Citation
Dividend initiation and differential information

Material Information

Title:
Dividend initiation and differential information an empirical investigation
Creator:
Wong, Thian Soon, 1958-
Publication Date:
Language:
English
Physical Description:
vi, 87 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Data transmission ( jstor )
Dividends ( jstor )
Information content ( jstor )
Interest rates ( jstor )
Investors ( jstor )
Net income ( jstor )
Prices ( jstor )
Quartiles ( jstor )
Statistical discrepancies ( jstor )
Statistics ( jstor )
Dissertations, Academic -- Finance, Insurance, and Real Estate -- UF
Dividends ( lcsh )
Finance, Insurance, and Real Estate thesis Ph. D
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1991.
Bibliography:
Includes bibliographical references (leaves 83-86).
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Thian Soon Wong.

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Resource Identifier:
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25622470 ( OCLC )

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DIVIDEND INITIATION AND
AN EMPIRICAL


DIFFERENTIAL INFORMATION: INVESTIGATION


BY


THIAN SOON WONG


















A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY




UNIVERSITY OF FLORIDA


1991















ACKNOWLEDGEMENTS


I gratefully


acknowledge


the encouragement,


patience


guidance committee


of


Professor


chairman.


I also


Robert


thank


Radcliffe, Professors


my


dissertation


Michael


Ryngaert,


Anand


Desai,


and Sanford


Berg


whose


help


and guidance


greatly


improved


this


dissertation.


I owe a special


debt


to C. Sloan Swindle


without


whose


support,


been


insight and


completed.


encouragement this


Finally,


I dedicate


this


study study


would


to


not have


my parents:


Grace


Foo Kai Chi and Wong


Cheng


Boon.


ii


and















TABLE OF CONTENTS


ACKNOWLEDGEMENTS ABSTRACT . CHAPTERS

1 INTRODUCT 2 INFORMATI

Prior Emp Causes fo

3 DATA AND

Data . .
Tests on

4 TESTS ON

The Overa


0 0 0 � 0 * 0 S �


ION . . . . .. ON AND DIVIDEND irical Work . . r Returns Volat METHODOLOGY . .


0 00 0
Quarterly QUARTERLY 11 Sample


0 0 500
Earnings EARNINGS


INITIATION i t - *0 as0 ility Decreases


0 0 * 0 0 0 0


page

ii iv



1 5


0 0 0 0 0


0 0 0 * S


0 O


. 0 0 0 S * 0 0 0 0 0 0


ANNOUNCEMENTS


The Sample Grouped by Informatio
The Market Reaction to Earnings

5 RETURNS VOLATILITY AROUND DIVIDE

Variance Changes in the Overall
Variance Ratio Tests . . . * The Timing of Variance Changes
The Effect of Clustered Data

6 SUMMARY AND CONCLUSIONS . . . REFERENCESE..... ... .. ���

BIOGRAPHICAL SKETCH . . . . . . .


0 0S 0


6
11

15 15 18

35 35 40 51 57 58
61 65 71 80 83

87


S 0 0 0 * 0 0
n Availability Changes . . .

ND INITIATION

Sample.....
* 0 � � . . S �


o . 0 5 5 ~ 5 0 O 5 0 5 0 S S S

0 5 0 0 9 ~ ~


iii















Abstract
the Un-


of Dissertation Pre iversitv of Florida


Requirements


DIVIDEND


asented to t in Partial


for the Degree


INITIATION


:11


of Doct


AND DIFFERENTIAL


'ie Graduate School Fulfillment of the
.or of Philosophy


INFORMATION:


AN


EMPIRICAL


INVESTIGATION


By


Thian


Soon


Wong


December


Chairman:


Major


Robert


Department:


Radcliffe Finance,


Insurance,


and Real


Estate


The information


transmission


hypothesis


states that


firms


begin


paying


to investors.

is partially


dividends


Evidence


as a means


that


substitutable


of transmitting


and earnings


dividend


has been


used


information information


to support


this


hypothesis.


This


is not


an appropriate


test.


Instead,


testable hypothesis proportion


implication


is that


of


of


dividend


publicly


the


information


initiation


disclosed


to


should


privately


transmission


increase


the


acquired


information.


The informativeness


of quarterly


earnings


announcements


before


and after


dividend


initiation


is examined using


returns


and standardized


variance


measures.


Under


the information


transmission hypothesis,


the standardized variance


of earnings


and dividend


announcements,


should


increase


after


dividend


iv


of


1991


m wo









initiation.


available


I find


Further,


information


that


while


firms should


returns


the least


experience


measures


the greatest


decrease


after


change. dividend


initiation,


the


low


standardized information


variance


measures do


availability


have


not.


Firms


larger


with


price


revaluations.


This


relationship


is not found with standardized


variance.


There


appears


to be


no evidence


that


the proportion


of publicly initiation.


disclosed


information


increases


after


dividend


The observed


decrease


in returns measures


of information


content


may be explained


by


a reduction


in returns


volatility


that


coincides


with


dividend


initiation.


Tests


are conducted


to detect initiation an effect


reducing


if


an information


is causing


could


bid-ask


effect


the returns


cause


spreads


returns


or noise


associated


with


dividend


volatility to decrease.


volatility


trading.


to decline


Variance


Such


by


ratio


tests


fail


to detect


any


such


decrease


following


dividend


initiation.


Analysis


of the timing


of variance


changes


reveals


that


dividend decreases


are equally


likely before


as after


dividend


initiation.


Therefore,


decreases


in returns


volatility


do not


systematically the contention


occur


that


after


dividend


information


initiation.


conveyed


by


This


weakens


the dividend


initiation


has caused


the volatility


decrease.


The observed


volatility


decrease


appears to be


caused


initiating dividends


by


which


amount


of


nave


firms


in the


proportion


of sample


large








period


1974-1977.


This


period


also


coincides


with


a decrease


in volatility


for the market


market volatility, t reduced. The general


in general.


he reduction conclusion


in firm is that


After


adjusting


volatility information


for


is much related


effects


do not


appear


to cause


the reduction


in returns


volatility.


vi















CHAPTER 1 INTRODUCTION


firms


pay


dividends


is


an enduring


puzzle


in finance.


One motivation


that


has been


advanced


is that


dividends


convey


information


dividends suggested formalized


Rock


or by


by


(1985)


to


investors.


dividend


Miller


Ross


and John


This


signalling


and Modigliani


(1977)


information hypothesis


(1961)


Bhattacharya


and Williams


(1985)


content


was


first


and subsequently


(1979) ,


among


Miller others.


addition proposed which in refer to


to this


that


signalling


initiating


formation


this


about


expanded


hypothesis,


dividends


some


may augment


firms is transmitted role for dividends as


researchers


the


have


process


to investors.


by

I


the information


transmission


hypothesis.


For example,


Asquith


and Mullins


(1983)


state


that


Dividend


policy


has several


attractive


information transmission mechanism.


focus of other a simple, c interpretation


future


prospects.


announcements must firm must either capital markets t
credibility of cas] visible compared w dividends are i


anticipate management (p. 94)


announcements, .omprehens ive of the firm's :


Unlike most be backed generate th :0 supply j


[ signals,


tith


other


nitiated,


a periodic is forced to


Unlik


dividen


signal recent


aspects as an .e the detailed


Is can be used as of management's


performance


announcements,


wit] is Lto


hi hard cash c


cold


and its dividend


cash.


r
Drconvlr


In addition


dividends


are a


announcements. shareholders


signal submit


by


to


manage


The


-ce the to the


lso highly 00. .Once apparently ment, and


a periodic review.


Why


of


and


In











Two recent (1988) report


studies results


by


Venkatesh


that


(1989)


and Healy


can be interpreted


and Palepu


to support


this


view


of why


firms


pay


dividends.


If dividend


information


substitutable announcements


for


before


earnings

dividend


information,


initiation


then


should


earnings


convey


more


information t announcements.

informativeness


by


the amount


:han


post-dividend


Venkatesh of quarterly


of price


finds earnings


revaluation)


initiation


that


the


announcements


decreases


after


earnings average (measured dividend


initiation.


Healy


and Palepu


find


that


the price


response


annual


earnings


information


is lower


in the


years


following


dividend


initiation.


I contend


that


the above


results


merely


show


that


earnings


and dividend


substitutability


information


is


substitutable.


is not compelling evidence of


Demonstrating the information


transmission


hypothesis.


Why


would


a firm


engage


in costly


information transmission


via dividend


payments


when


earnings


reports pursue


will


serve


an enhanced


the same


purpose.


information


I argue


transmission


that


a firm


policy


would


only


if


such


a policy


investors. investors


reduces


Firms


the cost


for which


of information


it


to acquire information


is


will


relatively


have


acquisition


costly


greater


for for


incentive


to disclose


information


through


dividends.


Consequently,


dividends


firm,


then


augment


the


the available


relative


public


amount


of


informatio private


about


the


information


acquisition


after


dividend initiation should


decrease.


is


to


if











I test comparing


the above


the


average


information standardize(


transmission d variance oI


hypothesis


by


announcements


from the


pre-


and post-dividend


initiation


periods.


The


use of


standardized


variances,


i.e.


announcement


period


variance


divided changes


by estimation period


in returns


volatility.


variance,


avoids


Standardized


problems


variance


with


measures


the amount dividends a privately periods. I

publicly


of "new"


i


innouncements acquired d

f dividend available ti


nformati relativ uring initiati


O


standardized variances of


[on


released


re to the amount surrounding nc .on alters the


privately


earnings and


acquired

dividend


at earnings


and


of information on-announcement


proportion


of


information, announcements


should


increase


after


dividend


initiation.


Further,


one would


expect


firms


with


low information


availability


relative


hypothesis,


to experience


informativeness


of public


I use two proxies


increase


announcements.


of information


in the


To test this

availability


(namely,

Journal


market


Index


value


news


of equity


items)


to divide


of Wall


Street


of dividend


initiating differences,


firms


across


into


five


groups, in the


groups.


relative


I then


test


for


of


these


public


announcements


before


and


after


dividend


initiation.


The results


from


this


study


do not support


the hypothesis


that dividend


initiation


changes


the relative


amount of


public


versus


no differential


the greatest


and number


a sample


informativeness


private


information.


Further,


I f ind











changes grouped


in relative


by


information


informativeness


across


of firms


availability.


If changes


findings


that


in information


Venkatesh


and Healy


are not the basis


and Palepu


report,


for the then what


is? Venkatesh post-dividend


finds


that


period.


returns


I present


variability


evidence


that


is lower


their


in the


results


are driven volatility


by


a contemporaneous


of firms


initiating


decline


dividends.


in the returns


This


decline


leads


Venkatesh


and Healy


and Palepu


to interpret


post-dividend


initiation ea I investigate


rnings


announcements


the nature


of this


as being returns v


less


informative.


olatility


decline.


I find

driven


no evidence


by


that


information


the decrease


related


in returns


volatility


causes.


This


paper


is organized


as follows.


Chapter


2 develops


implications


of the information


transmission


hypothesis.


critique


the Venkatesh


and Healy


and Palepu


studies


within the


framework


of this


for the decline


hypothesis. in returns


I also


discuss


volatility


possible


following


causes


dividend


initiation.


Chapter


3 presents


my


data


and the methodology


used to


examine


the information


content


of quarterly


earnings.


In Chapter


4,


I report


and discuss


the results


of tests


pre-


and post-dividend


5 presents summarizes


tests


initiation


on changes


quarterly


in returns


earnings.


volatility.


Chapter


Chapter


the results


is


the


on


samples


of this


study .
















INFORMATION


CHAPTER 2 AND DIVIDEND


INITIATION


Asquith


and Mullins


(1983)


hypothesize


that


dividends


provide


information


a vehicle


for communicating


the


firm's


management' s


current


and


superior

future


prospects.


Information


about


a firm


can come


from


two sources:


information information)


provided


by


and information


the


firm


privately


(publicly


acquired


by


released investors.


I assume


that


firms


have


a cost


advantage


in producing


information


about


themselves.


Therefore,


managers


prefer


release greater


information publicly


than


have


cost*1


One way


dividends


can transmit


information


to investors


by providing


substantiation


for the cash-flows


reported


in the


earnings announcement.


Without dividends,


investors would have


to seek


substantiation


from


other


costly


sources.


Venkatesh


(1989)


alludes


to this


for the post-dividend


less


in his


paper.


in returns volatility


is that


'information'


(announcements/rumors)


that could have


induced price reactions


in the pre-dividend


period"


(p. 176).


If dividends


play an


It is not necessary to assume that


altruistic purposes. By managers obtain a lower


managers


reducing the costs of
required rate of retu


do this


for


investigation, rn.


concerning


may


investors


to


produce


it at


is


accord


argument decrease


importance


His explanation


to pieces of











informative

private inf dividend in proportion


role,


ormation itiation


of


announcements,


information


the major tc acquisition

period.2 As


information


investors


privately.


stable will dE the firm


through


have


Therefore,


less


implication


crease


discloses


earnings


incentive


is that


in the post-


a greater


and dividend


to acquire


proportion


of


public


versus


private


information


will


increase


following


dividend


initiation.


I make


two assumptions


to test


this


hypothesis.


substitutable announcements.


First,


for


Second,


the information


information the total


conveyed imparted


amount


by


by


is


earnings


information


does


not change


with


dividend


initiation.


Prior


Empirical


Work


Venkatesh


(1989)


studies


a sample


of 72 NYSE


or AMEX


firms


that


initiated


quarterly


dividend


payments


between


1972-1983.


He collects


the announcement


dates


of quarterly


earnings


dividend


initiation. announcement


that


Using


for 14 quarters


raw and


as a measure


information


excess


before returns


of information


content


and after


from


dividend


the earnings


content,


he finds earnings


announcements


before


dividend


initiation


is significantly


greater.


Based


on this


evidence,


he concludes


that


earnings


The issues


implications framework by Verrecchia (


of information


are formally
Hellwig (19 1982) and Bhu


explored
80) , Diam


ishan


O


substitutability


and its


in a rational expectations ,nd and Verrecchia (1981),


(1989) .


the relative


dividends


of firm


payments


and


the average


of quarterly











and dividend information


information


is transmitted


are partially


by


substitutable


dividends.


Venkatesh


dividend


also


finds


initiation.


that returns volatility


His explanation


for this


is lowe


decrease


r after is that


investors


in the post-dividend


initiation


period


attribute


less


importance


to


information


arriving


during


non-


announcement


and


lower


investors passively


periods. observed


do not activ(


receive


This


leads


volatility. ely acquire


information


to smaller


Venkatesh information.


and decide


price


reactions


assumes Instead,


whether


that

they


to act


on


this


information.


Before


dividend


initiation,


they


are more


likely


to act


on non-announcement


period


information.


In the


post-dividend


period,


investors


receive


more


publicly


announced


information


and rely less


on non-announcement period


information.


The main


determine


about


future


focus


whether


of Healy

dividend


earnings.


They


and Palepu's initiations hypothesize


(1988)

convey


that


study


is to


information


investors


view


dividend cash, of enable i forecast


initiation


changes


as management


future earnings


investors


errors


should


changes.


to revise


in earnings


therefore


forecasts,


So,


substantiated


dividend


expectations announcements be reduced.


by


announcements


of earnings.


following


The


dividend


To test


this


hypothesis,


they


obtain


the price


response


(namely, surprise


two-day


cumulative


excess


returns)


and earnings


standardized


and that


earnings


by price)


(the


change


for5











annual


earnings


initiation.


They


announcements


regress


prior


the price


to and fo] response


lowing dividend on the earnings


surprise measures


to obtain


the earnings


the informativeness


response


of the earnings


coefficient.


surprise.


This


Other


things leads values


being equal,


to large


earnings


price


of the earnings


information


revaluations


that


and ther


response coefficient.


is unanticipated efore to larger


They


find


that


the earnings


response


coefficient


is lower


in the post-


dividend dividend


initiation initiation


period.


provides


Healy


and Palepu


information


which


argue


that


preempts


subsequent


earnings announcements.


Do Venkatesh's


and Healy


and Palepu's


results


regarding


earnings


provide


support


for the information


transmission


hypothesis?


argue


they


might


not.


These


studies


suggest


that


dividend


and earnings


information


are substitutable.


Managers


have


little


motivation


to initiate


dividends


if doing


merely


shifts


be conveyed


to dividends, by earnings.


information


This


that


would


is especially


otherwise


true


since


dividends


are costly


to investors.


First,


they


are costly


because


firm


dividends


chooses


the extent


instead


that


are taxed


to retain

al gains a


capitz


at ordinary


earnings,


income taxes


re postponed.


rates.


are r


Second,


I f the


reduced to dividends


must used


be paid


out of net income


for investment.


Replacing


that


would


otherwise


this source of


have


capital


been


incurs


transactions


costs


that


are ultimately


borne


by


investors.


dividends also can


so


Third ,


initiating


lead to shifts in











clienteles


costs

must


that


are costly


of administering a


play


documented


to investors.


dividend


a substantially


by


Venkatesh


policy.


greater


or Healy


Finally, Therefore,


informative


and Palepu


there are dividends


role


to justify


than their


cost.


I hypothesize


that


this


greater


role


must


be the increase


in publicly


costly


Healy


two


released


private


and Palepu


reasons.


information


information


studies


First,


and


a consequent


acquisition.


cannot


address


in the post-dividend


decrease


The Venkatesh


this


hypothesis


initiation,


there


two routes


through


which


public


information


is transmitted:


dividends public in


comparing neglecting


and earnings.


iformation


only


A priori,


is divided


earnings


the amount


we do


between


announcements,


of information


not know

these ti


how the total


wo routes.


the researcher


conveyed by dividends.


By

is He


can infer


it.


From


the effect inference,


of that


he


information,


can only


but cannot


conclude


that


quantify dividend


information


has substituted


for earnings


information.


As


an illustration,


information


is


a. This


assume amount


the total


remains


amount


constant


of available


for


pre-


and


post-dividend


initiation periods.


Suppose that before


dividend


initiation,

privately


half


(a/2)


acquired.


is publicly


After


dividend


disclosed an initiation,


id half


is


earnings


disclose accounts


a/3,


dividends


for the remaining


disclose


third.


a/3 and private


We


see that


acquisition


after


the information


dividend publicly


in

and for are


is


initiation,


two-thirds


of









10


disclosed.


Yet if


one observes


only


earnings


all


one can


conclude declined


is that


from


a/2


the information


to a/3.


We can


content conclude


of earnings


little


about


much


information


was transmitted


by


dividends.


Any attempt


gauge


the relative


proportion


information must examine the and dividend announcements.


of


public


informativeness


versus

of both


private earnings


The second


deficiency


in the Venkatesh


and Healy


and Palepu


studies


lies


in their


information content measures.


They


base


their


measures


on announcement


period


returns


that


do not


account


for the general reduction in returns


volatility.


This


decrease in returns volatility


may


be due to causes


unrelated


to any


information


effect associated with dividend


initiation.


Suppose


that


announcement period returns


variance


decreases


proportionally


with


the decrease in returns volatility.


Then


one


would


returns


observe


that


are


post-dividend


lower


in


initiation


magnitude


than


announcement pre-dividend


initiation distinguish


announcement


a decrease


returns.


If this


is


in announcement period


Sol


returns


one cannot


as being


due to information


related causes or otherwise.


A study


of the


nature of changes initiation is calle


in returns

d for. In


volatility


the next


around


section,


dividend I review


a decrease in returns volatility.


has

how


to


several possible causes for









11


Causes


for Returns


Volatility


Decreases


Information


related


causes.


In informationally


efficient


markets


where


new information


is rapidly


incorporated


into


price,


returns


volatility


is directly related


to the rate


information


reduction possible


arrival.


in the rate


that


when


Lower


volatility


at which


returns


may


information


on the firm's


be caused arrives.


underlying


by


It is


assets


become returns


less


uncertain


volatility


may


and


more


occur.


predictable,


However,


this


a decrease


effect


does


depend


on any changes


disseminated


in the


or processed


process


about


by


which


the firm.


information


The decision


initiate


dividends


may


coincide


with


the firm's


asset


returns


becoming less uncertain.


then


only


coincidentally


Decreases associated


in returns


with


dividend


volatility


are


initiation.


The initiation


of dividends


does


not


cause


the rate


information


An


arrival


alternative


to decrease. information


related


explanation


decreases


reduces


ways


by


in returns


noise

Black


trading. (1986).


volatility


Noise has b( I consider


is that


dividend


een interpreted


noise


initiation in various


as the difference


between


the security's


intrinsic


value


and its observed price.


This


mispricing


occurs


when


traders


over-react


activities


of other


traders


(French


and Roll


(1986)).


Greater


noise could


trading reduce


leads noise


to higher trading


- volatility. I if it induces


Dividend greater


initiation consensus


among traders.


of


in


not


is to


of


for


to


the









12


The bid-ask


spread.


Firms


with


large


bid-ask


spreads


tend


to have


greater


in several


ways.


returns


First,


volatility. the larger


This


relationship


the bid-ask


arises


spread


the


larger t] Observed


he difference


returns


between


are calculated


buy


and sell-side


using daily


transactions.


closing prices.


If


successive


closing


transactions


are alternatively buy and sell


transactions,


bid-ask greater


spread.


they would


Hence,


the observed


be conducted the greater


at opposite the bid-ask


edges


spread,


of the


the


return.


Bid-ask (Demsetz,


spreads increase with


1968


Ho and Stoll,


the dealer's


1981)


and


inventory


costs


the degree


of


informational


uninformed


asymmetry


dealer


(Bagehot,


between


1971,


informed Copeland


traders


and Galai,


and the


1983,


Glosten


and Milgrom,


1985).


It is possible


that


dividend


initiation may alter these


two factors


and therefore


reduce


bid-ask trading


spreads. volume.


For example,


This


reduces


dividend


the dealer's


initiation may


inventory


increase


costs


as


inventory reduce ii


turnover


nformationa


increases.

1 asymmetr)


Dividend y through


initiation greater


may


also


consensus


between


traders


and dealers.


Stock price.


Volatility decreases as stock price


increases.


This relationship


bid-ask observed


spread, returns


suppose stock


Al


occurs

higher


for two priced


reasons.


stocks


First,


will


and therefore lower volatility. Aas bid and ask prices of 9 7/8


for the


have


same


smaller


For example, and 10 1/8,









13


respectively; stock


B has bid and ask prices


of 19 7/8


and 20


1/8.

sell


The bid-ask


and buy


spread


transactions


is 2/8


for both


the observed


stocks.


return


For successive


for stocks


Aand


B is 2.5%


and 1.24%,


respectively.


Second,


as a firm's


stock


price


increases


(without


the firm


increasing positively


considering


debt) ,


related a firm


the firm's


leverage decreases.


to leverage.


in


This


a Modigliani-Miller


Volatility


can be easily


world


with


seen


is by


no taxes


and riskless


debt.


The return


on a leveraged


firm


= kv + (kv-r)


where


r is the riskless


the unleveraged


firm.


interest


The market


rate and


value


is the return


of debt


and equity


D and S, respectively.


Taking


variances


= or(1


Therefore,


stock


volatility


us depends


positively


on leverage


D/S.


Interest


rates.


leverage. decrease


rates.


Interest


Volatility


rates


An increase


leverage.


indirectly in interest


First,


also


changes


affect


rates


as interest


with


volatility


can both


rates


rise,


interest through


increase the value


or of


both


debt


and


proportionately


equity greater


both than


decline


debt;


this


but


leads


equity


to


declines


an increase


transfer from bond to


is


D
(-)
S


on is


Os


D + 8-


there is a wealth


in leverage.


Second,









14


equity


holders


that


decreases


leverage.


Christie


(1982)


studied


that


the net effect


high


interest


of interest rates on leverage.


rates


are associated


with


high


He finds returns


volatility.


If dividend


initiation


has information


related


effects,


volatility


can decrease


due to reductions


in noise trading and


bid-ask


spreads.


Volatility


can also


decrease


due to factors


unrelated


to information


effects.


The returns on the


assets


firms


initiating dividends


could


have


become


less uncertain


and thus could e>


prices


experience


are less


increases


volatile.


in stc


Dividend Dck price


initiating which le


firms id to


proportionally


Finally, changes


returns


smaller


bid-ask


volatility


spreads


and reduced


leverage.


can decrease due to market


wide


in interest


has


of


n.,


rates .
















DATA


CHAPTER 3
AND METHODOLOGY


Data


My


data


dividends identified


Security collect


sample


between


consists

1970-1986


by scanning


Prices


earnings


(CRSP)


of


332


firms


inclusive.


the 1988 NYSE/AMEX


information


that


These


Center


and NASDAQ


for approximately


initiated


firms


for Research


files.


were


in


I also


14 quarters


trading


days)


before


and after


the initial


dividend.


These


were


obtained


from


the 1987


quarterly


Compustat


files


and/or sample,


the Wall


firms


Street


must


meet


Journal


Index.


the following


To be included requirements.


in the


The initiating the firm's cc


dividend )rporate


is either the


history


first dividend


or the resumption


in of


dividend


payments


after


a hiatus


of at least


10


years.


The initial


dividend


cannot


be


an extra


or special


dividend. The firm


three


must


years


continue


after


paying


the initial


dividends dividend.


for at least


The firm


must


have


3 years


of daily


returns


data


before


and after


the initial


dividend.


