Determinations of Information Asymmetry Between Managers and Investor a Study on Panel Data

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Copyright © 2012 Younes Boujelbene and Lobna Besbes. This is an open access article distributed underthe Creative Commons ttribution License unported !.0" #hich permits unrestricted use" distribution" andreproduction in any medium" provided that original #or$ is properly cited. Contact author% YounesBoujelbene &'mail% younes.boujelbene()segs.rnu.tn

The Determinants of Information Asymmetry

between Managers and Investors: A Study on Panel Data

Younes Boujelbene1 and Lobna Besbes

2

1Economics, Unit of Research URDEE, FSEG, University of Sfax, TUNISIA

2Economics, Unit of Research UREA, FSEG, University of Sfax, TUNISIA

______________________________________________________________________________________________________________

Abstract

Than$s to the study o) 12* )irms listed on the +B, 2-0 inde" the researchers #ere able toidenti)y the determinants o) in)ormation asymmetry bet#een )irms/ managers and investors"using panel data over the period ranging )rom 1 to 200. The results o) the random e))ectsmodel sho# that most variables are not signi)icant. There is a problem o) heteroscedasticityand a problem o) autocorrelation. n this regard" the researchers have used the method o)generali3ed least s4uares to overcome these problems. The results obtained" by using thismethod" sho# that the trading volume" as #ell as the volatility o) stoc$ returns" has a positiveand signi)icant e))ect on in)ormation asymmetry. The coe))icient o) the insiders/ tradingvariable is high #hat sho#s the relevance o) this variable in the eplanation o) the in)ormationasymmetry problem. 5o#ever" the stoc$ price variable has a negative e))ect. These results areconclusive to corroborate the thesis o) variables relevance resulting )rom the theory o) mar$etsmicrostructure.

Keywords: n)ormation asymmetry" mar$et microstructure theory" volatility o) stoc$ returns"insiders6 trading.77777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777

Introduction

8ecently" the )inancial mar$ets have#itnessed a rapid evolution bothinstitutionally as #ell as in terms o) theiractivities. ndeed" several re)orms havebeen introduced to meet the ne#re4uirements o) a rapidly'changingmodern #orld tending to be moresophisticated due to technologicalprogress. n )act" globali3ation has madethe #orld mar$ets accessible to allinvestors" #orld#ide. This has generated agreater )inancial activity and a more largetrading volume.

n )act" the literature pertaining to mar$etmicrostructure provides a goodeplanation o) this notion o) in)ormation

asymmetry. t ta$es into account )inancialmar$et )rictions li$e taes" transactioncosts and )ees in order to analy3e therelationship bet#een the echangemechanisms and the price" setting process.These )rictions have led to a bid'as$ spreadthat has become the central theme o) themicrostructure theory. 9oreover"microstructure evo$es a deep study o) theprocess through #hich the investors/ latentdemands are translated in terms o) priceand volumes :9adhavan" 2000;.

+everal authors have emphasi3ed the )actthat asymmetric in)ormation is a))ected byseveral )actors% the trading volume:Bharath et al." 200;" the volatility o) stoc$returns :


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,orde and 8ydges" 200=;. long #ith these)actors" the asymmetric in)ormation seemsto be a))ected by the transactionsprobability o) the in)ormed as advanced by

&asley et al." :1=;" the analysts/ )orecasts:et" tostudy the principal cause o) the in)ormation

asymmetry problem and the adoptedsolution. Then" #e #ill determine thein)ormation asymmetry )actors. ,inally" totreat all aspects o) our econometric model%estimation" results and interpretations.

The Theory of Maret Microstructure

t is #orth noting that on the academiclevel" the development o) technology andcommunication media has brought an evengreater emphasis on the development o) aresearch )ield o) economics that is largelyuntapped" namely" the microstructuretheory o) the )inancial mar$ets. s a mattero) )act" this theory highlights the impact o)mar$et )rictions on price )ormation:9adhavan" 2000;. The literature o) themar$et microstructure provides analternative to the ?alrasian modelspertaining to the transactions6 behavior.The assumed per)ect competition as #ell as)ree trade. These models aim at all theaspects o) the securities6 transactionprocess. The earlier literature has )ocused

on the operations carried out by agents$no#n as mar$et ma$ers1 #ho arepro)essional agents buying and sellingsecurities upon re4uest. 9icrostructure

1 9ar$et ma$ers and )inancial intermediaries aredistinct. ,inancial intermediaries such as ban$strans)orm and repac$age their assets by buying andselling their liabilities di))erently )rom the mar$etma$er #ho buys and sells the same stoc$. The)inancial intermediary generally has short or longpositions on various stoc$s.

involves the integration o) in)ormation thatdepends primarily on the si3e o) declaredprices #hich includes transaction costs"orders and volumes" as #ell as on the

mar$et structure" that is to say its li4uidityand its echange mechanisms and )inallyon the agents6 intervention strategies.5ence" the e))iciency seems to be closelyassociated to the mar$et nature along #iththe competition o) the intervening parties.

