HoChiMinhCity,April2022 THESTATEBANKOFVIETNAM MINISTRYOFEDUCATIONA NDTRAINING BANKINGUNIVERSITYOF HOCHIMINHCITY NGUYENTHIENNHANEXAMINING HERDINGBEHAVIORIN VIETNAMESESTOCKMARKET GRADUATION THESISMAJOR[.]
Researchbackground
The Vietnamese stock market has experienced 21 years from its foundation in1998, including Ho Chi Minh Stock Exchange (HSX) and Ha Noi Stock Exchange.With only two listed companies at the early stage in 2000, the Vietnamese stockmarket has undergone many ups and downs with lots of memorable milestones OnSeptember 30, 2021, the Vietnamese stock market had 2,133 listed stocks Themarket value has reached over 8.3 million billion VND, equivalent to 133.83% ofGDP(accordingto theStateS e c u r i t i e s Commissionof Vietnam).
In the recent two years, this emerging market has entered a new period ofdevelopmentwithmany impressivemileposts.ThevalueoftheVN- Indexhasincreased significantly, especially since March 2020; the Vietnamese stock markethasbrokenmanyrecordsintermsofliquidityaswellasthenumberofnewaccounts At the end of 2021, there were 52 listed companies having capitalizationreaching more than 1 billion USD, VN- Index increased from about 1120 points to1498.28 points for gaining 378 points in ayear, and the average daily tradingvolume was more than 26.560 trillion Dong in
2021 The first outbreak of theCovid-
19pandemicoccurringinMarch2020hasaffectedthemarketindexadversely VN-Index dropped from 1000 to 650 in March 2020, equivalent to losing35% points Afterwards, the market has recovered from the bottom and experiencedam impressive growth until 2021 at 1498.28 points During the period fromMarch2020toDecember2021,investorsalsoexperiencedmanystrongdownward,unpredi ctable trading sessions and were difficult to explain Then, at each milestonewheretheVN-Indexwasabouttobreakthenewrecordinvalue,themarketexperienced strong declines from 100 points to 200 points So, is the market
2 reallyefficienta n d d o e s a n y mi sp ri ci ng o r b u b b l e s e x i s t s i n o u r e q u i t y market
?A f t e r a financial crisises or a correction of the market, herding behavior is often consideredtobeareasoncausingmispricing.
First of all, knowing what is an bubble hepls us understand more about the basisof herding phenomena Hashimoto (2020) defines an asset bubble as a deviation ofthe asset’s market value from its fundamental value and asset prices often higherthanthefundamentalvaluebeforethefinancialcrisis.However,accordingtostanda rd finance theory, an efficient market is defined as a market in which allpublishedinformationaboutabusinessreflectsaccurately andtimely onstockprices. This means that investors can base on the price determined in the market as asignaltohaveareasonableplantoallocateinvestmentcapital(EugeneF a m a , 1970) Besides that, EMH theory also says that people are rational, make decisionsreasonably, and have no biases in their future predictions So, if the market isefficient and people are rational, why do wes t i l l f a c e u p t o m a n y f i n a n c i a l c r i s e s and asset bubbles in the past? Through those economic depressions in Vietnam orover the world, the efficient market theory has started to be doubted about theprecision.
Behavioralfinancehasappearedandbeenabletointerpretthoseunusualfluctuations. Whileefficientmarkettheoryhighly appreciatestherationalityofinvestors, behavioral finance emphasis on the investors' behavior and understandinghowtheirpsychologicalfactorsinfluencethestockprices(Statman,2014). Behavioral finance is a new branch in finance that combines psychology theoriesand conventional finance to understand market behavior and investor's decisions; inother words, while the standard finance theory believes thatm a r k e t p a r t i c i p a n t s view stock prices rationally based on all current and future intrinsic and externalfactors, behavioral finance asserts that people are not entirely logical andmarketsare not fully efficient Statman (2014) mentiones four main foundation blocks ofstandardfinanceandofferedfouralternativefoundationblocksofbehavioralfinance.St andardfinanceisbasedon:1)Peoplearerational2)marketsareefficient
3)p e o p l e s h o u l d d e s i g n p o r t f o l i o s b y t h e r u l e s o f m e a n - v a r i a n c e p o r t f o l i o t h e o r y and do so, and 4) expected returns of investments are a function of risk BF offersfour alternative blocks: 1) People are normal 2) the market is not efficient 3) peopledesignportfoliosbytherulesofbehavioralportfoliotheory,and4 ) E x p e c t e d r eturnsofinvestments are describedbybehavioralassetpricingtheory.
