Empirical investigation of market value change in vietnam stock market

Background,researchmotivationandrationale

Stockpricevolatilitya n d itsdeterminantsr e m a i n a sourceo f c o n t r o v e r s y d e s p i t e y e a r s o f t h e o r e t i c a l andempiricalr e s e a r c h Investigationsofs h a r e pricecha ngesa p p e a r toyieldevidencethatchangesinfundamentalvariablesshouldjointl ybringa b o u t c h a n g e s ins h a r e pricesbothind e v e l o p e d a n d e m e r g i n g m a r k e t s H o w e v e r , t h e actualfundamentalfactorsfoundtoberelevantmayvaryfrommarket tomarket.I t isw i d e l y a g r e e d thata seto f fundamentalv a r i a b l e s a s s u g g e s t e d b y individualt h e o r i e s isn o d o u b t r e l e v a n t a s p o s s i b l e factorsa f f e c t i n g s h a r e p r i c e c h a n g e s intheshortandthelong-run.

As u b s t a n t i a l a mounto f r e s e a r c h h a s b e e n directedt o w a r d analyzingther e l a t i o n s h i p b e t w e e n s t o c k p r i c e volatilitya n d f i r m a t t r i b u t e s A m o n g t h o s e s u b s t a n t i al researchindevelopedmarket,itcanbelistedoutsomeoutstandingf indingss u c h a s Baskin(1989)andFamaandF r e n c h (1992)intheU n i t e d S t a t e s conte xta n d A l l e n andR a c h i m (1996)inA u s t r a l i a n c o n t e x t WhileB a s k i n (1989)rep ortsa stronglys i g n i f i c a n t r e l a t i o n s h i p b e t w e e n dividendyielda n d s t o c k pr icevolatility,AllenandRachim(1996)cannotfindanyevidencetosupportthish y p o t h e s i s butfindsanotherinterestingresultsrelatedtopayoutratio.

Event h o u g h V i e t n a m initiatesthes t o c k m a r k e t l a t e r thanm a n y o t h e r d e v e l o p e d c o u n t r i e s , therehas b e e n asubstantia lgrowth T h e fi rs t stock excha ngeinHoChiM i n h citywase s t a b l i s h e d in2 0 0 0 w i t h f o u r l i s t e d c o m p a n i e s Increasedforeigni n t e r e s t a n d th e p r i v a t i z a t i o n ofs ta te - own ed e n t e r p r i s e s l e a d toa r a p i d i n c r e a s e inl i s t i n g s Atthee n d o f 2009,t h e r e a r e a b o u t 2 5 0 firmsl i s t e d o n theHoC h i M i n h S t o c k Exchangeandthesmallerexc hangeinHanoi.

Mosto f thepreviouss t u d i e s o n determinantso f s t o c k returnvolatilityfocuso n w e l l - d e v e l o p e d marketswithlessattentiongiventothedevelopingmarkets Totheb e s t ofthea u t h o r k n o w l e d g e , therea r e v e r y fewstudiesthataddresstheissueo f s t o c k pricevolatilityandfundamentalfactorsintheVietnamesecontext.Thism o t i v a t e s thep r e s e n t s t u d y toexaminew h e t h e r f i r m c h a r a c t e r i s t i c s c a n affectthes t o c k pricevolatilityoftheVietnamesecompanies.Thisstudyfocusesonthesamei s s u e forV i e t n a m S t o c k market,a d e v e l o p i n g market.A p a r t fromu s i n g thel a t e s t d a t a , wedevelopthisstudybyincorporatingselectedvariablesforselectedpurposestoex aminet h e d e t e r m i n a n t s o f s t o c k pricevolatility.I n addition,industryeffectsa r e a lsotakenintoconsiderationofthisresearch.

Stock prices react to daily market news, prompting numerous studies on short-term event impacts In contrast, this research explores the long-term relationship between stock price volatility and firm attributes To achieve this, stock price volatility is calculated using the Parkinson (1980) method, which minimizes discrepancies between relevant share prices and fundamental ratios Ideally, quarterly data would be utilized; however, significant differences exist between internal financial statements and audited reports Additionally, regulations mandate the submission of only annual audited reports Consequently, this study employs annual data from audited reports to ensure accurate firm attributes.

Researchobjectivesandresearchquestions

Thisstudyisconductedtoanalyzethebehaviorofstockpricefromabroadp e r s p e c t i v e Themainpurposeofthispaperistodeterminetherelationshipbetweens t o c k price volatilitya n d firm at tr ib ut e s Wealso looka t theinfluenceofindustrye f f e c t o n s t o c k pricevolatility.I n a d d i t i o n , s t o c k pricebehaviorine a c h y e a r ande a c h indust ryisalsodiscussedinthisstudyinordertoidentifywhetherthereisany differenceinstockpricemovementfromoneyeartoanotheryear,fromonei n d u s t r y toanotherindustry.

Methodology

- Bivariatea n a l y s i s involvingr e g r e s s i n g thedependentv a r i a b l e PVa g a i n s t e a c h independentvariableseparately.

- Multivariatea na ly si s includingo r d i n a r y leastsquare r e g r e s s i o n , fixed e f f e c t r e g r e s s i o n andrandomeffectregression.

Contribution

This study employs new methods, including econometric testing and an econometric package, to analyze empirical results regarding stock price volatility in the Vietnamese stock market To the best of the author's knowledge, this is the first research that thoroughly investigates the characteristics of stock price volatility in Vietnam Our main contribution to the financial literature is an extensive empirical analysis of stock price movements and firm attributes over an extended time period By constructing a dataset of stock prices and incorporating detailed attributes of listed firms on the Ho Chi Minh City Stock Exchange, we achieve our research objectives Additionally, this study considers industry effects by including industry dummies to determine whether stock price volatility is biased toward specific industries within the Vietnamese stock market.

Secondly,thisresearchprovidesa usefulc a u t i o n forthei n v e s t o r s int e r m s o f realr e l a t i o n s h i p betweenstockpricevolatilityandfirmsattributes.

Lastbutnotleast, t h e limitationofdataconstraints inthiss t u d y may offersignalsforp olicymakerstomorestrictly regulateonaccountingstandardsandpublicationr u l e s

Structureofthethesis

Thist h e s i s d o e s n o t followconventionalmethodw h i c h d i v i d e s i n t o c h a p t e r s Wec o n s i d e r e a c h chapterc o v e r s a s e p a r a t e m a t t e r sothatwestructurethet h e s i s intopartswhichisabetterrepresentation.

Ourpaperisdividedinto6mainsections.Section1brieflyintroducesmajorc o n c e r n s o f thist h e s i s S e c t i o n 2 p r e s e n t s theoreticala s p e c t s ofs t o c k p r i c e volatilityf ocusingo n i m p a c t s fromf u n d a m e n t a l fa ct or s S e c t i o n 3 introducesd a t a d e s c r i p t i o n andhypothesisdevelopment.Section4describesmethodology.Ther e s u l t s oftheempiricalanalysis andtheirdiscussionsarethenpresentedinSection

5.Finally,Section6drawstheconclusionsofourstudy,followsbydiscussionson th e contributions,limitations,andimplicationsforfutureresearch.

Thestructurea n d methodologyo f thisthesisareguidedb y B r o o k s (2008)w i t h econ ometricapproachtoanempiricalinvestigation.

This section provides the theoretical background necessary for the models to be developed later It reviews the literature on stock price volatility, focusing primarily on fundamental analyses that examine the long-term relationship between stock price movements and firm attributes Additionally, it includes a brief overview of studies on the short-term relationship between stock price behavior and event announcements Most previous research has investigated the relationship between stock price volatility and dividend policy while considering other controlling variables This section summarizes these literature strands and highlights key fundamental factors for further analysis.

Sharepricesarethemostimportantindicatorsreadilyavailabletotheinvestorsfort h e i r decisiontoinvestornotinaparticularshare.Factorsaffectingstockpricesares t u d i e d fromdifferentpointsofview Several researchers examinetherelationship b e t w e e n stockpricesandselectedfactorswhichcouldbeeitherinternalorexternal. Theoriessuggestthatsharepricechangesareassociatedwithchangesinf u n d a m e n t a l v a r i a b l e s w h i c h a r e relevantf o r s h a r e valuations u c h a s payoutratio,dividendyie ld,c a p i t a l structure,e a r n i n g s , s i z e o f t h e f i r m a n d itsg r o w t h (Rappoport ,1986,Downs,1991).

Balla n d B r o w n ( 1 9 6 8 ) a r e t h e f i r s t tohighlightt h e r e l a t i o n s h i p b e t w e e n s t o c k pricesa n d informationd i s c l o s e d inthefinancialst at em en t s E m p i r i c a l r e s e a r c h ont h e valuer e l e v a n c e h a s i t s rootsinthet h e o r e t i c a l f r a m e w o r k one q u i t y v a l u a t i o n m o d e l s Ohlson(1995)depictsinhisworkthatthevalueofafi rmcanbeexpressedasalinearfunctionofbookvalue,earningsandothervaluerelevantinf ormation.

The relationship between fundamental factors and share price changes has been widely studied over short time frames, yet there is a scarcity of research focusing on long-term modeling Most studies utilize cross-sectional tests and event-based research methodologies, but few explore this relationship either cross-sectionally or inter-temporally A common limitation in these studies is the focus on only one or two fundamental factors, despite the existence of a broader range Additionally, while price revisions at the time of relevant disclosures are significant in short-term analyses, it is crucial to investigate their effects over extended periods, utilizing data spanning several years to measure the variables accurately.