,ollo#ing the literature on microstructure"Bharath et al." :200; have emphasi3ed the)act that transaction activities could o)tenbe measured by the daily volume o)echange. Yet" the latter6s application mightconceal some essential aspects as it does

not provide eact in)ormation about theechange type :i.e. #hether it is a purchaseor sale; seeing that each o) these cases hasits speci)ic implications on prices andli4uidity. ctually" the distinction bet#eenthose transactions initiated as purchasesand those initiated as sales can possibly beimplemented than$s to the recentdevelopment o) automated 4uotationsystems #hich allo# the delivery o) time'stamped databases. s a matter o) )act"mar$et microstructure literature has beencentred around the study o) the bid'as$spread as depicted by the relevant t#o#ell'$no#n types o) theories" namely" theinventory theories as #ell as theasymmetry ones.

8esearch on )inancial mar$etmicrostructure has largely contributed tothe increase o) its e))ectiveness by trying toeplain the lin$s that might eist bet#eenthese mar$et organi3ational systems" theinvestors6 characteristics and their order'placing strategies :+anvi and Caroline"

200;. n addition" it allo#s the study o) anadverse selection bet#een )irms6 managersand the investors. n )act" the in)ormationasymmetry prevails in the case #here acertain group holds in)ormation and theydo not transmit it to another group. Thepresence o) investors holding privatein)ormation :insiders; engenders anadverse selection problem to the mar$etmar$er since it changes his behavior anda))ects the setting up o) the bid'as$ spread.


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The Information Asymmetry between

Managers and Investors: !ause and

Solution

9yers :1*; argues that #hen managersare better in)ormed about investmentopportunities than the investors" themar$et is li$ely to penali3e the issuance o)such securities as e4uities" #hoseassessment is crucially related to theassessment o) such opportunities.There)ore" companies should resort toissuing stoc$s only as an ultimate step to beta$en" a)ter having undergone orimplemented some cheaper alternatives:internal cash" ban$ debt etc.;. n other#ords" the in)ormation pertaining to the

)irm management and concerning the)irm/s prospects as #ell as the value o) itsris$y securities vis'a'vis the mar$et" shouldlead to a )inancing hierarchy )avouring andgiving more priority to internal )inancingrather than to eternal )unding" along #itha pre)erence )or debts to e4uities :orproper capitals;.

n this respect" in)ormation asymmetry canbe reduced by means o) 9yers6s :1*;hierarchical )unding i.e. cash )lo#" debt andstoc$ issuance. Cash )lo# operations allo#

the strengthening o) the )inancial structureto support a business development. t mayalso resort to some speciali3ed cash'providing agencies such as ban$s" and thusgetting indebted to them. ctually" cash)lo# re)ers to the company6s )inancial statebe it stable or )ragile. Bernan$e and @ertler:10; sho# that a )ragile situationencourages the company to invest in ris$yprojects and increases the debt'relatedagency costs :the asset substitutionproblem;. 5o#ever" Charreau :1-;highlights the )act that applying sel)')inancing helps avoid agency costs.

The debt comes in a second placeA it playsan important role in reducing thein)ormation asymmetry bet#een themanagers and the investors. Li$e#ise" itcan increase the e4uity returns o) ,renchcompanies than$s to the leverage e))ectphenomenon. Besides" it becomes a meanso) supervising the manager6s investmentpolicy through the regular payment o)interest and the repayment it implies.

Lastly comes the share issuance. Thecompany ma$es use o) e4uity )inancing bymeans o) share issuing driven by itsmotivation to reinvest the pro)it earnings.

Thus" the hierarchy developed by 9yers:1*; loo$s as )ollo#s% internal )unds"debt and e4uity issues. This hierarchy playsan important role in reducing the ris$ o)being caught in situations o) under'investment and share issuance at muchreduced prices. +imilarly" it may limit thedistribution o) dividends and reduce capitalcosts by limiting the recourse to borro#ing.

