraught 

with negative returns. More importantly, countries where there is persistent absence of 

momentum display significant momentum once sentiment in taken into account. Japan, for 

example, has significant momentum during states of high sentiment. On the other hand, 

significant negative returns to the momentum portfolio are present during pessimistic periods. 

This is what deprives these markets of momentum. In addition to the local sentiment prevalent 

in the market, sentiment on a global scale influences momentum. In some cases, global 

sentiment appears to have a greater effect on momentum compared to local momentum. 

Sentiment prevalent during the portfolio holding period also dictates the level of momentum. 

The findings are robust to changes in the sentiment classification and proxy, and even after 

taking into account firm size and trading volume. 

The findings provide an interesting revelation to investors. Whilst higher sentiment 

periods provide investors with significant momentum portfolio returns, pessimistic periods 

do not yield any significant returns and in some cases could even lead to substantial losses. 

Investors seeking to implement momentum strategy in Asia and possibly elsewhere should be 

cautious of the sentiment prevalent at the time of portfolio formation. Moreover, global 

sentiment should also be taken into consideration. Implementing momentum strategy during 

pessimistic periods could prove to be disastrous. It should be noted that trading costs were not 

taken into account and this area could be an interesting consideration for future studies. 

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42


The Relationship of Crude Palm Oil Spot-Futures 

under Inflationary Expectation in Gold Market 

You-How Go 1 & Wee-Yeap Lau 2 

1 Faculty of Business and Finance, Universiti Tunku Abdul Rahman, 

Malaysia. 

2 Faculty of Economics and Administration, University of Malaya, Malaysia. 

Abstract: This study attempts to test whether the direction of information 

spillover between crude palm oil (CPO) spot and futures price corresponds to 

a long-term shift in gold price. While there is yet to be a study on the CPO 

spot-futures relationship under the inflationary expectation of gold price, this 

paper hypothesizes that market participants are bullish on gold price will use 

the commodity as an inflation hedge, and this put speculative pressure on future 

CPO price. Using daily data on CPO spot and futures returns from January 

1996 to November 2011, notably, it is found that: First, there is volatility 

spillover from current futures return to spot return during bullish period in gold 

due to increase in investor demand; Second, only contemporaneous volatility 

spillover between spot and futures returns exist during bearish period as 

investors become more risk averse. This study adds to another stylized fact that 

the upward trend of the gold price has economic content that leads to 

speculation of CPO price in the futures market. 

Keywords: Crude palm oil, gold trend, spot-futures relationship, information 

spillover, causality-in-variance. 

JEL classification: E31, G1, G13 


The inflationary expectation provides speculative opportunities in the futures market of 

commodities. Since the futures price is derived from the spot price of an underlying 

commodity, it provides information about market participants’ expectation of future spot 

prices and opportunities for price manipulation of a commodity. The reason is intervention in 

the futures market can influence producers’ decision in the commodity spot market. 

According to Newberry (1992), market participants tend to change their investments from 

common stocks, bonds or equities to commodity markets in order to face the expected 

inflation. 

Based on the study by Mahdavi and Zhou (1997), they find that commodity prices are 

often thought to incorporate arrival of new information faster than consumer prices. 

Meanwhile, Twomey et al. (2011) further demonstrate that commodities can be used to hedge 

against unexpected inflationary shock during the sample period of 1980-2011. The expected 

increases in a commodity price will provide opportunities for market participants to speculate 

the price of a commodity through the futures market. 

Several researchers such as Jaffe (1989), Narayan et al. (2010), Beckmann and Czudaj 

(2013), and Shahbaz et al. (2014) present various aspects of the role of gold in preserving 

price stability. For instance, Jaffe (1989) who finds that investors can hold gold as an 

alternative for a stock to hedge against inflation. Using the cointegration test, Narayan et al. 

(2010) find that futures markets of oil and gold are cointegrated at the maturities of a ten- 

 

Corresponding author: Wee-Yeap Lau. Tel.: 603-79673627. Email: wylau@um.edu.my 

43

You-How Go & Wee-Yeap Lau 

month contract during 1995-2009. Their findings suggest that investors transfer funds from 

stock market to gold market to hedge against inflation and secure profitability in their 

investment. As a consequence, a higher demand for gold will increase the gold price. 

Using a Markov-switching vector error correction model, Beckmann and Czudaj (2013) 

find that the gold has the ability to hedge the future inflation in the United States and the 

United Kingdom as compared to Japan and Euro Area, respectively. This implies that the gold 

cannot consistently provide an inflation hedge over time due to the occurrence of various 

economic turbulences at different time horizons. Based on the results of autoregressive 

distributed lag (ARDL) bounds testing approach, Shahbaz et al. (2014) find that the gold price 

can provide a good investment to hedge against inflation in Pakistan during the sample period 

of 1997-2011. 

Based on the existing findings, the upward movement of gold price can be used as an 

effective hedge against inflation in the long run, implying that an increase in expected 

inflation will encourage more people to invest in the gold. This leads to higher prices in gold 

and eventually influences the price movement of other commodities in the short run (Zhang 

and Wei, 2010). 1 In summary, the expected inflation will trigger higher demand for gold, and 

in return, a higher gold price can influence the price of other commodities. 

There are some reasons for conducting this study in the case of Malaysian crude palm oil 

(CPO) markets. First, being the world’s second largest producer of CPO, there is a growing 

demand for Malaysia to export CPO in form of biofuel and food to other emerging markets. 

To maintain a sufficient amount of commodity for biofuel and food consumption in the 

country, the Malaysian government has implemented the national policies related to energy 

and food. 

To reduce the dependency on fossil fuel, the National Biofuel Policy was implemented on 

March 21, 2006, to promote the use of biodiesel derived from palm oil as environmentally 

friendly and sustainable energy source (Unites States Department of Agriculture Foreign 

Agricultural Service, 2014). On the other hand, the National Agro-Food Policy was launched 

on September 28, 2011, to alleviate the issue of income inequality and poverty by ensuring 

steady and resilient food related industries based on agricultural sector (Ministry of 

Agricultural and Agro-Based Industry Malaysia, 2014). As a result of implementing these 

policies, changes of CPO price would affect demand for refined palm oil related products, 

thereby providing the direct or indirect influence on consumption pattern of CPO and 

inflation. 

Second, some studies on the role of gold as a hedge in the case of Malaysia are carried out 

from two perspectives. From the perspective of whether gold is a hedge or a safe haven in the 

stock market, Ghazali et al. (2013) perform systematic and conditional analyses using the 

daily data of 2001-2013 for domestic gold return (changes in the selling price of one troy 

ounce Kijang Emas) and stock return. Their results show that gold returns are negatively 

correlated with stock returns on average. This suggests that gold is a hedge. Meanwhile, its 

returns are mixed correlated with stock returns during financial stress, suggesting that the gold 

is a weak safe haven for stockholders. From the perspective of whether gold serves as a hedge 

against inflation, Ghazali et al. (2015) perform correlation and linear regression analyses 

using the daily data of 2001-2011 for domestic gold return and inflation. Their results show 

that the relationships between gold return and inflation, between gold return and expected 

inflation as well as between gold return and unexpected inflation are insignificant. In contrast, 

their finding suggests that a domestic gold price is not a good hedge against inflation. 

1 

By using linear and non-linear Granger causality approaches, Zhang and Wei (2010) find that the United States 

crude oil spot and London gold spot prices are cointegrated during 2000-2008. From the perspective of volatility, a 

change in gold price movement is found as the main factor driving a change in crude oil price in the short run. 

44

The Relationship of Crude Palm Oil Spot-Futures under Inflationary Expectation in Gold Market 

However, both studies overlook from the perspective of investor demand in the futures 

market point of view. Like those of other commodities, the spot-futures relationship for CPO 

in the short run is frequently determined by market participants’ decision. Their decision 

either is to use CPO for the production purpose or hold a physical stock of such commodity 

for the speculation purpose based on their expectation of a subsequent price rise (Go and Lau, 

2017). For those who have a high inflationary expectation, they tend to speculate CPO prices 

through the futures market by selling CPO futures contracts with higher prices to those who 

wish to acquire the physical stocks or inventories for CPO. This consequently provides a 

greater role of investor demand in the CPO futures market. 