Stockpricevolatilityanddividendpolicy

Itisw e l l k n o w n thatthemostimportantinternalfactorsa r e r e l a t e d tod i v i d e n d p olicyw h i c h includesdividendyielda n d p a y o u t r a t i o Differentr e s e a r c h e r s h a v e differentviewsabouttherelationshipamongdividendpolicyandstockprices.

The relationship between dividend payouts and stock price volatility, first introduced by Modigliani and Miller (1958), remains a topic of ongoing discussion and investigation They argued that a firm's value is independent of its dividend policy, asserting that stock price volatility is primarily determined by a company's earning ability However, subsequent studies by Miller and Rock (1985) and John and Williams (1987) suggest that this assertion holds true only if shareholders possess symmetric information regarding the company's financial status In practice, managers often withhold negative information while sharing positive insights with shareholders, only disclosing unfavorable data when compelled by regulations or financial constraints.

Frienda n d P u c k e t t (1964)initiatet h e w o r k o n r e l a t i o n s h i p b e t w e e n dividenda n d s t o c k pricevolatility.T h e y finda positiver e l a t i o n s h i p a m o n g d i v i d e n d a n d s t o c k p r i c e s

Jenson(1986)statesthatthereisapositiverelationshipbetweendividendandstockprice reaction.Hearguesthatdividendpayoutsreducethecostoffundsandi n c r e a s e thec a s h f l o w s o f thefirm.T h e c o m p a n y a f t e r payingc a s h dividendstostockholdersw ouldhavelessidlefundsinthehandsofmanagerstoinvestinlessornegativeNPVprojects.

In the United States, Baskin (1989) highlights a significant negative relationship between dividends and stock price volatility, proposing four models that connect dividends to stock price risk: duration effect, rate of return effect, arbitrage effect, and informational effect He recommends using control variables such as operating earnings, firm size, debt financing levels, payout ratio, and growth levels to assess the significance of the dividend-yield and price volatility relationship His findings indicate that dividend yield and payout ratio are negatively correlated with stock price volatility, while firm size, asset growth, and firm leverage positively influence stock price volatility.

Witha slightd i f f e r e n t approachfroms t o c k returnsnots t o c k prices,Famaa n d F r e n c h (1992)i n f e r thatdividenda n d cashflowvariabless u c h a s earning,i n v e s t m e n t andindustrialproductionmayserveasindicatorofstockreturns.

AllenandRachim(1996)failtofindanyevidencethatdividendyieldinfluencethes t o c k pricevolatilityinA u s t r a l i a However,theyfinda s i g n i f i c a n t p o s i t i v e c o r r e l a t i o n a m o n g s t o c k p r i c e v o l a t i l i t y a n d earningv o l a t i l i t y a n d l e v e r a g e , a n d a significantnegativerelationshipbetweenpricevolatilityandpayoutratio.A c c o r d i n g totheirr e s u l t s , thereisa negativecorrelationb e t w e e n s i z e a n d s t o c k pricevolatility,aslargecompaniesincurmoreliabilities.

Regardingtoe m e r g i n g markets,IrfanandN i s h a t (2003)ina s t u d y inP a k i s t a n a r g u e thatbothdividendpayoutratioanddividendyieldhavesignificantlynegati vee f f e c t onstockpricevolatility.MostoftheirfindingsaresimilartothoseofBaski n

FollowingIrfanandN i s h a t (2003),a numbero f studiesa r e conductedinP a k i s t a n regar dingtodi vi de nd policya n d s to ck pricevolatility.A s g h a r e t al.,

(2010)states t h a t pricev o l a t i l i t y anddividendyieldhaves t r o n g positivec o r r e l a t i o n butp r i c e volatilityish i g h l y n eg at iv e ly correlated with g row th ina sse ts Nazire t al.,

(2010)f i n d s thatdividendyieldandpayoutratiohavesignificantimpactonthesharep ricevolatility.T h e effectofdividendyieldons to ck pricevolatilityincrease duringthes t u d y i n g periodwhereaspayoutratiohasonlyasignificantimpactatlowerlevelofsig nificance.

Stockpricevolatilityandfirmage

P´astora n d V e r o n e s i (2003)finda n e g a t i v e c r o s s - s e c t i o n a l r e l a t i o n b e t w e e n volatilityandfirmage.Themedianreturnvolatilityof theUnitedStatesstocksfallsm o n o t o n i c a l l y from14%permonthfor1-year- oldfirmsto11%permonthfor10-year- oldf i r m s T h e a u t h o r s ’ modelp r e d i c t s highers t o c k volatilityforfirmsw i t h m o r e volatileprofitability,firmswithmoreuncertainaverageprofitability,andfirmsthat paynodividends.

Sstockpricevolatilityandtradingliquidity

Variousstudiesreportthattherea r e s i g n i f i c a n t r e l a t i o n s h i p s b e t w e e n volumea nds t o c k pricemovementandliquidity,duetothefactthattradingvolumeisasourceo f r i s k b e c a u s e o f theflowofinformation.F o r example,S a a t c c i o g l u a n d S t a r k s (1998)findthatv o l u m e l e a d s t o c k pricesc h a n g e s infouroutofthe s i x e m e r g i n g m a r k e t s Jonesetal.,(1994)foundthatthepositivevolatility- volumerelationd o c u m e n t e d bynumerousresearchersreflected apositiver elationshipbetween volatilityandthenumberoftransactions.Gallant,etal.,

(1992)investigatethepricea n d volumeco- movementusingdailydatafrom1928to1987forNewYorkStockE x c h a n g e a n d findpositivec o r r e l a t i o n b e t w e e n conditionalv o l a t i l i t y a n d v o l u m e S o n g , e t a l ,

(2005)e x a m i n e ther o l e s o f thenumberoft r a d e s , s i z e o f t r a d e s , a n d s h a r e volum einthevolatility- volumerelationintheShanghaiStockExchangeandc o n f i r m thatm a i n l y t h e n u m b e r oft r a d e s d r i v e s thevolatilityvolumerelation.I n addition,otherstudiesreportt hatstocktradingvolumerepresentsthehighestp o s i t i v e correlationtothee m e r g i n g s t o c k pricec h a n g e s ; thusr e p r e s e n t themostp r e d i c t e d v a r i a b l e s ini n c r e a s i n g pricev o l a t i l i t y inbothemerginga n d developings t o c k markets(Sabri,2004).

Otherfirmattributesandstockpricevolatility

In a study conducted in 1994, the joint linear effects of six variables on share price volatility were analyzed across three markets: Japan, Malaysia, and Singapore, using data from firms over a span of 16 years The findings revealed that these six variables significantly impact share price volatility in all three markets, although some were not significant in specific markets Notably, in the more analytically intensive Japanese market, changes in fundamental factors accounted for two-fifths of the variation in share price volatility In contrast, the developing markets of Malaysia and Singapore exhibited a lesser correlation, with larger portions of price variation remaining unexplained by the six firm-specific fundamental variables.

Inanotherstudy,AriffandKhan(2000)onasampleofh u n d r e d homogenousi n d u s t r i a l f i r m s , fourouto f t h e s e sixf a c t o r s a r e founds i g n i f i c a n t a n d explainedt w o - t h i r d ofsharepricevolatilityoverawindowoftwentyyearsforUSmarket.

IrfanandN i s h a t ( 2 0 0 3 ) identifythejoint- effectmultiplefactorsexerto n s h a r e priceso n K a r a c h i S t o c k E x c h a n g e inth el o n g run.Thes i g n i f i c a n t jointf a c t o r s o b s e r v e d arepayoutratio, size,l everageanddividendyield.Thisstudyundertakesinvestigationforpre-reform,post- reformandoverallperiod.

Afterr e v i e w i n g s o m e d i s t i n g u i s h e d w o r k s int h e field,itc a n bes e e n that m a n y w o r k s aredonesofaronthistopic.However,tothebestknowledgeoftheau thor,t h e r e areveryfewstudiesaboutstockpricefluctuationsandfirmattributesindevelo pingcountries,especially inVietnam.T he empirical evidenceofstockprice vol atilityinVietnamstockexchangeislackintheliterature.Thisgivesthecurrents t u d y greatrelevanceandistheimpetusfortheresearchertobegininvestigation.Inl i e u ofthec urrentliterature,thisresearchenrichestheliteraturebyexaminingw h e t h e r s t o c k pri cevolatilityisaffectedbyfirmattributesa s inpreviousr e l a t e d s t u d i e s

Thiss e c t i o n beginsb y presentinga d e t a i l e d descriptionofthem a i n d a t a s o u r c e s a n d anexplanationo f s e l e c t e d variables.Ther e m a i n e d p a r t ofs e c t i o n 3 f o l l o w s w i t h developmentofempiricalresearchhypothesis.

Datadescription

Thedataemployed inthisstudy include110l is te d companies intheHoChiMinhCi tyStockExchange(HOSE)overtheperiodfrom2007–

Dataofstockpricesforthepurposeofthisstudycomprisedailyclosingsharepriceso f 110 companiesfromtheHoChiMinhCityStockExchangeovertheperiodfrom0 1 Jan2007 to31Dec2009.Thesharepricesareadjustedfordividendsandstocks p l i t s inorder toreflectmoreaccuratereturns.