The Determinants of Asymmetric

Information

The Main Determinants of AsymmetricInformation

n this part" #e )ocus on studying the)actors a))ecting the asymmetry o)in)ormation bet#een the managers and theinvestors resulting )rom ris$y securities"namely% the trading volume" the volatility o)stoc$ returns" the insiders6 trading and thestoc$ price.

• Trading #olume

The interplay o) supply and demand allo#sto determine the transaction price o) eachstoc$ security. n )act" securities are traded)or cash" as buyers must have availablemoney and sellers must have stoc$s.ndeed" the outcome" i. e the payment anddelivery o) securities" ta$es placeimmediately a)ter negotiation. Chae :200-;points out that the trading volume isclosely lin$ed #ith various measures o)asymmetric in)ormation" and this volumedecreases #hen the earnings are

announced. dditionally" Bharath et al.":200; sho# that the inverse o) the average

daily trading volume :1−

it T ; positively

in)luences the asymmetry o) in)ormation.

H1: There Is a Positive Relationship

beteen the Inverse of the Average Daily

Tra!ing "olume an! the Information

Asymmetry#


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H): There Is a *egative Relationship

beteen the Share Price an! the

Asymmetric Information#

+ther Determinants of the AsymmetricInformation

n this part" the objective is to analy3e thee))ects o) the trading probability o) thein)ormed" the analysts/ )orecasts" the returnautocorrelation and the )irm si3e on thein)ormation asymmetry eisting bet#eenthe managers and the investors.

• The Trading Probability of the

Informed

n)ormed traders echange stoc$s on thebasis o) their private signal by sei3ing moreaggressive orders to ta$e advantage o) thenon'integration o) in)ormation in prices.The in)ormed agents purchase as soon asthe stoc$ is undervalued and sell #hen it isovervalued. Caball and


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Businesses #illing to ma$e projectedpro)its are )aced #ith t#o constraints. Gnthe one hand" auditors must epress theiropinion on the validity o) the managers6

selected assumptions be)ore thepublication o) such in)ormation. Gn theother hand" in case o) signi)icant error"companies are re4uired to justi)y andaccount )or the discrepancies in the)inancial statements.

• $eturn Autocorrelation

Clar$e and +hastri :2001; identi)y theinteraction bet#een the trading volumeand the stoc$ returns by means o) H2it " usedas a robust proy )or in)ormation

asymmetry. 9ore speci)ically" and inconsistence #ith Liorente et al." :2002;"this coe))icient needs to be positive andsigni)icant )or the stoc$s around #hichspeculation is important. t sho#s theimportance o) speculation #hile protectingthe stoc$ returns. The empirical #or$carried by Bharath et al." :200;"concerning some I.+. e4uities over theperiod 1!'2002" have come to theconclusion that the autocorrelation o)returns positively in)luences the

asymmetric in)ormation bet#een themanagers and the investors. Thiscoe))icient is calculated as )ollo#s%

)()()()()1(210 k k r k V C k r C C k R it it it it it it it it η +++=+

?ith V it !,"% Log o) trading volume o) theday $ )or the year tA

)(k r it % +toc$ returns o) the day $ )or the

year tA

)(k it η % 8esidual error term" #ith i J 1KT" tJ 1KT and $ J 1KT.

• (irm Si)e

symmetric in)ormation can also bemeasured by the )irm si3e. ndeed" Chae:200-; )ound that small )irms have higherin)ormation asymmetry than large )irms.Eemset3 :1=; notes that small )irms havehigh amounts o) internal in)ormation and#ide bid'as$ spreads due to the lo#number o) insiders. +ome researchers

measure the )irm si3e by the logarithm o)the mar$et capitali3ation o) the )irm"calculated at the end o) each trading dayand reduced to an average :8andi" 200*A

Comerton',orde and 8udge" 200=;. Gthersmeasure the )irm si3e by the logarithm o)the mar$et capitali3ation at the end o)Eecember each year :Chae" 200-A La)ond etal." 200;.

Selected Sam*le" #ariables

Measurement" Model Presentation"

$esults and Inter*retations

%electe! %ample, "ariables Measurement

an! Mo!el Presentation

Gur empirical study covers a sampleconsisting o) 12* ,rench )irms listed on the+B, 2-0 inde over the period ranging)rom 1 to 200+ ?e have obtained thedata )rom the data base Eatastream and)inancial reports" seen in the sites&uronet. The data have allo#ed us toproperly calculate and speci)y our model6svariables so as to eplain the asymmetry o)in)ormation lying bet#een managers andinvestors.