Aboutthedevelopmentofbehavioralfinance,thisfieldwasfirstt i m e mentioned in the 18 th century in the imperative works presented by Adam Smithnamed Theory of Moral Sentiments (1759) and Wealth of Nations (1776) Smith(1759) emphasizes the role of sentiments in decision-making After that, the 1960sand1970swereanew per io d ofB F' s development Tversky andKahne man,whoareknownas"FatherofBehavioralfinance,"gavetheirjudgmentswiththreeheu ristics,namelyavailability,representativeness,anchoring,andadjustment.Nowadays,beha vioralfinancehasimprovedourunderstandingofthe financialmarket, especially to some extent; it can interpret what standard financial theorycouldnot explain.
Herding behavior can be interpreted as a situation when traders make decisionsby imitating others' behavior (Spyrou, 2013), and a reason that causes herding,accordingtoBikhchandanietal.(1992),isthenon- transparencyoftheequitymarket Alot ofresearch investigate herding in different countries all overt h e world and some of them find that herding behavior is more pronounced in emergingmarket,seee.ginVoronkovaandBolh(2015).
Basedo n t h o s e i s s u e s m e n t i o n e d a b o v e a n d t h e c u r i o s i t y o f t h e a u t h o r a b o u t this phenomenonintheVietnameseequity market,theauthordecidedtodoaresearch of
"Examining herding behavior in Vietnamese stock market" aiming tohelp investors haveagreater understandingofherding in Vietnamese financialmarketandsupportingfortheirprogressofmakinginvestmentdecisions.
Researchgap
Therewerenumerousstudiesexaminingthepresenceofherdinginbothdeveloped and emerging countries on overt h e w o r l d T a n e t a l ( 2 0 0 8 ) ,
C h i a n g e t al.(2010),FuandLin(2010)andJu(2019)employscross- sectionalstandard deviation(hereafterCSSD)andcross-sectionalabsolutedeviation(herea f t e r CSAD) model to examine herding behavior in Chinese stock markets Choi & Skiba(2015) employ the model proposed by Sias (2004) and Choi & Sias (2009) forinvestigatingherdingin41countries;Kumaretal.(2020)appliesthecross-sectional absolute deviation model to detecting herding in Japan, Thailand, Taiwan,China,Indonesia,US,UK,IndiaandMalaysia.
In Vietnam, Tran and Truong (2011) examine herding by applying the GARCHto confirm the estimated result after using the Odinary least squared method todetect the phenomenon Vo and Phan (2017) use CSAD and CSSD model to detectherding in the Vietnamese stock market covering the period from
2005 to 2015 Theresultsindicate that herdingdoesexistintheVietnameseequitymarket. ThisresearchappliestheCSADmodeltotestherdingbehaviorintheVietnamese stock market over the period from 2016 to 2021, which is a moreupdated data set.
In some recenty e a r s , V i e t n a m e s e e q u i t y m a r k e t h a s e x p e r i e n c e d an outbreak of liquidity and a mass number of new investors joining the stockmarket The author aims to provide an overview of herding in the stock market byutilizeing the CSAD model proposed by Chang et al (2000) and employs quantileregressionanalysisto investigateherding.
Researchquestion
Does herding behavior exist in the Vietnamese stock market and how itaffectsthestockmarket?
Observing herding behavior in various market conditions, such as up/downmarketandhigh/lowtradingvolumemarket.
Researchobjectives
Thism a i n o b j e c t i v e i s t o e x a m i n e t o w h a t e x t e n t d o e s t h e h e r d i n g b e h a v i o r existintheVietnamesestockmarketfrom2016to2021bystudyingt h e relatio nship between the level of equity return dispersion measured by CSAD andtheoverallmarketreturn.
Besides that, this research aims to investigate herding behavior in differentconditions market.
Researchscopeandmethodology
Thisresearchutilizesanindirectapproachbyexaminingtherelationshipbetweenmar ketreturnandstockreturndispersioninordertoinvestigatethepresence of herding behavior in the Vietnamese stock market This study uses themodel presented by Chang et al (2000) and then modified by Chiang et al. (2010),whichisappliedcommonlyinresearchregardingherdlater.
The thesis conducts research based on a data sample of 1500 daily observationsof 270 companies listed on Ho Chi Minh City Stock Exchange (HSX) over theperiod of 2016-2021 In order to test the hypotheses, the author decided to use thetraditional OLS method and quantile regression analysis following Chiang et al(2010)
Researchstructure
The first chapter depicts the background of the research, researcher’s motivation,research objectives,generalmethodology and scope,researchgap and researchstructure.