To our best knowledge, thus far, there is no clear attempt on this perspective previously. 

Hence, it would be interesting to investigate whether the movement of gold price plays the 

role of inflationary expectation with respect to the spot-futures relationship in the case of 

Malaysian CPO. Among the research questions are: How market participants of CPO futures 

react when there is a rising trend in gold price and vice versa? What is the underlying market 

mechanism that prompts producers of a commodity and hedgers to react in a certain manner? 

Based on the behavioral finance point of view, in terms of an arbitrage mechanism, the 

associated rise or decline of the gold price leads to market participants to buy or sell CPO in 

spot and futures markets to gain excess revenue in the future inflation. As suggested by 

Kaufmann and Ullman (2009), Tilton et al. (2011), Bos and Van der Molen (2012), and 

Gulley and Tilton (2014), if market participants expect that the commodity futures price 

exceeds the current spot price, they would long futures contract to obtain riskless profits by 

buying a commodity now and holding in inventory until delivery in the future. This high 

inflationary expectation may lead to a rise of the arbitrage opportunity. 

There are two contributions to this study: First, to investigate whether speculation on CPO 

prices exists under the bullish gold market that acts as an inflation hedge; Second, to 

determine the decision on the appropriate response of CPO market participants toward 

volatility of spot and futures returns given a certain gold market trend. This study attempts to 

adopt the parametric approach which is developed by Cheung and Ng (1996). This approach 

seems to be more appropriate in examining the causality-in-variance between CPO spot and 

futures returns under upward and downward trends for gold. The reason is it can detect nonlinear 

causal relationship in mean (first moment) and variance (second moment) of both series 

based on cross-correlation functions of standardized residuals and their squares (Henry et al., 

2007). 

The rest of this study comprises the background of Malaysian CPO spot and futures 

activities. The subsequent section is to explain on the theoretical framework. Then, it is 

followed by an explanation of data and methodology. The next section is to discuss the 

empirical results. The last section is to conclude the discussion and suggest the implications 

of this study. 

2. Background of Malaysian Crude Palm Oil (CPO) Spot and Futures Activities 

CPO is one of the agricultural commodities that have sufficiently high economic value for 

Malaysia, where it is recognized as the golden crop after rubber. In this regard, the palm oil 

industry plays the role as the Malaysian economy’s backbone, making the country as the 

second largest world palm oil producer after Indonesia. As reported by Malaysian Palm Oil 

Board (2015), Malaysia has contributed to 39% of world production and 44% of world export 

for CPO in 2014. 

According to Malaysian Palm Oil Board (2015), palm oil industry has accounted for 6% 

of Malaysian gross domestic product in 2014. The industry has contributed to a higher export 

of palm oil products over the last few years. For example, its total export has increased from 

25.07 million tonnes in 2014 to 25.37 million tonnes in 2015 by 1.2%. However, a lower 

45


export price has led to declining total export revenue from RM63.62 billion in 2014 to 

RM60.17 billion in 2015 by 5.4%. Due to this, export revenue for palm oil has declined from 

RM44.50 billion in 2014 to RM41.26 billion in 2015 by 7.3%. 

The palm oil industry is divided into two major sectors. First, the upstream sector involves 

activities such as cultivating palms for the production of fruit bunches in plantation and 

processing fresh fruit bunches in the mill. Second, the downstream sector involves activities 

in refining and separating unrefined palm oil products in obtaining solid stearin fraction and 

liquid olein. According to the Performance Management and Delivery Unit (2010), palm oil 

industry based on upstream activities contributes 81.5% of the total export of the Malaysian 

palm oil industry as compared to an industry based on downstream activities that only 

contributes 4%. Since a high demand for refined palm oil products from South Korea, Turkey, 

Vietnam and Japan, oleochemical exports from Malaysia have increased from 2.83 million 

tonnes in 2014 to 2.85 million tonnes in 2015 (Malaysian Palm Oil Board, 2015). 

In 2005, Germany was the largest producer of biodiesel in the world with 1.6 million 

tonnes. Since the price of crude petroleum was recorded to be higher than U.S. Dollar 60 per 

barrel in 2006, many countries have started to seek renewable energy from vegetable oil. Palm 

oil has been successfully used as a biofuel because it is a viable and environmentally friendly. 

A low price of CPO during the period of August 2006 - June 2007 has made the biofuel 

industry in Malaysia viable. This has led to the percentage of palm oil production used for 

biofuel increased from 1% in 2006 to 7.9% in 2007 (Koizumi and Ohga, 2007). As a result, 

this made Malaysia became the world's second largest producer of biofuel in 2007 after 

Germany (European Biodiesel Board, 2007). However, a higher price of CPO in the 

subsequent period resulted in a thin profit margin which brought losses to the producers. 

Given the prominence of such commodity to the Malaysian economy, CPO futures 

contract is introduced since October 1980 as the first derivative instrument to be traded under 

the platform of Kuala Lumpur Commodity Exchange (KLCE). In November 1998, KLCE 

merges with the Malaysian Monetary Exchange (MME) to become the Commodity and 

Monetary Exchange (COMMEX). Afterward, the COMMEX is named to be the Malaysia 

Derivatives Exchange Berhad (MDEX). 

On April 17, 1993, the Bursa Malaysia Berhad and CME Group (Chicago Mercantile 

Exchange & Chicago Board of Trade) provide, operate and maintain futures and options 

exchange. Since the MDEX is 75% owned subsidiary of the Bursa Malaysia Berhad as 

compared to 25% owned subsidiary of the CME Group, the MDEX is renamed as the Bursa 

Malaysia Derivatives Berhad (BMD). Among derivatives exchanges around the world, palm 

oil related products are most popularly traded in the BMD. 

In 1994, the CPO futures market has well performed as its trading volume increased by 

59.5% as compared in 1993. Such performance has contributed to the palm oil production of 

7,672 thousand tonnes and about half of the total world palm oil production of 14,507 

thousand tonnes (Oil World, 2010). 

On December 31, 2009, total CPO futures contract traded has increased from 3,003,549 

contracts to 4,008,882 contracts steadily with the rising of demand from both China and India. 

From October 2010 to December 2010, CPO futures price was raised by 38% due to the 

declining CPO production from 17,564,937 tonnes to 16,993,717 tonnes (Malaysian Palm Oil 

Board, 2011). 

From a trading point of view, CPO futures price acts as the global benchmark of CPO 

price for various reasons such as price risk management and speculation. For example, 

plantation companies, refineries, exporters, and millers use CPO futures contracts to manage 

their risk and hedge against the risk of unfavorable movement in CPO price in the physical 

market. On the other hand, speculators use CPO futures to take a view on the likely movement 

of future spot price which can lead to profits or losses depending on whether they get it right 

46


or not. Lastly, producers and other market intermediaries use it as a price indicator to assess 

the best CPO pricing. 

3. Theoretical Framework 

To prevent any arbitrage opportunity, Pindyck (2001) describes that the relationship between 

spot and futures prices for a commodity should hold as in Equation (1). 

rT 

S 

t 

Ft 

T 

kT 

(1) 

t, T 

1 

, 

where ψ t,T is capitalized flow of marginal convenience yield over the period t to t + T, S t is 

spot price at time t, F t,T is futures price for delivery at time t + T, r T is risk-free T-period 

interest rate, and k T is the per unit cost of physical storage at time T. 

This study attempts to test whether the direction of information spillover between CPO 

spot and futures prices corresponds to the long-term shifts in gold price. Hence, the following 

two hypotheses for respective gold market trend are stated as: 

Hypothesis 1: When there is a continuous rise in the gold price (bull market), there will be 

volatility spillover from CPO futures returns to spot returns is due to increase in investor 

demand for CPO related products. 

Hypothesis 2: When there is a continuous decline in the gold price (bear market), there will 

be contemporaneous volatility spillover between CPO spot and futures returns as driven by 

market participants who are risk averse. 