Not


preceding and


one CRSP


tape


all


the returns


following source. F(


data


dividend ourteen f


for th(


initiation


irms


which


n. 3 year period are obtained from


were


on the NASDAQ


15


(900


(1)


(2)


(3)









16


One year appearance


announcement


must


elapse


between


on the NYSE/AMEX


of it's


initial


the


or NASDAQ dividend.


firm's


tapes


initial and the


In each

initial


14 quarter dividend,


period


preceding


announcement


dates


and following and earnings


price


data


for at least


10 quarters


must


be available.


This


eliminates


firms


that


only


report


annual


earnings


or f irms


merged


out of existence.


Table

dividend


1 presents initiations


an overview occurred in


of the sample.


the middle


Most


seventies.


of the Almost


65% of the sample


initiated


dividends


between


1975


and 1977.


This


could


controls


as posited


be due to the relaxation


or to cross-sectional


by


Marsh


and Merton


dependence


(1987).


of the Nixon n. in earnings c


More


than


price ,hanges three-


quarters quarters dividend


yearly


of the sample following di

payment frequ


and annual


paid


vidend


ency


payments.


at least


initiation.


was quarterly,


American


9 dividends


The mot followe


Exchange


in the 14 Dt common


by


listed


half firms


form


a slight


maj ority


followed


by


New York


Stock


Exchange


OTC firms. quarterly


More


than


earnings


90% of the sample


had at least


years


data.


tape when NYSE/AMEX tape when


they tape.


initiated
Similarly


they initiated


dividends , 6 firms dividends


had continuing data on the
which were on the NYSE/AMEX had prior NASDAQ data.


(4)


(5)


the and


and


of









17

TABLE 1
OVERVIEW OF SAMPLE CHARACTERISTICS


_______________Icumulative


Freq.


Percent


Freq.


Percent


___________________ ____________________ A - A _____________ L _______________ I _____________I Y Y I


Year of
Dividend Initiation


1970


.8


1.8


1971 4 1.2 10 3.0 1972 18 5.4 28 8.4 1973 31 9.3 59 17.8 1974 27 8.1 86 25.9 1975 62 18.7 148 44.6 1976 70 21.1 218 65.7 1977 49 24.8 267 80.4 1978 23 6.9 290 87.3 1979 12 3.6 302 91.0 1980 9 2.7 311 93.7 1981 6 1.8 317 95.5 1982 5 1.5 322 97.0 1983 6 1.8 328 98.8


19


86


.2


32


100.0


____________________ A L ________________ a ________________ ______________Y F 1 1


Number of Dividendsa


2-4


26


7.8


26


7.8


5-8 50 15.1 76 22.9 9-12 53 16.0 129 38.9


13


-16


20


1.1


32


100


.0


___________________ I ____________________ A � ______________ _______________ i _____________T 1 1


Dividend Payment Frequencyb


Annual


21


.3


21


6.3


Half 35 10.5 56 16.9 Quarter 217 65.4 273 83.2


Other'


59


17


.8


332


& I I I


100.0









18


TABLE


-- continued


Cumulative
, , i


Freq.


Percent


Freq.


Percent


NYSE 104 31.3 104 31.3 Exchanged AMEX 143 43.1 247 74.4

NASDAQ 85 25.6 332 100.0

16-19 12 3.6 12 3.6 Number of 20-23 42 12.7 54 16.3 Quarterly
Earningse 24-27 105 31.6 159 47.9
28-30 173 52.1 332 100.0
, , i i i~ iii i iii O


a Number
days f
b The fr


of dividends


ollowing "equency


which u


the initial
of dividend


were


declared


dividend. payments


within


in the 3


900 trading


year


period


following dividend Within the 3 year


firms


changed


frequency. firms chan


from


6 firms


ged


frc


from quarterly tc to half yearly


yearly with d Exchange on
initiated.


e Number


of


declaration information


initiation.


period


follow


an annual


changed


)m half half


froi


yearly yearly.


t(
2


to quarterly.


additional


which


firm


quarters


year-end


ing dividend to a half yE m annual to , o quarterly. ] firms change 1 firm chang


dividends


was trading


within


of the initial was available.


900 tr
dividend


when


initiation,


early quarte


payment rly. 44


firm changed from annual


from


half


to quarterly.
dividends were


ding days
for which


of


the


earnings


Tests


on Quarterl


Earnings


Measurinq


information


content.


The information


content


an earnings


announcement is computed


in the following way.


announcement announcement


(t+1)


period


(day


the announcement


consists


of 3 trading


the day preceding


date.


Beginning


days:

(t-1)


at days


the day


of the


and following t-2 and t+2,


the announcement


of


The


are


"M "


i


,'a(


period


surrounding


for 60 days


returns









19


collected.

dividends


These


returns


and other


form


earnings


the non-announcement


announcements


occur


period.


within


If


non-announcement


period,


the returns


from


these


announcements


are excluded.


The 60 non-announcement


period


returns


are used to


estimate


the market


model


for firm j:


= () +ij ,


r=i,60


where


are the returns


on day


T for the firm


j, r P


are the


returns


(including


dividends)


of


a value-weighted


index


all firms


on the tape,


and


ej and 3j


are OLS estimated


coefficients.


Excess returns uo, are calculated


- (aj c i ma)


I assume


zero


that


expected


excess

value.


returns


are normally


distributed


with


Three


measures


of information


content


are used.


The first


is the standardized


variance,


a variant


of Beaver's


U (see


Beaver,


1968) .


It is denoted


by


BVU.


This


is the ratio


announcement


period


returns


variance


to non-announcement


period


returns


or marginal


variance.


information


Beaver' s


conveyed


is


a measure


to the market by


of the "new" the earnings


dividends)


announcement


relative


to


the


average


information


also


disclosed


available


during


of


versus privatel)


the non-announcement


amounts


formation.


period.


It


of publicly I assume that


returns variances


this


Rj


for


Uj'


as


(and


of


is


a measure


the relative

y acquired inf


= Rjr


non-announcement


period


capture the


extent









20


of private


true


as firms


information acquisition.


may engage in other


This may not


forms


of public


be strictly disclosure,


for example,


minimized


earnings


if dividend


forecasts.


initiation


However,


does


their


effect


is


to changes


in the firm's


other


public


disclosures.


The


changes


use of standardized in returns volatility.


of circumstances.


First,


variances


This


also


is critical


if the market


were


controls


under two generally


for


sets

more


variable


during


a certain


period,


a direct


comparison


announcement


period


returns


would


bias


the test


of firm-


specific


price


response to


earnings


announcements.


This


bias


is exacerbated


if the sample


is clustered


in calendar


time


clustering


is coincident


with


changes


in market


volatility.


Second,


if


volatility Standardizing


there


is


a systematic


contemporaneous the announcement


with period


decrease dividend returns va


in


returns


initiation.


triance


by


the


non-announcement


arise


previously


from


period


returns


differences


results


variance avoids


in volatility.


may


be driven


by


problems


I suspect


returns


that

that


variance


decreases and


not by changes


in information


transmission.


Let A represent


the set of announcement


period


days


and N


represent estimate


the set of 60 non-announcement


the announcement


period


returns


period


variance


days .


to be the


sum of the squared excess


returns u


(T


EA)


divided


number


of days


within


the announcement


period,


period returns variance is


TA. Similarly, the sum of 60


not systematically


lead


of


and


may


reported


by


the


the non-announcement









21


squared


excess


returns


UT


(7r


c N)


divided


standardized


variance


is calculated


Var [ ujj,


Var [uJ


,rETA]


, rcTN]


1 2 TN 1'eTN


Standardized


the announcement


variance


period.


is calculated In the first


for two definitions


definition,


BVUE,


announcement announcement. Venkatesh's r


period


This


esults.


consists


definition


As BVUE


only


is used


measures


of


the


earnings


for comparison


the marginal


with


information


content


of earnings


relative


to the


average


information


in the


non-announcement substitutability


period,


of


this


earnings


ratio


and


is used


dividend


to examine


the


information.


Decreases


in post-dividend


initiation


BVUE's


indicate


that


dividend


announcements


have


preempted


earnings


announcements.


In the second


definition


I BVUD,


the announcement


period


consists


of the earnings announcement


announcement announcement. effect of div


post-dividend


occurring


This


ridends


within


definition as a public


initiation


period,


30


and the nearest


days


is used


to


disclosure


of


the


dividend earnings


measure the total mechanism. In the


information


is publicly


disclosed quarter,


through


BVUD


both


measures


dividends


and


the information


earnings.


content


In each


of all public


disclosures


(from


both


earnings


and dividends) in post-dividend


relative


by


as


60.


The


BVUj


of


the


to


private


information.


initiation


Increases









22


BVUD's


indicate


that


more


information


is released


publicly


than


acquired


privately.


I also


calculate


two


measures


which


are


based


announcement


period


returns.


They


are similar


to those used by


Venkatesh.


They


are measures of


price


revaluations


induced


information released at earnings announcements.


These measures


do not take


into


account


non-announcement period


returns


therefore,


the general


level


of returns


variability.


The first


measure,


RAW


. is the absolute


value


of the


sum of earnings


announcement


period


returns.


RAW


The second,


MAJ,


is the absolute


value


of the


sum of earnings


announcement


period


excess


MAJ


returns.


=1


=t+1 :=t-1


These


measures


differ


only


to the extent


that


the market


model


regression


accounts


for contemporaneous


market


movements.


Because


the explanatory


power


of market


model


regressions


typically


low,


these


measures


are quantitatively very


similar.


The values


for MAJ are usually


slightly


smaller


in magnitude


than


those


obtained


with


I define


the


pre-


and post-dividend


windows


to be 900 day


periods


(about


14 quarters)


preceding


and following


initiation of dividends.


on


by


and


=1


RjT


is


RAW.


the


t~l
E
T=t-i


UjT


A"j oint" announcement


occurs when









23


a dividend announcement.


is


declared I follow


within


2 days


Venkatesh


in


of


an


excluding


earnings


joint


announcements


from


the analysis.


I require


that


in each


window,


there


are at least


3 usable


earnings


announcements.


This


requirement


eliminates


7 firms


from


the sample.


The final


sample


consists


of 325 firms.


pre-


firm


j I


average


the information


initiation


content


quarters.


measures "Matched


differences"


are obtained


by subtracting


the


average


post-


dividend dividend


initiation initiation


information information


content content


from


the


for each


average


firm.


DRAWj DMAJ DBVUEJ DBVUAj


= RAWj,PRE =AJi, PRE
= j, PRE = BVUPRE


-RAWj, POST
-=Jj, POST
-BVUEj, POST
- ]D UAj, POST


The distribution


of matched


differences


i s then


analyzed


detect


the effect


of dividend


initiation.


Hvpotheses


and


test


statistics.


There


are


2 main


hypotheses.


The


first


is


that


dividend


information


substitutable


one would


expect


announcements


to


to observe decrease


information.


raw price


in


the


Under


this


reactions


post-dividend


hypothesis, of earnings


period.


the appropriate


For each


of all


and post-dividend


pre-


to


to earnings


is


test is


Therefore









24


DRAW

DMAJ


-o0
-o0


versus


versus


Ha Ha


DRAW DMAJ


Further,


if the total


amount


of information


or the general


level


of volatility


does


not increase


from


pre-to


post-


dividend measure


initiation pe

of information


n.riods, t content


then


the standardized


should


also


variance


decrease.


: DBVUE


- 0


versus


Ha


: DBVUE


second


hypothesis


is that


dividends


augment


transmission


of public


information


about


the firm.


Under


this


information


transmission


hypothesis,


the relative


information


content


of public


announcements


(both


earnings


and dividends)


to private


information


should


increase.


Therefore,


: DBVUD


- 0


versus


Ha


DBVUD


These


hypotheses are tested using standard


t statistics


Wilcoxon


signed-rank


statistics.


The nonparametric


Wilcoxon


signed-rank


test


is particularly


(for


suited


example,


to the


see Hollander


case


of paired


and Wolfe, replicates


1973) data


i.e.


pairs


of


are concerned


prewith


and post-treatment


a shift


in location


observations,


where


we


due to the application


of the treatment.


In this case,


the treatment is


the payment


techniques require


H0 H0


> 0


and


> 0.


H0


The


> 0.


the


H0


<0O.


and


of dividends .


Nonparametric


few assumptions









25


about


the underlying


obtained. I traditional


n particular, assumption


populations


from


nonparametric


that


which


procedures


the underlying


the data


forego


populations


normal.


The Wilcoxon


signed-rank


statistic


W is calculated


follows.


ranked


The absolute


in


ascending


values order.


of the matched


The ranks


of


differences


those


are


matched


differences


that


have


the sign


of the hypothesized


direction


(i.e.


DRAW>0,


DMAJ>O


I DBVUE> 0


and DBVUD<0)


are summed.


Denote


this


sum as


0I use the large


sample


approximation


to obtain


W �


- [


n(n+l)
4


n(n+l) (2n+l)
24


The statistic


has


an asymptotic


standard


normal


distribution.


a two tailed


test,


the null


hypothesis


is rejected


if 1w


> (a/2),


where


z(.)


denotes


the standard


normal


variable


a the significance


level.


Differential


information


availability.


I also


test


information transmission hypothesis by examining whether firms


grouped by content of


information


earnings


availability have different


announcements.


information


If information transmission


a valid


incentive


for initiating


dividends,


then


one would


expect


firms with


low information


availability


to experience


are the

are


as


T


For


and


is


the









26


the greatest


increase


in the relative


informativeness


public


announcements.


To test


this


hypothesis,


I group


firms


using value


two proxies


of equity


of information


and number


of Wall


availability


Street


(namely,


Journal


Index


market


news


items) .


these


I test public


for group


differences


in the


before


and


informativeness


after


of


dividend


initiation.


Previous


studies


(1989) ,


by


Atiase


Shores

(1985)


(1990) , Zeghal


Bhushan


(1984)


(1989) ,


and Grant


Lobo


(1980)


consistently


find


that


the earnings


announcements


for firms


with


low information


availability


have


greater


information


content.


These


studies


group


their


sample


firms


on proxies


information


availability


such


as firm size,


analyst


following,


trading number 4


volume,

of market


exchange makers a


listing,

and bid-asi


financial < spread.


media r( The most


eportage, commonly


used


proxy


of information


availability


is market


size.


example,

(1989),

earnings


have


Shores

Atiase


lower


already


(1990) (1985) a


announcements


information


have


more


Lobo


nnd Zeghal of large


content.


information


nd Mahmoud

(1984) al.


market


This availah


(1989), 1 report


Bhushan


that


capitalization


large

them ai


the


firms firms nd the


additional


contribution


of earnings


information


is smaller.


Large several


firms h reasons.


iave


Large


firms


information


may


have


availability


economies


of sc


due to ale in


producing


and disseminating


information


about


themselves.


Large


information to meet


of


announcements


Mahmoud


and


of


For


is because )le about t


greater


c o pr---;, n e


also


produce


more









27


regulatory


requirements.


Large


firms


tend


to have


more


shareholders third-party


a A large sh information


areholder providers


base


such


provides


brokerage


incentive


services,


financial


news-letters


etc.


, to produce


information about


firm.


I standardize


market


Standardization capitalization.


value takes


to


into


market

obtain


account


Standardization


may


value


of equity


firm


general


by


the


size .


market


important


if


information collection depends


not


on the magnitude


of market


capitalization


total total


universe


market


but


on the ranking


of firms.


value


changes


Relative


over


of the firms


rankings


time.


Since


will


relative


change


our sample


to the as the covers


a 1 ong


period,2


any


secular


change


in total


market


value


be important.


group


It turns


rankings


out that


greatly.


standardizatior


Out of 332 firms


i does 32 fir


not alter ms change


groups


when


ranked


by


market


value


of equity


alone.


The firm's


adjusted


firm


size (SHR)


is calculated


multiplied


as the total


by the price


days


before


the first


dividend


is declared


This


figure


is then


divided


by


the total


dollar


value


of all non-ADR


securities

1,000,000.


(TOTMV)


and multiplied


by


a scaling


factor


Studies


which


have


used


firm


size


have


covered


shorter period over which minimal.


changes


in total market value may


for


total


the firm's


the


adjusted


the


be


the


may


number


of shares outstanding


two


of


be


(Pt-2) �









28


SHR


Adjusted
Firm Size


X Pt-2


1,000,000


TOTMV


Price


data


the CRSP for NYSE


and number


tapes.


and AMEX


Prior


of shares to 1973,


listed


outstanding


total


securities.


market


From


1973,


are obtained


dollar


from


value


is


NASDAQ-traded


stocks


are included.


The number


of


news


items


appearing


in the Wall


Street


Journal


Index


measures


the


occurrence


of events


that


have


informative


value.


It is also


a measure


of financial


media


coverage.


A firm


with


a high


number


of


news


items


is assumed


to have sample, Journal


higher


information


the number


Index


of


during


news


the


availability.


items appearing


year


prior to the


For each


firm


in the Wall


declaration


in the Street


o f the


first


dividend is collected.


The firms


adjusted

divided


firm


into


in my size


five


sample


are ranked


and the number


groups.


Note


that


in ascending


of


news


the groups


items


based


order


by


and then


on news


items


number


example,


do not have


of


news


firms


characteristics


equal


items


numbers


of firms.


is discrete


in the third


group


of the information


This


causing


all have


is because


clustering.


news


availability


items. groups


the

For The are


shown


in Tables


2 and 3. There


appears


to be sufficient


variation


size


with


groups,


thE


respec =. mean


to firm


market


value


size.


For the adjusted


of equity


firm


is approximately


the preceding


I


two times


greater


than


group .









29


TABLE


CHARACTERISTICS


OF GROUPS


BASED


ON ADJUSTED


FIRM


SIZE


a SIZE
two
b NEWS


d


is the markE lays before is the numb(


value


of equity


the initial er of Wall S


dividend


street


(in millions


of dollars)


is announced.


Journal


Index news


items


in the year dividend.


prior


to the announcement


of the initial


Table


size yield

items


findings


and


4 shows


news


identical reported


the distribution


items.


While


groupings,


about


of Thompson,


them. Olsen


of firms


the information


large


firms


This


tend


grouped proxies


to have


is consistent


and Dietrich


(1987)


Wall Street Journal Index


by firm do not


more with


news


the


who examine news items.


GroupiN Var Mean SD Min Med Max

SIZEs 6.563 2.396 2.860 6.399 15.587
1 66
NEWSb 6.833 2.271 2 6 16

SIZE 13.144 3.558 6.274 13 .052 24.820
2 67 ... ...
NEWS 7.667 2.420 4 7 18

SIZE 23.303 6.088 10.621 22.727 38.900
3 67
NEWS 9.552 4.328 5 9 33

SIZE 50.667 17.750 26.837 45.794 127.630
4 66
NEWS 10.797 5.262 5 9 28

SIZE 308.83 584.906 50.474 128.265 3631.92
5 66
NEWS 13.078 7.895 5 10 50


the characteristics of









30


TABLE 3


CHARACTERISTICS


OF GROUPS


BASED


ON NUMBER


OF NEWS


ITEMS


a SIZE


is the market


two days


NEWS


before


is the numb


value


the init er of Wal


of equity (in ial dividend


ii


Street


millions


is


Journal


of dollars)


announced.


Index


news


items


in the year
dividend.


prior


to the announcement


of the initial


DISTRIBUTION


OF FIRMS


TABLE IN GROUPS


BASED


ON ADJUSTED


FIRM


SIZE


AND NUMBER


OF NEWS


ITEMS


ADJUSTED MARKET VALUE


NEWS


ITEMS


3


- ii


JI A A I A dl II ii v Y I Y II


17


28


10


Total


66


2 11 27 13 13 3 67 3 9 13 7 26 12 67 4 8 11 11 17 19 66


13


14


31


66


Total 48 92 46 79 67 332


GroupI N _Var Mean SD Min Med Max

SIZEs 23.727 26.846 2.860 14.018 138.52
1 48
_LINEWSb 4.826 0.5698 2 5 5

SIZE 43.899 91.479 2.880 13.312 512.290
2 92
92___ NEWS 6.424 0.497 6 6 7


SIZE 25.242 22.562 4.020 15.970 82.357
3 46
NEWS 8 0 8 8 8

SIZE 46.317 47.877 3.294 27.547 297.024
4 79
__ __NEWS 9.397 0.952 9 10 12

SIZE 238.728 586.243 10.621 55.544 3631.920
5 67
LNEWS 17.657 6.383 13 16 50









31


To test


for differential


information


contents


across


five


groups


the data


sample


is characterized


as a one-way


layout


design


experiment.


The data


consists


of N


nk (for


k=l, . . 15) information


observations availability


with


group.


observations


Note


that


in the kth


the number


of


observations


in each


group


need


not be equal


to each


other.


This


one-way


layout


is illustrated


in Figure


3.1.


D(j,k)I's


the matched observations.


differences


from


group


k with


individual


Information


Availability


Groups


1 2 3 4 5


D(1, 1)
D(2, 1)


D(1,2) D(2,2)


D(l,3) D(2,3)


D(1,4) D(2,4)


(1,5) (2,5)


D(n4,4)


D(n2, 2)


D(nl, 1)


D(n5 ,4)


D(n3,3)


Figure Sample Data


1
Design


the


are


= Ek











The basic


model


D(j ,k)


= M + Tk + ejk,


j=i, . . * ink


=1, ... ,5


where


is the unknown


overall


mean,


Tk


is the unknown


information


effect


in


group


k. I assume


the


errors


e are


mutually


independent


and


have


the


same


continuous


distribution.


The hypothesis


to be tested


: T


- 7 3


-- 74


: 71 7_ 2>r3 >T4


= T5


versus


T5


where


at least


one of the inequalities


is strict.


The firms


lower


information


availability


groups


have


larger


information


effects.


I use the Jonckheere-Terpstra


statistic


(see


Hollander


Wolfe,


1973)


to test


the hypothesis.


It is based


on the number


group


observations which


are less than


each


of the


group


observations,


where


> j


For 5


groups,


there


are 10 of


these


Mann-Whitney


counts Uii.


ni nj s=l t=l


i) D(t,j) ]


32


is


is


H0

Ha


the


in


of


and


T-









33


where


1 if a

Mann-Whitney


counts.


0 otherwise.


The stronger


Let JT


are the


be the


group


sum of the


effects,


larger


is JT-


The large


sample


approximation


J is


nk 2]/4


JT -I [N2-k1
k=l


[N2(2N+3)


-i
k=l


nk2 (2nk+3) ]


It has


an asymptotic


N(0, 1)


distribution.


In addition,


use the Kruskal-Wallis


(see


Hollander


Wolfe,


1973)


statistic


to detect


i f there


any


differences


r across


groups.


The alternative


hypothesis


is that


the r's


are not all equal.


observations. in ascending


groups


It is based


All matched difference:


order.


and summed.


These

Let rk


ranks


be the


on the ranks s in the sample


are then


divided


of sample are ranked


into


sum of the ranks


their


of the


observations


in


group


k. The Kruskal-Wallis


statistic


H is


given


5
N121
N (N+I)k=l


- 3 (N+l)


large


samples,


has


an


asymptotic


chi-squared


distribution


with


k-1 degrees


of freedom.


The above


tests are equivalent


to parametric


analysis


variance


F tests.


However,


when the


distributions


of matched


differences a inappropriate.


re not normal,

Further, the


the


use of F statistics


Jonckheere-Terpstra


is


the


1 72


and


in


by


For


of


test is a


0 (a ,b) =








34


comprehensive


problems


test


associated


which with


does


multiple


not suffer


from


comparisons


the usual


such


as the


Bonferroni


or Tukey


methods.















CHAPTER


TESTS


ON QUARTERLY


EARNINGS


ANNOUNCEMENTS


The Overall


Sample


The overall


sample consists


of 325 firms.


Each


firm


must


have at


least


3 usable quarterly


earnings


(joint


announcements


are omitted)


For each


firm,


or post-dividend


initiation quarterly


periods. earnings


announcements in each


period are averaged.


Summary statistics


for the cross-sectional


distribution


of average


information


content measures for


pre-


and post-dividend


initiation periods


are shown


in Table


5. The information


content measures


based

(MAJ),


on raw returns


the ratio


non-announcement


(RAW)


market


adjusted


excess


of earnings announcement returns


returns


variance


(BVUE)


returns


variance


and the ratio


to of


earnings


and dividend


announcement


returns


variance


to non-


announcement


dividend relevant


period


initiation


and


the


returns period,


ratio


variance


only


is


(BVUD) �


earnings


with


In the


announcements


respect


to


pre-


are


earnings


announcement returns variance


(BVU).


The information


content


measures

initiation


RAW and MAJ have period. The pre-d


larger

ividend


initiation


values


of the minimum,


the first


quartile,


median,


third


quartile


and maximum


always


exceed


those


from


the post-


35


in both pre-


the information content of


are


values


in the pre-dividend









36


TABLE


DISTRIBUTION EARNINGS ANN


OF THE AVERAGE


OUNCEMENTS


BEFOR


INFORMATION E AND AFTER


CONTENT OF QUARTERLY DIVIDEND INITIATION.


Sample Size = 325 firms

Pre-Dividend Period Post-DividendPeriod
q q i i


RAW


IMAJ


BVU


RAW


MAJ


BVUE


BVUD


Mean 5.79 5.67 2.007 4.52 4.39 1.908 1.862 SD 2.13 2.15 1.139 1.62 1.62 1.178 1.444 Minimum 1.14 1.52 0.459 0.02 0.85 0.230 0.528 Qja 4.29 4.13 1.309 3.42 3.34 1.173 1.205 Median 5.61 5.43 1.684 4.36 4.15 1.609 1.573 Q3a 6.98 6.89 2-.263 5.35 5.25 2.367 2.036 Maximum 13.31 12.82 7.663 11.65 11.43 9.589 15.081


a Ql=first


quartile;


Q3=third


quartile.


dividend quarterly revaluatio


initiation earnings

n is 5.79%.


period.