The next chapter presents the detailed methodology in terms of the model used andthequantileregressionmethod,aswellasthedatacollection.
The results of data analysis will be discussed in chapter four which contains theresults from using the traditional Ordinary Least Squares (OLS) approach and fromapplyingthenewmethod.
Finally, chapter five will summarize the main findings of the research, drawing outsome implications and the limitations, as well as further research direction, are alsomentioned.
This chapter will present the theoretical framework of herding behavior andsome relevant empirical research The theoretical framework depicts more detailedinformation of herding, such as the definition of this phenomena in stock market.Relevant empirical research provides studies of herding behavior and asymmetriceffect in diverse countries Besides, measurement of herding behavior in financialmarket is also presented In addition, the hypothesis is developed based on someargument.
Theoreticalliteraturereview
Firstly, there are plural ways to define the term herding or herding behavior sofar.Spyrou(2013)definesherdingorherdingbehaviorasaprocessw h e r e economic agents imitate each other actions and/or base their decisions upon theactionsofothers.Banerjee(1992)assertsthatherdingbehavioristheactofeveryone doing what everyone else is doing, even when their private informationsuggests doing something quite different Caparrlli et al (2004) state that the fear ofmakingamistakeaffectedinvestors'decisiontogowiththeflow,withtheconvictiont h a t a s h a r e d e r r o r s a v e s f a c e I n a d d i t i o n , N o f s i n g e r & S i a s ( 1 9 9 9 ) define herding as a group of investors trading in the same direction over a period oftime Clements et al (2016) claim that herding in financial markets reflects thesimilarityindecisionmaking.
So, how many type of herding are there? Bikhchandani and Sharma (2001)separate herding into two types: spurious herding and intentional herding.
"Spuriousherding" is when a group of investors faces the same information sets; so, it leads toa similar decision investment among investors and the outcome helps the marketmore efficient For example, investor A receives positive information about Apple'sstock and A decides to buy Apple After that, investor B also gets a positive signalabout Apple's stock and buys this stock also At that time, both investors exhibitedherding behavior, or correlated demand between last and current period In thissituation,thephenomenawould beconsidered unintentional herdingbec ause investors just simply follow the same signal and do not actually follow each other’strades.
On the contrary, “intentional herding” where investors mimic the decisions ofother investors and ignore their own information can lead to systematic risk, excessvolatility and driving stock’s market value far away from the intrinsic value. Thereare two models of some researchers that can be interpreted for this behavior. Firstly,accordingtoBanerjee(1992),AveryandZemsky(1998),Bikhchandanietal.
(1992),rationaltraderscopytheinvestmentactivityofothermarketinvestorsbecause they believe that these investors know something more than them or othershave relevant information The second explanation for herding behavior suggestedby Scharfstein and Stein (1990) is derived from reputation-based According to thismodel, institutions or professional investors are subject to reputational risk whenthey act differently from thecrowd These two types of herdingmay result inoppositeconsequences,butitisnoteasytodistinguishthem.
Therearemanyreasonsthatusedtoexplainthisphenomenasof a r Bikchandani et al (2001) point out three reasons for herding The first oneisinformational externalities When externalities affect the later investors who decidetoignoretheirowninformationandrelyoninvestmentdecisionsofe a r l i e r inve stors,aninformationcascadecropsup(Bikhchandanietal.,1992;Banerjee,1992).
Bikchandani et al (2001) also state that when the efficiency of theinformation is not common knowledge with investors,informationally inefficientherding may occur and can lead to price bubbles and mispricing Banerjee (1992)proposes a decision model and indicates that the decision-makers tend to mimic thebehavior of the initial group since theprevious groupmay know some relatedimportantinformation.
Secondly, reputation-based might lead to herding behavior when the investmentmanagers decide to mimic other manager's decision Scharfstein and Stein(1990);Trueman(1994);andGraham(1999)provideatheoryofherdingconsideringreputati onalconcernsoffundmanagers or analysts as areasonforherdingbehavior.
Reputational herding arise owing to appearing doubts about the ability or skill of amanager.Inotherwords,reputation-basedherdingresultswheninvestmentmanagers decide to ignore their private substantive private information and choosetomimicinvestmentdecisionsofothermanagers.Rajan(2006)indicatesthatherdi ng provides insurance the manager investment will not underperform his peers.Hence, this protects the manager’s performance and herding will occur if otherinvestment professionalsareinthesamesituation.