According to Hypothesis 1, when the gold price increases, market participants will expect 

the CPO spot price to increase in near terms as there is a comovement between CPO and gold 

prices. When the futures price rises more than the spot price, the capitalized flow of marginal 

convenience yield in Equation (1) will be quite small. Higher CPO futures price relative to 

spot price will also lead to a higher volatility spillover from futures to spot market. In other 

words, high futures volatility will decrease storage demand and the option value of keeping 

the commodity, hence market participants will long CPO futures contracts in order to hedge 

the increase of CPO spot price. This allows producers to hedge against future price volatility 

by allocating their inventories to buffer any fluctuation in consumption of CPO in the next 

period. 

After that, Hypothesis 2 is set to state that participants with the bearish expectation of gold 

market tend to react to the shocks immediately toward downward risk by shorting the futures 

contracts. The explanation to support Hypothesis 2 is a downward movement in the gold 

market will force market participants to relocate their resources from commodity to equity 

markets. This will cause a decline in CPO price and in contrast, an equity price will increase. 

The declining CPO price will cause storage costs to be insufficiently covered. 

Under this situation, market participants will decide to sell the commodity to minimize 

their losses. Their decision consequently contributes to a high volatility of CPO spot price 

which is often the unpredictable shifts in the supply and demand that make production costs 

to be higher in the short run. When the spot price exceeds the futures price, the capitalized 

flow of marginal convenience yield of CPO will be usually high (as shown in Equation (1)). 

A higher capitalized flow of marginal convenience yield is often associated with holding CPO 

as inventories when option value of keeping the commodity increases with a higher volatility 

of CPO spot. In order to reduce production costs, market participants need greater storage and 

inventory of CPO to relocate their production in the short run. 

47


4. Data 

This study uses daily closing prices of CPO spot and futures from January 1, 1996 to 

December 30, 2011, as measured in Ringgit Malaysia (RM). When a futures contract draws 

closer to maturity, the heterogeneity between consecutive contract and unusual market 

activity are often observed to happen and generate significant biases in the various time-series 

properties of the artificial price series (Ma et al., 1992). 

To avoid the possible biases, the constant maturity contract is chosen to ensure that all 

prices are measured at the same point in time. Therefore, this study uses the daily price for 3- 

month futures contract because this contract is the most active and liquid contract traded in 

the futures exchange. Meanwhile, this study chooses the price of spot-month continuous 

contract because such contract can provide information on price movement in the long term. 

The daily data of both prices are collected from the Thomson DataStream and Bursa 

Malaysia (refer to http://www.bursamalaysia.com). To reduce variation and achieve 

stationary movement, daily prices of CPO spot and futures are transformed into price changes 

in logarithmic terms (daily returns). 

This study identifies upward and downward trends in the gold market based on turning 

points in the London gold price movement. This study chooses gold that traded in the London 

Bullion Market because it is the largest over-the-counter market in trading gold and silver in 

the world followed by New York, Zurich and Tokyo. 

As observed in Figure 1, the downward trend (bear market) in the London gold price 

happens from January 17, 1996 to July 20, 1999. The sample period of this gold trend happens 

during the onset of the Asian financial crisis 1997-1998 and the post-Asian financial crisis in 

1999. It is observed that an upward trend (bull market) in the London gold price happens from 

November 7, 2005, to November 30, 2011. As compared to the crisis and non-crisis periods, 

it is further observed that both CPO spot and futures prices tend to move together with the 

gold price across time. 

The CPO futures price has an upward movement from 1998 onwards. The restructuring 

of the Malaysian derivative market to COMMEX in responding to the depreciation of Ringgit 

in November 1998 has seen the CPO futures contracts traded at RM2,700 per metric tonne, 

making palm oil as the top foreign exchange earner that exceeded the revenue derived from 

crude petroleum and petroleum products by a wide margin. 

To reduce dependency on fossil fuel as well as to stabilize palm oil prices through export, 

research and development activities as outlined in the National Biofuel Policy was launched 

on March 21, 2006 (Unites States Department of Agriculture Foreign Agricultural Service, 

2014). With this policy, the existence of bio-fuel for non-food usage in 2006 provided the 

most efficient pricing of CPO in the BMD. Consequently, CPO spot and futures prices have 

dramatically increased from 2006 to 2008 (as shown in Figure 1). 

From March 2008 to October 2008, both CPO spot and futures prices have dropped to 

RM1,418 and RM1,390 per metric tonne, respectively. Such scenario illustrated that the 

global financial crisis 2008-2009 has translated into a high volatility in CPO price. This high 

volatility made both spot and futures markets to be more uncertain over time. However, during 

the period of post-global financial crisis, the decline in demand of commodity globally 

resulted in high unsold inventory and low price. Subsequently, both palm oil prices have 

decreased to RM2,400 per metric tonne in 2012. 

48


Figure 1: Daily CPO spot and futures prices during bull and bear markets for the London gold, 

1996-2011. 

Source: Bloomberg (2011). 

Since volatility is recognized as a key determinant of the value of commodity-based 

contingent claims with market dynamics in the short run, so the preliminary step of this study 

is to compare volatility between CPO spot and futures returns. As shown in Table 1, a daily 

CPO spot return is slightly volatile than a daily CPO futures return during the gold bear market 

as reflected by their relative standard deviations. During a consecutive increase in the gold 

price, the volatility of CPO futures return is found to be slightly higher than the volatility of 

CPO spot return. This suggests that different movements of gold price in the long term lead 

to the presence of asymmetric information flow between CPO spot and futures returns. 

Table 1: Descriptive statistics for daily CPO returns 

Gold bear: 

Jan 17,1996 - Jul 20, 1999 

Gold bull: 

Nov 7, 2005 - Nov 30,2011 

CPO spot CPO futures CPO spot CPO futures 

Mean -0.0002 -0.0002 0.0005 0.0005 

Maximum 0.0771 0.0508 0.0929 0.0950 

Minimum -0.0747 -0.0614 -0.1104 -0.1089 

Standard deviation 0.0165 0.0156 0.0189 0.0196 

Skewness -0.1843 -0.2587 -0.3577 -0.2986 

Kurtosis 4.8983 3.8584 7.2478 6.1234 

Jarque-Bera 133.6745*** 35.9005*** 1144.2740*** 623.5885*** 

Observations 858 1480 

Notes: The daily CPO spot and futures returns in the natural logarithmic form. ** *denotes significance level at 

the 1 %. 

49


In addition, the Jarque-Bera test statistic provides a rejection of the null hypothesis of 

normality for both CPO returns. This non-normal distribution is further demonstrated by 

skewness and kurtosis, where both CPO returns in two different gold market trends are 

characterized by excess peaks that have kurtosis statistics to be substantially higher than 3. 

This characteristic in the data requires the use of generalized autoregressive conditional 

heteroskedasticity (GARCH)-type models to capture the volatility clustering of both CPO 

returns. This finding is consistent with those of Lean and Smyth (2015), who find that it is 

important to allow heteroskedasticity in CPO spot and futures returns when testing the 

efficient market hypothesis. 

There present structural changes which correspond to the 1998 Asian financial crisis and 

the 2008 global financial crisis (as observed in Figure 1). To avoid obtaining biased test 

statistics toward the non-rejection of a unit root, the existence of a unit root of both CPO 

returns is tested using a non-parametric test, namely Phillips-Perron (PP) test. 2 

In Table 2, an auxiliary regression of this unit root test is specified in two ways. The first 

one is to include a constant term (drift) only. The second one is to include constant term and 

time trend. Using two different ways for the auxiliary model, this test supports the rejection 

of the null hypothesis of a unit root at the 1% level of significance, implying that both returns 

are stationary series in level form. 

Table 2: Result of Phillips-Perron (PP) unit root test for daily CPO returns 

Gold bear : 

Jan 17,1996 - Jul 20, 1999 

Gold bull : 

Nov 7, 2005 - Nov 30,2011 

CPO spot CPO futures CPO spot CPO futures 

Constant -26.5082*** -27.4438*** -39.1993*** -40.1398*** 

Constant & Trend -26.6872*** -27.6250*** -39.1979*** -40.1367*** 

Notes: The daily CPO spot and futures returns in the natural logarithmic form. PP critical values are based on 

Mckinnon. The null hypothesis for the PP test is a series has a unit root (non-stationary). *** denotes 

significance level at the 1 %. 