(1) thec o m p a n i e s mustb e l i s t e d o n HOSEb y thee n d o f 2 0 0 7 and3 full- yearauditedfinancialstatementsandannualreportsareavailable;

Theinitialdata ofthisstudyconsistsof 116companieswh i c h arelisted onHOSE b y 31 Dec2007.H o w e v e r , s i n c e several observationsa r e notsimilar tothew h o l e s a m p l e , theyaretakenoutofthefinalsample.First,5financialfirmssuchasban k( S T B ) , s e c u r i t y c o m p a n y ( S S I ) , investmentfunds( M A F P F 1 , P RU B F 1 , V F

M V F 1 ) a r e excluded fromthepurviewo f thiss t u d y s in ce theyaresubjected toadi fferent regulatoryf r a m e w o r k thatd o e s n o t a p p l y o n otherl i s t e d c o m p a n i e s , givene i t h e r differentfinancialstatementformatsorspecificcharacteristicsofthefinanc ials e c t o r s S e c o n d , a manufacturingfirm,B a c h T u y e t CottonC o r p o r a t i o n (B BT),isa l s o e l i m i n a t e d f r o m t h i s s t u d y s i n c e theyh a d failedtodeliveritss t a t e m e n t f o r 2 0 0 8 andwasde- listedfromHOSE.Therefore,thefinalsamplecontains110companiesmatchingallselect ioncriteria,whichtogethercreates330observations.

Price volatility (PV) is derived from Parkinson's (1980) extreme value estimation of variance in stock returns This method calculates the annual range of stock prices by taking the difference between the maximum and minimum values, dividing it by the average of the high and low prices, and then raising the result to the second power Parkinson's approach is considered far superior to traditional estimation methods that rely solely on closing and opening prices This measure effectively captures changes in share prices on an annual basis The data for this variable is collected from the price timeline of each firm listed on HOSE, adjusted for dividends and stock splits.

Inthissubsection,webrieflyintroduceanumberoffirm- specificattributesusedint h e empiricala n a l y s i s T o enablee a s y comparison,wef i r s t c h o o s e e s s e n t i a l l y thes e l e c t e d attributesaspreviousresearches.Theseare:

(i) Earningvolatility( E V ) : isdefineda s ther a t i o ofthec o m p a n y ’ s earningsb e f o r e i n t e r e s t andtax( E B I T ) tot o t a l a s s e t s Thisiscalculatedf r o m t hec o n s o l i d a t e d auditedfinancialstatements.

(ii) Returnona s s e t s ( R O A ) : ism e a s u r e d a s n e t i n c o m e dividedb y theb o o k valueofa sse ts a t year- end.Thisiscalculatedf r o m thec o n s o l i d a t e d auditedfinancialstatements.

(iii) Returnonequity(ROE):ismeasureda s n e t i n c o m e d i v i d e d b y thebookvalu eofequitya t y e a r - e n d T h i s iscalculatedf ro m theconsolidated auditedfinancialstatements.

(iv) Assetg r o w t h (ASGR):iscalculatedt h r o u g h thenaturall o g a r i t h m ofthe r a t i o betweenthetotalassetsattheendofthefinancialyearandtotalassetsatthebe ginningo f th e samef i n a n c i a l y e a r s Thisisc a l c u l a t e d f r o m thec o n s o l i d a t e d auditedfinancialstatements.

(v) Currentratio(CURR):isusedasaproxyforshort- termfinancialdistress.Itiscalculateda s currentassetsdividedb y c u r r e n t liabiliti esa t year-end,a n d m e a s u r e s theabilityofthefirmtomeetitsshort- termpaymentrequirements.Thisiscalculatedfromtheconsolidatedauditedfi nancialstatements.

(vi) Leverageratio(LEVR):isa measureofl o n g - t e r m financiald i s t r e s s Itisd e f i n e d astheratiooftotalliabilitiestototalassets atyear- end.Thisisc a l c u l a t e d fromtheconsolidatedauditedfinancialstatements.

(vii) Dividendyield(DY):isthevalueo f allcashdividendsp a i d toc o m m o n stockho ldersdividedb y them a r k e t valueo f t h e firma t y e a r - e n d ThisisderivedfromthedividendtimelineonHOSE.

(viii) Payoutratio(POR):isthevalueofallcashdividendspaiddividedbytotale a r n i n g s Thisratioiscalculatedforeachyearandisderivedfromthedividendtimelin eonHOSE.

(ix) FirmS i z e ( S I Z E ):isthebookvalueoft o t a l a s s e t s a t they e a r - e n d Inther e g r e s s i o n s , weconsiderthen a t u r a l l o g a r i t h m oft o t a l a s s e t s Thisisc a l c u l a t e d fromtheconsolidatedauditedfinancialstatements.

(x) FirmAg e ( A G E ):isthen u m b e r ofy e a r p l u s onee l a p s e d s i n c e they e a r ofth ecompany’sIPO.Werefertothisvariableasthefirm’slistingage.Weaddo n e y e a r toavoida g e s o f z e r o T h e n , naturallogarithmiscalculated.T h e variationofthetransformedvariableissmallerandleadstolessbiasedr e s u l t s Thisiscollectedfromthecompanyprofile.

(xi) Liquidity( T O V R ):Wee m p l o y thet r a d i n g t u r n o v e r ratetoproxyf o r liquidi tyofthef i r m ' s s h a r e s It isdefineda s thetotal valueofstockstradedo v e r aye ardividedbythemarketvalueofthefirmattheyear- end.Thisisaproxyofliquidityemployedbymanypapers(Brennanetal.,1998,Cho rdiaeta l , 2001,D a t a r e t a l , 1 9 9 8 , R o u w e n h o r s t , 1999).Thisiscolletedf r o m thetradingtimelineonHOSE.

Our dataset categorizes firms into six distinct industries: basic materials, consumer goods and services, foods and beverages, industrials, real estate and construction, and others Specifically, among the 110 selected companies, there are 10 firms in the basic materials sector, 16 in consumer goods and services, 22 in foods and beverages, 22 in industrials, 29 in real estate and construction, and 11 in other industries Table 3.1 illustrates the composition of these industries.

Foodsandbeve rages I Realestates, ndustrials Construction &Materials

ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR

Mean 0.09434 0.37820 0.12195 0.76762 0.09875 2.54244 0.01870 0.37352 11.7719 0.66382 6.92857 Median 0.07515 0.31913 0.10322 0.54224 0.04349 1.80817 0.01852 0.37493 11.7140 0.69897 6.97598 Maximum 0.45786 2.33766 0.52608 6.05918 0.59652 14.3222 0.05455 1.21852 12.9860 1.20412 8.00721 Minimum 0.00839 0.03161 0.01589 -0.43878 0.00000 0.18280 0.00000 0.00000 10.9475 0.00000 4.50687 Std.Dev 0.06477 0.30964 0.07176 0.96777 0.13148 2.22471 0.01252 0.24138 0.45587 0.26191 0.55289

Mean 0.05682 0.21896 0.08959 0.20019 0.10338 2.54223 0.06627 0.65840 11.8338 0.76125 7.25934 Median 0.05204 0.21002 0.08481 0.10875 0.03356 1.73025 0.06534 0.50103 11.7763 0.77815 7.21287 Maximum 0.35415 1.35799 0.38924 2.33587 0.61648 19.4826 0.18519 20.6897 13.0333 1.23045 8.54394 Minimum -0.44500 -1.92080 -0.36580 -0.51451 0.00000 0.11378 0.00000 -1.66667 11.0224 0.30103 6.19913 Std.Dev 0.10038 0.36279 0.10423 0.38097 0.15039 2.78868 0.04987 1.98459 0.46732 0.20632 0.45180

ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR

Mean 0.07056 0.34251 0.09658 0.46391 0.13284 2.19502 0.04095 0.40971 11.88837 0.75854 7.23738 Median 0.05549 0.22685 0.08044 0.26180 0.07964 1.72576 0.02747 0.40554 11.85752 0.77815 7.27413 Maximum 0.45278 3.23416 0.60901 6.05918 0.65476 12.68903 0.18519 3.57143 13.15574 1.25527 8.37340 Minimum -0.32924 -0.88891 -0.31476 -0.26962 0.00000 0.46412 0.00000 0.00000 11.05594 0.00000 5.81903 Std.Dev 0.08460 0.43744 0.09858 0.83158 0.16005 1.69627 0.04259 0.44540 0.43583 0.22103 0.44535

Table3.2showsdescriptionofstockprice volatilityinVietnam ineachyear,e a c h i n d u s t r y andingeneralasawhole.

Themeanvalueandstandarddeviationofpricevolatilityin2007are0.51and0.48r e s p e c t i v e l y T h e m e a n valueands t a n d a r d d e v i a t i o n ofpricevolatilityin2 0 0 8 isl a r g e s t a t 1.48a n d 0.54respectively.Thevalueso f p r i c e v o l a t i l i t y in2009a r e m i n i m i z e d at0.32andmaximizedat2.79.

Them e a n valueso f p ri ce volatilitya r e h i g h e s t infoodsa n d b e v e r a g e s industrya t 1.17andlowestinothersindustryat0.83.

Ingeneral,theresultsfromtable3.2indicatethatthemeanvalueofpricevolatilityis1.09 withastandarddeviationof0.66,whichmeansthatitremainshighlyvolatileduringtheinves tigationperiod.

Table3.3presentsadescriptionoffirmattributesoflistedfirmsinVietnam.A m o n g t h e i n d e p e n d e n t v a r i a b l e s , them e a n ofROAremains0 0 8 w i t h s t a n d a r d d e v i a t i o n o f 0.09,w h i c h i n d i c a t e verylittlevolatility.Ononeh a n d , theleverager a t i o s offirmsspreadfrom0to0.65intheinvestigationperiod.Ontheotherhand,t h e minimum andm a x i m u m valuesofcurrentratio are0.11and 19.5respectivel y.Th e dividendyieldh a s meanvalueof0 0 4 a n d s t a n d a r d deviationof0 0 4 , w h i c h i m p l y lessvolatility.Meanwhile,meanvalueandstandarddeviationofpayoutratios t a y at0.48and1.18respectively.