Thirdly, compensation-based is one of the reasons can lead to herding. Trueman(2004)addressesthatananalystfavorsforecastingearningsclosertopreviousearning s expectation, so it can help analyst’s compensation higher by impactingcustomer’sassessmentoftheanalyst’sforecastingability.Bikhchandanietal.
(2001) mention in their research that if an investment manager's (i.e., an agent's)compensationisevaluatedbycomparingherperformancewithothersimilarprofe ssionals' one, then this distorts the agent's incentives and she ends up with aninefficientportfolio.Thismayalsoleadtoherdbehavior.
Herding may not totally negatively affect the efficiency of the stock market. Ifunintentional herding occurs, it can help the stock market gain its efficiency causeinvestors to react simultaneously to the same fundamental information and speed upthe adjustment of prices to new fundamentals (Lakonishok et al., 1992) On thecontrary, herding can also make the market less efficient if it does not base onfundamentals Intentional herding destabilizes the markets, with the potential tocreate, or at least contribute to, price bubbles, mispricing and crashes, see, e.g.,ScharfsteinandStein(1990)andBikchandanietal.(2001).
Barberis and Schleifer (2003) and Scharfstein and Stein (1990) assert that pricemovementsshouldreverseafterherdingdrivesstockpricesawayfromitsfundament als There is some empirical evidence on this aspect but not result in thesame conclusion Brown et al (2010) and Puckett and Yan (2008) find evidencesupportingh e r d i n g - r e l a t e d r e t u r n r e v e r s a l s u s i n g w e e k l y d a t a O n t h e o t h e r s i d e ,
Lakonishok et al (1992), Wermers (1999), and Sias (2004) find no evidence ofreturnreversals followingherds.
Herdingindifferentconditions
Apart from detecting herding in various countries worldwide, many studiesinvestigateh e r d i n g ind i f f e r e n t aspects, s u c h as the s i ze o f f i r m s , trad ingvolume andin up and downmarkets.
Abouttherelationshipbetweensize offirmsandherdingphenomena,Lakoni shoket al (1992) investigate herding using a sample of US equity funds and find thatintentional herding should be more prevalent in small stocks because these firmshave less public information; in other words, small stocks have lower quantity andquality of public information than large-cap stocks It is consistent with Scharfstein& Stein's (1990) interpretation that investment managers tend to sell small stockswhich others sell to avoid embarrassment; however, continuing to hold IBM – alarge-cap stock – when others sell Choi and Sias (2009), and Venezia et al (2011)alsoconfirma greater extentofherdinginsmall stocks.
Trading volume of the market is often choosed to detect the relationship withherdinginmanystudies.Intentionalherdingtheoryimpliesthatlowertradingvolumes or market liquidity tend to have higher herding levels A vast literaturehighlightstherelationbetweeninformationquality,marketliquiditya n d informat ion asymmetries For example, Vo and Phan (2017) investigated herding inthe Vietnamese stockm a r k e t o v e r t h e p e r i o d f r o m 2 0 0 5 t o
2 0 1 5 T h e f i n d i n g s reveal that herding is evident during both high and low trading volume days; thus, itis stronger in low volume days Diamond and Verrecchia (1991) predict higherinformation asymmetry in less liquid markets Suominen’s (2001) model suggeststhathighertradingvolume indicatesbetterinformationquality.
A vast amount of empirical studies have been conducted in order to examineherdingi n u p a n d d o w n m a r k e t s C h i a n g e t a l
( 2 0 1 0 ) e x a m i n e h e r d i n g i n 1 8 countries from Asia, Europe, America and Australia The results find that except fortheU S a n d L a t i n A m e r i c a n markets, herdingi s p r e s e n t i n b o t h upa n d d o w n markets, although herding asymmetry is more prevalent in Asian markets duringrising markets.
Tan et al (2008) examine herding behavior in dual-listed Chinese A-share andB-share stocks The outcome of the study finds evidence of herding within both theShanghai and Shenzhen A-share markets Herding occurs in both rising and fallingmarket conditions Herding behavior by A-share investors in the Shanghai market ismore pronounced under conditions of rising markets, high trading volume, and highvolatility,whilenoasymmetry is apparentintheB- sharemarket.Thiscanbeexplained that investors in the Shanghai market tend to be more optimistic andconfident of government support in rising markets owing to the Shanghai marketbeing comprised mainly of larger companies, which were formerly owned by thestate.