5. Methodology 

This study attempts to employ the non-linear approach since volatility and structural breaks 

in the CPO price level may lead to a non-linear linkage. The non-linear approach employed 

is a cross-correlation function (CCF) proposed by Cheung and Ng (1996). This approach can 

detect non-linear causal relationship in mean (first moment) and variance (second moment) 

of both series based on CCFs of standardized residuals and their squares (Henry et al., 2007). 

With a two-stage approach, CCFs have the ability to specify correctly the first moment 

dynamic (mean) and second moment dynamic (variance), detect significant causality of both 

series for a large number of observations at longer lags, and reveal useful information on the 

causality pattern (Cheung and Ng, 1996). 

In the first stage, correlograms of the partial autocorrelation function (PACF) is used to 

determine appropriate orders for autoregressive (AR) that maximizes the log likelihood 

function. Meanwhile, the orders for moving average (MA) are determined using correlograms 

of the autocorrelation function (ACF). The further examination of ACF and PACF 

correlograms on squared residuals from the conditional mean equation is to check the 

existence of GARCH effect. The conditional mean equation (ARMA) and conditional 

variance equation (GARCH) for both CPO returns are written as below. 

2 

Phillips and Perron (1988) develop the model that allows for testing a unit root in the presence of a structural change 

at a certain period (a detailed discussion can be seen in the books written by Davidson and MacKinnon (2004: 623) 

and Enders (2010: 229). 

50


P1 

P2 

R 

S , t 

a0 

ai 

RS 

, ti 

bi 

S , ti 

 

S , t 

1 

i1 

i1 

P3 

P4 

2 

2 

2 

S , t 

w i 

S , ti 

i 

S , ti 

i1 

i1 

 

2 

, ~ 

S , t 

 

t 

N(0, 

S,t 

) 

(2) 

(3) 

P1 

P2 

R 

F , t 

a0 

ai 

RF 

, ti 

bi 

F , ti 

 

F , t F , t t 1 

i1 

i1 

 

P3 

P4 

2 

2 

2 

F , t 

w i 

F , ti 

i 

F , ti 

i1 

i1 

2 

, ~ N(0, 

F ,t 

) (4) 

where R S,t is the daily CPO spot return at time t, σ 2 S,t is the conditional variance for CPO spot 

return at time t, ε S,t is the unexpected CPO spot return that cannot be predicted based on all 

information available up to the preceding period, R F,t is the daily CPO futures return at 

time t , σ 2 F,t is the conditional variance for CPO futures return at time t, and ε F,t is the 

unexpected CPO futures return that cannot be predicted based on all information available up 

to the preceding period. 

Based on Equation (2) - Equation (5), the number of lags for the dependent variable, 

forecasted error, squared error and conditional variance is based on the minimum Schwarz 

information criterion (SIC). These univariate equations should adequately account and 

explain the serial correlation of the data in the first and second moments in order to produce 

stationarity of standardized residuals in level and square forms. For the level form, they are 

denoted as U t and W t , respectively. For the square form, they are denoted as U 2 t and W 2 t , 

respectively. These standardized residuals are used further to compute a sample crosscorrelation 

(r) between CPO spot and futures returns at lag k using Equation (6) and 

Equation (7). 

UW 

k 

 

r 

2 2 

U W 

k 

 

r 

C 

C 

C 

k 

 

0C 

WW 

C 2 2 k 

 

U W 

2 C 

2 

UU 

UW 

2 

U U 

0 

0 2 

W W 

0 

where r UW (k) is the k th lag sample cross-correlation between standardized residuals for 

CPO spot and futures returns, C UW (k) is k-th lag sample covariance between standardized 

residuals for CPO spot and futures returns, C UU (0) is the sample variance of standardized 

residuals for CPO spot return, C WW (0) is the sample variance of standardized residuals for 

CPO futures return, r U 2 W2(k)is the k th lag sample cross-correlation between standardized 

residuals squared for CPO spot and futures returns, C U 2 W2(k) is k-th lag sample covariance 

between standardized residuals squared for CPO spot and futures returns, C U 2 U2(0) is the 

sample variance of standardized residuals squared for CPO spot return, and C W 2 W2(0) is the 

sample variance of standardized residuals squared for CPO futures return. 

To examine whether feedback in mean (variance) between both CPO returns occurs at a 

specified lag k, a standard normal critical value is used to test the null hypothesis of no 

feedback effect (Cheung and Ng, 1996: 37). If the absolute value from Equation (8) is greater 

than the critical value, the hypothesis testing reveals that there exists feedback in mean at a 

specific lag k. The rejection of the null hypothesis of no causal effect in variance between 

(5) 

(6) 

(7) 

51


both CPO returns is revealed when the absolute value from Equation (9) greater than the 

critical value. 

L 

k 

N0,1 

 

T rUW (8) 

L 

2 2 N 

W 

k 

0,1 

 

T r (9) 

U 

In the second stage, the resulting feedback in mean and variance are used further to 

reconstruct respective univariate equation (Equation (2) - Equation (5)) by adding the relevant 

lagged CPO returns and lagged squared CPO return. For example, to capture feedback in 

mean, lagged CPO futures and spot returns in the level form (R F,t−i &R S,t−i ) are included into 

Equation (2) and Equation (4) and become Equation (10) and Equation (12), respectively. 

Equation (11) and Equation (13) consist of lagged CPO futures and spot returns in the square 

2 2 

form (R F,t−i & R S,t−i ) to capture feedback in variance between both CPO returns. These 

augmented equations are improved because they have a better description of the temporal 

dynamics of CPO data. 

P1 

P2 

R , 

S , t 

a0 

ai 

RS 

, ti 

bi 

RF 

, ti 

 

S , t S , t 

t 

1 

i1 

i1 

 

2 

S, 

t 

2 

~ N(0, 

S,t 

) (10) 

w 

P3 

 

i1 

 

i 

2 

S, 

ti 

 

P4 

 

i1 

 

i 

2 

S , ti 

 

P5 

 

i1 

R 

2 

F , ti 

(11) 

P6 

P7 

R 

F , t 

a0 

ai 

RF 

, ti 

bi 

RS 

, ti 

 

 

F , t F 

 

1 

i1 

i1 

 

2 

F , t 

w 

P8 

 

i1 

 

i 

2 

F , ti 

 

P9 

 

i1 

 

i 

2 

F , ti 

 

2 

, 

, t t 

~ N(0, 

F ,t 

) (12) 

The sample CCFs of standardized squared residuals at a specific lag k are obtained from 

above equations and emphasized to be used further in searching the existence of the lead-lag 

pattern of interaction between CPO spot and futures returns. The significance of these 

correlations suggests that participants evaluate, assimilate and reflect the arrival of new 

information in the market, thereby affecting changes of the market volatility. 

6. Empirical Results 

During the bear market for gold, ACF, PACF and the minimum SIC indicate that conditional 

mean and variance of CPO spot returns are well explained by ARMA (5,6)-GARCH (1,1) 

process. For CPO futures return, it is explained by ARMA (4,4)-GARCH (4,4) process. 

During the bull market for gold, conditional mean and variance of the CPO spot and futures 

returns are well explained by ARMA (5,5)-GARCH (4,3) and ARMA (4,2)-threshold 

GARCH (4,4) process, respectively. These univariate equations are written as Equation (14) 

- Equation (21). 

P10 

 

i1 

R 

2 

S. 

ti 

(13) 

52


Gold bear period: 

R 

S, t 

a0 

a1RS 

, t 1 

... a5RS, 

t5 

b1 

S, 

t1 

... b6 

S, 

t6 

 

S, 

t (14) 

where ε S,t |φ t−1 ~N(0, σ 2 S,t ) 

2 

2 

2 

 

S, t 

w 1 

S, 

t1 

1 

S, 

t1 

(15) 

R 

F, t 

a0 

a1RF 

, t 1 

... a4RF 

, t4 

b1 

F, 

t1 

... 

b4 

F, 

t4 

 

F, 

t 

2 ) 

(16) 

where ε F,t |φ t−1 ~N(0, σ F,t 

2 

2 

2 

2 

2 

 

F , t 

w 1 

F , t1 

... 