Table3.4andtable3.5illustratethedescriptivestatisticsforallindependentv a r i a b l e s i.e.firmattributes.Thesetablesrevealthatbehaviorofeachfirmattributevariesf romonyeartoanotheryear,fromoneindustrytoanotherindustry.

Developingempiricalresearchhypotheses

Thiss e c t i o n p r o p o s e s s e v e r a l empiricalh y p o t h e s e s w h i c h a r e c o n s i s t e n t w i t h theliterature.T h e s e h yp ot he se s a l s o a l l o w u s tomakec o m p a r i s o n s b e t w e e n thec h a r a c t e r i s t i c s ofstockpricevolatilityinVietnamandothermarketsinclu ding developedmarketsuchastheUnitedStatesandA u s t r a l i a asw e l l a s emergingm a r k e t suchasPakistanandBangladesh.

Therelationb e t w e e n dividenda n d e a r n i n g s f o l l o w s thatgreatert h e v o l a t i l i t y ofearningsofafirm,thelessisthelikelihoodofdividendyieldbeingchangedbythef i r m ’ s m a n a g e m e n t H e n c e returnona ss e t s isd i r e c t l y relatedtos h a r e pricevolatil ity.

Weincludea v a r i a b l e tos e e theg r o w t h ina s s e t s b e c a u s e itisquitep o s s i b l e t h a t a n y otherrelationbetweendividendpolicyandstockpricevolatilitycouldbeo c c u r r e d D i v i d e n d payoutpolicyc o u l d bei n v e r s e l y linkedtog r o w t h a n d i n v e s t m e n t opportunities Therefore,weaddAssetsGrowthasacontrolvariabletor e f l e c t firmgrowth.

The level of debt financing significantly impacts a firm's asset value, as highlighted by Hamada (1972) and Sharpe (1964) in their capital structure theories High-risk firms, characterized by substantial debt, must generate returns that align with investor expectations, which can lead to increased share price volatility Modigliani and Miller (1958) argue that in competitive capital markets, a firm's value is independent of its financial structure; however, imperfections such as transaction costs, taxes, and information asymmetry mean that capital structure does influence share prices Additionally, smaller firms, often lacking diversification, attract less interest from financial institutions and investors, resulting in their stocks being less analyzed and more illiquid This scenario contributes to heightened price volatility for small firm stocks.

H4:Stock price volatility isn e g a t i v e l y influenced bycurrentratio,witha l l ot herf a c t o r s remainingconstant

Witkowska(2005)inaworkingpaperemployscurrentratioasameasureforstockvola tility.Currentratiomeasurestheabilityofthefirmtomeetitsshort- termp a y m e n t requirement.Ifafirmhaslowercurrentratio,theprobabilitytoen gageinshort- termdistressishigherandthusitsriskislargerthanfirmwithhighercurrentr a t i o As aresult,stockpricevolatilityishigheraswell.

Accordingtol i t e r a t u r e r e v i e w , dividendyieldisthem o s t importantfactorsa f f e c t i n g stock pricevo la ti l i ty (B as ki n, 1989).Hearguesthatthereisa significantnegative relationshipbetweendividendyieldandstockpricevolatility.

H6:S t o c k p r i c e v o l a t i l i t y isn e g a t i v e l y i n f l u e n c e d byd i v i d e n d p a y o u t rat io,withallotherfactorsremainingconstant

Thishypothesisisderivedfromthehy po th es is ofAllenandRachim(1996)w h i c h i n d i c a t e s a significantnegativer e l a t i o n s h i p p r i c e v o l a t i l i t y a n d p a y o u t ratio.T h e dividendp a y o u t p o l i c y a l s o expectedtob e negativelyr e l a t e d toi n v e s t m e n t o p p o r t u n i t i e s

Stockpricevolatilityisn e g a t i v e l y influencedb y f i r m size,withallotherf a c t o r s remainingconstant

The size of a firm significantly impacts its asset valuation and stock performance Smaller stocks tend to yield higher average returns; however, larger firms are generally better diversified, leading to lower risk and more stable share prices Research by Benishay (1961) and Atiase (1985) indicates that as a firm's size increases, its share price volatility decreases Consequently, smaller firms often experience more unstable stock prices due to their lack of diversification Additionally, investors in smaller firms may react more irrationally to new events, influencing their investment decisions Thus, the size of a firm also plays a crucial role in determining its dividend policy.

Itiswidelyacceptedthatfirmwithlongerhistoryismoreexperiencedinrunningitsb u s i n e s s Therefore,its defaultpr ob ab il it y isl o w e r which leadstoloweroperationr i s k Stockpriceofsuchfirmwithlowerriskislessvolatileasaresult.Thereasonisthatsto ckpricemaybelessvolatileformaturecompanywhichappearstobelessr i s k y andmor estable.Therefore,weanticipatefirmagetohavenegativeimpactons t o c k pricevolatil ity.

Thec o n c e p t oft h e v o l u m e i m p a c t isbuiltonthef a c t thatpricesn e e d v o l u m e tom o v e , thus,thehighvolatilityofstockpricesmaybeproducedasaconsequenceofv o l u m e v o l a t i l i t y a n d t r a d i n g activity.P r e v i o u s r e s e a r c h e s a l s o implya significant r e l a t i o n s h i p betweentradingvolumeandstockprices(Songetal.,2005,S a a t c c i o g l u andStarks,1998,Jonesetal.,1994).

No Variables Description Formula Expectedrelationtostockpricevolatility

2 ASGR Assetsgrowthrate ln(Total assets (t) – total assets (t-1)

8 AGE Firmage ln(1+year (t) - year (IPO) ) -

Wechoosetoutilizepaneldatainagreementwiththeliteraturewhichrecommendsitast hemostappropriatemethodforthefocusofourstudy.Hsiao(2006)mentionss o m e ofth eadvantagesofpaneldatainstudyinghistoricalseriesofasetofc o m p a n i e s : ite s t i m a t e s modelp a r a m e t e r s a c c u r a t e l y ; ito f f e r s toolstocounteractm o d e l missp ecificationsandomittedvariables;lastbutnot least,iteasesc o m p u t a t i o n andinterpretationofresults.

Descriptivestatisticsandcorrelationmatrix

Firstly,weimplementa descriptives t a t i s t i c s analysistoexaminedifferences s t o c k pr icemovementso f firmsindi ff er en t i n d u s t r i e s , differenty e a r s a n d ingeneral.I n addi tion,overalld e s c r i p t i o n o f independentv a r i a b l e s a n d descriptionine a c h y e a r a n d e a c h industrya r e a l s o presented.T h e resultsa r e describedinC h a p t e r 3 T h e c o r r e l a t i o n matrixwhichfollowshelpsustoidentifywhetherthereisanyperfectorn e

Bivariateanalysis

Thisprovidesa c r u d e t e s t o f t h e s i n g l e r e l a t i o n s h i p b e t w e e n commons t o c k p r i c e volatilityandthetheorysuggestedfundamentalvariablesindividually,thusprovidin gthei m p a c t o f e a c h variableo n s t o c k pricec h a n g e ifn o otherfactorisc o n s i d e r e d

Thesinglelinearregressionmodelisspecifiedasfollows: y i,t  X i,t  i,t whereyi,tdenotesthes t o c k p r i c e volatilityo f f i r m i a t t i m e t;Xi,tisa vectort h a t r e p r e s e n t s thefirmcharacteristicvariableoffirmiattimet;andεi,tistheerrorterm.Ofwhich,Xr epresentsforeachsingleindependentvariableatonetime.Indetails,9 singleregre ssionmodelsareconductedasfollows. y i,t   (ROA) i,t    i,t y i,t   (ASGR) i,t    i,t y i,t   (LEVR) i,t    i,t y i,t    CURR i,t    i,t y i,t   (DY) i,t    i,t y i,t   (POR) i,t    i,t y i,t   (SIZE) i,t    i,t y i,t   (AGE) i,t    i,t y i,t   (TOVR) i,t    i,t

Multivariateanalysis

OrdinaryLeastSquare(OLS)regression

Thisist h e b a s i c s p e c i f i c a t i o n o f them o d e l a n d itd o e s n o t takeintoa c c o u n t t hesp eci a l structureo f t h e p a n e l data,w i t h t h e doublec r o s s - s e c t i o n a l andtimed i m e n s i o n Thesimplest methodistoassumethattheintercep tandallcoefficients a r e c o n s t a n t a c r o s s timea n d i n d i v i d u a l s Thisapproachign oresthecharacteristicso f paneldataandestimatescoefficientsbyOLSregression.

While some shortcomings of the data have been identified, it remains crucial to control for potential autocorrelation of the error terms At this point, we have two modeling options: random effects or fixed effects If the dependent variables are influenced by individual time-invariant characteristics that are uncorrelated, the fixed effects model is appropriate as it eliminates the impact of these characteristics and evaluates the net effect of the predictors Conversely, if the differences among individuals are random and their characteristics are correlated, the random effects model is more suitable Given the sensitivity of this issue, we will follow the standard procedure by estimating the fixed effects model and conducting a Hausman test to compare fixed versus random effects.