In Vietnam, Vo and Phan (2017) detect herding in the Vietnamese stock marketin several market conditions and find that herding is more pronounced in downmarket thanupmarketbecause ofthe tendency of"flight to safety"duringb a d times.
Empiricalliteraturereview
Overall,manyempiricalstudieshavebeencarriedoutsincethe1990sconcentrating on detecting the existence of herding behavior in different marketconditions, perspectives and how it impacts the financial market Although some ofthem found no evidence of herd in the financial market, they have contributed abetterunderstandingofthisphenomenon.
Christie & Huang (1995) collect data from 1925 to 1988 and utilized cross- sectional standard deviation (CSSD) to investigate the herd in the US stock market.Nevertheless, there was no evidence of herd found Afterthat, Chang et al. (2000)present a more powerful model named cross-sectional absolute deviation (CSAD)utilized to test the herd which was developed on the basis of the CSSD model Theresearch detected herding behavior in multiple markets, such as the US, Hongkong,Japan,S o u t h K o r e a a n d T a i w a n T h e r e s u l t f o u n d e v i d e n c e o f herdingi n t w o emerging markets - South Korea and Taiwan, partial evidence of herding in JapananddocumentednoevidenceofherdingintheUSandHongKong.
Chiang et al (2010) employ the CSSD model proposed by Chisties & Huang(1995) and the CSAD model presented by Chang et al (2000) to examine herdingbehavior in Chinese stockmarkets The researchers collected dataofs t o c k s l i s t e d on the Shanghai and Shenzhen Stock Exchange over the period of January 1, 1996,to April 30, 2007 The outcome of the study shows evidence of herding in both upand down markets in Shanghai and Shenzhen A- share market; meanwhile, therewerenotanysignalsofherdinginB-sharemarkets. Caparrelli et al (2004) examine the herd presence in the Italian stock marketusing a sample from September 1, 1988, to January 8, 2001 The results find a non-linear relationship between dispersion and returns and support Christie and Huang’sconclusions that herding is present in extreme market conditions in this country.Another research which was carried out by Caporale et al (2008) to test herding inAthens Stock Market indicated the existence of herd behavior for the years 1998-2007 Applying the model suggested by Christie & Huang (1995) and Chang et al.(2000), the authors also broke the period of research into semi-annual sub-periods,herding is found during the stock market crisis of 1999 and investor behavior seemstohavebecomemorerationalsince2002.
Choi&Skiba(2015)employSias’smodeltodetectherdingeffectoninternational markets including 41 countries during the last quarter of 1999 to thefirstquarterof2010.Theresearchshowedstatisticallysignificantherdingpropensities in 41 target countries that have a significant presence of institutionalinvestors. Hwang & Salmon (2001) suggest a new measure and test of herding which isbased on the cross-sectional dispersion of factor sensitivity of assets within a givenmarket The authors examine herding in the US, UK, and SouthKorean stockmarkets.T h e r e s u l t s s h o w e d s t a t i s t i c a l l y s i g n i f i c a n t e v i d e n c e o f h e r d i n g t o w a r d s
”the market portfolio” during relatively quiet periods rather than when the market isunderstress.
There are several papers studying herding in emerging economies;for example,Hassan(2015)investigatesherdingbehaviorinthePakistanistockmarketemplo ying twomethodologies proposed by Chritie & Huang(1995) andC h a n g e t al.
(2000) The results showed the non-existence of herd behavior for the period2002-
2007 and found no support for the rational asset pricing model and investorbehaviorfoundinefficient.
Tan et al (2008) examine herding behavior in dual-listed Chinese A-share andB-share stocks in which the study uses Chang et al.'s modified model The resultsfound evidence of herding within both the Shanghai and Shenzhen A-share marketsin both rising and falling market conditions.Ju (2019) alsod e t e c t s h e r d i n g i n A - andB- sharemarketsapplyingtheCSADmodelandfoundtheprevalenceofherdingonbothA- andB-sharemarkets.
Bhaduri & Mahapatra (2013) examine the presence of herding in the Indianstock market utilizing symmetric properties of the cross-sectional return distributioninsteadofconventionaltestmethodologiestoidentifyherding.Usingt h a t alte rnativeapproach,thestudy foundevidenceofherdingintheIndianequitymarket during the sample period, which tended to be more pronounced during the2007crash.Thepaperalsofoundthattherateofincreaseinsecurityreturndispersion is relatively lower in the upmarket compared to down market days Fu &Lin (2010) use data from Jan 2004 to June 2009 to explore herding behavior andinvestors' asymmetric reactions to good news and bad news in China, but there wasno evidence of herding behavior in China stock market However, this study foundan interesting thing that low turnover stocks tend to herd than high turnover stockandinvestorstendtoherdinadownwardmarket.