4 

F , t4 

1 

F , t1 

... 

4 

F , t4 

(17) 

Gold bull period: 

R 

S, t 

a0 

a1RS 

, t 1 

... a5RS 

, t5 

b1 

S, 

t1 

... 

b5 

S, 

t5 

 

S,, 

t 

(18) 

where ε S,t |φ t−1 ~N(0, σ 2 S,t ) 

2 

2 

2 

2 

2 

 

S, t 

w 1 

S, 

t1 

... 

3 

S, 

t3 

1 

S, 

t1 

... 

4 

S, 

t4 

(19) 

R 

F, t 

a0 

a1RF 

, t 1 

... a4RF 

, t4 

b1 

F, 

t1 

b2 

F, 

t2 

 

F, 

t 

2 ) 

(20) 


2 

2 

2 

2 

2 

 

F , t 

w 1D 

1 

F , t1 

... 

4 

F , t4 

1 

F , t1 

... 

4 

F , t4 

(21) 

The maximum likelihood estimates and diagnostic statistics of the selected univariate 

equations are summarized in Table 3. One should note from Table 3 that ARCH and GARCH 

terms under both trends in the gold market are found to be significant. The sum of their 

coefficients is approximate to unity, suggesting that volatility persistence of both CPO returns 

is high and stationary. More importantly, Ljung-Box Q test on squared standardized residuals 

(Q² (10)) and ARCH-LM test imply that these selected equations adequately account a serial 

correlation of the data in the first and second moments in order to produce stationarity of 

standardized residuals in level and square forms. 

As observed in Table 4, during the bear market for gold, a cross-correlation between CPO 

futures and spot returns of 0.902 at lag 0 is statistically significant at the 1% level. This reveals 

an evidence of feedback in mean that runs contemporaneously between both CPO returns. In 

addition, a significant correlation of 0.0797 at the 5% level is interpreted as the mean of 

current CPO futures causes the mean of CPO spot after 4 days. Since feedback effects in mean 

at lag 0 and lag 4 provide significant cross-correlations of both CPO returns, both lagged 

terms of CPO futures returns are then inserted into Equation (14) to become Equation (22). 

To capture the occurrence of feedback in variance contemporaneously with a significant 

correlation of 0.7724 at the 1% level, the lag 0 of squared CPO futures return is incorporated 

into Equation (15) to become Equation (23). 

Under the same gold market trend, Table 4 shows that past mean of CPO spot return is 

correlated with the current mean of CPO futures return at lag 0 and lag 11, where the 

correlation of 0.9020 at lag 0 and correlation of 0.0718 at lag 11 are significant at the 1% and 

5% levels, respectively. To capture both correlations, lag 0 and lag 11 of CPO spot returns 

are included in Equation (16) to become Equation (24). At the 1% level, a correlation of 

0.0997 at lag 1 indicates that the variance of past CPO spot return requires 1 day affecting the 

variance of current CPO futures return. This dynamic correlation is taken into account 

together with the contemporaneous correlation by incorporating at lag 0 and lag 1 of squared 

CPO spot returns into Equation (17) to become Equation (25). 

53


Table 3: Maximum likelihood estimates of the univariate equations 

Gold bear: 

Jan 17,1996 - Jul 20, 1999 

Gold bull: 

Nov 7, 2005 - Nov 30, 2011 

Parameter CPO spot CPO futures CPO spot CPO futures 

Conditional mean equation: 

a 0 2.02 x 10 -6 

(2.07 x 10 -5 ) 

3.32 x 10 -6 

(0.0005) 

0.0002 

(0.0002) 

0.0006 

(0.0004) 

a 1 0.9084** 

(0.0240) 

0.1916** 

(0.0959) 

0.1148** 

(0.0584) 

0.7121*** 

(0.0325) 

a 2 0.2425** 

(0.0305) 

-0.0223 

(0.0835) 

-0.0795 

(0.0500) 

-0.8943*** 

(0.0367) 

a 3 0.6011** 

(0.0289) 

0.4758*** 

(0.0840) 

0.0295 

(0.0553) 

-0.049 

(0.0331) 

a 4 -0.8667** 

(0.0276) 

-0.6201*** 

(0.0667) 

-0.1978*** 

(0.0481) 

0.0583** 

(0.0260) 

a 5 0.0871** - 0.8872*** - 

(0.0245) 

(0.0561) 

b 1 -0.8385** 

(0.0047) 

-0.1636* 

(0.0978) 

-0.1268** 

(0.0644) 

-0.7288*** 

(0.0191) 

b 2 -0.2765** 

(0.0112) 

0.0515 

(0.0883) 

0.0961* 

(0.0541) 

0.9663*** 

(0.0213) 

b 3 -0.6497** -0.4541*** 0.0014 

- 

(0.0092) (0.0897) 

(0.0589) 

b 4 0.7953** 0.6203*** 

0.2339*** - 

(0.0067) (0.0732) 

(0.0527) 

b 5 -0.01 - -0.8930*** - 

(0.0068) 

(0.0637) 

b 6 0.0224** - - - 

(0.0083) 

Conditional variance equation: 

w 9.99 x 10 -6 ** 

(3.2 x 10 -6 ) 

1.80 × 10 -5 ** 

(7.71 x 10 -6 ) 

6.04 x 10 -3 * 

(3.3 x 10 -8 ) 

3.15 x 10 -6 ** 

(1.4 x 10 -6 ) 

θ 1 - - - 0.0170* 

(0.0088) 

α 1 0.1274** 

(0.0279) 

0.1317*** 

(0.0257) 

0.1078*** 

(0.0184) 

0.0618*** 

(0.0181) 

α 2 - 0.1104*** 

(0.0282) 

0.0023 

(0.0295) 

0.0506*** 

(0.01485) 

α 3 - 0.1040*** 

(0.0271) 

-0.1086*** 

(0.0218) 

0.074*** 

(0.0172) 

α 4 - 0.1064*** 

(0.0283) 

- 0.0728*** 

(0.0203) 

β 1 0.8364** 

(0.0335) 

-0.1014*** 

(0.0332) 

1.1106*** 

(0.0300) 

0.4974*** 

(0.0390) 

β 2 - -0.1190*** 

0.0299) 

-0.27556*** 

(0.0513) 

0.0412 

(0.0308) 

β 3 - -0.1540*** 

(0.0289) 

0.9711*** 

(0.0458) 

-0.6367*** 

(0.0319) 

β 4 - 0.8579*** 

(0.0275) 

-0.8078*** 

(0.0318) 

0.8377*** 

(0.0346) 

Log-likelihood 2389.9340 2450.5710 4082.0500 4031.7130 

ARCH-LM 0.0927 [0.7608] 2.5611 [0.6337] 0.9958 [0.8023] 1.4099 [0.8425] 

Q²(10) 6.6185 [0.1570] 9.5895 [0.4770] 5.78 [0.3280] 7.8009 [0.4530] 

Notes: ***, ** and * denote significance level at the 1%, 5% and 10%, respectively. Standard errors and p-values 

are reported into ( ) and [ ], respectively. 

54


During the bull market for gold, the empirical result in Table 4 shows that CPO spot and 

futures returns exhibit contemporaneous correlation in mean and variance with significant 

correlations of 0.8366 and 0.6294 at the 1% level, respectively. Additionally, feedback in 

mean is found to run from current CPO futures return to future CPO spot return at lag 1, 

providing that a significant cross-correlation of 0.0897 at the 1% level. Based on these 

findings, lag 0 and lag 1 of CPO futures returns are included into Equation (18). To capture 

feedback in variance in the same direction, lag 0 of squared CPO futures return is included in 

Equation (19). Both updated equations are written as Equation (26) and Equation (27). 