For


announcements


pre-dividend


the


For post-dividend


mean


initiation


initiation


raw


price


quarterly


earnings

dividend


announcements,


initiation


mean


the mean


market


is 4.52%.


adjusted


Similarly,


price


the


revaluation


pre-


is


5.67%


with


the post-dividend


initiation mean being


4.39%.


These


figures


For raw returns,


are similar


Venkatesh


to those


obtains


reported


by


a pre-dividend


Venkatesh.1 initiation


1
study
75 NYS


The differences are as follows. '


3E


or AMEX


firms


between Jenkatesh


which


pa


my study and the Venkatesh examines a smaller sample of
id quarterly dividends. His


estimation period precedes the quarterly earnings announcement date. He uses a lagged Scholes-Williams technique to obtain his market model regressions.









37


mean


of 5.84%.


In the post-dividend


initiation


period,


reports


following


a mean


of 4.91%


(preceding)


(4.01%)


dividends


for earnings announcements.


announcements


Results


for


market


adjusted


excess


returns


are similarly


comparable.


The standardized variance


information content measures


show


a lesser for BVUE

dividend


initiation


decrease.


and BVUD


The post-dividend


are 1. 908


initiation mean value


period,


the


average


and 1. 862


of 2. 007. dividend


initiation


compared


mean


with


values


the


pre-


For the post-dividend


announcement


variance


is less


than


the average


earnings


announcement


variance.


Thus


the combined announcement


average


(BVUD)


variance


is less


than


of


earnings


for earnings


and dividend announcements


alone


(BVUE) �


Whether


these


differences


are statistically


significant


examined


by comparing matched


differences.


The distribution


for matched statistics


statistics greater in


differences


and


the


to test whether


is tabulated


nonparametric


the


the pre-dividend


average


initiation


in Table Wilcoxon


information period. TI


6. I use t signed-rank


content


is


ie evidence


for RAW and MAJ is unambiguous.


Both


t and Wilcoxon


signed-


rank


tests


support


the hypothesis


that


the


average


post-


dividend


initiation


quarterly


earnings


announcement


is less


informative.


About


70% of the sample


experiences


a decrease


earnings


price


informativeness.


The


average


reduction


is about


and dividend


he


is


in


1. 2%. This


earnings


information


suggests


that









38


might


be substitutable.


My sample


yields


results


entirely


consistent


with


those


reported


by


Venkatesh.


The results


from


the standardized


variance


measures


information


content


measures


are in less


accordance


with


raw price decrease


measures. following


Just


over


dividend


half


the sample


initiation.


experience


DBVUE


has


insignificant


decline.


Thus,


there


appears to be


a reduction


in the informativeness


of earnings announcements


relative


average


informativeness


in non-announcement


days.


More


importantly,


in


however,


a direction opposite


DBVUD


has


to that


a significant hypothesized.


change


but it is


The variance


of


excess


returns


for public


announcements


(both


earnings


dividends


announcements)


relative


to the non-announcement


periods


is decreased


once


dividends


have


been


initiated.


Contradictory


to the information


transmission


hypothesis,


proportion


privately


of information


acquired


decreases


publicly


after


released

dividend


relative


to that


initiation.


I also


test


the normality


of the distribution


of the


matched differences


the value


probability distribution significance


of information


of the Shapiro-Wilk


of


rejecting


is normal.


level


the


content measures.


D statistic


null


The null


for all distributions.


The lower


the greater


hypothesis


is rejected


that


the

the


at the 5%


The non-normality of


the distributions


of matched


differences


justifies


the


use of


nonparametric test statistics.


of


the


the


an


to


and


the









39


CROSS-SECTIONAL


TABLE
DISTRIBUTION


OF MATCHED


DIFFERENCES


Sample Size = 325 firms
i'


DRAWa


DMAJa


DBVUEa


DBVUDa


Mean 1.27 1.29 0.099 0.145 Std. Dev. 2.53 2.51 1.546 1.729 Minimum -6.99 -6.51 -7.098 -13.632 Q1b -0.15 -0.36 -0.785 -0.503 Median 1.37 1.23 0.048 0.114 Q3b 2.76 2.95 0.873 0.907 Maximum 9.17 9.80 6.237 6.518 Dc 0.9654 0.9695 0.9364 0.8367 Prob < D 0.0368 0.0421 0.0000 0.0000 No. Positive 241 226 170 177 % Positive 77.2 69.5 52.3 54.5 Tde 9.05 9.24 1.15 1.52 Prob > T 0.0001 0.0001 0.1250 0.0654 Wdf 8.42 8.46 1.17 2.82 Prob > W 0.0001 0.0001 0.1210 0.0024


a Difference
content of for the ra


of average


pre-


quarterly earnings,


w price


information


and post-dividend


for eaci content


firm.


information For example,


measure,


DRAWj


-- R j, PRE


- RAWj, POST


b Ql=first quartile; Q c Shapiro-Wilk test of d Test of H0: DRAW=0


H0:


DMAJ=0


Ho: DBVUE= Ho: DBVUD=


=0
=0


e t-statistic. f Wilcoxon signed-rank


3=third H0: Disi versus
versu Versus I versus


quartile. tribution


is normal.


Ha: DRAW> 0,


Ha:


DMAJ>I0,


Ha: DBVUE>O, H a: DBVUD>0.


statistic.


I









40


The Sample


Grouped


by Information


Availability


In this


section,


I analyze


the matched


differences


across


information transmission


availability hypothesis,


groups.


one


Under


would


the


expect


information


to


see


the


informativeness


of earnings announcements to decrease


as one


goes


from


low information


availability


to high


information


availability


firms.


Table


7 shows


the distribution


of DRAW


grouped


by adjusted


firm


size.


For all


groups,


the differences


are significantly positive.


The Wilcoxon signed-rank tests and


t-tests


are significant at


Kruskal-Wallis


H statistic


levels well below


the 5% level.


indicates that the group


means


DRAW


do differ


across


groups.


The Jonckheere-Terpstra


statistic smaller


indicates as market


that value


the


group


increases.


means This


are decreasingly


relationship


is


significant


at


the


5%


level.


Post-dividend


initiation


quarterly


earnings


average


price revaluations


are less


than


the pre-dividend group means shows


initiation


that


period.


the decrease


An examination


tends


to be less


of the


for high


market


value


firms.


Low market


value


firms


experience


a drop


of 1.5%


in the


announcements.


decrease. monotonic. decreases


average


High


However,


Firms


than


market


this


revaluation


value


in the lowest


those


occurring


at earnings


firms experience only


relationship


market value


in the next


is


not


group


have


a 0.6%


strictly


smaller


quintile.


Table


8 shows


the distribution


o f DMAJ


grouped


by adjusted


the same general results with


The for


in


firm size. We obtain


DMAJ. The









41


group group group


means decrease


1 is less


than


from that


means is less strong


Tables


9 and 10 show the


group


of


group


than


2 to


group


5. The mean


2. The difference


for


between


for DRAW.


distributions


for the standardized


variance


significant


measures.


change


In general,


in


means


there after


does


not


dividend


appear


to be


initiation.


any


P


values


for the t-tests


and Wilcoxon


signed


rank


tests


generally


insignificant.


The only


exception


to this


is


group


2 for DBVUD


which


shows


a decrease.


The Kruskal-Wallis


statistics


fail


to reject the


null


hypothesis


of equal


group


means.


The Jonckheere-Terpstra statistics do


evidence that


changes in relative informativen


not provide any ess increases as


one goes


from


smaller


to larger


firms.


Tables


11


differences


to


14


for the


show


groups


the


based


distributions


on number


of


of Wall


matched Street


Journal


Index


news


items.


In general,


the results


obtained


from


using


this


information


availability


proxy


are similar


those


obtained


from


adjusted


firm


size.


Within


groups,


the t


and W statistics


show


that


the


mean


matched


differences


DRAW


and DMAJ


are all greater


than


zero


at conventional


significance levels.


The Kruskal-Wallis


statistic


shows


that


the


group


statistic


means

shows


are not all equal.


that


The Jonckheere-Terpstra


groups with more news items


have


smaller


mean decreases.


are


to


for









42


DISTRIBUTION


OF DRAW


TABLE 7
FOR PORTFOLIOS


BASED


ON FIRM


SIZE


FIRM SIZE PORTFOLIOS (1=SMALL, 5=LARGE)

1 2 3 4 5 N 65 64 67 66 63 Minimum -6.9927 -4.7255 -5.6859 -5.3322 -3.6112 Qla 0.0597 -0.1516 0.1686 -0.2734 -0.8354 Median 1.7415 1.7134 1.7880 1.0386 0.4658 Q3a 3.2472 3.5687 2.8520 2.2331 1.7973 Maximum 9.1723 8.4802 7.7009 6.5617 6.2081 Mean 1.4319 1.6038 1.5852 1.0865 0.6213 SD 3.1280 2.5617 2.3125 2.3807 2.0822 T 3.69 5.01 5.61 3.71 2.37 Pr > ITI 0.0005 0.0001 0.0001 0.0001 0.0210 W 3.76 4.29 4.87 3.39 2.18 Pr > IWl 0.0001 0.0001 0.0001 0.0006 0.0292 Hb 9.6564* jc 2.6670*


a Ql=first


b Test
not
rej e(
c Test
one


quartile;


Q3=


of H0: 'r =T2=...=7" the same. H is th ct the null at the of H0: '1=--= 5


Sl


trict


inequality.


third
5 versu


quartile.


Ha:


le Wallis-Kru 5% level if


versus


at least


one mean


skal statistic. H<9.49.


Ha: 71>_T2>. �.>5


J is


the


with


(r) is Cannot


at least


Jonckheere-Terpstra


statistic.
J<1.1645.
* Significant


Cannot


rej ect


the null


at the 5% level


if


at the 5% level


using


a one-tailed


test .












DISTRIBUTION


OF DMAJ


43

TABLE 8
FOR PORTFOLIOS


BASED


ON FIRM


SIZE


FIRM SIZE PORTFOLIOS (1=SMALL,_5=LARGE)

____1 2[13 2 4 15 N 65 64 67 66 63 Minimum -6.1745 -6.5091 -3.5399 -4.8611 -2.8960 Qva -0.1286 -0.5404 0.1559 -0.5313 -0.8236 Median 1.3908 1.8688 1.4322 0.7621 0.5403 Q3b 2.5956 3.8226 3.0869 2.9983 1.9154 Maximum 9.8017 8.5145 7.4536 6.4123 5.4624 Mean 1.2713 1.6674 1.6423 1.1678 0.6591 SD 2.9604 2.7884 2.2282 2.4313 1.9437 T 3.46 4.78 6.03 3.90 2.69 Pr > ITI 0.0010 0.0001 0.0001 0.0002 0.0091 W 3.52 4.19 5.08 3.35 2.36 Pr > IWI 0.0004 0.0001 0.0001 0.0008 0.0182 Hc 7.2795 1jid L_2.0676*


a Ql=first


quartile;


Q3=third


quartile.


Test


of H0:


not the


sam


r 1=-T 2-e. H


is


versus


=T5
the


Ha:


at least


Wallis-Kruskal


one mean


statistic.


(T) is Cannot


reject null at


C Test
one


the 5% level if H<9.49.


of H0: T ='T 2= ...=T5


strict


versus


inequality.


Ha: '1T2>'" 0> T 5


J is


the


with


at least


Jonckheere-Terpstra


statistic.
J<1. 645.
* Significant


Cannot


rej ect


the null


at the 5% level


if


at the 5% level


using


a one-tailed


test .









44


DISTRIBUTION


OF DBVUE


TABLE 9
FOR PORTFOLIOS


BASED


ON FIRM


SIZE


FIRM SIZE PORTFOLIOS (1=SMALL,_5=LARGE)


N 65 64 67 66 63 Minimum -7.0975 -2.8549 -3.3461 -2.3720 -2.8119 Qla -1.0527 -0.7551 -0.9388 -0.6734 -0.5887 Median -0.1229 0.2910 0.0761 0.1267 -0.0641 Q3a 0.9738 1.0409 0.9714 0.6136 0.8085 Maximum 5.4849 4.8623 6.2366 3.5934 2.4277 Mean -0.2467 0.3007 0.2396 0.1151 0.0835 SD 2.2479 1.4633 1.6395 1.0479 0.9478 T -0.88 1.64 1.20 0.89 0.70 Pr > ITI 0.3797 0.1051 0.2359 0.3756 0.4868 W -0.49 1.26 0.67 0.61 0.55 Pr > IWI 0.6242 0.2076 0.5028 0.5418 0.5824 Hb 1.6994 jc -0.3855


Ql=first


quartile;


Q3=third


quartile.


Test not t


of Ho: 1r=IT2=. ..=I5 :he same. H is th


versus Ha:


at least


e Wallis-Kruskal


one mean


statistic.


(T) is Cannot


reject the


C Test
one


of H0: strict


statistic. J<1.645.


null


at the 5% level if H<9.49.


TI--T2--...=--5 versus


inequality.


Cannot


rej ect


J the


Ha: r>r2>.>. .>r5 with at least is the Jonckheere-Terpstra n, null at the 5% level if









45


DISTRIBUTION


OF DBVUD


TABLE FOR POR


10
,TFOLIOS


BASED


ON FIRM


SIZE


FIRM SIZE PORTFOLIOS (1=SMALL,_5=LARGE)
, i iU


SI i
N 65 64 67 66 63 Minimum -7.2114 -2.7647 -13.6318 -10.3739 -1.0485 Q1a -0.7629 -0.3808 -0.5740 -0.5135 -0.3751 Median 0.0467 0.2756 0.0064 0.2192 -0.0104 Q3a 0.8941 1.1312 0.9512 0.8536 0.7990 Maximum 5.3639 4.8751 6.5185 3.4881 3.0709 Mean -0.0263 0.4357 0.0094 0.1248 0.1936 SD 1.9576 1.3613 2.3879 1.6614 0.8251 T -0.11 2.56 0.03 0.61 1.86 Pr > ITI 0.9140 0.0129 0.9745 0.5438 0.0673 W 0.09 2.27 0.92 1.74 1.14 Pr > IWI 0.9282 0.0232 0.3576 0.0818 0.2542 Hb 2.5566 jc -0. 1831


Ql=first


Test
not t


reject Test o one s


quartile;


of H0 .: 1='r2=. .. the same. H is


the null


Q3=third =7,,, versu


quartile.


, Ha:


at least


the Wallis-Kruskal


one mean


statistic.


(T) is Cannot


at the 5% level if H<9.49.


f H0: 71= "2= .�. . =75 versus


trict


inequality.


Ha:


is


r >r2->. ��1>r5 with at least the Jonckheere-Terpstra


statistic.
J<1.645.


Cannot


rej ect


the null


at the 5% level


if









46


The results


Tables


13


and


for the distributions


14,


respectively,


of DBVUE


provide


and DBVUD


in


no discernable


pattern.


With


the exception


of


group


3,


none


of the


group


means Terpst


decrease


are significantly ra J statistic i


in the relative


different ndicates t


from

that


information


i zero. there


The Jonckheere-


is


content


no monotonic of earnings


announcements


as one goes


from


low to


high


news


item.


However,


the Wallis-Kruskal


H statistic


indicates


that


the relative


information


content


between


groups


are not all equal.


In general,


supportive and DRAW,


the results


of each


other.


from Based


these


stratified


on the returns


the decrease in group means


tests are not


measures,


DMAJ


increases as one goes


from


high


market value


(news


item)


firms to low market


value


(news


items)


firms.


This


is consistent with


the information


transmission


informativeness


hypothesis.


of


Based on returns measures alone,


quarterly


earnings


reports


information


decrease


availability


after


dividend


firms


initiation.


experience


However,


the


once


greatest we adjust


for the contemporaneous


level


of returns


volatility,


this


information


effect


no longer


persists.


The


mean


standardized


variance


measures,


DBVUE


and DBVUD,


are not statistically


different exhibit an informatior


following


dividend


y significant availability


change


initiation.


in


groups.


going


There


Neither


from 1 appears


do they


Low to high


to be


no


monotonic


difference


in the


mean


change


in total


information


by information availability.


the low


for


for firms grouped


content


( DBVUD )









47


TABLE


DISTRIBUTION


OF DRAW


11


FOR PORTFOLIOS
ITEMS


BASED


ON NO. OF NEWS


FIRM SIZE PORTFOLIOS (1=LEAST, 5=MOST)

1 2 3 4 5 N 45 92 40 80 68 Minimum -5.6416 -5.0269 -6.9927 -5.6859 -4.2648 Q1a -0.7078 0.1753 0.4185 0.0847 -0.9469 Median 1.7415 1.5886 1.7555 1.3645 0.5221 Q3a 3.3723 3.2141 2.7990 2.5838 1.6834 Maximum 9.1723 7.7009 8.9110 5.9192 6.5617 Mean 1.5995 1.5428 1.4653 1.2798 0.5568 SD 3.0830 2-.4669 2.8481 2.1635 2.3436 T 3.48 6.00 3.25 5.29 1.96 Pr > ITI 0.0011 0.0001 0.0024 0.0001 0.0543 W 3.30 5.32 3.20 4.72 1.80 Pr > wI 0.0010 0.0001 0.0014 0.0001 0.0588 Hb 10.2878* jc 2.6193*


Ql=first


b Test


quartile;


of H0: T1=72=.


not the


rej ect C Test o:
one s


same.


Q3=third


quartile.


versus Ha:


* -=T5


at least


H is the Wallis-Kruskal


the null at
f HO T 1--:72


trict


statistic.
J<1. 645.
* Significant


inequa Cannot


the 5% level =r5 versus Ha lity. J is


rej ect


one mean


statistic.


if H<9.49.
: 1>'T2>... >_r5


the


the null


with


(r) is Cannot


at least


Jonckheere-Terpstra at the 5% level if


at the 5% level


using


a one-tailed


t~est.









48


TABLE


DISTRIBUTION


OF DMAJ


FOR PORTFOLIOS


BASED


ON NO. OF NEWS


ITEMS


FIRM SIZE PORTFOLIOS (1=LEAST,_5=MOST)

1 2 3 4 5
___ Ii ~ 23 1 4 1 5
N 45 92 40 80 68 Minimum -6.5091 -5.2964 -6.1745 -3.5399 -4.0065 Q1a -0.3792 -0.0396 0.0547 -0.1967 -0.8519 Median 1.8883 1.3843 2.0159 1.2342 0.3210 Q3a 3.4979 3.1635 3.3842 2.4626 1.7938 Maximum 9.8017 7.4536 7.1067 6.1621 5.9768 Mean 1.8230 1.4624 1.6080 1.1782 0.6300 SD 3.3569 2.4725 2.7286 2.0246 2.1987 T 3.64 5.67 3.73 5.20 2.36 Pr > ITI 0.0007 0.0001 0.0006 0.0001 0.0210 W 3.41 5.67 3.73 5.20 2.36 Pr > IWI 0.0006 0.0001 0.0003 0.0001 0.0182 Hb 9.4949* jc 2.7262*


Ql=first


quarti e;


Q3=third


quartile.


of H0:


not the


rej ect c Test o:
one s


sam


T 1=T2=
ie. H


the null


f H0: trict


statistic.
* Significant


is


versus


the


Ha:


at least


Wallis-Kruskal


at the 5% level


1=T 2.. .=T5 versus inequality. J


Cannot


rej ect


one mean


statistic.


(r) is Cannot


if H<9.49.


Ha: Ti>T2>...>T5


is


the null


with


at least


the Jonckheere-Terpstra if J<1.645.


at 5% level using


12


Test


a one-tailed


test .









49


TABLE


DISTRIBUTION


OF DBVUE


FOR PORTFOLIOS


BASED


ON NO. OF NEWS


ITEMS


FIRM SIZE PORTFOLIOS (1=LEAST,_5=MOST)

1[ 2 3 4 5 N 45 92 40 80 68 Minimum -7.0975 -3.9293 -1.5222 -3.3461 -7.0369 Q1a -0.0897 -1.0469 -0.2689 -0.4310 -0.8351 Median 0.0336 0.0293 0.7221 0.0897 -0.1813 Q3a 1.0041 1.0610 1.3258 0.6437 0.4573 Maximum 4.4923 6.2366 4.8623 5.1343 2.8748 Mean 0.0024 0.1548 0.6755 0.0632 -0.2103 SD 2.1131 1.7655 1.1946 1.1790 1.2715 T 0.01 0.84 3.58 0.48 -1.36 Pr > ITi 0.9940 0.4024 0.0009 0.6328 0.1771 W 0.45 0.38 3.28 0.70 -1.46 Pr > awl 0.6528 0.7040 0.0010 0.4840 0.1442 Hb 11.8148* jc 1.4030


Ql=first


Test


of H0


not the


rej ect Test o one s


quartile; " 1= T2="


same.


Q3=third =T5 versus


quartile.


Ha:


at least


H is the Wallis-Kruskal


one mean


statistic.


(T) is Cannot


the null if H<9.49.


�H: T1T2=. 7


trict


statistic.
J>l. 645.
* Significant


versus


inequality.


Cannot


rej ect


J


Ha: T1 >T2>.�.>T5


is the


the null


with


at least


Jonckheere-Terpstra at the 5% level if


at the 5% level


13


using


a one-tailed


test .









50


TABLE


DISTRIBUTION


OF DBVUD


FOR PORTFOLIOS


BASED


ON NO.


OF NEWS


ITEMS


FIRM SIZE PORTFOLIOS (1=LEAST, 5=MOST)

1 2 3 4 5 N 45 92 40 80 68 Minimum -7.2114 -13.6318 -1.3339 -5.7861 -3.1993 Q1a -0.7629 -0.5921 0.1358 -0.3647 -0.5277 Median 0.1868 0.0714 0.7632 0.1048 -0.0717 Q3a 1.0446 1.1932 1.2732 0.7652 0.4795 Maximum 4.0260 6.5185 4.8751 3.4881 3.1993 Mean 0.0482 0.1039 0.7711 0.0608 -0.0028 SD 1.9836 2.4677 1.1524 1.0976 1.0661 T 0.16 0.40 3.58 0.48 1.36 Pr > ITI 0.8713 0.6873 0.0001 0.6223 0.9828 W 0.80 1.04 3.78 1.39 -0.48 Pr > IWI 0.4238 0.2984 0.0002 0.1646 0.6312 Hb 11.8098* jc 111.2855


Ql=first


Test


S


quartile; : T 2"** ame. H is


the null at


H0�
rict


statistic.
J<1. 645.
* Significant


Cannot


Q3=third


versus


the


rej ect


quartile.


Ha:


at least


Wallis-Kruskal


one mean


(7'r)


is


Cannot


the 5% level if H<9.49.


sus Ha: J is the nL


at least


the ill


14


of H0


not the
reject I o Test of
one st]


T1=T2=. . . =T5 VE inequality.


statistic.


r1>r2>... >T5 with


Jonckheere-Terpstra at the 5% level if


at the 5% level


e r


using


aone-tailed


test .









51


This


contradicts


one would released


expect


the information the increase in


transmission


the proportion


for low


hypothesis


of publicly information


availability


firms.


This


returns


inconsistency information


investigation


of


between content


returns


the standardized


measures

volatility


calls


variance


for


and


further dividend


initiation.


returns


It is possible


volatility


that a contemporaneous


the results


decrease


obtained.


This


investigated


in the next


chapter.


The Market


Reaction


to Earnins


Chanaes


going


contemporaneous


on


decline


to


examine


in returns


the


variance,


effect


of


I analyze


price


reaction


to reported


quarterly


earnings


change.


This


section


provides


further


proof


that


what


has been


previously


interpreted


as a decrease


in earnings


informativeness


caused


dividend


initiation


may


be due to


a contemporaneous


decrease


in returns


volatility.


The relationship


between market price


reaction and


reported


earnings


is modeled as


CERjq


- Po


/31D


+ i3AEjq


+)34DAEjq


The 3-day


cumulated


excess


returns


for the day prior


of and the day following


the earnings


announcement


date


for quarter


information


to be greatest


as


around


is producing


in is


Before


the the


by


day


(1)


to


the


and f irm


is denoted


by CERjq.*


The dummy









52


variable


D takes


on the value


1 if the quarterly


earnings


announcement


comes


from


the post-dividend


initiation


period


and is


zero


otherwise.


The earnings


surprise


or standardized


earnings deflated earnings


is the quarterly


by


the firm's


share


price


in earnings


two days


per


before


announcement.


EPSjq


- EPSjq-1


Pj ,t-2


The change random-walk


in quarterly expectations


earnings model. D


is appropriate


eflating


by


based


the share


on a

price


captures


formed


any expectations


through


about


the forthcoming


gathered


between


earnings earnings


announcements.


regression


approach


allows


us


to


incorporate


information


contained


in the earnings


report


(namely,


change

above


in earnings


analysis


per share)


based


on


which


groups.


we could


This


not do in the


for the


potential


bias


that


could


occur


if earnings


changes


smaller

similar


or less


to


variable


the approach


in the post-dividend


used


by Healy


period.


and Palepu


This


(1988) .