Gur model is #ritten" )or each stoc$ i :i J 1... ... .... T;" as )ollo#s%

it it it it it it it it it it PRICE IT VRT ASY ε β β β β α +++++= −

432

1

1

?ith A- it % The in)ormation asymmetry ismeasured by 8oll :1*;/s e))ective bid'as$spread!" #hich is calculated on daily basisthen brought bac$ on average during theyear t %

R- it

)])1(),(cov()1(200))1(),(cov(200[ −−−−− k r k r I k r k r I moy it it it it it it

?here cov :rit :$;" rit :$'1;; is thecovariance o) daily stoc$ returns6 duringthe year tA it J 1 i) Mcov :rit :$;" rit :$'1;;N O0and it J 0 other#ise.

1−

it T % The inverse o) daily trading volume"

calculated on average during the year tA

! The in)ormation asymmetry is measured by 8oll:1*;/s model :8+it ; )or any stoc$ i over day $ in year

t.


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VRit % The standard deviation o) the dailystoc$ returns" calculated on average duringyear tA

it IT % The ratio o) the sum o) purchases andsales o) insiders* and the trading volume"calculated on average during the year tA

D8C&it % Logarithm o) the average dailyclosing share price during year tA

it % &rror corresponding to the e4uation"#ith i J 1KT and t J 1KTA

1 it # 1 2it # 1 'it # 1 it % ?eight o) each eogenousvariable on the endogenous variable +YA

3 it % The model constant.

Results an! Interpretations

Be)ore testing the signi)icance o) thevariables" #e rec$on it necessary to applythe 5ausman test to estimate thecorrelation bet#een individual e))ects andeplanatory variables. This speci)icationtest )ollo#s Chi's4uare la# #ith


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Table -: $esults of the $andom ,ffect Model

AS. !oef Mean Std ,rr t Prob / 012 !onf Interval 3

T4- .!*-2= 1.**!- .!2==* 0.*1 0.=22 '1.!=-*2 2.1-=*2

#$ .021-- .!*22! .1!=-2 0.=* 0.-** '.0!2*10 .0=21*!

IT .=**-2- .1=!*2 .0=20! .2 0.000 .-0*- .*=1

P$I!, '.0-=-*

.!112! .200=2*1 '0.!1 0.0* '.*0!211* .!0*-21

!ons .0=!-*-- .01** .0*2-=!= 2.0 0.000 .01!2--= .12!=-=

?hile re)erring to the estimation results"the in)ormation asymmetry increases thetrading volume. The inverse o) the tradingvolume" the volatility o) stoc$ returns andthe stoc$ price are not signi)icant. ,rom

this result" the hypothesis 4# 42 and 4 could not be assessed. The variableinsiders6 trading presents a positive andsigni)icant sign o) 1F )or the randome))ects model. The results )ound related tothis variable support the conclusions o)


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Table &: 5eteroscedasticity Test

AS. !oef Std ,rr t Prob / 012 !onf Interval 3

T4- .0!-2 . 0010!*2 !2.! 0.000 .*01-=!! .*0-2!!=

#$ .01*22=! .00001*2 *1.12 0.000 .01!-=0 .01!-2

IT .*012 .0002- !.-- 0.000 .!1-22 .!-0==

P$I!, '.0=2-*1 .00021!- '!2.=* 0.000 '.02-=!1 '.0=2=-*

!ons .1=20--! .000!12- -21.2! 0.000 .10112* .1122!=

, :*"12!-; J 1*.1* A Drob Q , J 0.0000

t is also essential to sho# the presence o) aproblem o) autocorrelation through thecalculated , statistic.

Table 6: Autocorrelation Test

To correct these t#o problems" #e haveopted )or the @enerali3ed Least s4uaretechni4ue to overcome the eistence o)heteroscedasticity problem as #ell as the

problem o) inter'individual autocorrelationo) a )irst order autoregression type.

• $esults of ,stimates by the Method of

7east S8uares 9enerali)ed and Their

Inter*retations

Table : $egression $esults via 97S

AS. !oef Std ,rr t Prob / 012 !onf Interval 3

T4- .=020- .0=2-*1 =.=2 0.000 .*--!=1 .1*2=-

#$ .1=011 .0*-2 1. 0.0-2 '.002-* .!!20*2

IT .-*2!!= .2*--1! !.22 0.00- .!12-=!! 1.-==0!P$I!, '.*-*2-22 .00-2!= '2.0 0.02 '.*!01- '.*-!=2-1

!ons .01-2-= .011!2- 1.! 0.1=2 '.00=2- .0!-2*

?ald chi2:*; J =. A Drob Q chi2 J 0.0=*!