Based on the past mean of CPO spot and the current mean of CPO futures returns during 

the bull trend for gold, their cross-correlation of 0.0564 at lag 9 is found as significant at the 

5% level. In views of this correlation, lag 9 of CPO spot return is included along lag 0 of CPO 

spot return into Equation (20) to become Equation (28) to capture the feedback effect in mean 

from CPO spot return to CPO futures return. For conditional variance perspective, the 

contemporaneous squared CPO spot return is included into Equation (21) to become Equation 

(29). The augmented equations are written as below. 

Gold bear period: 

R 

S, t 

a0 

a1RS 

, t1 

... a5RS, 

t5 

b1 

S, 

t1 

... b6 

S, 

t6 

b7 

RF, 

t 

 

 

 

(22) 

b 

8R F , t 4 

 

S, 

t 

2 ) 

where ε S,t |φ t−1 ~N(0, σ S,t 

2 

2 

2 

2 

 

S, t 

w 1 

S, 

t 1 

1 

S, 

t1 

 

2RF 

, t 

(23) 

R 

F, t 

a0 

a1R 

F, 

t1 

... a4RF 

, t4 

b1 

 

F, 

t1 

... 

b4 

F, 

t4 

b5 

RS 

, t 

b 

6R S , t 11 

 

F, 

t 

2 ) 

 

(24) 


2 

2 

2 

2 

2 

2 

 

F , t 

w 1 

F , t 1 

... 4 

F , t 4 

1 

F , t 1 

... 4 

F , t 4 

5RS 

, t 

(25) 

2 

6R S , t1 

Gold bull period: 

R 

S, t 

a0 

a1RS 

, t1 

... a5RS 

, t5 

b1 

S, 

t1 

... b5 

S, 

t5 

b6 

RF 

, t 

 

 

 

(26) 

b 

7R F , t 1 

 

S, 

, t 

2 ) 

where ε S,t |φ t−1 ~N(0, σ S,t 

2 

2 

2 

2 

2 

2 

 

S, t 

w 1 

S, 

t 1 

... 

3 

S, 

t3 

1 

S, 

t1 

... 

4 

S, 

t4 

5RF 

, t (27) 

R 

F, t 

a0 

a1RF 

, t1 

... a4RF 

, t4 

b1 

F, 

t1 

b2 

F, 

t2 

b3RS 

, t 

b 

4R S , t 9 

 

F, 

t 

2 ) 

 

(28) 


2 

2 

2 

2 

2 

 

F , t 

w 1D 

1 

F , t1 

... 4 

F , t4 

1 

F , t1 

... 4 

F , t 

 

(29) 

2 

5R S , t 

4 

55


Table 4: Cross-correlation in the levels and squares of standardized residuals resulting from Table 3 

Gold bear : Jan 17, 1996 - Jul 20, 1999 Gold bull : Nov 7, 2005 - Nov 30, 2011 

Lag (i) 

Level Square Level Square 

S(-i)F FS(+i) S(-i)F FS(+i) S(-i)F FS(+i) S(-i)F FS(+i) 

0 0.9020*** 0.7724*** 0.8366*** 0.6294*** 

1 0.0546 0.0211 0.0997*** 0.0102 -0.0065 0.0897*** 0.0141 0.0197 

2 -0.0112 -0.0169 -0.0186 -0.0052 0.0110 0.0140 0.0105 -0.0019 

3 -0.0253 0.0384 0.0321 0.0142 -0.0082 -0.0226 -0.0157 0.0151 

4 0.0300 0.0797** 0.0316 0.0333 0.002 -0.0049 0.0099 0.0132 

5 0.0383 0.0355 -0.035 -0.0354 0.0070 0.0028 0.0447 0.0510 

6 -0.0088 -0.0348 -0.0309 -0.0109 -0.0102 -0.0013 -0.0133 -0.0069 

7 0.0396 0.0309 0.0336 -0.0271 0.0338 0.0126 -0.0011 0.0280 

8 0.0556 -0.0005 -0.0347 -0.0396 0.0148 0.0102 -0.0195 -0.0319 

9 0.0141 0.0108 -0.012 0.0241 0.0564** 0.0241 -0.0321 0.0073 

10 -0.0362 -0.0319 0.0328 0.0513 0.0058 0.0292 0.0192 0.0199 

11 0.0718** 0.0272 -0.0156 -0.0221 0.0007 -0.0058 0.0307 0.0284 

12 0.0343 0.0158 -0.013 0.0083 0.0343 0.0202 -0.0005 -0.0001 

13 0.0013 -0.0242 -0.0177 -0.0068 -0.0216 -0.0329 -0.0344 0.0166 

14 0.0630 0.0567 0.0511 0.0151 0.0026 -0.0278 -0.0326 0.0018 

15 0.0611 0.0440 0.0325 0.0313 0.0270 0.0486 0.036 0.0149 

16 0.0435 0.0193 0.0074 0.0407 0.0415 0.0484 -0.0356 -0.0296 

17 0.0125 -0.0340 0.0098 0.0647 0.0357 0.0241 0.0163 -0.0192 

18 0.0439 0.0246 -0.0550 -0.0305 0.0039 0.0173 0.0037 -0.0065 

19 0.0530 0.0123 -0.0484 -0.0287 0.0045 -0.0043 -0.0077 0.0074 

20 0.0634 -0.0040 0.0108 -0.0142 0.0140 0.0178 -0.0202 -0.0004 

Notes: S and F denote as daily CPO spot and futures returns. Critical values at 1% are 2.58 and critical values at 5% are 1.96. *** and ** indicate statistical significance at 

the 1% and 5% levels, respectively. “S(-i)F” represents cross-correlations for lag-effect of past daily CPO spot return on current daily CPO futures return, while 

“FS(+i)” represents cross-correlations for lead-effect of current daily CPO futures return on future daily CPO spot return. The significant cross-correlation in “Levels” 

column reveals evidence of feedback effect in mean of two series. In the “Squares” column, it reveals as evidence of feedback effect in variance. 

56


Based on the maximum likelihood estimated augmented equations (Equation (22) - 

Equation (29)) in Table 5, log-likelihood values of these equations are higher than univariate 

equations. This suggests that the inclusion of these lagged terms of CPO returns significantly 

increase the explanatory power of augmented estimated models, where it is further dictated 

by the Ljung-Box Q and ARCH-LM diagnostic statistics in terms of its adequacy. 

Table 5: Maximum likelihood estimates of the augmented equations 

Gold bear: 

Jan 17,1996 - Jul 20, 1999 

Gold bull: 

Nov 7, 2005 - Nov 30, 2011 


Conditional mean equation: 

a 0 -1.34 x 10 -5 

(1.8 x 10 -5 ) 

8.88 x 10 -5 

(0.0001) 

2.75 x 10 -6 

(0.0001) 

0.0001 

(9.59 x 10 -5 ) 

a 1 0.036*** 

(0.0112) 

-0.0128 

(0.0122) 

-0.7859*** 

(0.1795) 

-0.0339** 

(0.0124) 

a 2 0.0039 

(0.0064) 

0.0185** 

(0.0090) 

0.0718*** 

(0.0218) 

0.0417** 

(0.0131) 

a 3 -0.0036 

(0.0067) 

0.0179 

(0.0128) 

-0.0107 

(0.0156) 

-0.0119 

(0.0109) 

a 4 0.7503*** 

(0.0832) 

0.0076 

(0.0104) 

0.0142 

(0.0127) 

-0.0021 

(0.011) 

a 5 -0.0228* - 0.013439 - 

(0.0121) 

(0.0119) 

b 1 -0.1164*** 

(0.0402) 

-0.015 

(0.0402) 

0.4631*** 

(0.179) 

-0.2627*** 

(0.0312) 

b 2 -0.057* 

(0.0326) 

-0.0067 

(0.0345) 

-0.3919*** 

(0.0805) 

-0.0538* 

(0.0295) 

b 3 -0.0359* 

(0.0216) 

-0.0122 

(0.0275) 

-0.0534 

(0.0347) 

0.9595*** 

(0.0121) 

b 4 -0.7990*** 

(0.0719) 