The average


price


response


to earnings announcement


for the


sample


is


in the post-dividend


go. Any change


initiation


in the period


average


price


is captured


response


by j1,


average


market


price


reaction


to


a given level


of earnings


is 92- It is expected


change


share


the


AEjq


information


The


the


controls


whole


are


is


the


and


indicating


to be positive


that


surprise









53


the market improvements


responds (declines)


positively


The


averag(


(negatively) e post-dividen


to earnings Ld initiation


adjustment


to 82


If dividends


is 83 " enable


investors


to revise


their


expectations


of earnings,


will


their


be reduced.


forecast However,


errors


measures


in the post-dividend period


of unexpected


earnings


which

ref lec


only incorporate t this additional


quarterly informatio


earnings changes n obtained from c


will


not


dividends.


Standardized estimates of


quarterly unexpected


earnings earnings.


changes Therefore,


will


the m


be noisier arket price


response reduced.


to


our measure


Coefficient


of earnings


92 is expected


surprise


should


to be negative.


I estimate


each


firm


in my


equation


sample,


(1) using 8 quarters


ordinary


least


preceding and


squares. following


initial


dividend


are collected.


A quarter


is excluded


if the


announcement announcements


is


within


joint,


2 days


i.e.


of each


earnings


other.


and


A quarter


dividend


is also


excluded


i f there


was no data


on net income,


share


price


shares


outstanding


to calculate


the standardized


earnings.


require


that


a firm


had 4 usable


pre-


and post-dividend


initiation


quarters.


This


requirement


excludes


58


firms


leaving


274 firms


for the regression analysis.


For comparison,


I also


ran


the regression


using


measure BVUE.


be


For the


or


the


adjusted


variance









54


BVUEjq


= P


+ 31 AEjq


+ 94DI AEjq I


This regression market response and the reported


initiation.


thus


analyzes


adjusted


quarterly


Because


the relationship


for the firms's


earnings


the earnings


: before surprise


between


returns


and after


BVUE


the


volatility


dividend


is based


on


squared absolute


excess value


returns


and is always


of the earnings


surprise


positive,


use the


as the independent


variable.


The interpretation


on P2


and 93


remains


the same.


The results


of the regressions


are shown


in Table


15. For


the 3-day


cumulated excess returns


CER


, the sample


mean


5.4832


and


is


highly


dividend-initiating


revaluation


firms


at quarterly


significant. experienced


earnings


This


indicates


an average


during


5.5%


the 2 year


that price period


preceding


dividend


initiation.


The coefficient


fl


is -0.8237


and is highly


significant.


For the quarters


in the 2 year


period


reaction


following


is reduced


dividend


by


initiation


0.82%.


These


the


results


average are of


price course


restatements


of those


obtained


in Table


The coefficient


02


is 0. 1925


and significant,


indicating


that


the announcement


of


a 1%


increase


in standardized


earnings However,


on average


leads


to


the coefficient


For post-dividend


a 0.2%


has


quarterly


increase


a positive


earnings


in stock


value


announcement


price.


of 0.1828. nts, a 1%


increase


in standardized


earnings


on average


gives


rise


in stock


(2)


g0


is


5.


to


+81GD


0.38%


increase


price.


This


result


directly









55


MARKET


PRICE


TABLE RESPONSE TO


15
STANDARDIZED EARNINGS


a Model estimated


+PD


+ 3A Eq


+ 04DAEq


Model estimated is


BVUEq


= PO


+PD + P3


+ D I AEq


CER is the cumulated 3 day excess returns; one day prior to, the day of and the day following the quarterly earnings announcement. D is a dummy variable which takes on the value 1 if the quarterly earnings occurs after dividend initiation and is zero otherwise. The standardized earnings is the change in earnings per share from the prior quarter divided by the stock price two trading days before the


earnings


announcement.


AE =


EPSq


- EPSq_1
Pt-2


t-statistics
* Significant
** Significant


a
a


in parentheses. t the 1% level t the 5% level


using using


two-tailed two-tailed


test. test.


Dep. Var. CERa BVUEb 80 5.4832 (47.538**)c 1.9508 (24.385**) 11 -0.8237 (-5.025**) -0.0698 (-0.613)

P2 0.1925 (2.174*) 0.0131 (0.214) P3 0.1828 (0.480) 0.1232 (0.465)

F 10.376** 0.224

Adj. R2 0.0072 -0.0006

No. of Firms 274 No. of Obs. 3901


is


CERq


=O









56


contradicts


the notion that


dividends


convey


information


that


leads


investors to better


forecast


earnings.


The coefficient


93 is, however,


not statistically


significant.


My regression


results


yield


results


diametrically


opposite


to those


obtained


significantly


by Healy


negative


930.


and Palepu I suspect


(1988) .


that


They


this


obtain


difference


arises


out of


a difference


in regression


technique


and model


specification.


unrelated


Healy


regressions


and Palepu technique


apparently use for estimation.


a seemingly


This


their these


estimates


firm


of 0


specific


and /32


coefficients,


specific.


model


In obtaining specification


excludes

volatility negative.


the


Al1


is thus


coefficient.


forced


onto


Any


P3


decrease


which


in


biases


returns


it to be


variance results.


measure


for the regression of informativeness


The relative


informativeness


using


is consistent


of quarterly


with


prior


earnings


response


announcement


after


at quarterly


periods


dividend


earnings


information.


announcements


is not significantly


The


relative related


market


to


non-


to the


earnings


reported,


nor does


this


responsiveness


change


after


dividend initiation.


to be firm


allows


their


The results


does


the standardized


not change















CHAPTER


RETURNS


VOLATILITY


AROUND


DIVIDEND


INITIATION


In the previous


chapter,


I examined


the information content


of quarterly initiation.


r earnings announcements before a The information content measures


Ind after


used


dividend


are directly


related


to the volatility


for the observed


decrease


of returns. in the pric


A possible e reactions


explanation associated


with


post-dividend


initiation


earnings


announcements


is that


returns


volatility


has decreased.


Declines the process


in returns


by


which


volatility information


may


be caused


is impounded


by changes in in price. For


example,


dividend


initiation


may


induce


greater


consensus


among


traders.


This


decreases


noise


trading


and bid-ask


spreads


and consequently returns variance.


However,


volatility


also


may


decrease


due to causes that


are independent


information motivated experience increases proportionally smaller


Finally,


reason.


in


Dividend


stock


bid-ask


the observed volatility


price


spreads


decreases


initiating which lE


and reduced


in


Eac


firms I to


leverage.


my sample


could


be due to unrelated


market


wide


changes


in interest


rates.


this


chapter,


I perform


several


tests


to distinguish


causes of


the observed


volatility


decrease.


I conclude


that


57


of


any


In


the









58


information


is not the only


plausible


cause


for volatility


decline.


Variance


Changes


in the Overall


Sample


Table


16 shows


the distribution


of percentage


changes


variances


calculated


for different


sampling


periods


around


dividend and 100


initiation.


trading


The sampling


days


periods


immediately


cover


before


900,


500,


and after


declaration


of the


initial


dividend.


Fewer


firms


are available


for the longer


sample


periods


because


of the longer


sequence


of returns


required.


For the 100,


250,


500 and 900 day


sampling returns,


periods,


I require


respectively.


at least


Variances


90, 240,


are calculated


480 and 850 as the sum of


the squared approximately


The percentage


continuously


equals change


E[R2]


compounded for short


in variance


returns


since


measurement


Var[R]


intervals.


is


Percentage variance


change


= 100lx [i


VARPOST ] VARPRE


A variance


decrease


after


dividend


initiation


gives


rise


positive


percentage


change.


The


mean


100 day) measured


percentage


sampling returns vo


change


periods.

latility.


is positive


This


for all


indicates


The median


is


(except


a decline


a better


the in


statistic


since the mean percentage variance


to


in


250

the


to


is


of location


change








59


DISTRIBUTION OF


TABLE 16
PERCENTAGE VARIANCE CHANGES


a Percentage


variance


changes are


calculated as


Percentage variance change =


loo [1 VARPOST] 10 0 x [ 1 VARpsT


Variances are calculated using daily continuously compounded stock returns from sample periods of 900, 500, 250 and 100 trading days before and after the declaration
day of the initial dividend.
b For 900, 500, 250, and 100 day sampling periods, a firm is
included if it has 850, 480, 240, and 90 valid returns,
respectively.
C Number of firms in sample with positive percentage variance
changes, i.e. variance decreases after dividend initiation. d Shapiro-Wilk test of normality. e Test of H0: Percentage variance change = 0.


Perioda 900 500 250 100 Sampleb 225 304 327 331 Min -260.12 -310.63 -382.41 -1196.15 Q1 13.05 -12.14 -20.03 -25.86 Median 39.44 33.65 20.33 9.68 Q3 55.20 54.58 43.73 36.83 Max 80.59 86.77 80.31 81.33 Mean 26.94 13.50 3.52 -10.06 SD 46.29 59.78 61.69 92.38 No. Pos.c 182 212 216 186 % Pos. 80.9 69.7 66.1 56.2 Dd 0.8884 0.8354 0.8152 0.6024 Pr>IDI 0.0 0.0 0.0 0.0 Te 8.47 3.94 1.03 -1.98 Pr>ITI 0.0001 0.0001 0.3035 0.0484 We 8.59 5.79 4.09 1.14 Pr>I W1 0.0001 0.0001 0.0001 0.2559


A









60


downwardly


biased.1


The medians


are positive


for all sampling


periods sampling


and always


period,


the


greater


more


than


the means.


apparent is the


The longer


volatility


the


decline.


The median


percentage


change


is 39% for the 900 day sample


period,


this


declines


to 10% for the 100 day sample


period.


The proportion


of firms


experiencing


volatility


declines


also


increases


with


longer


sampling


periods:


56% for the 100 day


sample


period


increasing


to 81% for the 900 day sample


period.


The Wilcoxon percentage


(except


signed-rank


variance


the 100 day)


tests


change sample


of the null


is


zero


hypothesis


are rej ected


periods.


When


examining


changes


in stock


return


variance,


it is


important


to consider


changes


in market


volatility


through


time.


This


is particularly


so for my


sample which


has dividend


initiation


dates


dispersed


over


17 years


and where


there


also


considerable


possible


that


any


clustering


observed


in the middle


change


in market


seventies.


variance


It is around


time


of


contemporaneous


divider changes


initiation


in market


is


To address


this


possibility,


I divide


firm


variances


estimate


of market


volatility.


This


estimate


is obtained


from


the contemporaneous


returns


on an equally


weighted


market


index


of all


firms


on the relevant


data


tape.


This


adjustment


is in the spirit


of


a heteroskedasticity


adjustment,


where


From Jensen's


that


the


for all


the


is


explained


volatility.


by


by


an


the


>E [VpRE]/E [VposT] �


inequality,


E [ VpRE/VposT ]









61


stock' s market. changes


return


Table where


variance is assumed


17 shows


the firm


proportional


the distribution


variances


have


to that


of the


of percentage variance


been


adjusted


contemporaneous estimate


of the market


variance.


It


appears


that


changes


in market


volatility


explain


part


of the variance


decline.


Median


standardization


percentage


but


are still


changes positive.


are


smaller


The Wilcoxon


after


signed-


rank


sample


tests can


period.


only


This


rej ect


provides


equal some


variances


evidence


for the 900 day


that


clustering


the data


sample


may explain


the observed


variance


decreases.


Variance


Ratio


Tests


Consider


returns


calculated


over


intervals


of k days


where


If daily


returns


follow


a random


walk,


the variance


these


k-day returns


should


be k times


the variance


of daily


returns.


French


and Roll


(1986)


model


a stock's


return


having


3 components:


a rational information


(intrinsic


value)


component, component.


a noise


If noise


or mispricing


component


and bid/ask error


and


components


a bid/ask


error


are temporary


components,


over


time,


they


will


be corrected


and induce


negative


autocorrelat ions.


This


negative


autocorrelation


causes


the k-day


variance to be


less


than


k times


the daily


variance. variance


A variance


ratio


(VR)


is the ratio


of k-day


returns


to daily returns variance


by


of


k>l.


of


as


and Roll


(1986)


(French








62


DISTRIBUTION


OF MARKET


TABLE ADJUSTED


17
PERCENTAGE VARIANCE


CHANGES


a Percentage


variance


changes are calculated as


Percentage variance change = 100 x [1


VARPOST]
VARpR


Variances are calculated using daily continuously compounded stock returns from sample periods of 900, 500, 250 and 100 trading days before and after the declaration day of the initial dividend. Firm variances are divided by the returns variance of a value-weighted market index. b For 900, 500, 250, and 100 day sampling periods, a firm is
included if it has 850, 480, 240, and 90 valid returns,
respectively.
C Number of firms in sample with positive percentage variance
changes, i.e. variance decreases after dividend initiation. d Shapiro-Wilk test of normality. e Test of H0: Percentage variance change = 0.


Perioda 900 500 250 100 Sampleb 225 304 327 331 Min -198.86 -272.93 -406.08 -803.86 Q1 0.36 -31.79 -31.85 -43.60 Median 28.73 13.29 6.28 5.22 Q3 48.10 45.73 39.83 30.41 Max 90.54 80.75 79.44 79.81 Mean 17.73 -2.74 -6.50 -10.06 SD 46.33 66.64 66.25 92.15 No. Pos.c 169 181 184 176 % Pos. 75.1 59.5 56.3 53.2 Dd 0.8452 0.8544 0.8367 0.6862 Pr>IDI 0.0 0.0 0. 0.0 Te 5.74 -0.72 -1.77 -3.85 Pr>ITI 0.0001 0.4736 0.0769 0.0001 We 7.21 1.84 0.75 -1.70 Pr>IWI 0.0001 0.0666 0.4556 0.1359









63


call


these


actual-to-implied


variance


ratios) .


1 Var
k


Var (Rl)


where


Rk is the k-day


return.


Under


the null


hypothesis


that


returns


follow


a random


walk,


the VR should


be equal


If there


are transitory


components,


the VR should


be smaller


than


one.


the transitory


used


French


in recent


(1988) ,


The VR therefore


bid/ask studies


Lo


allows


and noise


by


and


French


Mackinlay


us to gauge components.


and Roll


(1988)


the effect


VRs have


(1986) ,


and


Fama


Kaul


of


been


and and


Nimalendran


and Richardson


(1990) .


and S


Cochrane (1 mith (1991)


L988)


Lo and Mackinlay


analyze


the properties


(1989),

of the


VR test


statistic.


In general,


they


find


that


the VR test


statistic


is robust


and has higher


relative


power


compared


with


alternative


test statistics.


If dividend


initiation


reduces


the bid/ask


error


component


(either


due to improved


liquidity


or reduced


information


asymmetry)


or noise


trading


(better


informed


traders


with


greater consensus)


then post-dividend


initiation VRs


should be


closer to


the summary


one than


pre-dividend


statistics


for 10-day


initiation


and 20-day


VRs.


Table


return


18 shows


variances


It


of es basic


can be shown


;timated


that


VR(k)


autocorrelations


measurement


interval


can be written


of returns


(daily


in


as a function


measured


over


our case).


+ -[ (k-l) P
k


+ (k-2) P2


0 a + Pk-i]


(Rk)


to


one.


VRk


the


VRk =









64

TABLE


PRE-


DISTRIBUTION
AND POST-DIVIDEND


OF VARIANCE RATIOS


INITIATION


FOR


500 DAY SAMPLE


PERIODS.


Sample = 310

VRo 1IVR20


PRE


POST


DIFFa


PRE


POST


DIFFa


Minimum 0.294 0.326 -0.867 0.215 0.250 -1.093 Q1 0.775 0.794 -0.180 0.743 0.709 -0.178 Median 0.949 0.950 0.018 0.915 0.917 0.030 Q3 1.166 1.142 0.171 1.211 1.148 0.234 Maximum 1.942 1.921 1.114 2.532 2.078 1.504 Mean 0.993 0.983 0.010 0.985 0.960 0.025 Tb 0.61 (0.5406d) 1.18 (02392 Wc 0.51 (0.6077d) 1.31 (0.1921a)


a For each
dividend


firm,


the difference


initiation variance ratios


between


pr


is DIFF


ee- and = PRE -


post
- POST.


t test


of H0:


DIFF


- 0.


Wilcoxon signed-rank


test


of H0:


DIFF


d Significance


level


of two-tailed


test.


obtained


from


500 day sample


periods


before


and after


dividend


initiation. multiplying


The 10-day


10 (20)


500 day sample


20-day


returns.


and post-dividend


period


(20-day)


successive


there


The VRs are


initiation


returns


returns.


10-day


daily


are fifty


all less than


periods.


Mean


are obtained


Therefore, an twenty


one for both


and median


by


in


five prepre-


dividend


initiation significant


initiation


VRs but (Wilcoxon


VRs this


are


smaller


difference


signed-rank test).


than


post-dividend


is not statistically


Therefore,


I cannot


18


= 0.









65


conclude


that


variance decreases are due


to reductions


in bid-


ask spreads


or noise


trading.


The Timing


of Variance


Changes


results


show


that


the


decrease


in


the


price


revaluations


at earning


announcements


in the post-dividend


initiation


returns


information


period


may


variability.


effect


be related


This


associated


decline


with


to the general


may


be


dividend


decline


caused


by


initiation.


ascribe variance


causality


changes


to this should


information


occur


after


effect


the


requires


initiation


that


of


dividends.


systematically


On the other


occur


hand, after


if variance


dividend


changes


initiation,


do not


the


plausibility questionable.


of


an dividend


initiation


information


effect


In this


section


I I analyze


the timing


of variance


changes.


I first successil


examine percentage ve 250 day sample p


varian( periods


changes


beginning


calculated 750 trading


over days


prior


to dividend


initiation.


Due to the long


series


returns


required,


only


272 firms


are included


in the sample.


Table


changes. declines the year


19 shows


the distribution


The sample


in the


year


of these


of firms experience


prior


to (period


percentage


significant


[-250,-i])


variance variance


as well


as


the initiation


The


in an To


is


of


- - - - - - -I I - -I


I


of dividends.


(period


[ 1,250] )


af ter









66


TABLE


SUMMARY (250


a Percentage
calculated


STATISTICS DAY SAMPLE I


variance


OF PERCENTAGE


PERIOD)


changes


VARIANCE


FOR SUCCESSIVE


for period


CHANGES ?ERIODS


(-500,-251)


as


Percentage


variance


change


= 00x [i


VAR(-500,


-251), ]


VAR(-750,-501)


The period
dividend ir 15. Day 0 i b Number of f
changes, i. C Test of H0:


(1,250) nitiation


is comparable percentage va


Ls the declaration date


irn
e.


to the


pre-


Lriance changes of the initial


and postin Table dividend.


is in sample with positive percentage variance variance decreases after dividend initiation.


Percentage


variance


change


- 0.


Therefore


the variance


decline


does


not


occur


strictly


after


dividend


initiation.


There


is significant


variance


decline


year


prior


to dividend


initiation.


To better


successive


examine


overlapping


when variance declines


periods


of 100 trading


occur,


days.


I examine For each


firm,


I collect


400 trading


days


of returns:


200 before


initiation of dividends


19


Sample 272 Perioda -500,-251 -250,-1 1,250 251,500 Median 7.266 13.29 6.28 5.22 Mean -5.052 -2.74 -6.50 -10.06 No. pos.b 129 164 161 136 % Pos. 47.4 60.2 59.1 50.0 Wc 0.98 4.40 2.75 1.20 Pr>IWI 0.3297 0.0001 0.0026 0.2324


are


the


in


the


.0


I divide


these


400 days


and 200 after.









67


into seven 100


day samples


with each


sample overlapping


prior from


from from


sample


day


day day


-200

-150

-100


by


50 days.


to -101;


to -51;


Sample sample


sample


period period


period


one consists two consists


three


to -1; and so on. The final


consists


sample


of returns of returns of returns


period


seven


consists


of returns


from


day


101 to 200.


The sample


periods


are shown


in Figure


-200


2.


-100


I I H I


Sample
periods


Sample


Periods


Figure 2 for Detecting


Variance


Changes


Within


each


sample


period,


I test


whether


a significant


variance


decrease


has occurred.


For each


firm,


I count


number


of significant variance


decreases


occurring


in the


pre-


dividend dividend


initiation initiation


sample sample


periods

periods


1, 2 and 3 and the post-


5, 6,


and 7. Denote


these


counts


as COUNT1


and COUNT2,


respectively.


If


change is more likely


a variance initiation,


the


100
I


200
1


the


I| I , i


to occur following


dividend









68


COUNT2


is going


to be greater


than


COUNT1.


The test


therefore


COUNT2-COUNT1


I test


this


= 0


hypothesis


versus

using


H1:


the


COUNT2-COUNTI


Wilcoxon


> 0


signed-rank


statistic.


To detect


a variance


decrease


within


a sample


period,


I use


a test statistic (1977, 1979). LE daily returns R1


normal


variates.


T* which et M be a

,R2 f * ,RM

Further,


has been


extensively


consecutively


which


examined


observed


are assumed


let the variances of


by


sequence


Hsu of


to be independent


these


variables


be represented


by a121(T22 10*1


respectively.


The problem


to test


H0:


whether
2 2 2 U.1 = 2


a variance


0 0 &


shift


= cM = 02


6 has occurred,


(ao2


is unknown)


against


* * 0 = kM2


= 2


co


- Uk+2


0 0 *


= cM -


c0 +6,


16 >0,


where


k is


(c02+6) ! (0,o) .


unknown Define


(k=1,2,


S M-l)�


Xi= (Ri-A)


6 is


unknown


The


and


test


statistic


T is defined


as


S
l=l


(i-1)


xi


I O

(M-I)


is


is


2


Ha:


U~k+l1


xi


i=1


i-l, 2, ...,M.









69


Under


the null


hypothesis


of


no variance


shift,


this


test


statistic 1) (M+2) ].


has mean


use


E[T]=


the


and variance


sample


VAR[T]= (M+I) / [6 (M-


approximation


0.5)/(VAR[T] )1


which


is distributed


asymptotically


standard


normal.


In this


test,


I calculate


T


for each


sample


period


with


M=100. sample


level


firms3.


I deem

period of just


that


a variance


if T*<-2.6.


under


0.5%.


This


decrease


corresponds


firm


to


sample


83 had


in that


is 328


variance variance


changes


increases.


in the 7 sample


189 firms


(58%


periods.


56 firms


of the sample)


had only


had at least


one variance decrease


in the 7 sample


periods.


The results


analysis


are


shown


in


Table


20.


The


frequency


distribution


show


a preponderance


dividend


initiation


(i.e.


decreases f variance


in sample


by sample changes


periods


periods


does


occurring


5, 6 and 7).


not


after This


is confirmed


by


the Wilcoxon


signed-rank


statistic's


value


0.70.


I conclude


from


this


analysis


that


the decrease


in returns


variability

initiation.


does


not


Variance


occur


decreases


systematically


occur


with


after equal


dividend frequency


before


and after


dividend


initiation.


Four


an uninterru subsequently


OTC firms


pted


were


series


excluded


because


they


of consecutive returns.


did not have These firms


traded on either the AMEX


large


T*= (T-


has occurred


Of the 328 firms


The total examined,


a significance


size


no significant


this


of variance


of


of


or NYSE .









70


ANALYSIS


TABLE 20 OF THE TIMING OF


VARIANCE


DECREASES


a The announcement


A variance


date


decrease is


of the initial


deemed


to have


dividend n. occurred


is day if T*


'1.
<-2.6


for that


sample


period.


Variance before a


decrease nd after


occur with


dividend


equal


initiation.


COUNT2


- COUNT 1


against


Variance
dividend


decreases


occur


more


initiation.


COUNT2


- COUNT1


where


COUNT1


TOTAL


OCCURRING


NUMBER


OF


IN SAMPLE


VARIANCE PERIODS 1


DECREASES 2 OR 3 FOR


FIRM j.


TOTAL


OCCURRING FIRM j.


NUMBER


OF


IN SAMPLE


VARIANCE PERIODS 5


DECREASES 6 OR 7 FOR


The Wilcoxon


signed-rank


statistic


W is 0.70.


Cannot Sample


reject null at the


is 328 firms.


Sample 1 2 3 4 5 6 7
Period

Beginning -200 -150 -100 -50 1 51 101
Returns ____________ ____Ending -101 -51 -1 50 100 150 200 Returna
Decrease sb 36 41 31 33 44 39 35


H0:


frequency


=0


frequently


after


> 0


COUNT2


5% significance level.


size









71


The Effect


of Clustered


Data


I examine


the possibility


that


specific


periods are causing


the observed


decrease


in firm


volatility.


Just


over


75% of


sample


firms


initiated


dividends


in the period


1972-77,


with


over


50% occurring


between


1975-1977.


The effect


of interest


rates on volatility


is particularly


relevant


to


my sample.


seventies experienced


volatile


changes


in interest


rates.


particular,


interest


rates


rose


from


1972-73


and fell


from


1974-76. It is plausible my sample, pre-dividend


that


for


periods


a large


number


coincided


witt


of firms in

ia rising


interest


rate


volatility)


environment


and post-dividend


(and


periods


coincided


high with


returns falling


interest


rates


(low


returns


volatility).


If the decrease


in firm


volatility


is independent


secular variance


influences,


declines


the proportion


should


of firms


experiencing


Table


21 shows percentage variance changes


calculated


for 500


sample


periods


distributed


by


the


year


of dividend


initiation. experiencing


The table variance


shows


that


declines


the proportion


is not constant


of firms


across


years.