In another asset class, Kumar et al (2020) investigate herding in commoditymarketsofmajorAsianeconomies.Thestudyfindsevidenceofanti- herdingbehaviorinIndia,Malaysia,andTaiwan,whereasJapandoesnotshowany herding or anti-herding patterns Herding evidence was found in China and Indonesia aswell Besides that, in Singapore and Thailand, herding is detected in the downwardmarket,whileintheUSandUKshowsthatherdingdoesnotdependonthedevelop ment status of the market Zhou et al (2013) present a modified CSADapproach to investigate herding behavior and herding asymmetry The empiricalresults found no herding behavior exists in China's overall carbon market As forherding asymmetry, no herding behavior exists in both up and down markets and inthe markets with high and low trading volumes, as well as in the markets with highvolatility.
InV i e t n a m e s e s t o c k m a r k e t , V o & P h a n ( 2 0 1 7 ) i n v e s t i g a t e t h e e x i s t e n c e o f herd behaviorin Vietnam stockmarket applying them o d e l p r o p o s e d b y C h r i s t i e and Huang (1995) and Chang et al (2000) Using a set of data of 299 companieslistedin H oC h i M i n h C i t y StockE x c h a n ge c o v e r i n g t h e t i m e pe r i o d 2 0 0 5 - 2 0 1 5 , the results indicate the evidence of herding over the whole period researched andasymmetriceffectundervariousmarketconditionsandtradingvolume.
Tran and Truong (2011) examine the existence of herding in the Vietnamesestockmarketandtheasymmetriceffectconditionalonthedirection ofmarketmovements After collecting data of daily price series of all securities in Ho ChiMinh City Stock Trading Center covering the period of 2002 to 2007, the authorsapplied GARCH(1,1) model to reduce drawbacks in OLS method The result foundthe presence of herding in this emerging market; however, there is no evidence forasymmetriceffectinthiscase.
Authors PeriodStudied Models Countrie s MainFindings
CH US Theresultsdidnots h o w the presenceofherdf o r bothd a i l y a n d m o n t h l y returns.
Hong Kong, Japan,So uthKor ea,and Taiwan
Theresultfounde v i d e n c e of herding in two emergingmarkets - South Korea andTaiwan, partial evidence ofherdinginJapananddocum ented no evidence ofherdingintheUSandHong Kong.
CH,CKK China Theresultss h o w e d evidence of herding in bothupanddownmarketsinSh anghai and Shenzhen A- sharemarket;meanwhile,the reweren o t a n y s i g n a l s ofh e r d i n g i n
Italia Herdbehaviorisevidentdurin g extrememarketconditionsin terms of both sustainedgrowth rate and high stocklevels according to the CHmodel.
Caparole Weeklya n d m o n t h CH,CKK Greece Herdingisfoundduringthe l y etal.
Theresearchshowedstatistic allysignificantherdingprope nsitiesin41target countries that have asignificantpresenceof institutionalinvestors.
Theresultsshowedstatisticallysig nificantevidenceofh e r d i n g towards”themarketportfoli o” during relativelyquietperiodsrathertha nwhenthemarketisunder stress.
CH,CKK Pakistan The results showed the non- existence of herd behaviorfortheperiod2002- 2007andfoundnosupportf o r therationalassetpricingmo delandinvestor behaviorfoundinefficient. Tanetal.
China The results found evidenceof herding within both theShanghai and Shenzhen A-share markets in both risingandf a l l i n g m a r k e t conditions.
Ju(2019) Available period:July1,1992,to June
CH,CKK China Theresultsindicatedherding is prevalent in bothA-andB- sharemarkets.Spill- overeffectsrelatedto herdingdonotexist.
India Thepaperfoundthattherate of increase in securityreturndispersionisrel ativelylowerintheupmarketco mparedtodown marketdays.
Vietnam HerdingisreportedinVietnames estockmarket,particularlyin themedianand lower quantile of returndispersion distribution. Theresultsalsorevealthatherdin g is more pronouncedind o w n m a r k e t t h a n i n u p market.
CCK Vietnam Theresultfoundthepresence of herding in thisemerging market; however,thereisnoevidence forasymmetrice f f e c t i n t h i s case.