Fixedeffectsregression

Oneofcommonlyusedmethodsforpaneldataregressionistorelaxtheassumptiono f th ec o n s t a n t i n t e r c e p t a c r o s s cross- sectionalunitsw h i l e continuingtoholdthea s s u m p t i o n o f t h e c o n s t a n t coeffici entsfori n d e p e n d e n t v a r i a b l e s Thismethodisc a l l e d theFixedEffects Mode lbecausetheinterceptofeachcross- sectional unitisa s s u m e d tonotchange overtime.Thedifferences ofintercept acrossimaybeduetothespecificc h a r a c t e r i s t i c s o f c r o s s - s e c t i o n a l u n i t s T h e s i m p l e s t typeso f fixede f f e c t s modelsallowtheintercepti ntheregressionmodeltodiffercross- sectionallyb u t noto v e r t i m e , whilea l l o f thes l o p e e s t i m a t e s a r e fixedbothc r o s s - s e c t i o n a l l y a n d overtime.

Oneo f t h e a d v a n t a g e s inthefixede f f e c t s modelisthatitise a s y tou s e , butthism e t h o d iscostlyintermsofthedegreesoffreedomduetotheinclusionofnumerousdummyv ariablesinthemodel.Ifthenumberofcross- sectionalunitsisint h e t h o u s a n d s , estimationsu s i n g thefixede f f e c t s m o d e l m a y b e t i m e - c o n s u m i n g a n d e x c e e d thecapabilit ies ofan y computer( G r e e n e , 2002).I n ad dition,thefixede f f e c t s modelisnotproperformeasuringt h e effectoft i m e - i n v a r i a n t variables( G u j a r a t i , 2002).Thefixedeffectmodelincludesthedumm yvariableswhichcouldh a v e a l i n e a r r e l a t i o n s h i p w i t h thet i m e - i n v a r i a n t v a r i a b l e s , r e s u l t i n g inmulticollinearity.

Randomeffectregression

The error components model, often referred to as the random effects approach, posits that each entity has a distinct intercept term that remains constant over time This model assumes that the relationships between explanatory and explained variables are consistent both cross-sectionally and temporally Specifically, under the random effects model, the intercepts for each cross-sectional unit are derived from a common intercept, which is uniform across all units and time periods, along with a random variable that varies across entities but remains stable over time, reflecting the random deviation of each entity's intercept from the overall global intercept.

Theselectionofthepaneldataregressionmodeldependsonthecorrelationbetweena r a n d o m disturbance,u i ,a n d otherindependentv a r i a b l e s I fu iis correlatedw i t h otherin dependentv a r i a b l e s , t h e fixedeffectsmodelisappropriatefore s t i m a t i n g c o e f f i c i e n t s Therandomeffectsmodel,includingu ic o r r e l a t e d withotherr e g r e s s o r s , maycause a n inconsistency problemduetoth eo mi t te d variables( G r e e n e 2002) Ifthenumberofindependentvariablesissufficiently largeandthedataa r e r a n d o m l y d r a w n froma larges a m p l e , ther a n d o m e f f e c t s modelismoreappropriatethanth efixedeffectsmodel.

F-statistictest

ThisisaredundantfixedaffecttestusedtotestbetweenOLSregressionmodelandf i x e d e f f e c t m o d e l s Theresultsofthis t e s t helpustoidentify wh i c h modelisthem o s t s uitable.

Hausmantest

Thisisc o n d u c t e d tod e c i d e whether c h o o s i n g fixede f f e c t modelorr a n d o m ef fe ct m o d e l ismoreappropriate.Hausman(1978)proposesamethodtotestmodels p e c i f i c a t i o n b y c o m p a r i n g t w o s e t s o f e s t i m a t e s T h e b a s i c ideao f theH a u s m a n

HuynhThiBichThao 30 testistocomparee s t i m a t e s fromther a n d o m effectsmodelw i t h t h o s e fromthef i x e d effectsmodelunderthenullhypothesisthatbothmodels’estimatesarec o n s i s t e n t I f thed i f f e r e n c e b e t w e e n t h e t w o seto f e s t i m a t e s a r e large,then u l l h y p o t h e s i s isrejectedandtheconclusionisinfavorofthefixedeffectsmodel.

Thesep a n e l datat e c h n i q u e s a n d r e l a t e d testsa r e guidedbyB a l t a g i (200 8)a n d H s i a o (Hsiao,2006).

To ensure the validity of our results, we conducted several robustness checks in addition to the F-statistic test and Hausman test We hypothesize that the relationship between stock volatility and firm characteristics is influenced by broader industry patterns rather than individual firm differences To illustrate industry characteristics affecting stock volatility, we examined whether stock volatility varies across specific industries in HOSE by employing six dummy variables to represent different sectors For clarity, we categorized the industries into six groups: Basic Materials (DUM1), Consumer Goods and Services (DUM2), Foods and Beverages (DUM3), Industrials (DUM4), Real Estate, Construction & Materials (DUM5), and Others (DUM6) Furthermore, we conducted regressions using different years and industries to enhance our analysis.

Int h i s s e c t i o n , wep r e s e n t t h e empiricalfindingsa n d a n in- deptha n a l y s i s ofther e s u l t s Thiss e c t i o n beginsw i t h n e c e s s a r y st ep s fort e s t i n g m u l t i - c o l l i n e a r i t y includingc o r r e l a t i o n matrixa n d calculationofv a r i a n c e i n f l a t i o n f a c t o r

This section employs OLS regression, fixed effects, and random effects models for overall estimations, followed by an empirical test to justify the use of panel data analysis in this dissertation Initially, the F-statistics test will be presented to compare the fixed effects model with OLS regression Subsequently, the Hausman test (1978) will be utilized to evaluate the two panel data regression methods: the fixed effects model and the random effects model, accompanied by the empirical regression results Year-by-year regressions will be conducted for each industry, culminating in comprehensive analyses of the hypothesis tests and an assessment of the robustness of the results.

Correlationmatrix

Atthef i r s t glance,itc a n b e s e e n thatpricevolatilityp o s i t i v e l y c o r r e l a t e s w i t h l e v e r a g e , currentr a t i o , dividendyield,payoutratio,f i r m a g e a n d liquidity.H o w e v e r , pricevolatilityn e g a t i v e l y c o r r e l a t e s w i t h returno n a s s e t s , a s s e t g r o w t h a n d f i r m s i z e T h e lowi n t e r - c o r r e l a t i o n s a m o n g t h e e x p l a n a t o r y variablesu s e d int h e regressionsindicate noreasontosuspectserious.

Ita l s o c a n b e s e e n thatthec o r r e l a t i o n c o e f f i c i e n t s b e t w e e n e x p l a n a t o r y v a r i a b l e s arel o w e r than0 5 6 , s u g g e s t i n g thatt h e r e isn o multicollinearityp r o b l e m a m o n g t h e s e independentv a r i a b l e s A n o t h e r importantnoted r a w n fromt h i s correlationm a t r i x testisthatitfiguresoutlowc o r r e l a t e d relationshi pbetween dividendyielda n d payoutratioofonly0.19.Thisresultisnotsimilartomostof previousstudies.

PV ROA ROE EV ASGR LEVR CURR DY POR SIZE AGE TOVR

Tofurthertestwhethermulticollinearityexists,theVarianceInflationFactor(VIF )isemployed.TheresultsofthistestarepresentedinTable5.2.ThemeanofVIFis1 3 3

V I F fori n d i v i d u a l v a r i a b l e s arealsoverylow.Thisindicatesthattheexplanat oryvariablesusedinther e g r e s s i o n modelarenotsubstantiallycorrelatedwitheachoth er.

Bivariateanalysis

Table5 3 reportst h e testresultso f r e g r e s s i o n b e t w e e n s t o c k pricevolatility(PV)a n d eachoftheindependentvariables.Thisinvestigatesandgeneratessimpler e l a t i o n s h i p betweendependentvariableandeachofindependentvariables e pa ra te l y Theresultsofthisregressionshowsthatreturnsonassets(ROA),leverageratio(

L E V R ) , currentratio( C U R R ) , payoutratio( P O R ) a n d f i r m s i z e ( S I Z E ) a r e n o t s i g n i f i c a n t l y e x p l a i n i n g thes t o c k pricevolatility( P V ) T h e othe rfourvariablesaresignificantbutnotsubstantialenoughtoexplainalargeportionofpric ev a r i a t i o n e x c e p t ford i v i d e n d yield( D Y ) Similarlytor e s u l t s f r o m p r e v i o u s s t u d i e s (Baskin,1989,AllenandRachim,1996),thehighestthecoefficientofdividen dy i e l d iss t o o d a t h i g h e s t of4 6 4 a s c o m p a r e d tothec o e f f i c i e n t s o f otherv a r i a b l e s Contrarytothetheorieswhich arepresented inhypotheses,assetgrowth(

A S G R ) , dividendy i e l d (DY)a n d f i r m age(AGE)h av e s i g n s thata r e oppositetot h o s e p r e d i c t e d b y t h e o r i e s ins i m p l e r e g r e s s i o n t e s t A m o n g t h e seto f s i g n i f i c a n t v a r i a b l e s , onlyliquidity(TOVR)showsthesimilarsigntheoretically.

Variables Coef Prob Coef Prob Coef Prob Coef Prob Coef Prob Coef Prob Coef Prob Coef Prob Coef Prob

Note:ThedependentvariableisPV,*,**,***indicatessignificanceatthe10%,5%and1%respectively.

Multivariateanalysis

Overallregressionresults

Firstly,a n OrdinaryLeast Square (OLS) modelregressingstock pricevolatilityo nt h e nineindependentvariabless h o w s thatt h e e x p l a n a t o r y p o w e r o f themodeli s

0.30which meansthat30% ofthevariationinthedependent variable isexplainedb y t heusedsetofindependentvariables(Table5.4,model1A).