In particular,


the periods


1972-73


and 1978-79


have


majority of the period


firms


1975-76


experiencing variance increases. Conversely,


which


accounts


for almost


40% of


my sample


of dividend


initiating


firms


has above average


proportion


4
(1989)


See,


for example,


Livingston


p . 37.


my


The


In


consequently


day


of


not cluster around


certain


periods.


the


of


(1990)


p .,


or Madura









72


TABLE


21


OF PERCENTAGE


BY YEAR


VARIANCE


OF DIVIDEND


CHANGES


INITIATION


Year No.a a pos.b % Pos . Mean Median

1970 6 5 83.3 20.4 24.5 1971 4 4 100.0 34.2 33.2 1972 18 1 5.6 -73.1 -33.2 1973 29 4 13.8 -57.6 -63.9 1974 24 16 66.7 4.5 19.1 1975 58 57 98.3 54.5 56.8 1976 62 62 100.0 53.4 57.2 1977 47 33 70.2 13.1 26.6 1978 20 9 45.0 -22.4 -4.3 1979 11 4 36.4 -7.2 -4.6 1980 9 6 66.7 8.0 31.1 1981 6 5 83.3 21.8 35.0 1982 3 3 100.0 19.8 16.8 1983 5 4 80.0 23.7 21.7 1986 3 0 0.0 -60.1 -69.0


a Number b Number
changes C Sample


of sample of sample


firms firms


i.e. variance


firms


with


of sample


initiating
with posit decreases.


tive


percentage


firms initiatin


in


that


percentage


variance ch ig dividends


year.


variance


a


nges as in that


(500


DISTRIBUTION


DAY SAMPLE


PERIOD)


a percentage year.


dividends


positive









73


variance


decreases.


This


pattern


is also


found


for variances


calculated


for 900,


250 and 100 day sample


periods.


Table changes


22 shows


adjusted


by


the distribution


a estimate


of percentage


of the contemporaneou


variance s market


variance.


Note


that


after


adjusting


for market


variance,


mean and median

period 1975-76.


experience


percenta Firms


decreases


ge variance initiating


in variances


change

dividen


but o f


is negative f ds in those


a smaller


for the years


magnitude


than


the market.


The behavior


of sample


firm


variances


through


time


plotted variance weighted


in Figures


of


an index


average


3 through 5. of dividend


of all firms


Figure 3 plots initiating firms on the NYSE/AMEX


the monthly


(VAR)


and


and NASDAQ


tapes. monthly


For each returns


month


(m=1/70


variance


for


to 12/86),


VAR is the average


a set of dividend


initiating


firms


within


Nm 0


A firm


24 months


is included of month m.


in Nm


if it initiated


dividends


Varj,


The monthly


returns


variance


for the firm


is calculated


Varj ,m


where


Tm


is the number


of trading


days


in month


m. If


a firm


has less

from VAR.


than


15 valid


returns


in any


month,


it is excluded


the


is


VARm


Nm

Nmj=


Tm
=1


R2j


as


VAR thus represents


variance


monthly


for


the mean









74


DISTRIBUTION


OF MARKET


TABLE ADJUSTED


22
PERCENTAGE


VARIANCE


CHANGES


DAY SAMPLE


PERIOD)


BY YEAR


OF DIVIDEND INITIATION


Year No. pos.b % Pos . Mean Median 1970 6 2 33.3 -3.9 -9.7 1971 4 1 25.0 -14.9 -15.6 1972 18 14 77.8 28.8 58.1 1973 29 29 100.0 51.4 51.8 1974 24 22 91.7 31.0 37.7 1975 58 9 15.5 -48.4 -36.5 1976 62 24 39.7 -50.5 -16.2 1977 47 35 74.5 18.6 32.0 1978 20 18 90.0 45.2 53.3 1979 11 11 100.0 39.9 42.5 1980 9 6 66.7 17.3 34.5 1981 6 5 83.3 25.0 33.9 1982 3 2 66.7 -6.5 8.8 1983 5 1 20.0 -38.6 -22.9 1986 3 3 100.0 63.7 61.8


a Number b Number
changes C Sample


of sample of sample


firms


firms
firms


initiating with posit decreases.


rive


in


that


year.


variance


hanges


as


a percentage year.


of sample firms initiating


dividends


in that


(500


i.e. variance


with


dividends


positive


percentage


percentage variance c









75


the sample


of dividend


initiating


firms


through


a moving


window


of 24 months.


From


1/70


to 12/72,


the equally-weighted


VarM


is the monthly


NYSE/AMEX


market


returns


index.


variance


From


1/73


12/86,


VarM


is


a weighted


average


of the monthly


returns


variance indices.


of the equally-weighted


NYSE/AMEX


and NASDAQ


market


VarMm


= wVarNYSE/AMEX,m


+ (1-W) VarNASDAQ,m


The weight NYSE/AMEX variances


w corresponds


firms


to the


in the sample


of the market


indices


proportional


(w


= 247/332) .


are calculated


representation


of


The monthly


in the same


way


as for individual


firms.


Figure


3 shows


the wide


variations


in returns


variance


the sample.


The returns


variance


is particularly


high


in the


years


1973


to 1976.


The sample


variance


behaves


similarly


the wider market.


Therefore,


variations


in return variance


not peculiar


to the sample


of dividend


initiating


firms.


Figure


4 plots


the monthly


variance


and the distribution of


firms

total


initiating dividends,


sample


INIT) .


This


expressed


figure


as a percentage


illustrates


of the


the effect


of


clustering

initiating


in the sample.


dividends


between


The large


1975


and 1977


proportion


follows


of firms


a period


of


above


average


returns


variance.


This potentially could


explain


the reported


decrease


in returns


variance


and quarterly


revaluations.


of

to


of


to


are


price


earnings









76


Figure


Monthly


Returns


Firms


(VAR


Variance
.) and a


of


an Index


Weighted


of Dividend


Market


Index


Initiating


(VarM) .


Figure


rate. Rates:


Reserve


The


5 plots source


Money


the monthly variance and


for the 3 month


and Capital


Bulletin.


Market


The figure


T-bill


Rates

shows


the 3-month


rate


tables


that


T-bill


is the Interest


from


the Federal


the increase


in


returns preceded


variance


by increases


in the middle


seventies


in interest rates.


This


appears


to be


is not to


say


that interest


10

9

8

7





-3




2

1 _ - N 7001 7101 7201 7301 7401 7501 7601 7701 7801 7901 8001 8101 8201 8301 8401 8 6601
VAR - VarM


- I


rates


have caused


the changes


in returns









77


volatility.


They


could


both


be reactions


some


other


macro-


economic


event,


such


as the oil shock


of 1973.


Figure


Monthly
Firms


Returns S(VAR)


Variance


of


an Index


and the Distribution c


Dividends


of Dividend )f Firms Ini


Initiating
Ltiating


INIT)


a Values o: b Multiply


n the vertical axis values on the verti


are in percentages. cal axis by 10-3.


7001 7101 7201 7301 7401 7501 7601 7701 7801 7901 8001 8101 8201 8301 8401 8501 8801
b
% INITbVAR


I











































Monthly
Firms


Returns (VAR) a


Variance


78


Figure


of


nd the 3-Mont


an Index o h Treasury


f Dividend


Bill


Rate


Initiating (T-Bill)


a Source:


Federal


Reserve


Bulletin.


Values


on the vertical


axis are b Multiply


in percentages. values on the vertical


axis


The general


conclusion


that


can be drawn


from


the above


analysis

dividend factors. negative


is that


variance


initiation Variance


decreases


i are not caused decreases do not


seemingly


by


associated


information


reduce


with


related


the degree


of


autocorrelation


7001 7101 7201 7301 7401 7501 7601 7701 7801 7901 8001 8101 8201 8301 8401 8501 8601 T-Billa VAR b


by


I


10-3.


reductions in


would indicate


that









79


noise


trading


or


bid-ask


spreads.


Neither


do


they


systematically


occur


following


dividend


initiation.


variance seventies coincide


decreases


when with


appear


a large periods


to be


occur


proportion


mostly


of dividend


of low interest


rates


in the middle


initiations


preceded


by


periods


of high


interest


rates.


Variance


decreases


could


therefore


be driven


by changes


in interest


rates


or other


macro-economic


events.


The evidence


in this


and the previous


chapter


tends


to reject


the hypothesis


that there are long


effects associated with dividend


The


run


information


initiation.















CHAPTER


SUMMARY


AND CONCLUSIONS


It has been


suggested


that


dividends


play


a role


transmitting


information


to investors.


This


hypothesis


motivated


the examination of the


relationship between


dividend


initiation


found


that


and earnings


announcements.


Previous


the price reactions at earnings


research


announcements


has

are


on average


result


lower


has been


after


dividends


interpreted


have


to support


been


initiated.


the contention


This that


dividends preempted


has conveyed


information


information.


that


would


Dividend


otherwise


information


have


been


has


conveyed


by earnings


reports.


I contend


that


this


conclusion


is invalid.


Showing


that


price


reactions


have


declined


merely


shows


that


earnings


dividends implication

dividends r appropriate


information of dividends


is


substitutable.


The


as an information source


educe costly private


empirical


measure


information


is


therefore


testable is whether


acquisition.


The


not earnings


returns


variance


alone


but standardized


returns variance.


non-announcement


activities


of


: returns investors,


reflects then s


the private tandardiz ing


information announcement


returns gives a


variance


measure


by


the surrounding


of the importance


non-announcement


of the public


returns


announcement.


80


in


has


and


If









81


If private dividends,


information


investors


acquisition


should


place


is reduced


greater


reliance


by having


on the


firm's


public


announcements,


both


dividends


and earnings.


addition,


this


reliance


should


be greatest


for firms


for which


information


is least


readily


available.


I do not detect


any


difference


in the relative


measures


the information


content


of earnings announcements


before


after sample


dividend


of firms


initiation.


as well


This


as when


result


holds


the sample


for the total


is grouped


by


proxies


of


information


availability.


The


information


transmission


hypothesis


is not supported.


What


is puzzling


is that


there


is


a significant


reduction


raw price


reactions


for post-dividend


initiation earnings


announcements.


I contend


that


this


reduction


is caused


genera

with eviden


1 decrease dividend ce that th


in the volatility


initiation. e reduction


of returns


Regression


which


analysis


in post-dividends


earni


coincides provides .ngs price


reactions reduction


is related


to the variance


in the information


content


decrease


and not to


of earnings.


It can be argued


that


an information


effect


associated


with


dividend initiation causes the


decrease


in returns


volatility.


For example,


i f the


initiation of


dividends


induces greater


consensus


among


traders,


noise trading


or bid-ask


spreads


be reduced.


I test


for reductions


in variance


ratios


before


and after


dividend


initiation.


I do not find


any significant


Further, one should


In


of


and


in


by


may


change.


o f variance


observe


the frequency









82


declines timing of decreases dividend


to be higher


variance appear


after


dividend


changes show with equal


initiation.


no evidence frequency


Tests


of this.


before


of the


Variance


and


after


initiation.


The findings


hypothesis


that


of this dividends


study play


are not supportive


an informational


of the


role .


The


search


for a valid reason


why


firms


initiate


dividend


payments


must


still


continue.


However,


it does


provide


evidence


that


market


is


sufficiently


efficient


such


that


from dividends are minor.


the


any


informational effects









83


REFERENCES


Asquith, P.
Dividend Business


and D. W. Mullins, 1983, "The Payments on Shareholders' 56, 77-96.


Impact Wealth,


of
of


Initiating
Journal of


Atiase, R., 1985,
Capitalization, Announcements,"


"Predisclosure Information, Firm and Security Price Behavior Around Earnings Journal of Accounting Research 23, 21-36.


Bagehot, W., 1971,
Analysts Journal


"The 22,


Only Game in Town," 12-14.


Financial


Black,


F. , 1986,


"Noise", Journal of Finance 41,


529-543.


Beaver, W.
Earnings Selected Research


H., 1968, "The Information Content Announcements," Empirical Research Studies, Supplement to the Journal 6, 67-92.


of in
of


Annual Accounting:
Accountinc


Bhattacharya, S.,
Policy, and the of Economics 10


1979 ," 'Bird 259-2


Imperfect Information, in the Hand' Fallacy,", 70.


Dividend Bell Journal


Bhushan, R., 1989, "Collection of Information
Traded Firms: Theory and Evidence," Journal
and Economics 11, 183-206.


About Publicly of Accounting


Christie,
Stock
432.


A. A., 1982, "The
Variances,1" Journal


Stochastic Behavior of of Financial Economics


Common 10, 407-


Cochrane,
Journal


J. H., 1988, "How Big of Political Economy


is the Random Walk in GNP?," 96, 893-920.


COMPUSTAT, Standard and Poor's Compustat Services, Englewood,
Colo.


Copelandr
Bid-Ask


T. , and Spread,


D.


Galai, Journal


1983, "Information Effects of Finance 38, 1457-1469.


on the


CRSP Daily Stock Master Returns and Index, Center for Research
in Security Prices, University of Chicago, Chicago, Ill.


Demsetz, H., 1968, "The
Journal of Economics


Cost 82,


of
33-5


Transacting," Quarterly 13.









84


Diamond, D. W.,andaRE. Verrecchia,
Aggregation in a Noisy Rational Journal of Financial Economics 9,


1981, "Informational Expectations Economy," 221-235.


Grant, E. B., 1980, "Market
Amounts Information of Accounting Research 18,


Implications of Differential Interim Information," Journal 255-268.


Fama, E. F. and K. R. French, .
Components of Stock Prices,
96, 246-273.


1988, "Permanent and Temporary " Journal of Political Economy


Federal Reserve Bulletin, Board of Governors of the Federal
Reserve System, Washington, D. C.


French, K.,
Arrival Journal


an of of


d R. Roll, 1986, "S Information and Financial Economics


tock
the 17,


Return Variances: The Reaction of Traders," 5-26.


Glosten, L. ,
Prices in Traders,"


and P. Milgrom, 1985,
a Specialist Market wi Journal of Financial


"Bid, Ask and Transaction th Heterogeneously Informed Economics 14, 71-100.


Healy, P., and K. Palepu, 1988
by Dividend Initiations
Financial Economics 21, 14


"Earnings Information Conveyed and Omissions," Journal of 9-175.


Hellwig, M. F.,
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498.


1980, "
Markets,


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the Aggregation of Journal of Economic


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in 477-


T., and H. Transaction Economics 9


Stoll, 1981, "Optimal Dealer Pricing under and Return Uncertainty," Journal of Financial 47-74.


Hollander, M. and
Methods, (New


D. A.
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Wolfe, 1973, Nonparametric Statistical NY: Wiley).


Hsu, D. A.,
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1977,
Appliec


"Tests of Variance Shift at an Unknown Time d Statistics 26, 279-284.


Hsu, D. A., 1979,
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Parameter Price and American


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Air Traffic Statistical


John, K.
Taxes: 1053-1


and J. Williams, 1985, "Dividends, Dilution and A Signalling Equilibrium," Journal of Finance 40,
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of


Ho


I









85


Kaul G. and M
Errors of Economics


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Market Overreaction?," Journal of 28, 67-93.


Bid-Ask Financial


Livingston, M., 1990, Money and
Instruments and Their Uses,
Prentice-Hall).


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Lo, A. W
Not
Speci


* and A. Follow fication


C. MacKinlay, Random Walks Test," Review


1988, "Stock
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Lo, A. W. and A.
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Differential Information and Security Return Variability,"
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Madura,
(St.


Paul


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Marsh, T., and R. Merton,
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Miller, M.
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H. and and the
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F. Modigliani, 1961, Valuation of Shares,


"Dividend " Journal


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Miller, M. H.
Asymmetric


and K. Rock, 1985, "Dividend Policy Information," Journal of Finance 40,


Under 1031-1051.


Richardson, M. and T. Smith, 1991,
with Tests for Serial Correlati Paper, Rodney L. White Center Wharton School, University of


"Robust Power on in Stock Pr: for Financial Pennsylvania.


Ross, S. A., 1976, "The Determination of
The Incentive-Signalling Approach,
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Shores, D., 1990, "The Associa-"tion
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Thompson, R. B., II, C. Olsen, and J.
"Attributes of News About Firms: Specific News Reported in the Wall Journal of Accounting Research 25,


Between Interim and Earnings Announcements," 28g 164-181.


R. Dietrich, 1987, An Analysis of FirmStreet Journal Index," 245-274.









86


Venkatesh, P. C., 1989, "The Impact of Dividend Initiation
the Information Content of Earnings Announcements Returns Volatility", Journal of Business 62, 175-1970


Verrecchia, R. E., 1982, "Information Acquisition
Rational Expectations Economy," Econometrica 50,


in a Noisy
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Wall Street Journal Index,


Dow Jones and Co., New York, N.


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Financial Statements,"
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and the Informational Content of Journal of Financial and
299-310.


on and


Y e









87


BIOGRAPHICAL


SKETCH


Thian


Soon


Wong was


born


in Johor


Bahru,


Malaysia,


on March


1958.


He received


a bachelor's


degree


in electrical


engineering Malaysia, ir


from


n 1982


the University


and a Master


of Malaya,


of Business


Kuala


Lumpur,


Administration


from


the University


of Florida


in


1986.


He expects


to receive


Doctor


of Philosophy


degree


in finance


from


the University


Florida in 1991.


19,


of









I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and its fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.


/ I - / / ,
Robert C. Radcliffe, Chairman Professor of Finance, Insurance, Real Estate


and


I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and its fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.


I

Anand Desai Assistant Profe Insurance, and R(


.ssor of eal Estate


Finance,


I certify that I have read this study and that in opinion it conforms to acceptable standards of schola presentation and its fully adequate, in scope and quality, a dissertation for the degree of Doctor of Philosophy.


Michael D Assistant Insurance


2.
Ryngaert Professor of and Real Estate


I certify that I have read this study and that in opinion it conforms to acceptable standards of schola presentation and its fully adequate, in scope and quality, a dissertation for the degree of Doctor of Philosophy.


my rly as


SanforVBerg Professor of Economics


This dissertation was sulaitted to the Graduate Faculty of the Department of Finance, Insurance, and Real Estate in the College of Business Administration and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy.


Dean, Graduate School


my
rly as


Finance,




Full Text

PAGE 1

DIVIDEND INITIATION AND DIFFERENTIAL INFORMATION AN EMPIRICAL INVESTIGATION BY THIAN SOON WONG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 1991 • •

PAGE 2

ACKNOWLEDGEMENTS I gratefully acknowledge the encouragement, patience and guidance of Professor Robert Radcliffe, my dissertation committee chairman. I also thank Professors Michael Ryngaert, Anand Desai, and Sanford Berg whose help and guidance greatly improved this dissertation. I owe a special debt to C. Sloan Swindle without whose support, insight and encouragement this study would not have been completed. Finally, I dedicate this study to my parents: Grace Foo Kai Chi and Wong Cheng Boon. «r 11

PAGE 3

TABLE OF CONTENTS page ACKNOWLEDGEMENTS ii ABSTRACT iv CHAPTERS 1 INTRODUCTION 1 2 INFORMATION AND DIVIDEND INITIATION 5 Prior Empirical Work 6 Causes for Returns Volatility Decreases .... 11 3 DATA AND METHODOLOGY 15 Data 15 Tests on Quarterly Earnings 18 4 TESTS ON QUARTERLY EARNINGS ANNOUNCEMENTS ... 35 The Overall Sample 35 The Sample Grouped by Information Availability . 40 The Market Reaction to Earnings Changes .... 51 5 RETURNS VOLATILITY AROUND DIVIDEND INITIATION . 57 Variance Changes in the Overall Sample 58 Variance Ratio Tests 61 The Timing of Variance Changes 65 The Effect of Clustered Data 71 6 SUMMARY AND CONCLUSIONS 80 REFERENCES 83 BIOGRAPHICAL SKETCH 87 • • • 111

PAGE 4

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DIVIDEND INITIATION AND DIFFERENTIAL INFORMATION: AN EMPIRICAL INVESTIGATION By Thian Soon Wong December 1991 Chairman: Robert Radcliffe Major Department: Finance, Insurance, and Real Estate The information transmission hypothesis states that firms begin paying dividends as a means of transmitting information to investors. Evidence that dividend and earnings information is partially substitutable has been used to support this hypothesis. This is not an appropriate test. Instead, a testable implication of the information transmission hypothesis is that dividend initiation should increase the proportion of publicly disclosed to privately acquired information . The informativeness of quarterly earnings announcements before and after dividend initiation is examined using returns and standardized variance measures. Under the information transmission hypothesis, the standardized variance of earnings and dividend announcements, should increase after dividend IV

PAGE 5

initiation. Further, firms which have the least amount of available information should experience the greatest change. I find that while returns measures decrease after dividend initiation, standardized variance measures do not. Firms with the low information availability have larger price revaluations. This relationship is not found with standardized variance. There appears to be no evidence that the proportion of publicly disclosed information increases after dividend initiation. The observed decrease in returns measures of information content may be explained by a reduction in returns volatility that coincides with dividend initiation. Tests are conducted to detect if an information effect associated with dividend initiation is causing the returns volatility to decrease. Such an effect could cause returns volatility to decline by reducing bid-ask spreads or noise trading. Variance ratio tests fail to detect any such decrease following dividend initiation . Analysis of the timing of variance changes reveals that dividend decreases are equally likely before as after dividend initiation. Therefore, decreases in returns volatility do not systematically occur after dividend initiation. This weakens the contention that information conveyed by the dividend initiation has caused the volatility decrease. The observed volatility decrease appears to be caused by a large proportion of sample firms initiating dividends in the v

PAGE 6

period 1974-1977. This period also coincides with a decrease in volatility for the market in general. After adjusting for market volatility, the reduction in firm volatility is much reduced. The general conclusion is that information related effects do not appear to cause the reduction in returns volatility. vi

PAGE 7

CHAPTER 1 INTRODUCTION Why firms pay dividends is an enduring puzzle in finance. One motivation that has been advanced is that dividends convey information to investors. This information content of dividends or dividend signalling hypothesis was first suggested by Miller and Modigliani (1961) and subsequently formalized by Ross (1977), Bhattacharya (1979), Miller and Rock (1985) and John and Williams (1985) among others. In addition to this signalling hypothesis, some researchers have proposed that initiating dividends may augment the process by which information about firms is transmitted to investors. I refer to this expanded role for dividends as the information transmission hypothesis. For example, Asquith and Mullins (1983) state that Dividend policy has several attractive aspects as an information transmission mechanism. Unlike the detailed focus of other announcements, dividends can be used as a simple, comprehensive signal of management's interpretation of the firm's recent performance and its future prospects. Unlike most announcements, dividend announcements must be backed with hard cold cash. The firm must either generate this cash or convince the capital markets to supply it. In addition to the credibility of cash signals, dividends are also highly visible compared with other announcements. . . . Once dividends are initiated, shareholders apparently anticipate a periodic signal by management, and management is forced to submit to a periodic review, (p. 94) 1

PAGE 8

2 Two recent studies by Venkatesh (1989) and Healy and Palepu (1988) report results that can be interpreted to support this view of why firms pay dividends. If dividend information is substitutable for earnings information, then earnings announcements before dividend initiation should convey more information than post-dividend initiation earnings announcements. Venkatesh finds that the average informativeness of quarterly earnings announcements (measured by the amount of price revaluation) decreases after dividend initiation. Healy and Palepu find that the price response to annual earnings information is lower in the years following dividend initiation. I contend that the above results merely show that earnings and dividend information is substitutable. Demonstrating substitutability is not compelling evidence of the information transmission hypothesis. Why would a firm engage in costly information transmission via dividend payments when earnings reports will serve the same purpose. I argue that a firm would pursue an enhanced information transmission policy only if such a policy reduces the cost of information acquisition for investors. Firms for which it is relatively costly for investors to acquire information will have greater incentive to disclose information through dividends. Consequently, if dividends augment the available public information about the firm, then the relative amount of private information acquisition after dividend initiation should decrease.

PAGE 9

3 I test the above information transmission hypothesis by comparing the average standardized variance of announcements from the preand post-dividend initiation periods. The use of standardized variances, i.e. announcement period variance divided by estimation period variance, avoids problems with changes in returns volatility. Standardized variance measures the amount of "new" information released at earnings and dividends announcements relative to the amount of information privately acquired during surrounding non-announcement periods. If dividend initiation alters the proportion of publicly available to privately acquired information, standardized variances of earnings and dividend announcements should increase after dividend initiation. Further, one would expect firms with low information availability to experience the greatest increase in the relative informativeness of public announcements. To test this hypothesis, I use two proxies of information availability (namely, market value of equity and number of Wall Street Journal Index news items) to divide a sample of dividend initiating firms into five groups. I then test for differences, across groups, in the relative informativeness of these public announcements before and after dividend initiation. The results from this study do not support the hypothesis that dividend initiation changes the relative amount of public versus private information. Further, I find no differential

PAGE 10

4 changes in relative informativeness across samples of firms grouped by information availability. If changes in information are not the basis for the findings that Venkatesh and Healy and Palepu report, then what is? Venkatesh finds that returns variability is lower in the post-dividend period. I present evidence that their results are driven by a contemporaneous decline in the returns volatility of firms initiating dividends. This decline leads Venkatesh and Healy and Palepu to interpret post-dividend initiation earnings announcements as being less informative. I investigate the nature of this returns volatility decline. I find no evidence that the decrease in returns volatility is driven by information related causes. This paper is organized as follows. Chapter 2 develops the implications of the information transmission hypothesis. I critigue the Venkatesh and Healy and Palepu studies within the framework of this hypothesis. I also discuss possible causes for the decline in returns volatility following dividend initiation. Chapter 3 presents my data and the methodology used to examine the information content of guarterly earnings. In Chapter 4 , I report and discuss the results of tests on preand post-dividend initiation guarterly earnings. Chapter 5 presents tests on changes in returns volatility. Chapter 6 summarizes the results of this study.