-0.0429 

(0.0257) 

-0.0517 

(0.0321) 

-0.0207* 

(0.0117) 

b 5 0.0981** 0.939*** 

-0.0308 - 

(0.0396) (0.013) 

(0.0252) 

b 6 0.0373 

0.0185* 

0.8498*** - 

(0.0283) (0.0097) 

(0.0124) 

b 7 0.9587*** - 0.7485*** - 

(0.0118) 

(0.1521) 

b 8 -0.7261*** - - - 

(0.0775) 

Conditional variance equation: 

w 4.8 x 10 -6 *** 

(6.7 x 10 -7 ) 

5.43 x 10 -6 *** 

(1.44 x 10 -6 ) 

1.2 x 10-6*** 

(3.2 x 10 -7 ) 

4.6 x 10 -7 ** 

(1.8 x 10 -7 ) 

θ 1 - - - -0.0062 

(0.0086) 

α 1 0.6001*** 

(0.0657) 

0.4116*** 

(0.0559) 

0.4517*** 

(0.0513) 

0.4204*** 

(0.0458) 

α 2 - -0.0086 

(0.0526) 

-0.1964*** 

(0.0715) 

-0.0212*** 

(0.006) 

α 3 - 0.1049*** 

(0.0252) 

-0.1343** 

(0.0606) 

0.0549*** 

(0.0073) 

α 4 - -0.1823*** 

(0.035) 

- -0.3865*** 

(0.0392) 

Notes: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Standard errors 

are reported into ( ). 

57


Table 5: (Continued) 

Gold bear: 

Jan 17,1996 - Jul 20, 1999 

Gold bull: 

Nov 7, 2005 - Nov 30, 2011 


Conditional variance equation (continued): 

β 1 0.1796*** 

(0.0356) 

0.1258 

(0.1426) 

β 2 0.0349*** 

(0.0045) 

-0.2759*** 

(0.0601) 

β 3 - 0.4124*** 

(0.0801) 

β 4 - 0.0075 

(0.044) 

β 5 - 0.0285*** 

(0.0039) 

0.5467*** 

(0.1443) 

0.2806* 

(0.1463) 

-0.0714** 

(0.03) 

-0.0113 

(0.0192) 

0.0313*** 

(0.0041) 

0.1923*** 

(0.0407) 

-0.0855*** 

(0.011) 

0.8831*** 

(0.0133) 

-0.1171*** 

(0.036) 

0.0184*** 

(0.0016) 

β 6 - 0.0095 

- - 

(0.0061) 

Loglikelihood 

3310.4220 3292.0640 5217.8420 5124.4540 

ARCH-LM 0.6243 [0.4295] 0.4195 [0.9808] 1.4846 [0.6858] 1.0902 [0.8958] 

Q²(10) 4.5465 [0.3370] 1.5693 [0.9550] 3.1481 [0.6770] 9.4281 [0.3070] 

Notes: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Standard errors 

and p-values are reported into ( ) and [ ], respectively. 

After capturing the interaction of CPO spot and futures returns, volatility persistence for 

the respective CPO return and sample CCFs of standardized residuals (level and square forms) 

from lag 0 to lag 20 are summarized in Table 6 and Table 7, respectively. Their results of 

spillover effects in mean and volatility are further used to test Hypothesis 1 and Hypothesis 2. 

Based on Table 6 and Table 7, there are three findings to support Hypothesis 1 during the 

upward trend of the gold market. First, the included current squared CPO futures return in 

Equation (27) is found to absorb a large portion of volatility persistence for CPO spot return 

by 0.1343 (as shown in Table 6, it reduces from 0.9999 to 0.8656). Second, the variation of 

current CPO futures return takes 20 days to increase variation of future CPO spot return 

(“Squares” column in Table 7). Third, a correlation of 0.1141 between standardized squared 

residuals of CPO spot and futures returns at the lag of 20 days is significant at the 1% level 

(“Squares” column in Table 7). These findings suggest that market participants who have a 

bullish expectation on gold price tend to expect that inflationary pressure will increase 

volatility in the CPO futures market. Hence, their attention would turn to CPO futures returns 

to predict CPO spot returns. 

Table 6: Volatility persistence of CPO spot and futures returns 

Model specification Univariate equation Augmented equation 

Conditional Conditional Sum of Sum of 

Sum of Sum of 

Sum 

mean variance 

GARCH ARCH 

GARCH ARCH 

Sum 

Gold bear: Jan 17,1996 - Jul 20, 1999 

S ARMA(5,6) GARCH(1,1) 0.8364 0.1274 0.9638 0.1796 0.6000 0.7797 

F ARMA(4,4) GARCH(4,4) 0.4835 0.4525 0.9360 0.2697 0.3257 0.5954 

Gold bull: Nov 7, 2005 - Nov 30, 2011 

S ARMA(5,5) GARCH(4,3) 0.9983 0.0016 0.9999 0.7445 0.1210 0.8656 

F ARMA(4,2) TGARCH(4,4) 0.7396 0.2593 0.9988 0.8727 0.0675 0.9402 

Notes: S and F denote as daily CPO spot and futures returns. TGARCH stands for threshold GARCH model. 

Volatility persistence is measured through the sum of coefficient values for ARCH and GARCH terms. 

58


Table 7: Cross-correlation in the levels and squares of standardized residuals resulting from Table 5 

Gold bear : Jan 17,1996 - Jul 20, 1999 Gold bull : Nov 7, 2005 - Nov 30, 2011 

Lag (i) 

Level Square Level Square 

S(-i)F FS(+i) S(-i)F FS(+i) S(-i)F FS(+i) S(-i)F FS(+i) 

0 -0.9120*** 0.7313*** -0.8731*** 0.7181*** 

1 0.0322 -0.0275 -0.0003 -0.0271 0.0015 -0.0309 -0.0138 -0.0050 

2 0.0025 -0.0249 -0.0172 -0.0242 -0.0326 -0.0625** -0.0200 -0.0202 

3 -0.0142 -0.0250 0.0206 -0.0165 0.0205 -0.0244 -0.0052 0.0067 

4 -0.0047 -0.0034 -0.0070 0.0039 0.0196 -0.0030 -0.0199 -0.0207 

5 -0.0330 -0.0294 0.0078 -0.0192 0.0455 -0.0198 0.0378 0.0051 

6 0.0680** 0.0426 -0.0116 0.0009 -0.0198 -0.0383 -0.0340 -0.0195 

7 0.0222 0.0032 0.0132 -0.0224 0.0316 0.0194 -0.0175 -0.0196 

8 0.0330 -0.0114 0.0178 -0.0112 0.0211 0.0313 -0.0015 -0.0295 

9 -0.0120 -0.0066 -0.0124 -0.0145 0.0700*** 0.0482 -0.0291 -0.0154 

10 -0.0478 -0.0228 -0.0007 -0.0369 0.0200 0.0623** -0.0193 -0.0203 

11 0.0006 -0.0228 -0.0048 -0.0143 0.0189 0.0220 -0.0103 -0.0204 

12 -0.0020 -0.0228 -0.0278 -0.0173 0.0127 0.0015 -0.0294 -0.0111 

13 0.0568 0.0491 -0.0230 -0.0214 -0.0153 -0.0016 -0.0057 -0.0234 

14 -0.0277 -0.0167 -0.0211 -0.0219 0.0202 0.0100 -0.0028 -0.0104 

15 -0.0233 -0.0079 0.0111 0.0245 -0.0183 0.0018 -0.0120 0.0042 

16 0.0297 -0.0163 -0.0243 0.0292 -0.0136 0.0032 -0.0254 -0.0210 

17 0.0216 0.0329 0.0044 0.0134 -0.0048 -0.0190 -0.0042 -0.0011 

18 -0.0164 0.0028 -0.0011 0.0397 0.0046 0.0157 0.0100 -0.0103 

19 -0.0742** -0.0431 0.0307 -0.0060 0.0007 0.0193 -0.0004 -0.0084 

20 -0.0012 -0.0206 0.0028 0.0209 -0.0583 -0.0404 0.0325 0.1141*** 

Notes: S and F denote as daily CPO spot and futures returns. Critical values at 1% are 2.58 and critical values at 5% are 1.96. *** and ** indicate statistical significance at the 

1% and 5% levels, respectively. “S(-i)F” represents cross-correlations for lag-effect of past daily CPO spot return on current daily CPO futures return, while “FS(+i)” 

represents cross-correlations for lead-effect of current daily CPO futures return on future daily CPO spot return. The significant cross-correlation in “Levels” column reveals 

evidence of mean dependence of two series. In the “Squares” column, it reveals as evidence of variance dependence. 