Next,duetothecommoncharacteristicsofpaneldata,paneldatamayhaveheterokadas ticityind a t a T h e s e effectsa r e e i t h e r fixede f f e c t o r r a n d o m e f f e c t A f i x e d ef fectm o d e l a s s u m e s differencesininterceptsacrossfirmso r timep e r i o d s , w h e r e a s arandomeffectmodelexploresdifferencesinerrorvariances Ao ne - wa y m o d e l includesonlyonesetofdummyvariables(e.g.,firmsortimes),whileat wo-waymodelconsiderstwosetsofdummyvariables(e.g.,firmsandyears).

Oursampleisb a l a n c e d p a n e l w i t h 3 3 0 o b s e r v a t i o n s T h e fixede f f e c t model sa r e e s t i m a t e d forc r o s s - s e c t i o n a n d p e r i o d e f f e c t s r e s p e c t i v e l y ( M o d e l 2 A a n d 3 A inTab l e 5.4)andtogether(Table5.4,model4A).

Thedatainthisstudyisquiteshortpaneldatawithonlythreeyearsdataisa v a i l a b l e To performbetweenestimatesitrequiresthatthenumberofcross- sectiontobegreaterthanthenumbersofcoefficientsinthemodel.Duetothisdatalimitatio n,onlyc r o s s - s e c t i o n r a n d o m e f f e c t m o d e l ise s t i m a t e d (Table5 4 , model5 A )

Model1A Model2A Model3A Model4A Model5A

Periodfixed(dummyvariables) Cross- sectionfixed(dummyvariables)Perio Cross- sectionran

Variable Coefficient Prob Coefficient Prob Coefficient Prob Coefficient Prob Coefficient Prob

% l e v e l inc r o s s - s e c t i o n f i x e d e f f e c t model.I t i m p l i e s thats t o c k pricevolatilityofl i s t e d fir msonHOSEisexplainedbyfirmperformanceandstockpricebehaviorisn e g a t i v e l y af fe ct ed by r e t u r n o f a ss e t s Thisfindingd o e s n o t supporth y p o t h e s i s 1 o f our study.However,thesignsofcoefficientsofreturnonassetsinothermodelsa r e positi vebutnotstatisticallysignificant.

Dividendyieldispositiveandstatisticallysignificantat1%levelinmodels1A,2Aa n d 5 A.Thisconfirmsthatstockpricebehaviorispositivelyaffectedbyfirmdividendy i e l d T hisfindingisi n c o n s i s t e n t w i t h thehypothesisofB a s k i n (1989)t h a t thereisa neg ativerelationshipb e t w e e n dividendyielda n d s t o c k p r i c e volatility.Thisfindingisa l s o inconsistentwiththeh y p o t h e s i s o f Irfan a nd N i s h a t (2003)whichreportsthatdivi dendyieldhasstrongnegativeassociation withstockpricevolatility.H o w e v e r , thi sr e s u l t supportst h e previouss t u d y ofA s g h a r e t a l ,

(2010)w h i c h s t a t e s thatpricevolatilitya n d dividendyieldhaves t r o n g p o s i t i v e correlation.Similartothefindingso f m o s t ofpr ev io us s t u d i e s , dividendyieldh a s t h e largesteffectonstockpricevolatilityamongthesetofvariables.

Firm size is positively correlated with stock price volatility at a significant level of 10% in Model 2A, but negatively impacts stock price volatility in Models 1A, 3A, and 5A This positive association aligns with findings by Baskin (1989) and Irfan and Nishat (2003), while the negative correlations support the hypothesis proposed by Allen and Rachim (1996) Additionally, our seventh hypothesis is corroborated by the negative coefficient of return on assets Theories suggest that small firms may experience greater stock price instability compared to larger firms due to their lower diversification However, these results may reflect the unique characteristics of developing markets like Pakistan and Vietnam.

Thec o e f f i c i e n t off i r m ageispositiveands t a t i s t i c a l l y significanta t 1 % levelin m o d e l s 2A a n d 5A an d at10% significance levelinmodel1A.Thisindicatest ha t t h e olderthef i r m i s , thelargeritss t o c k p r i c e volatilityi s Thisfindingcontrasts witht h e r e s u l t ofP ´ a s t o r a n d V e r o n e s i ( 2 0 0 3 ) w h i c h d e c l a r e s a n e g a t i v e r e l a t i o n b e t w e e n volatilitya n d f i r m a g e T h e r e f o r e , h y p o t h e s i s 8 isn o t supportedb y thisf i n d i n g s

Moreover,liquidityispositiveandsignificantat1%levelinallmodels.Ourresultissimil artothefindingo f G a l l a n t et.al.

(1992),findinga positiver e l a t i o n s h i p b e t w e e n stockvolatilityandvolume.Thi sfindingindicatesthatthelargerthet r a d i n g valueofstockis,themorethestockpricevo latiles.

Assetgrowthisfoundtoben e g a t i v e c o r r e l a t e d w i t h s t o c k pricev o l a t i l i t y b u t n o t significantinmostofmodels.Thisnegativeassociation isdifferentfromtheresulto f previousstudies(Baskin,1989,IrfanandNishat,2003).Bycontrast,thisresultiss i m i l a r toinvestigationof(Asgharetal.,2010,Naziretal.,2010).

Wealso dono t findanyevidencetosupporttheideaofAllenan d Rachim (1996)t h a t t h e r e isa significantnegativerelationshipb e t w e e n pricev o l a t i l i t y a n d p a y o u t r a t i o asthecoefficientsofpayoutratioarepositivebutnotsignificantinourr e g r e s s i o n r esults.I t m e a n s thats t o c k pricevolatilityisn o t e x p l a i n e d byp a y o u t r a t i o oft hefirm.

Aredundantfixede f f e c t t e s t isu s e d tot e s t b e t w e e n OLSmodela n d fixede f f e c t m o d e l s T h e hypothesist e s t i n g OLSregressionm o d e l a g a i n s t c r o s s - s e c t i o n a l h e t e r o g e n e i t y h a s F - s t a t i s t i c F(109,211)=1 7 2 (p- value:0 0 0 0 4 ) , sothenullh y p o t h e s i s o f n o c r o s s - s e c t i o n a l h e t e r o g e n e i t y isr e j e c t e d T h e hypothesistestingOLSa g a i n s t t emporalheterogeneityh a s F - s t a t i s t i c s F(2,318)i.99( p - v a l u e : 0.0000),sothenullhypothesisofnotime- dimension heterogeneityisa l s o rejected (Table5.5,section 1).Inaddition,theexpl anatorypower ofmodel2 isbetterthanm o d e l 3 A T h e s e t e s t r e s u l t s concludethat a c r o s s - s e c t i o n a l fixede f f e c t modelisp r e f e r r e d tothesimpleOLSmodel.

To determine the appropriate model between fixed effects and random effects, we conducted the Hausman specification test, which compares the estimators of both methods The null hypothesis of this test posits that the difference in coefficients estimated by the two methods is not systematic, allowing for the use of the random effects model However, with a p-value of less than 1%, we reject the null hypothesis, indicating that the random effects model is not suitable, and we prefer the fixed effects specification Consequently, our analysis is based on the results of the cross-section fixed effects model, although we also discuss findings from OLS, period fixed effects, and random

Cross-sectionF-statistic 1.721046 (109,211) 0.0004 Cross-sectionChi-square 209.908094 109 0.0000

Overallregressionwithindustrydummies

Inordertov a l i d a t e o u r r e g r e s s i o n r e s u l t s , industryd u m m i e s v a r i a b l e s a r e furtherconcludedtoe x a m i n e w h e t h e r s t o c k pricevolatilityf a v o r s a specifici n d u s t r y I n otherw o r d s , t h e s e testsverifyift h e r e isa n y differenceofs t o c k price behaviora m o n g classifiedindustries.

Table5 6 reportsther e g r e s s i o n r e s u l t s o f r e g r e s s i o n s w h e n weinclude d u m m y v a r i a b l e s tocontrol forindustry effect.Ov e r a l l , theR 2o f themodels w i t h industryd u m m y isaslightlyimprovedascomparedtotheR 2o f themodelswithoutin dustryd u m m y Itmeansthattheexplanatorypoweroftheestimationmodelwithind ustry

HuynhThiBichThao 40 dummyisb e t t e r t h a n thatofthee s t i m a t i o n modelw i t h o u t i n d u s t r y dummy.T h e r e s u l t s arethesameintermsofthenumberofsignificantvariablesandthedirecti onsofrelationship.Ofwhich,theresultsfromOLSandrandomeffectm o d e l s s h o w thatfoodsa n d beverages,industrialsandr e a l estates, construction& m a t e r i a l s s e c t o r s a r e positivelya s s o c i a t e d w i t h s t o c k p r i c e v o l a t i l i t y M e a n w h i l e , t h e findingsfromperiod- fixedeffectmodelindicate thatallthesixindustrieshavea f f e c t s onstockpricev olatility.

Model1A Model1B Model3A Model3B Model5A Model5B

OLSw/odummy OLSwithdummy Periodfixedw/ odummy Periodfixedwithd ummy Cross- sectionrandomw/ Cross- sectionrandomwithd Variable Coefficient Prob Coefficient Prob Coefficient Prob Coefficient Prob Coefficient Prob Coefficient Prob

Note:-ThedependentvariableisPV,*,**,***indicatessignificanceatthe10%,5%and1%respectively.

Regressionresultsineachyear

Thissub- sectionrepresentstheresultso f r e g r e s s i o n s w h e n w e runthemodelf o r e a c h yearfr om2007to2009.Thefindingsareasfollows.