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CHAPTER 2 INFORMATION AND DIVIDEND INITIATION Asquith and Mullins (1983) hypothesize that dividends may provide a vehicle for communicating management's superior information concerning the firm's current and future prospects. Information about a firm can come from two sources: information provided by the firm (publicly released information) and information privately acquired by investors. I assume that firms have a cost advantage in producing information about themselves. Therefore, managers prefer to release information publicly than have investors produce it at greater cost. 1 One way dividends can transmit information to investors is by providing substantiation for the cash-flows reported in the earnings announcement. Without dividends, investors would have to seek substantiation from other costly sources. Venkatesh (1989) alludes to this argument in his paper. His explanation for the post-dividend decrease in returns volatility is that "investors accord less importance to pieces of 'information' (announcements/rumors) that could have induced price reactions in the pre-dividend period" (p. 176) . If dividends play an 1 It is not necessary to assume that managers do this for altruistic purposes. By reducing the costs of investigation, managers obtain a lower required rate of return. 5

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6 informative role, the major testable implication is that private information acquisition will decrease in the postdividend initiation period . 2 As the firm discloses a greater proportion of information through earnings and dividend announcements, investors have less incentive to acquire information privately. Therefore, the relative proportion of public versus private information will increase following dividend initiation. I make two assumptions to test this hypothesis. First, the information conveyed by dividends is substitutable for information imparted by earnings announcements. Second, the total amount of firm information does not change with dividend initiation. Prior Empirical Work Venkatesh (1989) studies a sample of 72 NYSE or AMEX firms that initiated quarterly dividend payments between 1972-1983. He collects the announcement dates of quarterly earnings and dividend payments for 14 quarters before and after dividend initiation. Using raw and excess returns from the earnings announcement as a measure of information content, he finds that the average information content of quarterly earnings announcements before dividend initiation is significantly greater. Based on this evidence, he concludes that earnings 2 The issues of information substitutability and its implications are formally explored in a rational expectations framework by Hellwig (1980), Diamond and Verrecchia (1981), Verrecchia (1982) and Bhushan (1989).

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7 and dividend information are partially substitutable and that information is transmitted by dividends. Venkatesh also finds that returns volatility is lower after dividend initiation. His explanation for this decrease is that investors in the post-dividend initiation period attribute less importance to information arriving during nonannouncement periods. This leads to smaller price reactions and lower observed volatility. Venkatesh assumes that investors do not actively acquire information. Instead, they passively receive information and decide whether to act on this information. Before dividend initiation, they are more likely to act on non-announcement period information. In the post-dividend period, investors receive more publicly announced information and rely less on non-announcement period information . The main focus of Healy and Palepu's (1988) study is to determine whether dividend initiations convey information about future earnings. They hypothesize that investors view dividend changes as management forecasts, substantiated by cash, of future earnings changes. So, dividend announcements enable investors to revise expectations of earnings. The forecast errors in earnings announcements following dividend initiation should therefore be reduced. To test this hypothesis, they obtain the price response (namely, two-day cumulative excess returns) and earnings surprise (the earnings change standardized by price) for 5

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8 annual earnings announcements prior to and following dividend initiation. They regress the price response on the earnings surprise to obtain the earnings response coefficient. This measures the informativeness of the earnings surprise. Other things being equal, earnings information that is unanticipated leads to large price revaluations and therefore to larger values of the earnings response coefficient. They find that the earnings response coefficient is lower in the postdividend initiation period. Healy and Palepu argue that dividend initiation provides information which preempts subsequent earnings announcements. Do Venkatesh ' s and Healy and Palepu' s results regarding earnings provide support for the information transmission hypothesis? I argue they might not. These studies suggest that dividend and earnings information are substitutable. Managers have little motivation to initiate dividends if doing so merely shifts to dividends, information that would otherwise be conveyed by earnings. This is especially true since dividends are costly to investors. First, they are costly because dividends are taxed at ordinary income rates. If the firm chooses instead to retain earnings, taxes are reduced to the extent that capital gains are postponed. Second, dividends must be paid out of net income that would otherwise have been used for investment. Replacing this source of capital incurs transactions costs that are ultimately borne by investors. Third, initiating dividends also can lead to shifts in

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9 clienteles that are costly to investors. Finally, there are costs of administering a dividend policy. Therefore, dividends must play a substantially greater informative role than documented by Venkatesh or Healy and Palepu to justify their cost . I hypothesize that this greater role must be the increase in publicly released information and a consequent decrease in costly private information acquisition. The Venkatesh and Healy and Palepu studies cannot address this hypothesis for two reasons. First, in the post-dividend initiation, there are two routes through which public information is transmitted: dividends and earnings. A priori . we do not know how the total public information is divided between these two routes. By comparing only earnings announcements, the researcher is neglecting the amount of information conveyed by dividends. He can infer the effect of that information, but cannot quantify it. From inference, he can only conclude that dividend information has substituted for earnings information. As an illustration, assume the total amount of available information is a. This amount remains constant for preand post-dividend initiation periods. Suppose that before dividend initiation, half (a/2) is publicly disclosed and half is privately acquired. After dividend initiation, earnings disclose a/3, dividends disclose a/3 and private acquisition accounts for the remaining third. We see that after dividend initiation, two-thirds of the information is publicly

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10 disclosed. Yet if one observes only earnings all one can conclude is that the information content of earnings has declined from a/2 to a/3. We can conclude little about how much information was transmitted by dividends. Any attempt to gauge the relative proportion of public versus private information must examine the informativeness of both earnings and dividend announcements. The second deficiency in the Venkatesh and Healy and Palepu studies lies in their information content measures. They base their measures on announcement period returns that do not account for the general reduction in returns volatility. This decrease in returns volatility may be due to causes unrelated to any information effect associated with dividend initiation. Suppose that announcement period returns variance decreases proportionally with the decrease in returns volatility. Then one would observe post-dividend initiation announcement returns that are lower in magnitude than pre-dividend initiation announcement returns. If this is so, one cannot distinguish a decrease in announcement period returns as being due to information related causes or otherwise. A study of the nature of changes in returns volatility around dividend initiation is called for. In the next section, I review several possible causes for a decrease in returns volatility.

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11 Causes for Returns Volatility Decreases Information related causes. In informationally efficient markets where new information is rapidly incorporated into price, returns volatility is directly related to the rate of information arrival. Lower volatility may be caused by a reduction in the rate at which information arrives. It is possible that when returns on the firm's underlying assets become less uncertain and more predictable, a decrease in returns volatility may occur. However, this effect does not depend on any changes in the process by which information is disseminated or processed about the firm. The decision to initiate dividends may coincide with the firm's asset returns becoming less uncertain. Decreases in returns volatility are then only coincidentally associated with dividend initiation. The initiation of dividends does not cause the rate of information arrival to decrease. An alternative information related explanation for decreases in returns volatility is that dividend initiation reduces noise trading. Noise has been interpreted in various ways by Black (1986). I consider noise as the difference between the security's intrinsic value and its observed price. This mispricing occurs when traders over-react to the activities of other traders (French and Roll (1986)). Greater noise trading leads to higher volatility. Dividend initiation could reduce noise trading if it induces greater consensus among traders.

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12 The bid-ask spread. Firms with large bid-ask spreads tend to have greater returns volatility. This relationship arises in several ways. First, the larger the bid-ask spread the larger the difference between buy and sell-side transactions. Observed returns are calculated using daily closing prices. If successive closing transactions are alternatively buy and sell transactions, they would be conducted at opposite edges of the bid-ask spread. Hence, the greater the bid-ask spread, the greater the observed return. Bid-ask spreads increase with the dealer's inventory costs (Demsetz, 1968 , Ho and Stoll, 1981 ) and the degree of informational asymmetry between informed traders and the uninformed dealer (Bagehot, 1971 , Copeland and Galai, 1983, Glosten and Milgrom, 1985 ) . It is possible that dividend initiation may alter these two factors and therefore reduce bid-ask spreads. For example, dividend initiation may increase trading volume. This reduces the dealer's inventory costs as inventory turnover increases. Dividend initiation may also reduce informational asymmetry through greater consensus between traders and dealers. Stock price . Volatility decreases as stock price increases. This relationship occurs for two reasons. First, for the same bid-ask spread, higher priced stocks will have smaller observed returns and therefore lower volatility. For example, suppose stock A has bid and ask prices of 9 7/8 and 10 1/8,

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13 respectively; stock B has bid and ask prices of 19 7/8 and 20 1/8. The bid-ask spread is 2/8 for both stocks. For successive sell and buy transactions the observed return for stocks A and B is 2.5% and 1.24%, respectively. Second, as a firm's stock price increases (without the firm increasing debt) , the firm's leverage decreases. Volatility is positively related to leverage. This can be easily seen by considering a firm in a Modigliani-Miller world with no taxes and riskless debt. The return on a leveraged firm k s is k s = k v + (k v -r) (^) where r is the riskless interest rate and k v is the return on the unleveraged firm. The market value of debt and equity is D and S, respectively. Taking variances °S = a V^ + -g) Therefore, stock volatility a s depends positively on leverage D/S. Interest rates. Volatility also changes with interest rates. Interest rates indirectly affect volatility through leverage. An increase in interest rates can both increase or decrease leverage. First, as interest rates rise, the value of both debt and equity both decline but equity declines proportionately greater than debt; this leads to an increase in leverage. Second, there is a wealth transfer from bond to

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14 equity holders that decreases leverage. Christie (1982) has studied the net effect of interest rates on leverage. He finds that high interest rates are associated with high returns volatility . If dividend initiation has information related effects, volatility can decrease due to reductions in noise trading and bid-ask spreads. Volatility can also decrease due to factors unrelated to information effects. The returns on the assets of firms initiating dividends could have become less uncertain and thus prices are less volatile. Dividend initiating firms could experience increases in stock price which lead to proportionally smaller bid-ask spreads and reduced leverage. Finally, returns volatility can decrease due to market wide changes in interest rates.

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CHAPTER 3 DATA AND METHODOLOGY Data My data sample consists of 332 firms that initiated dividends between 1970-1986 inclusive. These firms were identified by scanning the 1988 Center for Research in Security Prices (CRSP) NYSE/AMEX and NASDAQ files. I also collect earnings information for approximately 14 quarters (900 trading days) before and after the initial dividend. These were obtained from the 1987 quarterly Compustat files and/or the Wall Street Journal Index . To be included in the sample, firms must meet the following requirements. (1) The initiating dividend is either the first dividend in the firm's corporate history or the resumption of dividend payments after a hiatus of at least 10 years. The initial dividend cannot be an extra or special dividend . (2) The firm must continue paying dividends for at least three years after the initial dividend. (3) The firm must have 3 years of daily returns data before and after the initial dividend. 1 1 Not all the returns data for the 3 year period preceding and following dividend initiation are obtained from one CRSP tape source. Fourteen firms which were on the NASDAQ 15

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16 (4) One year must elapse between the firm's initial appearance on the NYSE/AMEX or NASDAQ tapes and the announcement of it's initial dividend. (5) In each 14 quarter period preceding and following the initial dividend, announcement dates and earnings and price data for at least 10 quarters must be available. This eliminates firms that only report annual earnings or firms merged out of existence. Table 1 presents an overview of the sample. Most of the dividend initiations occurred in the middle seventies. Almost 65% of the sample initiated dividends between 1975 and 1977. This could be due to the relaxation of the Nixon price controls or to cross-sectional dependence in earnings changes as posited by Marsh and Merton (1987). More than threequarters of the sample paid at least 9 dividends in the 14 quarters following dividend initiation. The most common dividend payment frequency was quarterly, followed by half yearly and annual payments. American Exchange listed firms form a slight majority followed by New York Stock Exchange and OTC firms. More than 90% of the sample had at least 5 years of quarterly earnings data. tape when they initiated dividends had continuing data on the NYSE/AMEX tape. Similarly, 6 firms which were on the NYSE/AMEX tape when they initiated dividends had prior NASDAQ data.

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17 TABLE 1 OVERVIEW OF SAMPLE CHARACTERISTICS Cumulative Freq . Percent Freq. Percent Year of Dividend Initiation 1970 6 1.8 6 1.8 1971 4 1.2 10 3 . 0 1972 18 5.4 28 8.4 1973 31 9 . 3 59 17.8 1974 27 8 . 1 86 25.9 1975 62 18 . 7 148 44 . 6 1976 70 21.1 218 65.7 1977 49 24 . 8 267 80.4 1978 23 6.9 290 87.3 1979 12 3 . 6 302 91.0 1980 9 2.7 311 93.7 1981 * 6 1.8 317 95.5 1982 5 1 . 5 322 97.0 1983 6 1.8 328 98.8 1986 4 1.2 332 100.0 Number of Dividends 3 2-4 26 7.8 26 7.8 5-8 50 15.1 76 22.9 9-12 53 16.0 129 38.9 13-16 203 61.1 332 100.0 Dividend Payment Frequency b Annual 21 6.3 21 6.3 Half 35 10.5 56 16.9 Quarter 217 65.4 273 83.2 Other c 59 17 . 8 332 100.0

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18 TABLE 1 -continued Cumulative Freq . Percent Freq. Percent NYSE 104 31.3 104 31.3 Exchange d AMEX 143 43 . 1 247 74.4 NASDAQ 85 25.6 332 100.0 16-19 12 3 . 6 12 3 . 6 Number of 20-23 42 12 . 7 54 16.3 Quarterly Earnings 6 24-27 105 31.6 159 47.9 28-30 173 52 . 1 332 100.0 a Number of dividends which were declared within 900 trading days following the initial dividend. b The frequency of dividend payments in the 3 year period following dividend initiation. c Within the 3 year period following dividend initiation, 5 firms changed from an annual to a half yearly payment frequency. 6 firms changed from annual to quarterly. 44 firms changed from half yearly to quarterly. 1 firm changed from quarterly to half yearly. 2 firms changed from annual to half yearly to quarterly. 1 firm changed from half yearly with additional year-end dividends to quarterly. d Exchange on which firm was trading when dividends were initiated . e Number of quarters within 900 trading days of the declaration of the initial dividend for which earnings information was available. Tests on Quarterly Earnings Measuring information content. The information content of an earnings announcement is computed in the following way. The announcement period consists of 3 trading days: the day of the announcement (day t) , the day preceding (t-1) and following (t+1) the announcement date. Beginning at days t-2 and t+2, returns for 60 days surrounding the announcement period are

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19 collected. These returns form the non-announcement period. If dividends and other earnings announcements occur within this non-announcement period, the returns from these announcements are excluded. The 60 non-announcement period returns are used to estimate the market model for firm j : R jr = <*j + PjKr' r =1 , 60 where R jT are the returns on day r for the firm j, R mT are the returns (including dividends) of a value-weighted index for all firms on the tape, and and (3^ are OLS estimated coefficients. Excess returns u jT are calculated as u . = R. (a, + 8±R„) JT JT ' J TUT/ I assume that excess returns are normally distributed with zero expected value. Three measures of information content are used. The first is the standardized variance, a variant of Beaver’s U (see Beaver, 1968) . It is denoted by BVU. This is the ratio of announcement period returns variance to non-announcement period returns variance. Beaver's U is a measure of the "new" or marginal information conveyed to the market by the earnings (and dividends) announcement relative to the average information available during the non-announcement period. It is also a measure of the relative amounts of publicly disclosed versus privately acguired information. I assume that non-announcement period returns variances capture the extent

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20 of private information acquisition. This may not be strictly true as firms may engage in other forms of public disclosure, for example, earnings forecasts. However, their effect is minimized if dividend initiation does not systematically lead to changes in the firm's other public disclosures. The use of standardized variances also controls for changes in returns volatility. This is critical under two sets of circumstances. First, if the market were generally more variable during a certain period, a direct comparison of announcement period returns would bias the test of firmspecific price response to earnings announcements. This bias is exacerbated if the sample is clustered in calendar time and clustering is coincident with changes in market volatility. Second, if there is a systematic decrease in returns volatility contemporaneous with dividend initiation. Standardizing the announcement period returns variance by the non-announcement period returns variance avoids problems that may arise from differences in volatility. I suspect that previously reported results may be driven by returns variance decreases and not by changes in information transmission. Let A represent the set of announcement period days and N represent the set of 60 non-announcement period days. I estimate the announcement period returns variance to be the sum of the squared excess returns u T (r e A) divided by the number of days within the announcement period, T A . Similarly, the non-announcement period returns variance is the sum of 60

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21 squared excess returns u T (r e N) divided by 60. The standardized variance is calculated as BVU, J Var[u jT , t eT A ] Var [ u jT , t £ T N ] ^ E u JT A reT A E V u. : rp JT reT N Standardized variance is calculated for two definitions of the announcement period. In the first definition, BVU E , the announcement period consists only of the earnings announcement. This definition is used for comparison with Venkatesh's results. As BVU E measures the marginal information content of earnings relative to the average information in the non-announcement period, this ratio is used to examine the substitutability of earnings and dividend information. Decreases in post-dividend initiation BVU E 1 s indicate that dividend announcements have preempted earnings announcements. In the second definition, BVU D , the announcement period consists of the earnings announcement and the nearest dividend announcement occurring within 30 days of the earnings announcement. This definition is used to measure the total effect of dividends as a public disclosure mechanism. In the post-dividend initiation period, information is publicly disclosed through both dividends and earnings. In each quarter, BVU D measures the information content of all public disclosures (from both earnings and dividends) relative to private information. Increases in post-dividend initiation

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22 BVU d 's indicate that more information is released publicly than acquired privately. I also calculate two measures which are based on announcement period returns. They are similar to those used by Venkatesh. They are measures of price revaluations induced by information released at earnings announcements. These measures do not take into account non-announcement period returns and therefore, the general level of returns variability. The first measure, RAW, is the absolute value of the sum of earnings announcement period returns. t.i raw j = I E R J T =t-l The second, MAJ, is the absolute value of the sum of earnings announcement period excess returns. T= t + 1 MAJj = I E U J.I T =t-l These measures differ only to the extent that the market model regression accounts for contemporaneous market movements. Because the explanatory power of market model regressions is typically low, these measures are quantitatively very similar. The values for MAJ are usually slightly smaller in magnitude than those obtained with RAW. I define the preand post-dividend windows to be 900 day periods (about 14 quarters) preceding and following the initiation of dividends. A "joint" announcement occurs when

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23 a dividend is declared within 2 days of an earnings announcement. I follow Venkatesh in excluding joint announcements from the analysis. I require that in each window, there are at least 3 usable earnings announcements. This requirement eliminates 7 firms from the sample. The final sample consists of 325 firms. For each firm j, I average the information content measures of all preand post-dividend initiation quarters. "Matched differences" are obtained by subtracting the average postdividend initiation information content from the average predividend initiation information content for each firm. DRAW. = KX^.PRE RAW. vJ DMAJ . J MAJ^pre M7 CT J dbvu ej = BVUj.PRE BVU^ dbvu a j BVU^pre BVU; j , POST E j , POST A j , POST The distribution of matched differences is then analyzed to detect the effect of dividend initiation. Hypotheses and test statistics. There are 2 main hypotheses. The first is that dividend information is substitutable to earnings information. Under this hypothesis, one would expect to observe raw price reactions of earnings announcements to decrease in the post-dividend period. Therefore the appropriate test is

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2 4 H 0 : DRAW = 0 versus H a : DRAW > 0 and H 0 : DMAJ = 0 versus H a : DMAJ > 0 . Further, if the total amount of information or the general level of volatility does not increase from pre-to postdividend initiation periods, then the standardized variance measure of information content should also decrease. H 0 : DBVU e = 0 versus H a : DBVU E > 0 . The second hypothesis is that dividends augment the transmission of public information about the firm. Under this information transmission hypothesis, the relative information content of public announcements (both earnings and dividends) to private information should increase. Therefore, H 0 : DBVU d = 0 versus H a : DBVU d < 0 . These hypotheses are tested using standard t statistics and Wilcoxon signed-rank statistics. The nonparametric Wilcoxon signed-rank test (for example, see Hollander and Wolfe, 1973) is particularly suited to the case of paired replicates data i.e. pairs of preand post-treatment observations, where we are concerned with a shift in location due to the application of the treatment. In this case, the treatment is the payment of dividends. Nonparametric techniques require few assumptions

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25 about the underlying populations from which the data are obtained. In particular, nonparametric procedures forego the traditional assumption that the underlying populations are normal . The Wilcoxon signed-rank statistic W is calculated as follows. The absolute values of the matched differences are ranked in ascending order. The ranks of those matched differences that have the sign of the hypothesized direction (i.e. DRAW>0, DMA J > 0 , DBVU E >0 and DBVU d < 0) are summed. Denote this sum as T + . I use the large sample approximation to obtain W. T . _ ^ n (n+1) W = N n (n+1) ( 2n+l) 24 The statistic has an asymptotic standard normal distribution. For a two tailed test, the null hypothesis is rejected if |w > z(a/2), where z(.) denotes the standard normal variable and a the significance level. Differential information availability. I also test the information transmission hypothesis by examining whether firms grouped by information availability have different information content of earnings announcements. If information transmission is a valid incentive for initiating dividends, then one would expect firms with low information availability to experience

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26 the greatest increase in the relative informativeness of public announcements. To test this hypothesis, I group firms using two proxies of information availability (namely, market value of equity and number of Wall Street Journal Index news items) . I test for group differences in the informativeness of these public announcements before and after dividend initiation . Previous studies by Shores ( 1990 ) , Bhushan (1989) , Lobo and Mahmoud ( 1989 ), Atiase ( 1985 ), Zeghal ( 1984 ) and Grant (1980) consistently find that the earnings announcements for firms with low information availability have greater information content. These studies group their sample firms on proxies of information availability such as firm size, analyst following, trading volume, exchange listing, financial media reportage, number of market makers and bid-ask spread. The most commonly used proxy of information availability is market size. For example, Shores ( 1990 ), Lobo and Mahmoud (1989), Bhushan (1989), Atiase ( 1985 ) and Zeghal ( 1984 ) all report that the earnings announcements of large market capitalization firms have lower information content. This is because large firms already have more information available about them and the additional contribution of earnings information is smaller. Large firms have greater information availability due to several reasons. Large firms may have economies of scale in producing and disseminating information about themselves. Large companies also produce more information to meet

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27 regulatory requirements. Large firms tend to have more shareholders. A large shareholder base provides incentive for third-party information providers such brokerage services, financial news-letters, etc., to produce information about the firm. I standardize the firm's market value of equity by the total market value to obtain adjusted firm size. Standardization takes into account the general market capitalization. Standardization may be important if information collection depends not on the magnitude of market capitalization but on the ranking of the firms relative to the total universe of firms. Relative rankings will change as the total market value changes over time. Since our sample covers a long period, 2 any secular change in total market value may be important. It turns out that standardization does not alter the group rankings greatly. Out of 332 firms, 32 firms change groups when ranked by market value of equity alone. The firm's adjusted firm size is calculated as the total number of shares outstanding (SHR) multiplied by the price two days before the first dividend is declared (P t 2 ) • This figure is then divided by the total dollar value of all non-ADR securities (TOTMV) and multiplied by a scaling factor of 1 , 000 , 000 . 2 Studies which have used firm size have covered a shorter period over which changes in total market value may be minimal .

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28 Adjusted Firm Size SHR X P t _ 2 TOTMV X 1,000,000 Price data and number of shares outstanding are obtained from the CRSP tapes. Prior to 1973, total market dollar value is for NYSE and AMEX listed securities. From 1973, NASDAQ-traded stocks are included. The number of news items appearing in the Wall Street Journal Index measures the occurrence of events that have informative value. It is also a measure of financial media coverage. A firm with a high number of news items is assumed to have higher information availability. For each firm in the sample, the number of news items appearing in the Wall Street Journal Index during the year prior to the declaration of the first dividend is collected. The firms in my sample are ranked in ascending order by adjusted firm size and the number of news items and then divided into five groups. Note that the groups based on news items do not have egual numbers of firms. This is because the number of news items is discrete causing clustering. For example, firms in the third group all have 8 news items. The characteristics of the information availability groups are shown in Tables 2 and 3. There appears to be sufficient variation with respect to firm size. For the adjusted firm size groups, the mean market value of eguity is approximately two times greater than the preceding group.

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29 TABLE 2 CHARACTERISTICS OF GROUPS BASED ON ADJUSTED FIRM SIZE Group N Var Mean SD Min Med Max 1 66 SIZE 3 6.563 2 . 396 2 .860 6.399 15.587 NEWS b 6.833 2 . 271 2 6 16 2 67 SIZE 13 . 144 3 . 558 6.274 13.052 24.820 NEWS 7.667 2.420 4 7 18 3 67 SIZE 23.303 6.088 10.621 22.727 38.900 NEWS 9 . 552 4 .328 5 9 33 4 66 SIZE 50.667 17 .750 26.837 45.794 127.630 NEWS 10.797 5.262 5 9 28 5 66 SIZE 308 .83 584 .906 50.474 128.265 3631.92 NEWS 13 . 078 7 . 895 5 10 50 a SIZE is the market value of equity (in millions of dollars) two days before the initial dividend is announced. b NEWS is the number of Wall Street Journal Index news items in the year prior to the announcement of the initial dividend . Table 4 shows the distribution of firms grouped by firm size and news items. While the information proxies do not yield identical groupings, large firms tend to have more news items reported about them. This is consistent with the findings of Thompson, Olsen and Dietrich (1987) who examine the characteristics of Wall Street Journal Index news items.