HerdingprevailsinChinaandIndonesia However,India, Malaysia, and
Malaysia TaiwanTh ailandJap anUS UK
Taiwan exhibit anti- herdingbehavior In Singapore andThailand,herdingisdetect edinthebearishmarketstate,wh ileJapandoes not show any herdingoranti- herdingpatterns.Furtheranalys isinvolvingthe US and UK shows thatherding does not depend onthedevelopmentstatusofth em a r k e t H e r d i n g i n emerging markets seems tobemostlydrivenby volatility.
Brazil Theresultssuggestedthatherd ing behaviordependsonhightradi ng volume,highvolatilityofret urns, market downturn, andtrading imbalance triggered by sellers.
CH,CKK China Therewasnoevidenceofherdin gbehaviorinChinastockm a r k e t T h i s s t u d y founda n i n t e r e s t i n g t h i n g thatlowturnoverstockstendto herdthanhighturnoverstock a n d investorst e n d t o h e r d i n a downwardmarket.
Note:CH, C C K and T I P r e f e rs t oC hr is tie a n d Hua ng M o d e l ; C h a n g , C hen g and KhoranaModelandTradingImbalancePictureModel.
Hypothesisdevelopment
WeiandGelos(2002)indicatethattherearetwomainreasonsforherding:1 )informationalcascadesand2) co mp ensa ti on - base d incentives I n thecaseof informationalcascaded,theinvestormaybeinduc edtoignorehisownsignalandmimicthebehaviorofothersduetoimperfectinfor mationandnoisysignalsaboutfundamentals.ItisconsistentwithBikhchandani&Shar ma(2000),theresearchersassertthatlackofinformationinamarketcanleadtopric ebubblesormispricing.Thus,i n v e s t o r s t e n d t o m i m i c t h e b e h a v i o r o f a n i n i t i a l g r o u p o f i n v e s t o r s i n t h e erroneousbeliefthatthisgroupknowssomething.Al thoughtheVietnamesestockmarkethasgonethrough lots ofups anddowns sinceit wasestablished in 2000, thisemergingmarket still fac es uptoseveralissues, suchasinformationtransp arencyanddisclosure,illegaltransactions, pricemanipulation, andshortag eoflegislation framework.Therefore,thefollowinghypothesisisestablishedandtested inordertoinvestigate the presence and prevalence of herding in this emerging stock market:Hypothesis1:HerdingbehaviorexistsintheVietnamesestockmarket.
Besides investigating the presence of herding behavior, the researchers alsostudied this phenomenon in different market conditions Vo & Phan (2017) comparethe strength of herding between up and down market conducting based on daily dataand the outcome indicates that herding indownsidem a r k e t i s s t r o n g e r t h a n i n upsidemarket Ju (2019) examines herdingi n b o t h A - a n d B - s h a r e m a r k e t a n d foundt h a t i n v e s t o r s o n A - s h a r e m a r k e t h e r d o n l y w h e n t h e m a r k e t i s d o w n f o r valueorlargestock portfolio Thisphenomenon maybeinterpretedasthefea rofpotentiall o s s w h e n t h e m a r k e t i s d e c r e a s i n g l o o m s l a r g e r t h a n t h e p l e a s u r e o f potentialgainwhenthemarketisincreasing(Tversky&Kahname n,1986).Adayhavingmarketreturngreaterthanzeroisconsideredtobea“upmarketda y”,whilea“downmarketday”hasmarketreturnlessthanzero.Basedontheabove argument, the author decides to test the following hypothesis in this study:Hypothesis2:Herdingbehaviorismorepronouncedinadecliningmarketthan inarisingmarket.
In addition, several studies have detected level of herding behavior in marketwithhighandlowtradingvolume.Vo&Phan(2017)examineherdinginVietnames e stock market and find that herding is evident during high and lowtrading volume days; however, it is stronger in low volume state On the contrary,Signorelli et al
(2020) investigate herding in Brazillian equity market.the resultsshowed evidence of herding in high trading volume state while it did not happen inlow volume. Hassan et al (2012) test herding phenomena in Pakistani stock marketbut find no evidence of it in both high and low volume state A day trading sessionhaving trading volume greater than than the previous 30-day moving average isconsidered to be a “high trading volume day” On the contrary, “low trading volumedays have trading volume less than the previous 30-day moving average So, it isinteresting the author to test herding in high and low volume state following thehypothesis below:
Hypothesis 3: Herding behavior is more pronounced in a high trading volumedaysthaninlowtradingvolumedays.