The results obtained using the @L+ sho#changes in the degree o) signi)icance o)variables ecept that o) the insiders6trading o) #hich remains signi)icant at a1F level. The per)ormed regression sho#s

that the variable coe))icient : 1−it T ; is

positive" though signi)icant. This resultdi))ers )rom the conclusions reached byChae :200-; #hich demonstrate a negativeand signi)icant coe))icient at a -F level.But" it is consistent #ith our hypothesis 4'#hich states that the inverse o) the average

daily trading volume positively a))ects the

u !oef Std ,rr t Prob / 012 !onf Interval 3

7- '1.00021- .!-*2-- '2.*2 0.010 '1.!-= '.2*!2-=

7& 2.00-* .1=-22! 11.* 0.000 1.=- 2.!*-2

76 .-11! .02=-! =. 0.000 .*11- .=-21

7 .0!!-= .!-2-1 0.0 0.1* '.=-01*1 .==20=-

!ons .0!=2 .002-- -.0* 0.000 .0221*- .0-!

Drob Q , J 0.0000


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in)ormation asymmetry bet#een managersand investors.

The stoc$ returns volatility variable sho#s

a positive and signi)icant sign o) 10F )or@L+. The stoc$ returns volatility is)re4uently used by the investors li$e ameasuring instrument o) the selectionadverse problem. The principal reason o)the reali3ation o) positive returns is theinsiders6 privileged access to in)ormation.That enables them to $no# the moment#hen the shares are undervalued. Theobtained results pertaining to this variableconsolidate the conclusions reached by Ranet al." :2001; as opposed to those o) Chungand Li :200!;.

Through the use o) @enerali3ed Least+4uare techni4ue" the insiders/ tradingvariable remains signi)icant. ts very highcoe))icient indicates the importance o) thisvariable in the eplanation o) thein)ormation asymmetry. This result isconsistent #ith that o) Sabbour et al.":2000; #ho )ind that the insiders/ trading"on the Canadian mar$et" increase thein)ormation asymmetry.

The stoc$ price has a negative e))ect on thein)ormation asymmetry. This con)irms thehypothesis 5* #hich supposes thepresence o) a negative relationshipbet#een the share price and thein)ormation asymmetry. The )ound resultcon)irms the one )ound by Comerton',ordeand 8ydge :200=; but it contradicts theresult )ound by ttig et al." :200=;.The highcoe))icient o) the stoc$ price sho#s themajor role o) this variable in theeplanation o) the in)ormation asymmetry.

!onclusion

Gur study eamines the determinants o)in)ormation asymmetry bet#een managersand investors #hich are trading volume"stoc$ returns volatility" insiders6 tradingand stoc$ price. The results" )ollo#ing theestimation o) our basic model" sho# thatthe increased trading volume leads to anincrease in in)ormation asymmetry. Thestoc$ returns volatility is a relevant )actorin the eplanation o) the adverse selectionproblem. The coe))icient o) the insiders6

trading variable is high #hat sho#s theimportance o) this variable in theeplanation o) the in)ormation asymmetryproblem in relation to other variables. The

stoc$ price is an indicator o) in)ormation#hich sho#s the inverse relationshipbet#een this variable and adverseselection problem.

$eferences

ttig" >." ,ong" ?.' 9." @adhoum" Y. Lang"L. 5. D. :200=;. U&))ects o) Large+hareholding on n)ormation symmetryand +toc$ Li4uidity"V 5ournal o6 Ban,ingan7 8inance# !0" 2-'22.

Bernan$e" B. @ertler" 9. :10;.U,inancial ,ragility and &conomicDer)ormance"V 9he uarterl; 5ournal o6


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Comerton',orde" C. 8ydge" S. :200=;.UEirector 5olding" +harehoderConcentration and lli4uidity"V ?or$ingDaper" niversit; o6 -;7ne; -C 200=.

Cushing" E. 9adhavan" . :2000;. U+toc$8eturns and Trading at the Close"V 5ournalo6 8inancial Mar,ets# &lsevier" !" *-'=.

Eemset3" 5. :1=;. UCorporate Control"nsider Trading and 8ates o) 8eturn"V

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Determinations of Information Asymmetry Between Managers and Investor a Study on Panel Data