59


The result from Table 6 and Table 7 supports Hypothesis 2 that posits market participants 

who are a bearish expectation on gold price tend to react faster to the arrival of new 

information on CPO spot-futures returns following three findings. First, the incorporating lag 

0 and lag 1 of squared CPO spot returns as explanatory variables in Equation (25) sharply 

reduce volatility persistence for CPO futures return by 0.3406 (as shown in Table 6, it reduces 

from 0.9360 to 0.5954). Second, market participants' response to volatility in CPO spot 

market towards the futures market contributes to a significant contemporaneous correlation 

of 0.7313 between standardized squared residuals of both CPO returns at the 1% level 

("Squares” column in Table 7). Third, the contemporaneous correlation of 0.7313 during the 

gold bear market is slightly stronger than a significant contemporaneous correlation of 0.7181 

during the gold bull market (“Squares” column in Table 7), suggesting that market 

participants who have a bearish expectation on gold price are risk averse in responding 

volatility in the CPO futures market based on CPO spot volatility. 

7. Conclusion 

Our study proposes the hypothesis that expected inflationary shock as reflected by the 

movement of higher gold prices or bullish on a longer time horizon would raise the 

speculative pressure in the determination of future CPO price. As a consequence, CPO futures 

will rise above CPO spot, the futures volatility will be higher than the spot volatility, resulting 

in lower storage demand and the option value of keeping the inventory, investors will take 

long positions in CPO futures to cover a high marginal cost in the future. This theoretical 

exposition is supported by our empirical findings that there will be dynamic information 

spillover from current CPO futures return to spot return during the gold bullish trend, in 

addition to the presence of contemporaneous information spillover. 

We further propose the hypothesis that during the period of deflation shock as shown by 

the bearish trend in gold prices, there will be less speculative pressure on future CPO price. 

Instead, there will be a strong contemporaneous correlation between CPO spot and futures 

returns as shown from our empirical result. This study adds to another stylized fact that the 

upward movement of gold prices has economic content and will be able to cause speculation 

of CPO prices through the futures market. If the above hypotheses are true, speculators will 

not only affect the CPO spot prices during contango, they may also respond to CPO futures 

prices by predicting CPO spot prices based on their bullish expectation on gold prices as a 

signal of inflation hedge. 

Based on the finding, this study suggests the following policy implications: Firstly, the 

upward movement of gold price will be an indicative of a future rise in CPO price. To 

speculate on the increase in the price of CPO, investors can long CPO futures contracts to 

insulate them from a high inflation. Secondly, when CPO price is expected to fall, the 

downward movement of gold price would be a signal for the investors to implement shortselling 

activities. 

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62

PAST MFA CONFERENCE 

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Host: Universiti Teknologi MARA, Terengganu 


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Host: Universiti Utara Malaysia (UUM) 

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Host: Multimedia University (MU) 

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CHIEF EDITORS OF CAPITAL MARKETS REVIEW 

1993-2001 ............................................ Mohd Salleh Majid, Bursa Malaysia 

2002-2003 ............................................ Kok Kim Lian, University of Malaya 

2004-2011 ............................................ Fauzias Mat Nor, Universiti Sains Islam Malaysia 

2012-2016 ............................................ Obiyathulla Ismath Bacha, International Centre for 

............................................................. Education in Islamic Finance (INCEIF) 

2017 - Present ...................................... Catherine Soke-Fun Ho, Universiti Teknologi MARA 

INDEXING 

Capital Markets Review is indexed by Research Papers in Economics (RePEc) and MyJurnal by 

Malaysia Citation Centre (MCC). 

INSTRUCTION FOR AUTHORS 

1. The cover page should contain the title of the manuscript, the author(s) and their affiliation(s). Title 

should be typewritten in bold and in 14pt fonts. Author’s name and affiliation should be typewritten in 

single spacing using 10pt fonts with affiliations typed in italics. All text on this page should be centre 

aligned. Contact of corresponding author and acknowledgement should be mentioned in the footnote 

in 9pt fonts with a symbol *. Author must provide complete correspondence information – Author’s 

name, telephone number and email address. 

2. Manuscripts may be written in either Bahasa Melayu or English. Only original and unpublished 

works will be considered. The first page of text shows the title of the manuscript with an abstract of 

about 100-150 words and a maximum of 6 keywords identifying the main topics of the manuscript. 

JEL classification numbers should be included after the keywords. 

3. The paper starts after the JEL classification, with all pages numbered consecutively at the bottom 

right. Heading of main section (e.g. 1. Introduction) and headings of subsections (e.g. 3.1 Data 

Sample) should be typed in bold. Headings of subsequent subsections (e.g. 3.1.1 Data Source) should 

be typed in italics. 

4. Tables and figures should be embedded in the text. All tables and figures should be numbered 

consecutively with Arabic numerals, have a brief title, and be referred to in the text. The entire table 

should be presented in one page unless too long. Landscape table is acceptable. Vertical lines should 

not be used in the table. Explanatory notes should be placed at the bottom of the table. The word 

‘Notes’ precedes the table notes. Tables and their respective titles should be aligned to the left. Figures 

and their respective titles should be aligned to the centre. All figures should be provided as high 

quality printouts, suitable for reproduction. 

5. The whole manuscript should be typewritten in single spacing using 10pt fonts, except for footnotes 

(9pt fonts), tables (maximum 9pt fonts), figures (maximum 9pt font) and explanatory notes for the 

tables (8pt fonts). 

6. Responsibilities for the accuracy of bibliographic citations lie entirely with the authors. Submission 

to Capital Markets Review should follow the style guidelines described in the American Psychological 

Association (APA). All references should be identified at the appropriate point in the text by inserting, 

within parentheses the last name of author and year of publication, e.g. (Author, 2003). List all the 

items cited by authors in alphabetical order in a separate sheet entitled References. References and 

appendices follow in respective order. 

7. Please email your manuscript to Associate Professor Dr. Hooy Chee Wooi, Managing Editor, 

Capital Markets Review: cmr.mfa@gmail.com 

8. In case of doubt, contact the managing editor at Tel: +604-6533889.


The Capital Markets Review (CMR) is the official journal of Malaysian Finance 

Association. CMR has been in publication since 1993 by the Kuala Lumpur Stock 

Exchange (now known as Bursa Malaysia) and the Research Institute of Investment 

Analyst Malaysia (RIIAM). Since 1994, CMR took over and published CMR under the 

financial support by Bursa Malaysia. 

CMR publishes paper in both English and Bahasa Malaysia. CMR publishes double-blind 

refereed articles in various aspects of finance, including Asset Pricing, Banking, Corporate 

Finance, Corporate Governance, Financial Accounting, International Finance, Market 

Microstructure, and Risk and Insurance. The Journal welcomes empirical and theoretical 

contributions that have not been previously published. Please send your manuscript to the 

Managing Editor. 

The Journal is available free to members of the MFA during annual MFA conference. The 

subscription price for individuals is RM50.00 per volume and RM100.00 for institutions 

(including postage and handling) for subscribers in Malaysia and Singapore. For overseas 

subscribers, the subscription price is USD50.00 per volume. 

Enquiries relating to membership and subscription should be addressed to: 

Managing Editor, Malaysian Finance Association 

c/o Associate Professor Dr. Chee-Wooi Hooy 

School of Management 


11800 Penang 

Malaysia 

Tel: +604-6533889 

Email: cmr.mfa@gmail.com 

The Journal of the MALAYSIAN FINANCE ASSOCIATION and 

BURSA MALAYSIA

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