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30 TABLE 3 CHARACTERISTICS OF GROUPS BASED ON NUMBER OF NEWS ITEMS Group N Var Mean SD Min Med Max 1 48 SIZE 3 23.727 26.846 2 .860 14.018 138.52 NEWS b 4 . 826 0.5698 2 5 5 2 92 SIZE 43 . 899 91.479 2 . 880 13 .312 512.290 NEWS 6.424 0.497 6 6 7 3 46 SIZE 25.242 22 . 562 4 . 020 15.970 82.357 NEWS 8 0 8 8 8 4 79 SIZE 46.317 47 . 877 3 .294 27.547 297.024 NEWS 9 . 397 0 .952 9 10 12 5 67 SIZE 238 . 728 586.243 10.621 55.544 3631.920 NEWS 17 . 657 6. 383 13 16 50 SIZE is the market value of equity (in millions of dollars) two days before the initial dividend is announced. NEWS is the number of Wall Street Journal Index news items in the year prior to the announcement of the initial dividend . TABLE 4 DISTRIBUTION OF FIRMS IN GROUPS BASED ON ADJUSTED FIRM SIZE AND NUMBER OF NEWS ITEMS NEWS ITEMS i 2 3 4 5 Total ADJUSTED MARKET VALUE 1 17 28 10 9 2 66 2 11 27 13 13 3 67 3 9 13 7 26 12 67 4 8 11 11 17 19 66 5 3 13 5 14 31 66 Total 48 92 46 79 67 332

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31 To test for differential information contents across the five groups the data sample is characterized as a one-way layout design experiment. The data consists of N = E k n k (for k=l,...,5) observations with n k observations in the kth information availability group. Note that the number of observations in each group need not be equal to each other. This one-way layout is illustrated in Figure 3.1. D(j,k) 's are the matched differences from group k with j individual observations . Information Availability Groups 1 2 3 4 5 D ( 1 , 1 ) D ( 2 , 1 ) D(ni,l) D ( 1 , 2 ) D ( 2 , 2 ) D ( n 2 , 2 ) D ( 1 , 3 ) D ( 2 , 3 ) D (n 3 , 3 ) D(l,4) D ( 2 , 4 ) D(n 4/ 4) D ( 1 , 5 ) D ( 2 , 5) D(n 5 , 4) Figure 1 Sample Data Design

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32 The basic model is D ( j , k) = n + r k + e jk , j=l , . . . , n k =1 , . . . , 5 where /i is the unknown overall mean, r k is the unknown information effect in group k. I assume the errors e are mutually independent and have the same continuous distribution . The hypothesis to be tested is H 0 : t 1 = t z =t 3 = t 1i = r 5 versus H a : t 1 > r 2 > r 3 > r A > r 5 where at least one of the inequalities is strict. The firms in the lower information availability groups have larger information effects. I use the Jonckheere-Terpstra statistic (see Hollander and Wolfe, 1973) to test the hypothesis. It is based on the number of group j observations which are less than each of the group i observations, where i > j. For 5 groups, there are 10 of these Mann-Whitney counts . n i n j u ij = ££0[ D (s,i) D(t, j) ] S = 1 t = l

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33 where
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34 comprehensive test which does not suffer from the usual problems associated with multiple comparisons such as the Bonferroni or Tukey methods.

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CHAPTER 4 TESTS ON QUARTERLY EARNINGS ANNOUNCEMENTS The Overall Sample The overall sample consists of 325 firms. Each firm must have at least 3 usable quarterly earnings (joint announcements are omitted) in both preor post-dividend initiation periods. For each firm, the information content of quarterly earnings announcements in each period are averaged. Summary statistics for the cross-sectional distribution of average information content measures for preand post-dividend initiation periods are shown in Table 5. The information content measures are based on raw returns (RAW) , market adjusted excess returns (MAJ) , the ratio of earnings announcement returns variance to non-announcement returns variance (BVU E ) and the ratio of earnings and dividend announcement returns variance to nonannouncement period returns variance (BVU D ) . In the predividend initiation period, only earnings announcements are relevant and the ratio is with respect to earnings announcement returns variance (BVU) . The information content measures RAW and MAJ have larger values in the pre-dividend initiation period. The pre-dividend initiation values of the minimum, the first quartile, median, third quartile and maximum always exceed those from the post35

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36 TABLE 5 DISTRIBUTION OF THE AVERAGE INFORMATION CONTENT OF QUARTERLY EARNINGS ANNOUNCEMENTS BEFORE AND AFTER DIVIDEND INITIATION. Sample Size = 325 firms Pre-Dividend Period Post-Dividend Period RAW MAJ BVU RAW MAJ BVU e BVU d Mean 5.79 5.67 2 . 007 4 . 52 4 . 39 1.908 1.862 SD 2 . 13 2 . 15 1.139 1.62 1.62 1.178 1.444 Minimum 1.14 1 . 52 0.459 0.02 0.85 0.230 0.528 Ql a 4 .29 4 . 13 1.309 3 . 42 3 . 34 1.173 1.205 Median 5.61 5.43 1.684 4 .36 4 . 15 1.609 1.573 Q3 a 6.98 6.89 2 .263 5.35 5.25 2 .367 2 . 036 Maximum 13.31 12 . 82 7 . 663 11 . 65 11.43 9 . 589 15.081 Ql=first quartile; QS^third quartile. dividend initiation period. For pre-dividend initiation quarterly earnings announcements, the mean raw price revaluation is 5.79%. For post-dividend initiation quarterly earnings announcements, the mean is 4.52%. Similarly, the predividend initiation mean market adjusted price revaluation is 5.67% with the post-dividend initiation mean being 4.39%. These figures are similar to those reported by Venkatesh. 1 For raw returns, Venkatesh obtains a pre-dividend initiation 1 The differences between my study and the Venkatesh study are as follows. Venkatesh examines a smaller sample of 75 NYSE or AMEX firms which paid quarterly dividends. His estimation period precedes the quarterly earnings announcement date. He uses a lagged Scholes-Williams technique to obtain his market model regressions.

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37 mean of 5.84%. In the post-dividend initiation period, he reports a mean of 4.91% (4.01%) for earnings announcements following (preceding) dividends announcements. Results for market adjusted excess returns are similarly comparable. The standardized variance information content measures show a lesser decrease. The post-dividend initiation mean values for BVU e and BVU D are 1.908 and 1.862 compared with the predividend initiation mean value of 2.007. For the post-dividend initiation period, the average dividend announcement variance is less than the average earnings announcement variance. Thus the combined average variance of earnings and dividend announcement (BVU D ) is less than for earnings announcements alone (BVU E ) . Whether these differences are statistically significant is examined by comparing matched differences. The distribution for matched differences is tabulated in Table 6. I use t statistics and the nonparametric Wilcoxon signed-rank statistics to test whether the average information content is greater in the pre-dividend initiation period. The evidence for RAW and MAJ is unambiguous. Both t and Wilcoxon signedrank tests support the hypothesis that the average postdividend initiation quarterly earnings announcement is less informative. About 70% of the sample experiences a decrease in earnings price informativeness. The average reduction is about 1.2%. This suggests that earnings and dividend information

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38 might be substitutable. My sample yields results entirely consistent with those reported by Venkatesh. The results from the standardized variance measures of information content measures are in less accordance with the raw price measures. Just over half the sample experience a decrease following dividend initiation. DBVU E has an insignificant decline. Thus, there appears to be a reduction in the informativeness of earnings announcements relative to the average informativeness in non-announcement days. More importantly, however, DBVU d has a significant change but it is in a direction opposite to that hypothesized. The variance of excess returns for public announcements (both earnings and dividends announcements) relative to the non-announcement periods is decreased once dividends have been initiated. Contradictory to the information transmission hypothesis, the proportion of information publicly released relative to that privately acquired decreases after dividend initiation. I also test the normality of the distribution of the matched differences of information content measures. The lower the value of the Shapiro-Wilk D statistic the greater the probability of rejecting the null hypothesis that the distribution is normal. The null is rejected at the 5% significance level for all distributions. The non-normality of the distributions of matched differences justifies the use of nonparametric test statistics.

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39 TABLE 6 CROSS-SECTIONAL DISTRIBUTION OF MATCHED DIFFERENCES Sample Size = 325 firms DRAW 3 DMAJ a DBVU E a DBVU D a Mean 1.27 1.29 0.099 0.145 Std. Dev. 2 . 53 2 . 51 1.546 1.729 Minimum -6.99 -6.51 -7.098 13.632 Ql b -0. 15 -0.36 -0.785 0.503 Median 1.37 1.23 0.048 0.114 Q3 b 2 .76 2 .95 0.873 0.907 Maximum 9 . 17 9 . 80 6 .237 6.518 D° 0.9654 0 .9695 0.9364 0.8367 Prob < D 0.0368 0 . 0421 0.0000 0.0000 No. Positive 241 226 170 177 % Positive 77 . 2 69 . 5 52 . 3 54.5 rpd, e 9 . 05 9 .24 1.15 1.52 Prob > T 0 . 0001 0 . 0001 0.1250 0.0654 W d ’ f 8 .42 8.46 1.17 2.82 Prob > W 0 . 0001 0 . 0001 0.1210 0.0024 a Difference of average preand post-dividend information content of quarterly earnings, for each firm. For example, for the raw price information content measure, DRAWj = HSW j>PRE RAWj ( P0ST b Ql=first quartile; Q3=third quartile. c Shapiro-Wilk test of H 0 : Distribution is normal. d Test of H 0 : DRAW=0 versus H a : DRAW>0, H 0 : DM A J = 0 versus H a : DM A J > 0 , H 0 : dbvu e =o versus H a : DBVU e >0 / H 0 : DBVU d =0 versus H a : DBVU d >0. t-statistic . Wilcoxon signed-rank statistic.

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40 The Sample Grouped by Information Availability In this section, I analyze the matched differences across information availability groups. Under the information transmission hypothesis, one would expect to see the informativeness of earnings announcements to decrease as one goes from low information availability to high information availability firms. Table 7 shows the distribution of DRAW grouped by adjusted firm size. For all groups, the differences are significantly positive. The Wilcoxon signed-rank tests and t-tests are significant at levels well below the 5% level. The Kruskal-Wallis H statistic indicates that the group means for DRAW do differ across groups. The Jonckheere-Terpstra J statistic indicates that the group means are decreasingly smaller as market value increases. This relationship is significant at the 5% level. Post-dividend initiation quarterly earnings average price revaluations are less than in the pre-dividend initiation period. An examination of the group means shows that the decrease tends to be less for high market value firms. Low market value firms experience a drop of 1.5% in the average revaluation occurring at earnings announcements. High market value firms experience only a 0.6% decrease. However, this relationship is not strictly monotonic. Firms in the lowest market value group have smaller decreases than those in the next quintile. Table 8 shows the distribution of DMAJ grouped by adjusted firm size. We obtain the same general results with DMAJ. The

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41 group means decrease from group 2 to group 5. The mean for group 1 is less than that of group 2. The difference between group means is less strong than for DRAW. Tables 9 and 10 show the distributions for the standardized variance measures. In general, there does not appear to be any significant change in means after dividend initiation. Pvalues for the t-tests and Wilcoxon signed rank tests are generally insignificant. The only exception to this is group 2 for DBVU d which shows a decrease. The Kruskal-Wallis statistics fail to reject the null hypothesis of equal group means. The Jonckheere-Terpstra statistics do not provide any evidence that changes in relative informativeness increases as one goes from smaller to larger firms. Tables 11 to 14 show the distributions of matched differences for the groups based on number of Wall Street Journal Index news items. In general, the results obtained from using this information availability proxy are similar to those obtained from adjusted firm size. Within groups, the t and W statistics show that the mean matched differences for DRAW and DMAJ are all greater than zero at conventional significance levels. The Kruskal-Wallis statistic shows that the group means are not all equal. The Jonckheere-Terpstra statistic shows that groups with more news items have smaller mean decreases.

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42 TABLE 7 DISTRIBUTION OF DRAW FOR PORTFOLIOS BASED ON FIRM SIZE FIRM SIZE PORTFOLIOS ( 1=SMALL, 5=LARGE) 1 2 3 4 5 N 65 64 67 66 63 Minimum -6.9927 -4 . 7255 -5.6859 -5.3322 -3 . 6112 Ql a 0.0597 -0. 1516 0.1686 -0.2734 -0.8354 Median 1.7415 1.7134 1.7880 1.0386 0.4658 Q3 a 3 . 2472 3 . 5687 2 . 8520 2 .2331 1.7973 Maximum 9 . 1723 8 . 4802 7 .7009 6.5617 6.2081 Mean 1.4319 1.6038 1.5852 1.0865 0.6213 SD 3 . 1280 2 . 5617 2 .3125 2 . 3807 2 . 0822 T 3 . 69 5.01 5.61 3 .71 2.37 Pr > | T 0.0005 0.0001 0.0001 0.0001 0.0210 w 3 . 76 4 . 29 4 . 87 3 .39 2 . 18 Pr > | W 0 . 0001 0 . 0001 0.0001 0.0006 0.0292 H b 9 . 6564 * J c 2 . 6670* a Ql=first quartile; Q3=third quartile. b Test of H 0 : r 1 =r 2 : =. . . =r 5 versus H a : at least one mean (r) is not the same. H is the Wallis-Kruskal statistic. Cannot reject the null at the 5% level if H<9.49. 0 Test of H 0 : r 1 =r 2 =...=r 5 versus H a : r 1 >r 2 >...>r 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null at the 5% level if J<1. 645. * Significant at the 5% level using a one-tailed test.

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43 TABLE 8 DISTRIBUTION OF DMAJ FOR PORTFOLIOS BASED ON FIRM SIZE FIRM SIZE PORTFOLIOS ( 1=SMALL, 5=LARGE) 1 2 3 4 5 N 65 64 67 66 63 Minimum -6.1745 -6.5091 -3 . 5399 4 . 8611 2.8960 Ql a -0.1286 -0.5404 0.1559 0.5313 0.8236 Median 1.3908 1.8688 1.4322 0.7621 0.5403 Q3 b 2 . 5956 3 . 8226 3 . 0869 2.9983 1.9154 Maximum 9 .8017 8 . 5145 7 .4536 6.4123 5.4624 Mean 1.2713 1.6674 1.6423 1.1678 0.6591 SD 2 .9604 2 .7884 2 .2282 2.4313 1.9437 T 3 .46 4 . 78 6.03 3 .90 2.69 Pr > | T 0.0010 0 . 0001 0.0001 0.0002 0.0091 W 3 . 52 4 . 19 5.08 3 .35 2.36 Pr > | W | 0.0004 0.0001 0 . 0001 0.0008 0.0182 H c 7 . 2795 J d 2 . 0676* a Ql=first quartile; Q3=third quartile. b Test of H 0 : r 1 =T 2 =...=r 5 versus H a : at least one mean (r) is not the same. H is the Wallis-Kruskal statistic. Cannot reject null at the 5% level if H<9.49. c Test of H 0 : r 1 =r 2 =. . .=r 5 versus H a : t 1 >t 2 >. . . >r 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null at the 5% level if J<1 . 64 5 . * Significant at the 5% level using a one-tailed test.

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44 TABLE 9 DISTRIBUTION OF DBVU E FOR PORTFOLIOS BASED ON FIRM SIZE FIRM SIZE PORTFOLIOS ( 1=SMALL, 5=LARGE) 1 2 3 4 5 N 65 64 67 66 63 Minimum -7 . 0975 -2 . 8549 -3 . 3461 -2 . 3720 -2.8119 Ql a -1.0527 -0.7551 -0.9388 -0.6734 -0.5887 Median -0.1229 0.2910 0.0761 0.1267 -0.0641 Q3 a 0.9738 1. 0409 0.9714 0.6136 0.8085 Maximum 5.4849 4 .8623 6.2366 3 . 5934 2.4277 Mean -0 . 2467 0 .3007 0 .2396 0.1151 0.0835 SD 2 . 2479 1.4633 1.6395 1.0479 0.9478 T -0.88 1.64 1.20 0.89 0.70 Pr > T 0.3797 0.1051 0.2359 0.3756 0.4868 w -0 . 49 1.26 0 . 67 0.61 0.55 Pr > W 0.6242 0 .2076 0.5028 0.5418 0.5824 H b 1.6994 J c -0.3855 Ql=first quartile; Q3=third quartile. Test of H 0 : r 1 =r 2 =...=r 5 versus H a : at least one mean (r) is not the same. H is the Wallis-Kruskal statistic. Cannot reject the null at the 5% level if H<9.49. Test of H 0 : t 1 =t 2 =. . .=r 5 versus H a : r 1 >r 2 >. . ,>t 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null at the 5% level if Jcl.645.

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45 TABLE 10 DISTRIBUTION OF DBVU d FOR PORTFOLIOS BASED ON FIRM SIZE FIRM SIZE PORTFOLIOS ( 1=SMALL, 5=LARGE) 1 2 3 4 5 N 65 64 67 66 63 Minimum -7 .2114 -2 .7647 -13 . 6318 -10.3739 -1.0485 Ql a -0.7629 -0.3808 -0.5740 -0.5135 -0.3751 Median 0.0467 0.2756 0.0064 0.2192 -0.0104 Q3 a 0.8941 1. 1312 0.9512 0.8536 0.7990 Maximum 5.3639 4 . 8751 6.5185 3 .4881 3.0709 Mean -0 . 0263 0.4357 0.0094 0.1248 0.1936 SD 1.9576 1 . 3613 2 . 3879 1.6614 0.8251 T "0. 11 2 . 56 0.03 0.61 1.86 Pr > T 0 .9140 0 . 0129 0.9745 0.5438 0.0673 w 0.09 2 . 27 0 . 92 1.74 1.14 Pr > w 0.9282 0.0232 0.3576 0.0818 0.2542 H b 2 . 5566 J c -0 . 1831 a Ql=first quartile; Q3=third quartile. b Test of H 0 : r 1 =r 2 =...=r 5 versus H a : at least one mean (r) is not the same. H is the Wallis-Kruskal statistic. Cannot reject the null at the 5% level if H<9.49. c Test of H 0 : t 1 =t 2 =. . .=r 5 versus H a : r 1 >r 2 >. . .>r 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null at the 5% level if J<1.645.

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46 The results for the distributions of DBVU E and DBVU d in Tables 13 and 14, respectively, provide no discernable pattern. With the exception of group 3, none of the group means are significantly different from zero. The JonckheereTerpstra J statistic indicates that there is no monotonic decrease in the relative information content of earnings announcements as one goes from low to high news item. However, the Wallis-Kruskal H statistic indicates that the relative information content between groups are not all equal. In general, the results from these stratified tests are not supportive of each other. Based on the returns measures, DMAJ and DRAW, the decrease in group means increases as one goes from high market value (news item) firms to low market value (news items) firms. This is consistent with the information transmission hypothesis. Based on returns measures alone, the informativeness of quarterly earnings reports for low information availability firms experience the greatest decrease after dividend initiation. However, once we adjust for the contemporaneous level of returns volatility, this information effect no longer persists. The mean standardized variance measures, DBVU E and DBVU d , are not statistically different following dividend initiation. Neither do they exhibit any significant change in going from low to high information availability groups. There appears to be no monotonic difference in the mean change in total information content (DBVU d ) for firms grouped by information availability.

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47 TABLE 11 DISTRIBUTION OF DRAW FOR PORTFOLIOS BASED ON NO. OF NEWS ITEMS FIRM SIZE PORTFOLIOS ( 1=LEAST , 5=MOST) 1 2 3 4 5 N 45 92 40 80 68 Minimum -5.6416 -5.0269 -6.9927 -5.6859 -4.2648 Ql a -0.7078 0.1753 0.4185 0.0847 -0.9469 Median 1.7415 1.5886 1.7555 1.3645 0.5221 Q3 a 3 . 3723 3.2141 2 . 7990 2 . 5838 1.6834 Maximum 9 . 1723 7 .7009 8 .9110 5.9192 6.5617 Mean 1 . 5995 1.5428 1.4653 1.2798 0.5568 SD 3 . 0830 2 .4669 2 . 8481 2 . 1635 2 .3436 T 3 .48 6 . 00 3 .25 5.29 1.96 Pr > T 0 . 0011 0 . 0001 0.0024 0.0001 0.0543 w 3 .30 5. 32 3 .20 4 .72 1.80 Pr > | W 0.0010 0.0001 0 . 0014 0.0001 0.0588 H b 10 . 2878* J° 2 . 6193* a Ql=first quartile; Q3=third quartile. b Test of H 0 : t 1 =t 2 =. . .=r 5 versus H a : at least one mean (r) is not the same. H is the Wallis-Kruskal statistic. Cannot reject the null at the 5% level if H<9.49. c Test of H 0 : r 1 =r 2 =. . .=r 5 versus H a : r 1 >r 2 >. . . >r 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null at the 5% level if Jcl.645. * Significant at the 5% level using a one-tailed test.

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48 TABLE 12 DISTRIBUTION OF DMAJ FOR PORTFOLIOS BASED ON NO. OF NEWS ITEMS FIRM SIZE PORTFOLIOS ( 1=LEAST, 5=MOST) 1 2 3 4 5 N 45 92 40 80 68 Minimum -6.5091 -5.2964 -6. 1745 -3 . 5399 -4 . 0065 Ql a -0.3792 -0.0396 0.0547 -0.1967 -0.8519 Median 1.8883 1.3843 2 . 0159 1.2342 0.3210 Q3 a 3 .4979 3 . 1635 3 .3842 2 .4626 1.7938 Maximum 9 .8017 7 .4536 7 . 1067 6.1621 5.9768 Mean 1 . 8230 1.4624 1.6080 1.1782 0.6300 SD 3 .3569 2 .4725 2 . 7286 2 . 0246 2 . 1987 T 3 . 64 5.67 3 .73 5 .20 2 .36 Pr > T 0.0007 0.0001 0 . 0006 0.0001 0.0210 W 3.41 5.67 3 . 73 5.20 2.36 Pr > j W 0 . 0006 0 . 0001 0.0003 0.0001 0.0182 H b 9 . 4949* J c 2 . 7262* a Ql=first quartile; Q3=third quartile. b Test of H 0 : t 1 =t 2 =. . .=r 5 versus H a : at least one mean (r) is not the same. H is the Wallis-Kruskal statistic. Cannot reject the null at the 5% level if H<9.49. c Test of H 0 : t 1 =t 2 =. . .=r 5 versus H a : r 1 >r 2 >. . .>t 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null if J<1.645. * Siqnificant at 5% level usinq a one-tailed test.

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49 TABLE 13 DISTRIBUTION OF DBVU E FOR PORTFOLIOS BASED ON NO. OF NEWS ITEMS FIRM SIZE PORTFOLIOS ( 1=LEAST, 5=M0ST) 1 2 3 4 5 N 45 92 40 80 68 Minimum -7 . 0975 -3 .9293 -1.5222 -3 . 3461 -7.0369 Ql a -0.0897 -1.0469 -0.2689 -0.4310 -0.8351 Median 0.0336 0. 0293 0.7221 0.0897 -0.1813 Q3 a 1.0041 1.0610 1.3258 0.6437 0.4573 Maximum 4 .4923 6.2366 4 .8623 5.1343 2.8748 Mean 0 . 0024 0.1548 0.6755 0.0632 -0.2103 SD 2 . 1131 1.7655 1. 1946 1.1790 1.2715 T 0.01 0 . 84 3 . 58 0.48 -1.36 Pr > | T 0.9940 0 .4024 0.0009 0.6328 0.1771 w 0.45 0 . 38 3 . 28 0.70 -1.46 Pr > w| 0 . 6528 0 .7040 0 . 0010 0.4840 0.1442 H b 11.8148* J c 1.4030 a Ql=first quartile; Q3=third quartile. b Test of H 0 : r 1 =r 2 =...=r 5 versus H a : at least one mean (r) is not the same. H is the Wall is-Kruskal statistic. Cannot reject the null if H<9.49. c Test of H 0 : t 1 =t 2 =. . .=r 5 versus H a : r 1 >r 2 >. . .>r 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null at the 5% level if J>1 .645. * Siqnificant at the 5% level usinq a one-tailed test.

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50 TABLE 14 DISTRIBUTION OF DBVU d FOR PORTFOLIOS BASED ON NO. OF NEWS ITEMS FIRM SIZE PORTFOLIOS (1=LEAST, 5=MOST) 1 2 3 4 5 N 45 92 40 80 68 Minimum -7 .2114 -13 . 6318 -1.3339 -5.7861 -3.1993 Ql a -0.7629 -0.5921 0.1358 -0.3647 -0.5277 Median 0.1868 0.0714 0.7632 0.1048 -0.0717 Q3 a 1.0446 1. 1932 1.2732 0.7652 0.4795 Maximum 4 . 0260 6.5185 4 . 8751 3 .4881 3 . 1993 Mean 0.0482 0. 1039 0 . 7711 0.0608 -0.0028 SD 1 .9836 2 .4677 1 . 1524 1.0976 1.0661 T 0.16 0.40 3 . 58 0.48 1.36 Pr > T 0.8713 0.6873 0 . 0001 0.6223 0.9828 w 0.80 1.04 3 . 78 1.39 CO • 0 1 Pr > w 0 .4238 0 . 2984 0.0002 0.1646 0.6312 H b 11.8098* J c 1.2855 a Ql=first quartile; Q3=third quartile. b Test of H 0 : r 1 =r 2 =...=r 5 versus H a : at least one mean (r) is not the same. H is the Wallis-Kruskal statistic. Cannot reject the null at the 5% level if H<9.49. c Test of H 0 : r 1 =r 2 =. . .=t 5 versus H a : . .>r 5 with at least one strict inequality. J is the Jonckheere-Terpstra statistic. Cannot reject the null at the 5% level if J<1. 645. * Significant at the 5% level using a one-tailed test.