Withoutindustrydummies Withindustrydummies Coefficient t-Statistic Prob Coefficient t-Statistic Prob

Note:ThedependentvariableisPV,*,**,* * * indicatessignificanceatthe10%,5%and1%respectively.

In2007,stockpricevolatilityissignificantlypositivelyaffectedbyreturnonassets,a s s e t growth,payoutratioandliquidity.Bycontrast,stock pricevolatilityissignificantlynegativ elyaffectedb y dividendyieldandfirmsize.Regardingt o themodelwithindustrydummi es,theresultsarealsothesamewiththeexceptionthats t o c k pricevolatilityfavorsothersi ndustry.

Coefficient t-Statistic Prob Coefficient t-Statistic Prob

1.78withsignificantlevelof1%.Thisisthereservedtrendasopposedtotheoriesandresults f r o m others i n g l e yearregression.Firms i z e andl i q u i d i t y respectivelymaintaintheir negativeandpositiverelationshipwithstockpricevolatility.Wealsocannotfindanyevid encetosupportthetheoreticalsignificantrelationshipbetweendividendyieldandstockpri cevolatilityin2008,whichissimilartothefindingsofA l l e n andRachim(1996).Howev er,unlikeAllenandRachim(1996),payoutratioi s alsofoundinsignificantinthisyear.Regar dingtothemodelwithindustrydummies,theresultsimplythats t o c k pricevolatilitydoesn o t favoranyspecificindustry.

Coefficient t-Statistic Prob Coefficient t-Statistic Prob

In 2009, stock price volatility was positively influenced by return on assets, asset growth, and liquidity, while negatively impacted by firm size, supporting Hypothesis 1 However, dividend yield did not show significant effects, contradicting the findings of Baskin (1989) regarding the relationship between dividend yield and stock price volatility When considering industry dummies, the results indicated that stock price volatility favored sectors such as basic materials, consumer goods and services, food and beverages, and real estate, construction, and materials Additionally, the regression model with industry dummies demonstrated an increase in explanatory power, rising from 0.25 to 0.39.

In summary, return on assets positively impacts stock price volatility, except for the year 2008 Asset growth rate also shows a significant positive correlation with stock price volatility, while results from 2008 are not significant Firm leverage and firm age coefficients are positive in 2007 and 2009 but negative in 2008, though firm leverage lacks statistical significance across all years Current ratios negatively affect stock price volatility but are not statistically significant Dividend yield has a negative impact on stock price volatility, supporting Baskin's (1989) hypothesis, although it shows an adverse correlation in 2008 The payout ratio positively influences stock price volatility in 2007 and 2008, while negatively correlating in 2009 Firm size coefficients are consistently negative and significant each year, aligning with most theories Additionally, liquidity coefficients are positive and significant annually, indicating that higher trading values of firm stocks lead to increased volatility Lastly, there is no evidence supporting a significant relationship between stock price volatility and firm age in any year.

Regressionresultsforeachindustry

2,whichexplains72%variationforvariationinthestockpricevolatility.TheR 2o f thismod elisratherhighincomparisonwithotherfiveindustries.Thereforetheoverallexplanatorypo werofthemodelishighestinbasicmaterialindustry.Thecoefficientoffirmleverageis2 4 9 whichi s significantat1 0 % levelof.Thevariablefirmsizeexhibitsnegativerelationshipwi thstockpricevolatility.Itimpliesthesmalleracompanyis,thebiggeritsstockpricevolatilit yis.ThisfindingissimilartothatofAllenandRachim( 1 9 9 6 ) Thecoefficientof liquidityis0.71whichissignificantat1%level.Thisimpliesvalidityofthemodelthroughtra dingvalueofstock.Thecoefficientofothervariablessuchasreturnona s s e t s , assetgr owth,dividendyieldandfirmagearepositivebutinsignificantatanylevelofconfidence. 5.2.4.2 Consumergoodsandservicesindustry

TheR 2o f 0.47i n d i c a t e s that4 7 % o f stockpricevolatilityisexplainedb y thevariabl esusedinthemodel.Thecoefficientsreturnonassets,firmleverage,dividendyieldandpayo utratioare2.97,3.54,5.19,0.56respectivelyandare significantat5%,5%,1%and5%respectively.Thesepositiverelationshipsimplythatthe betterperformancefirmachieves,thehigherleveragefirmuses,thehigherdividendyieldfir moffersandthelargertradingvolumeis,thebiggeritsstockpricevolatilityi s Animporta ntfindinginthismodelisthesignificantp o s i t i v e relationshipbetweenr e t u r n ona s s e t s ands t o c k pricevolatilityw h i c h i s disliket h o s e inotherindustries.

TheR 2is 0.58,explaining58%variationinthedependentvariablei.e.stockpricevolatility.Re turnonassetshasnegativeimpacto n s t o c k pricevolatilityw h i c h issignificantat1 0 % lev el.Dividendyieldv a r i a b l e remainsitsp o s i t i v e relationshipw i t h stockpricevolatilit yinfoodandbeverageindustrywhichissignificantat1% level.Thecoefficiento f dividendyield is5.71whichindicatest h a t dividendyieldexplai ns5.71%variationinthestockpricebehaviorofthefirms.Thecoefficientofliquidityis0.53 whichisalsosignificantat1%level.Thisimpliesthattradingvaluehaspositiverelationshipwi thstockpricevolatility.

TheR 2of 0.48indicatesthat4 8 % o f thestockpricebehaviorisexplainedbythevariablesused inthemodel.Thecoefficientsofdividendyieldandliquidityare8.06and0 5 3 respectivelyw h i c h area l l s i g n i f i c a n t at1 % level.Thisimpliesthatthehigherdividendyieldfirmsu ndertakeandthelargertradingvaluefirmstockis,thehighervolatilitytheirstockpricesare.H owever,thesignofcoefficientofreturnona s s e t s isnegativeat1.62whichissignificanta t10%level.

TheR 2o f themodelis35.7,w h i c h isthelowestasopposedtoR 2u s e d i n themodelsforotheri n d us t r i e s Thesignof coefficientoffirmleverageisnegativeinthismodel,whichisdiffer entfromthesignsinbasicmaterialsindustryandconsumergoodandserviceindustry.Divide ndyieldandliquiditycontinuesignalingpositiverelationshipwithstockpricevolatilitywit h10%and1%levelofsignificancerespectively.Conversely,payoutratiointhisindustryhas negativerelationshipwithstockpricevolatilitywhichimpliesthatthehigherdividendfirms payouttoinvestors,thelowerstockpricevolatilityis.Thisfindingissimilartothato f Baskin(1989)andAllenandRachim(1996).

TheR 2of themodelis0 6 9 w h i c h meansthat6 9 % o f s t o c k pricevolatilityisexplain edbyvariablesusedinthismodel.Thesignofcoefficientofreturnonassetsi s negativeat4.0 5whichissignificantat1%level.Firmleverageandpayoutratios h o w negativerelations hipwithstockpricevolatilitywhiledividendyieldandliquidityarep o s i t i v e l y correlate dwith stockpricevolatility.Animportantobservationisthatthecoefficientofdividendyieldis15.52 ,whichisquitehighincomparisontotheotherindustries.Thecoefficientsofreturnonas sets,firmleverage,dividendyieldandpayoutratioaresignificantat1 % levelw h e r e a s the coefficientofliquidityissignificantat10%levelofconfidence.

Insummary,dividendyieldhaspositiverelationshipw i t h s t o c k p r i c e volatilityina l l in dustriesexcludingbasicmaterialsindustrywithsignificantlevel atleast 10%howeverinsi gnificantatanylevelofconfidenceinb a s i c materialindustry.Thisindicatesthatthehigherdi videndyieldfirmsp u r s u e , thelargers t o c k price volatilitiesare.Liquidityi s alsopositivelycorrelatedwithstockp r i c e volatilityina l l ind ustrieswithsignificantlevelofatleast10%.

InOLSregressionmodels,theregressionresultsarealsothesamewhenwegraduallysubs tituter e t u r n o n a s s e t s byreturno n equityandearningvolatility.I n fixedeffectmodels,e arningvolatilitydoesn o t playsignificantr o l e whilea s s e t g r o w t h ratedoes.

OLSregression Model15(WithROA) Model16(WithROE) Model17(WithEV)

Variable Coefficient t-Statistic Prob Coefficient t-Statistic Prob Coefficient t-Statistic Prob

Cross-sectionfixedeffect Model18(WithROA) Model19(WithROE) Model20(WithEV)

Variable Coefficient t-Statistic Prob Coefficient t-Statistic Prob Coefficient t-Statistic Prob

Theoverallresearchandempiricalfindingsofpreviouschaptersaresummarizedinthissecti on.Contributionstotheliteraturearethendescribed,followed byad i s c u s s i o n oft helimitationsofthestudyandtheimplicationsforfutureresearch.

Reviewsoffindings

Stock price volatility in Vietnam is a compelling subject, focusing on how various firm attributes, such as return on assets, asset growth rate, firm leverage, current ratio, dividend yield, payout ratio, firm size, firm age, and liquidity, influence stock price behavior This study analyzes a sample of 110 listed companies on the Ho Chi Minh City Stock Exchange from 2007 to 2009 Employing various econometric techniques for panel data analysis, including OLS, fixed effects, and random effects estimations, the research aims to provide more accurate insights into long-term stock price dynamics in the Vietnamese market.