Thischapterpresentsthedatacollectionandtheregressionmodeloftheresearch.Thea uthorintroduceshowdataiscollectedandprovidesasampledescription of the data collection Besides that, regression models for investigatinghypotheses and levels of herding in different markets are also shown in this chapter.Inaddition,thechapteralsopresentsregressionmethodology,includingtwoappr oachesfor modelestimation:OLSandQuantile Regression(QREG).
Datacollection andsampledescription
The author uses the daily closed price of all stocks listed on Ho Chi Minh StockExchange (HSX) to examine the existence of herding in Vietnamese stock market.Thedailydatasetissecondarydatacoveringtheperiodfrom01/04/2016to31/12/202 1andis collectedfromthe website ofHSX andcophieu68.com Thereare
409stockslistedonHSXandaftercollectingandfiltering,thefinalsamplecomprises270com paniesequivalentto270stockswhichprovided1500dailyobservationsovertheperiodstudied. Theselectedstocksmustmeet“goingconcern”requirementduringthepe riodof 2016-2021 and are listed on Ho Chi Minh Stock Exchang (HSX) Besides that,the data set is divided into 4 small data sets in order to give more insights aboutherdingphenomenoninvariousmarketconditions.
A day having market return greater than zero is considered to be a “up marketday”, while a “down market day” has market return less than zero In terms oftrading volume, a day trading session having trading volume greater than than theprevious 30-day moving average is considered to be a “high trading volume day”.Onthecontrary,“lowtradingvolumedayshavetradingvolumelessthantheprevious30- daymovingaverage.
Regression model fortestingthehypotheses
Regressionmodel
Two models which are widely used in detecting herding behavior are the cross- sectional standard deviations (hereafter referred to as CSSD) method proposed byChristie&Huang(1995)andthecross- sectionalabsolutedeviation(hereafterreferredtoasCSAD)introducedbyChangetal. (2000).
Christie & Huang (1995) employ the cross-sectional standard deviation
𝑡 𝑡 where𝑅𝐷𝑡is the return dispersion at time t.𝐼 𝐿 is an indicator variable at time ttaking on the value of unity when the market return at time t lies in the extremelowertailofthedistribution,and0otherwise.Similarly,𝐼 𝑈 i sanindicatorv ariable with a value of unity when the market return at time t lies in the extreme upper tailof the distribution, and 0 otherwise However, there are some drawbacks of theChristie & Huang model Munkh-Ulzii et al (2018) mentioned that one of thedrawbacks related to the Christie & Huang (1995) approach is that it requires thedefinition of extreme returns The term of extreme return is arbitrary, so in reality,traders do not always find the same in their opinion about extreme return,and thecharacteristics of the return distribution may change over time Moreover,
Chiang etal (2010) has pointed out that herd behavior may be present during the entire returndistributionandbecomemoredominantduringtheperiodofmarketstresses,whi le
Christie & Huang’s model recognizes herding under the condition of extreme returnonly.T h e r e f o r e , t h e a u t h o r d e c i d e s t o e m p l o y an o p t i o n a l m e t h o d t o t h e C h r i s t i e and Huang (1995) approach for herding was proposed by Chang et al (2000) –CSAD method This method is developed basing on the approach of Christie &Huang(1995):
|𝑅 𝑚,𝑡 |istheabsoluteequallyweightedaveragestockreturninthedual-listedportfolio consisting N company during the period t γ1is the coefficient of absoluteequally weighted average stock return and γ2is the coefficient of squared marketreturnor“herdingcoefficient” as well.
The model included independent and dependent variables which were describedthecalculationasfollows:
Dependentvariable:thedependentvariableinthisresearchwasreturndispersion measuredbycross-sectionalabsolutedeviation, which was expressedas:
CSAD = 1 ∑𝑁|𝑅 −𝑅 | (3) t 𝑁 i,𝑡 i,𝑡 𝑚,𝑡 whereNisthenumberoffirmsintheportfolio,𝑅 𝑚,𝑡is thereturno f m a r k e t portfolioattimet and𝑅 i,𝑡is thereturnofstockiattimet.
Independent variables: the independent variable in this research was marketreturn From the original data were closed price of VN-Index, the author calculatesdailymarketreturnasthefollowingformula:
Chiang et al (2010) state that an increase in market return can lead to anincrease in stock return dispersion because each asset has its own sensitivity tomarket return and every investors make their investing decision independently innormalmarketcondition;asaresult,𝑅 𝑚,𝑡and CSADarelinear.So,ifγ2