This study utilizes a comprehensive dataset on stock price volatility and firm attributes to analyze stock price behavior in Vietnamese firms It identifies the joint effects of multiple factors on long-term stock prices, revealing that stock price volatility is negatively influenced by returns on assets, as demonstrated through cross-section fixed and random effect models Notably, dividend yield exhibits the most significant impact on stock price volatility across all models, supporting previous research such as Baskin (1989) Additionally, firm size, firm age, and trading liquidity are found to positively affect stock price volatility at a significant level The research also highlights industry-specific effects, with sectors such as food and beverages, industrials, real estate, and construction materials positively influencing stock price volatility.

Contribution

Althoughthereareseverallimitationstothisdissertation,theempiricalfindingscanb e view edinthelighto f thecontributionstothetheoreticalmodels.Thiss t u d y contributestothelite raturebyprovingempiricalresultsonstockpricevolatilityinVietnams t o c k markettakin gintoaccountsfirmattributeimpacts.Regardingtomethodology,a variousmethodso f esti mationsareconsideredinthispaper.I n addition,industrydummyvariablesareaddedi n ther egressionmodelemployings e v e r a l industrydata.Furthermore,thisstudyemploysva riousrobustnesscheckstovalidatet h e r e g r e s s i o n resultswhicharen o t wellconducte dinpreviousstudiesrelatedtopaneldataanalysisofstockpricevolatility.

Limitationsandrecommendationsforfutureresearches

NX Theseshorteddatalimitsthegeneralizationofthefindings.Theempiricalfindin gswouldbefurtherenrichedifmoredatawereincludedi n theempiricaltest.

- Thiss t u d y appliesthe“topd o w n ” m e t h o d whichemploysthes a m e indepen dentvariablesetsfromthewholesampletoanyspecificindustry.

- Examinet h e relationshipb e t w e e n stockp r i c e volatilityandfirmattributesrela tivetogeneralmarketincludinglistedfirmso n HOSEandHNX,non- financialfirmsandfinancialfirmsandoveralengthierperiodoftime.

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AppendixA:RegressionresultsR eg res si o n resultsofModel1A

Variable Coefficient Std.Error t-Statistic Prob.

AdjustedR-squared 0.421572 S.D.dependentvar 0.663858S.E.ofregression 0.504894 Akaikeinfocriterion 1.745040Sumsquaredresid 53.78762 Schwarzcriterion 3.115016Loglikelihood -168.9316 Hannan-Quinncriter 2.291503F-statistic 3.032055 Durbin-Watsonstat 3.022667Prob(F-statistic) 0.000000

Cross- sectionfixed(dummyvariables)Periodfixed(du mmyvariables)

Method:PanelEGLS(Cross- sectionrandomeffects)Sample:20072009

AdjustedR-squared 0.293286 S.D.dependentvar 0.646027 S.E.ofregression 0.543091 Sumsquaredresid 94.38313 F-statistic 16.17055 Durbin-Watsonstat 1.986452 Prob(F-statistic) 0.000000

Method:PanelEGLS(Cross- sectionrandomeffects)Sample:20072009

AdjustedR-squared 0.295589 S.D.dependentvar 0.649667 S.E.ofregression 0.545260 Sumsquaredresid 93.65211 F-statistic 10.86122 Durbin-Watsonstat 2.019390 Prob(F-statistic) 0.000000

AdjustedR-squared 0.572824 S.D.dependentvar 0.427241S.E.ofregression 0.279239 Akaikeinfocriterion 0.531551Sumsquaredresid 1.793413 Schwarzcriterion 0.985038Loglikelihood 1.229412 Hannan-Quinncriter 0.684135F-statistic 5.767832 Durbin-Watsonstat 2.634010Prob(F-statistic) 0.000327

No CODE COMPANYNAME INDUSTRY LISTEDDATE

1 ABT BentreAquaproductImportandExportJointStockCompany Food&Beverages 25-Dec-06

2 ACL CuulongFishJointStockCompany Food&Beverages 5-Sep-07

3 AGF AngiangFisheriesImport&ExportJointStockCompany Food&Beverages 2-May-02

5 ANV NamvietCorporation Food&Beverages 7-Dec-07

6 BBC BibicaCorporation Food&Beverages 17-Dec-01

7 BHS BienhoaSugarJointStockCompany Food&Beverages 20-Dec-06

9 BMP BinhminhPlasticsJointStockCompany RealEstate,Construction&Materials 11-Jul-06

10 BT6 620ChauthoiConcreteCorporation RealEstate,Construction&Materials 18-Apr-02

11 CII HochiminhCityInfrastructureInvestmentJointStockCompany RealEstate,Construction&Materials 18-May-06

13 COM Materials-PetroleumJointStockCompany ConsumerGoodsandServices 7-Aug-06

14 CYC ChangYihCeramicJointStockCompany RealEstate,Construction&Materials 31-Jul-06

15 DCC DESCONConstructionCorporation RealEstate,Construction&Materials 12-Dec-07

16 DCT DongnaiRoofsheetandConstructionMaterialJointStockCompany RealEstate,Construction&Materials 10-Oct-06

17 DHA HoaanJointStockCompany RealEstate,Construction&Materials 14-Apr-04

19 DIC DICInvestmentandTradingJointStockCompany RealEstate,Construction&Materials 28-Dec-06

25 FMC SaotaFoodsJointStockCompany Food&Beverages 7-Dec-06

26 FPC FullPowerJointStockCompany RealEstate,Construction&Materials 25-Jul-06

28 GIL BinhthanhImport-ExportProductionandTradeJointStockCompany ConsumerGoodsandServices 2-Jan-02

33 HAS HanoiPost-TelecominicationConstructionandInstallationJointstockCompany RealEstate,Construction&Materials 19-Dec-02

35 HBC HoabinhConstructionandRealEstateCorporation RealEstate,Construction&Materials 27-Dec-06

36 HDC Baria–VungtauHouseDevelopmentJointStockCompany RealEstate,Construction&Materials 8-Oct-07

41 HT1 Hatien1CementJointStockCompany RealEstate,Construction&Materials 13-Nov-07

43 ICF InvestmentCommerceFisheriesCorporation Food&Beverages 18-Dec-07

44 IFS InterfoodShareholdingCompany Food&Beverages 17-Oct-06

46 ITA TantaoInvestment-IndustryCorporation RealEstate,Construction&Materials 15-Nov-06

47 KDC KinhdoCorporation Food&Beverages 12-Dec-05

48 KHA KhanhhoiImportExportJointStockCompany RealEstate,Construction&Materials 19-Aug-02

51 LAF LonganFoodProcessingExportJointStockCompany Food&Beverages 15-Dec-00

52 LBM LamdongBuildingMaterialsJointStockCompany RealEstate,Construction&Materials 20-Dec-06

55 MCV CavicoVietnamMiningandConstructionJointStockCompany RealEstate,Construction&Materials 11-Dec-06

57 MPC MinhphuSeafoodCorporation Food&Beverages 20-Dec-07

58 NAV NamvietJointStockCompany RealEstate,Construction&Materials 22-Dec-06

59 NKD NorthKinhdoFoodJointStockCompany Food&Beverages 15-Dec-04

60 NSC NationalSeedJointStockCompany Food&Beverages 21-Dec-06

61 NTL TuliemUrbanDevelopmentJointStockCompany RealEstate,Construction&Materials 21-Dec-07

72 RIC RoyalInternationalCorporation RealEstate,Construction&Materials 31-Jul-07

74 SAV SavimexCorporation RealEstate,Construction&Materials 9-May-02

75 SC5 ConstructionJointStockCompanyNo5 RealEstate,Construction&Materials 18-Oct-07

76 SCD ChuongduongBeveragesJointStockCompany Food&Beverages 25-Dec-06

SongdaUrban&IndustrialZoneInvestmentandDevelopmentJointStockComp any RealEstate,Construction&Materials 6-Jul-06

81 SMC SMCTradingInvestmentjointstockcompany RealEstate,Construction&Materials 30-Oct-06

82 SSC SouthernSeedCorporation Food&Beverages 1-Mar-05

84 TAC TuonganVegetableOilJointStockCompany Food&Beverages 26-Dec-06

86 TCR TaiceraEnterpriseCompany RealEstate,Construction&Materials 29-Dec-06

87 TDH ThuducHousingDevelopmentCorporation RealEstate,Construction&Materials 14-Dec-06

89 TNA ThiennamTrading-ImportExportJointStockCompany ConsumerGoodsandServices 20-Jul-05

93 TRI SaigonBeverageJointStockCompany Food&Beverages 28-Dec-01

94 TS4 SeafoodJointStockCompanyNo4 Food&Beverages 8-Aug-02

95 TSC Techno-AgriculturalSupplyingJointStockCompany Food&Beverages 4-Oct-07

98 UIC IdicoUrbanAndHouseDevelopmentJointStockcompany RealEstate,Construction&Materials 12-Nov-07

100 VHC VinhhoanCorporation Food&Beverages 24-Dec-07

101 VIC VincomJointStockCompany RealEstate,Construction&Materials 19-Sep-07

104 VIS Vietnam-ItalySteelJointStockCompany BasicMaterials 25-Dec-06

105 VNE VietNamElectricityConstructionJointStockCorporation RealEstate,Construction&Materials 9-Aug-07

106 VNM VietnamDairyProductsJointStockCompany Food&Beverages 19-Jan-06

108 VSH Vinhson-SonghinhHydropowerJointStockCompany Others 18-Jul-06

Tiêu đề	Empirical Investigation of Market Value Change in Vietnam Stock Market
Tác giả	Huỳnh Thị Bích Thảo
Người hướng dẫn	Assoc. Prof, Ph.D. Trần Huy Hoàng
Trường học	University of Economics
Chuyên ngành	Finance Banking
Thể loại	Master Thesis
Năm xuất bản	2011
Thành phố	Ho Chi Minh City

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