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51 This contradicts the information transmission hypothesis as one would expect the increase in the proportion of publicly released information to be greatest for low information availability firms. This inconsistency between the standardized variance and returns information content measures calls for further investigation of returns volatility around dividend initiation. It is possible that a contemporaneous decrease in returns volatility is producing the results obtained. This is investigated in the next chapter. The Market Reaction to Earnings Changes Before going on to examine the effect of the contemporaneous decline in returns variance, I analyze the price reaction to reported quarterly earnings change. This section provides further proof that what has been previously interpreted as a decrease in earnings informativeness caused by dividend initiation may be due to a contemporaneous decrease in returns volatility. The relationship between market price reaction and reported earnings is modeled as CER jq = 0 O + ( 3 , D + 03 AE jq + ( 3 , DAE jq (1) The 3-day cumulated excess returns for the day prior to, the day of and the day following the earnings announcement date for quarter q and firm j is denoted by CER jq . The dummy

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52 variable D takes on the value 1 if the quarterly earnings announcement comes from the post-dividend initiation period and is zero otherwise. The earnings surprise or standardized earnings is the quarterly change in earnings per share deflated by the firm's share price two days before the earnings announcement. EPS jq EPS jq -i P j.t-2 The change in quarterly earnings is appropriate based on a random-walk expectations model. Deflating by the share price captures any expectations about the forthcoming earnings formed through information gathered between earnings announcements . The regression approach allows us to incorporate information contained in the earnings report (namely, the change in earnings per share) which we could not do in the above analysis based on groups. This controls for the potential bias that could occur if earnings changes are smaller or less variable in the post-dividend period. This is similar to the approach used by Healy and Palepu (1988). The average price response to earnings announcement for the whole sample is (3 0 . Any change in the average price response in the post-dividend initiation period is captured by (3 lr and the average market price reaction to a given level of earnings surprise is /3 2 . It is expected to be positive indicating that

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53 the market responds positively (negatively) to earnings improvements (declines) . The average post-dividend initiation adjustment to / 3 Z is /? 3 . If dividends enable investors to revise their expectations of earnings, their forecast errors in the post-dividend period will be reduced. However, measures of unexpected earnings which only incorporate quarterly earnings changes will not reflect this additional information obtained from dividends. Standardized quarterly earnings changes will be noisier estimates of unexpected earnings. Therefore, the market price response to our measure of earnings surprise should be reduced. Coefficient /3 2 is expected to be negative. I estimate equation (1) using ordinary least squares. For each firm in my sample, 8 quarters preceding and following the initial dividend are collected. A quarter is excluded if the announcement is joint, i.e. earnings and dividend announcements within 2 days of each other. A quarter is also excluded if there was no data on net income, share price or shares outstanding to calculate the standardized earnings. I require that a firm had 4 usable preand post-dividend initiation quarters. This requirement excludes 58 firms leaving 274 firms for the regression analysis. For comparison, I also ran the regression using the variance adjusted measure BVU E .

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54 BVU E, jq = Po + + /3
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55 TABLE 15 MARKET PRICE RESPONSE TO STANDARDIZED EARNINGS Dep. Var. CER a BVU E b Po 5.4832 (47 . 538**) c 1.9508 (24.385**) Pi -0.8237 (-5.025**) -0.0698 (-0.613) P 2 0.1925 (2.174*) 0.0131 (0.214) P 3 0.1828 (0.480) 0.1232 (0.465) F 10.376** 0.224 Adj . R 2 0.0072 -0.0006 No. of Firms 274 No. of Obs. 3901 a Model estimated is CER = P 0 + P X D + P 3 AE + P 4 DAE, Model estimated is BVU e = p 0 + P,D + P 3 |AE q | + P 4 D | AE q CER is the cumulated 3 day excess returns; one day prior to, the day of and the day following the quarterly earnings announcement. D is a dummy variable which takes on the value 1 if the quarterly earnings occurs after dividend initiation and is zero otherwise. The standardized earnings is the change in earnings per share from the prior quarter divided by the stock price two trading days before the earnings announcement. EPS q EPS q-l P t -; 0 t-statistics in parentheses. * Significant at the 1% level using a two-tailed test. ** Significant at the 5% level using a two-tailed test.

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56 contradicts the notion that dividends convey information that leads investors to better forecast earnings. The coefficient ;0 3 is, however, not statistically significant. My regression results yield results diametrically opposite to those obtained by Healy and Palepu (1988). They obtain a significantly negative /3 3 . I suspect that this difference arises out of a difference in regression technique and model specification. Healy and Palepu apparently use a seemingly unrelated regressions technique for estimation. This allows their estimates of /3 0 and f3 z to be firm specific. In obtaining these firm specific coefficients, their model specification excludes the f3 1 coefficient. Any decrease in returns volatility is thus forced onto (3 3 which biases it to be negative . The results for the regression using the standardized variance measure of informativeness is consistent with prior results. The relative informativeness of quarterly earnings does not change after dividend information. The market response at quarterly earnings announcements relative to nonannouncement periods is not significantly related to the earnings reported, nor does this responsiveness change after dividend initiation.

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CHAPTER 5 RETURNS VOLATILITY AROUND DIVIDEND INITIATION In the previous chapter, I examined the information content of quarterly earnings announcements before and after dividend initiation. The information content measures used are directly related to the volatility of returns. A possible explanation for the observed decrease in the price reactions associated with post-dividend initiation earnings announcements is that returns volatility has decreased. Declines in returns volatility may be caused by changes in the process by which information is impounded in price. For example, dividend initiation may induce greater consensus among traders. This decreases noise trading and bid-ask spreads and consequently returns variance. However, volatility also may decrease due to causes that are independent of any information motivated reason. Dividend initiating firms experience increases in stock price which lead to proportionally smaller bid-ask spreads and reduced leverage. Finally, the observed volatility decreases in my sample could be due to unrelated market wide changes in interest rates. In this chapter, I perform several tests to distinguish the causes of the observed volatility decrease. I conclude that 57

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58 information is not the only plausible cause for volatility to decline . Variance Changes in the Overall Sample Table 16 shows the distribution of percentage changes in variances calculated for different sampling periods around dividend initiation. The sampling periods cover 900, 500, 250 and 100 trading days immediately before and after the declaration of the initial dividend. Fewer firms are available for the longer sample periods because of the longer sequence of returns required. For the 100, 250, 500 and 900 day sampling periods, I require at least 90, 240, 480 and 850 returns, respectively. Variances are calculated as the sum of the squared continuously compounded returns since Var[R] approximately equals E[R 2 ] for short measurement intervals. The percentage change in variance is Percentage variance change = 100 x [1 VAR POST VAR PRE ] A variance decrease after dividend initiation gives rise to a positive percentage change. The mean percentage change is positive for all (except the 100 day) sampling periods. This indicates a decline in measured returns volatility. The median is a better statistic of location since the mean percentage variance change is

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59 TABLE 16 DISTRIBUTION OF PERCENTAGE VARIANCE CHANGES Period 3 900 500 250 100 1 Sample b 225 304 327 331 Min -260. 12 -310.63 -382 .41 -1196.15 Q1 13 . 05 -12 . 14 -20.03 -25.86 Median 39.44 33 . 65 20.33 9.68 Q3 55.20 54 . 58 43.73 36.83 Max 80.59 86.77 80.31 81.33 Mean 26.94 13 . 50 3 . 52 -10.06 SD 46.29 59 .78 61.69 92 . 38 No. Pos . c 182 212 216 186 % Pos . 80 . 9 69 . 7 66.1 56.2 D d 0 . 8884 0.8354 0.8152 0.6024 Pr> | D | 0.0 0.0 0.0 0.0 rpe 8 . 47 3 . 94 1.03 -1.98 | Pr> | T | 0.0001 0.0001 0 .3035 0.0484 w e 8 . 59 5 . 79 4 . 09 1.14 Pr> | W | 0.0001 0. 0001 0 . 0001 0.2559 Percentage variance changes are calculated as Percentage variance change = 100 x [1 VAR POST VAR PRE ] Variances are calculated using daily continuously compounded stock returns from sample periods of 900, 500, 250 and 100 trading days before and after the declaration day of the initial dividend. b For 900, 500, 250, and 100 day sampling periods, a firm is included if it has 850, 480, 240, and 90 valid returns, respectively . 0 Number of firms in sample with positive percentage variance changes, i.e. variance decreases after dividend initiation. d Shapiro-Wilk test of normality. ® Test of H 0 : Percentage variance change = 0.

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60 downwardly biased. 1 The medians are positive for all sampling periods and always greater than the means. The longer the sampling period, the more apparent is the volatility decline. The median percentage change is 39% for the 900 day sample period, this declines to 10% for the 100 day sample period. The proportion of firms experiencing volatility declines also increases with longer sampling periods: 56% for the 100 day sample period increasing to 81% for the 900 day sample period. The Wilcoxon signed-rank tests of the null hypothesis that the percentage variance change is zero are rejected for all (except the 100 day) sample periods. When examining changes in stock return variance, it is important to consider changes in market volatility through time. This is particularly so for my sample which has dividend initiation dates dispersed over 17 years and where there is also considerable clustering in the middle seventies. It is possible that any observed change in market variance around the time of dividend initiation is explained by contemporaneous changes in market volatility. To address this possibility, I divide firm variances by an estimate of market volatility. This estimate is obtained from the contemporaneous returns on an equally weighted market index of all firms on the relevant data tape. This adjustment is in the spirit of a heteroskedast icity adjustment, where the i From Jensen's inequality, E [ V PRE /V P0ST ] > E [V pre ]/E [Vpo ST ] .

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61 stock's return variance is assumed proportional to that of the market. Table 17 shows the distribution of percentage variance changes where the firm variances have been adjusted by a contemporaneous estimate of the market variance. It appears that changes in market volatility explain part of the variance decline. Median percentage changes are smaller after standardization but are still positive. The Wilcoxon signedrank tests can only reject equal variances for the 900 day sample period. This provides some evidence that clustering of the data sample may explain the observed variance decreases. Variance Ratio Tests Consider returns calculated over intervals of k days where k>l . If daily returns follow a random walk, the variance of these k-day returns should be k times the variance of daily returns. French and Roll (1986) model a stock's return as having 3 components: a rational information (intrinsic value) component, a noise or mispricing component and a bid/ask error component. If noise and bid/ask error components are temporary components, over time, they will be corrected and induce negative autocorrelations. This negative autocorrelation causes the k-day variance to be less than k times the daily variance. A variance ratio (VR) is the ratio of k-day returns variance to daily returns variance (French and Roll (1986)

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62 TABLE 17 DISTRIBUTION OF MARKET ADJUSTED PERCENTAGE VARIANCE CHANGES Period 3 900 500 250 100 Sample 13 225 304 327 331 Min -198 .86 -272 . 93 -406.08 -803.86 Q1 0.36 -31.79 -31.85 -43.60 Median 28.73 13 . 29 6.28 5.22 Q3 48 . 10 45.73 39.83 30.41 Max 90.54 80.75 79 . 44 79.81 Mean 17 .73 -2 . 74 -6.50 -10.06 SD 46.33 66 . 64 66.25 92.15 No. Pos . c 169 181 184 176 % Pos. 75. 1 59 . 5 56.3 53.2 D d 0.8452 0 .8544 0 . 8367 0.6862 Pr> D 0 . 0 0 . 0 0. 0 0.0 5.74 -0 . 72 -1.77 -3.85 Pr> T 0 . 0001 0 .4736 0 . 0769 0.0001 w e 7.21 1 . 84 0.75 -1.70 Pr> W 0.0001 0 . 0666 0.4556 0.1359 Percentage variance changes are calculated as Percentage variance change = 100 x [1 post^ VAR pre Variances are calculated using daily continuously compounded stock returns from sample periods of 900, 500, 250 and 100 trading days before and after the declaration day of the initial dividend. Firm variances are divided by the returns variance of a value-weighted market index. b For 900, 500, 250, and 100 day sampling periods, a firm is included if it has 850, 480, 240, and 90 valid returns, respectively . c Number of firms in sample with positive percentage variance changes, i.e. variance decreases after dividend initiation. d Shapiro-Wilk test of normality. ® Test of H 0 : Percentage variance change = 0.

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63 call these actual-to-implied variance ratios) . ^ Var (R k ) VR k = — Var (R 1 ) where R k is the k-day return. Under the null hypothesis that returns follow a random walk, the VR should be equal to one. If there are transitory components, the VR should be smaller than one. 2 The VR therefore allows us to gauge the effect of the transitory bid/ask and noise components. VRs have been used in recent studies by French and Roll (1986) , Fama and French ( 1988 ), Lo and Mackinlay ( 1988 ), and Kaul and Nimalendran ( 1990 ). Cochrane ( 1988 ), Lo and Mackinlay (1989), and Richardson and Smith ( 1991 ) analyze the properties of the VR test statistic. In general, they find that the VR test statistic is robust and has higher relative power compared with alternative test statistics. If dividend initiation reduces the bid/ask error component (either due to improved liquidity or reduced information asymmetry) or noise trading (better informed traders with greater consensus) then post-dividend initiation VRs should be closer to one than pre-dividend initiation VRs. Table 18 shows the summary statistics for 10-day and 20-day return variances 2 It can be shown that VR(k) can be written as a function of estimated autocorrelations of returns measured over the basic measurement interval (daily in our case) . VR k 1 + [ (k-1) P x + (k-2) P 2 + ... + p k .J Jx.

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64 TABLE 18 DISTRIBUTION OF VARIANCE RATIOS FOR PREAND POST-DIVIDEND INITIATION 500 DAY SAMPLE PERIODS. Sample = 310 VRio VR Z0 PRE POST DIFF a PRE POST DIFF 3 Minimum 0.294 0.326 -0.867 0.215 0.250 -1.093 Q1 0.775 0.794 -0.180 0.743 0.709 -0.178 Median 0.949 0.950 0 . 018 0.915 0.917 0.030 Q3 1. 166 1. 142 0. 171 1.211 1.148 0.234 Maximum 1.942 1.921 1. 114 2 . 532 2 . 078 1.504 Mean 0.993 0.983 0 . 010 0.985 0.960 0.025 rpb 0.61 ( 0 . 54 06 d ) 1.18 ( 0 . 2 392 d ) w c 0.51 ( 0 . 6077 d ) 1.31 ( 0 . 192 l d ) a For each firm, the difference between preand postdividend initiation variance ratios is DIFF = PRE POST. b t test of H 0 : DIFF = 0. c Wilcoxon signed-rank test of H 0 : DIFF = 0. d Significance level of two-tailed test. obtained from 500 day sample periods before and after dividend initiation. The 10-day (20-day) returns are obtained by multiplying 10 (20) successive daily returns. Therefore, in a 500 day sample period there are fifty 10-day an twenty five 20-day returns. The VRs are all less than one for both preand post-dividend initiation periods. Mean and median predividend initiation VRs are smaller than post-dividend initiation VRs but this difference is not statistically significant (Wilcoxon signed-rank test) . Therefore, I cannot

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65 conclude that variance decreases are due to reductions in bidask spreads or noise trading. The Timing of Variance Changes The results show that the decrease in the price revaluations at earning announcements in the post-dividend initiation period may be related to the general decline in returns variability. This decline may be caused by an information effect associated with dividend initiation. To ascribe causality to this information effect requires that variance changes should occur after the initiation of dividends. On the other hand, if variance changes do not systematically occur after dividend initiation, the plausibility of an dividend initiation information effect is questionable . In this section, I analyze the timing of variance changes. I first examine percentage variance changes calculated over successive 250 day sample periods beginning 750 trading days prior to dividend initiation. Due to the long series of returns required, only 272 firms are included in the sample. Table 19 shows the distribution of these percentage variance changes. The sample of firms experience significant variance declines in the year prior to (period [-250,-1]) as well as the year after (period [1,250]) the initiation of dividends.

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66 TABLE 19 SUMMARY STATISTICS OF PERCENTAGE VARIANCE CHANGES (250 DAY SAMPLE PERIOD) FOR SUCCESSIVE PERIODS Sample 272 Period 3 -500,-251 -250,-1 1,250 251,500 Median 7 .266 13 . 29 6.28 5.22 Mean -5.052 -2 .74 -6.50 -10.06 No. Pos . b 129 164 161 136 % Pos . 47 . 4 60 . 2 59 . 1 50.0 W c 0.98 4.40 2.75 1.20 Pr> | W | 0 . 3297 0 . 0001 0.0026 0.2324 Percentage variance changes for period (-500,-251) are calculated as Percentage variance change = 100 x [1 ( ~ 500 -r : ? jil ] VAR (-? so / -501) The period (1,250) is comparable to the preand postdividend initiation percentage variance changes in Table 15. Day 0 is the declaration date of the initial dividend. Number of firms in sample with positive percentage variance changes, i.e. variance decreases after dividend initiation. Test of H 0 : Percentage variance change = 0. Therefore the variance decline does not occur strictly after dividend initiation. There is significant variance decline in the year prior to dividend initiation. To better examine when variance declines occur, I examine successive overlapping periods of 100 trading days. For each firm, I collect 400 trading days of returns: 200 before the initiation of dividends and 200 after. I divide these 400 days

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67 into seven 100 day samples with each sample overlapping the prior sample by 50 days. Sample period one consists of returns from day -200 to -101; sample period two consists of returns from day -150 to -51; sample period three consists of returns from day -100 to -1; and so on. The final sample period seven consists of returns from day 101 to 200. The sample periods are shown in Figure 2 . -200 -100 0 100 200 Sample periods 135 7 2 4 6 Figure 2 Sample Periods for Detecting Variance Changes Within each sample period, I test whether a significant variance decrease has occurred. For each firm, I count the number of significant variance decreases occurring in the predividend initiation sample periods 1, 2 and 3 and the postdividend initiation sample periods 5, 6, and 7. Denote these counts as C0UNT1 and C0UNT2 , respectively. If a variance change is more likely to occur following dividend initiation,

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68 C0UNT2 is going to be greater than C0UNT1. The test is therefore , H 0 : C0UNT2-C0UNT1 = 0 versus H x : C0UNT2 -C0UNT1 > 0 I test this hypothesis using the Wilcoxon signed-rank statistic. To detect a variance decrease within a sample period, I use a test statistic T* which has been extensively examined by Hsu (1977, 1979). Let M be a consecutively observed sequence of daily returns R 1 ,R 2 ,...,R M which are assumed to be independent normal variates. Further, let the variances of these variables be represented by a : 2 , a 2 2 , . . . , a M 2 , respectively. The problem is to test whether a variance shift 6 has occurred, H 0 : a-L 2 = a 2 2 = . . . = a M 2 a 0 2 i (a 0 2 is unknown) against H a : ? 2 (Ji = Go = ak M 2 = a 0 2 ; 2 _ k+1 = a k+2 a 2 _ M a 0 2 + 0 , where k is unknown (k=l , 2 , . . . , M-l) ; is unknown and (a 0 2 +
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69 Under the null hypothesis of no variance shift, this test statistic has mean E[T] =! $ and variance VAR[T] = (M+l) / [ 6 (Ml)(M+2)]. I use the large sample approximation T*=(T0. 5)/ {VAR[T] } % which is distributed asymptotically standard normal . In this test, I calculate T* for each sample period with M=100. I deem that a variance decrease has occurred in that sample period if T*<-2.6. This corresponds to a significance level of just under 0.5%. The total firm sample size is 328 firms 3 . Of the 328 firms examined, 83 had no significant variance changes in the 7 sample periods. 56 firms had only variance increases. 189 firms (58% of the sample) had at least one variance decrease in the 7 sample periods. The results of this analysis are shown in Table 20. The frequency distribution of variance decreases by sample periods does not show a preponderance of variance changes occurring after dividend initiation (i.e. in sample periods 5, 6 and 7). This is confirmed by the Wilcoxon signed-rank statistic's value of 0.70. I conclude from this analysis that the decrease in returns variability does not occur systematically after dividend initiation. Variance decreases occur with equal frequency before and after dividend initiation. 3 Four OTC firms were excluded because they did not have an uninterrupted series of consecutive returns. These firms subsequently traded on either the AMEX or NYSE.

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70 TABLE 20 ANALYSIS OF THE TIMING OF VARIANCE DECREASES Sample Period 1 2 3 4 5 6 7 Beginning Return 3 -200 -150 -100 -50 1 51 101 Ending Return 3 -101 -51 -1 50 100 150 200 Decreases 13 36 41 31 33 44 39 35 a The announcement date of the initial dividend is day 1. b A variance decrease is deemed to have occurred if T <—2.6 for that sample period. H 0 : Variance decreases occur with equal frequency before and after dividend initiation. COUNT2 COUNT1 = 0 against H a : Variance decreases occur more frequently after dividend initiation. C0UNT2 C0UNT1 > 0 where COUNT1 = COUNT2 = TOTAL NUMBER OF VARIANCE DECREASES OCCURRING IN SAMPLE PERIODS 1, 2 OR 3 FOR FIRM j . TOTAL NUMBER OF VARIANCE DECREASES OCCURRING IN SAMPLE PERIODS 5, 6 OR 7 FOR FIRM j . The Wilcoxon signed-rank statistic W is 0.70. Cannot reject null at the 5% significance level. Sample size is 328 firms.

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71 The Effect of Clustered Data I examine the possibility that specific periods are causing the observed decrease in firm volatility. Just over 75% of my sample firms initiated dividends in the period 1972-77, with over 50% occurring between 1975-1977. The effect of interest rates on volatility is particularly relevant to my sample. The seventies experienced volatile changes in interest rates. In particular, interest rates rose from 1972-73 and fell from 1974-76.'' It is plausible that for a large number of firms in my sample, pre-dividend periods coincided with a rising interest rate environment (and consequently high returns volatility) and post-dividend periods coincided with falling interest rates (low returns volatility). If the decrease in firm volatility is independent of secular influences, the proportion of firms experiencing variance declines should not cluster around certain periods. Table 21 shows percentage variance changes calculated for 500 day sample periods distributed by the year of dividend initiation. The table shows that the proportion of firms experiencing variance declines is not constant across the years. In particular, the periods 1972-73 and 1978-79 have a majority of firms experiencing variance increases. Conversely, the period 1975-76 which accounts for almost 40% of my sample of dividend initiating firms has above average proportion of A See, for example, Livingston (1990) p. 3 or Madura (1989) p. 37.

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72 TABLE 21 DISTRIBUTION OF PERCENTAGE VARIANCE CHANGES (500 DAY SAMPLE PERIOD) BY YEAR OF DIVIDEND INITIATION Year No . a Pos . b % Pos . c Mean Median 1970 6 5 83 . 3 20.4 24.5 1971 4 4 100 . 0 34.2 33.2 1972 18 1 5.6 -73 . 1 -33.2 1973 29 4 13 . 8 -57 . 6 -63.9 1974 24 16 66 . 7 4 . 5 19.1 1975 58 57 98 . 3 54 . 5 56.8 1976 62 62 100 . 0 53 . 4 57.2 1977 47 33 70.2 13 . 1 2 6.6 1978 20 9 45 . 0 -22 . 4 -4 . 3 1979 11 4 36.4 -7 . 2 -4 . 6 1980 9 6 66 . 7 8 . 0 31.1 1981 6 5 83 . 3 21.8 35.0 1982 3 3 100 . 0 19 . 8 16.8 1983 5 4 80 . 0 23.7 21.7 1986 3 0 0 . 0 -60.1 -69 . 0 a Number of sample firms initiating dividends in that year. b Number of sample firms with positive percentage variance changes, i.e. variance decreases. c Sample firms with positive percentage variance changes as a percentage of sample firms initiating dividends in that year.

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73 variance decreases. This pattern is also found for variances calculated for 900, 250 and 100 day sample periods. Table 22 shows the distribution of percentage variance changes adjusted by a estimate of the contemporaneous market variance. Note that after adjusting for market variance, the mean and median percentage variance change is negative for the period 1975-76. Firms initiating dividends in those years experience decreases in variances but of a smaller magnitude than the market. The behavior of sample firm variances through time is plotted in Figures 3 through 5. Figure 3 plots the monthly variance of an index of dividend initiating firms (VAR) and a weighted average of all firms on the NYSE/ AMEX and NASDAQ tapes. For each month (m=l/70 to 12/86) , VAR is the average monthly returns variance for a set of dividend initiating firms N m . A firm is included in N m if it initiated dividends within 24 months of month m. VAR m = irS Var h.n iN m i =1 N m The monthly returns variance for the firm j is calculated as m Var bm = £ R % X =1 where T m is the number of trading days in month m. If a firm has less than 15 valid returns in any month, it is excluded from VAR. VAR thus represents the mean monthly variance for

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TABLE 22 DISTRIBUTION OF MARKET ADJUSTED PERCENTAGE VARIANCE CHANGES (500 DAY SAMPLE PERIOD) BY YEAR OF DIVIDEND INITIATION Year No . a Pos . b % Pos . c Mean Median 1970 6 2 33 . 3 -3 . 9 -9.7 1971 4 1 25 . 0 -14 . 9 -15.6 1972 18 14 77 . 8 28 . 8 58 . 1 1973 29 29 100 . 0 51.4 51.8 1974 24 22 91.7 31.0 37.7 1975 58 9 15 . 5 -48 . 4 -36.5 1976 62 24 39 . 7 -50 . 5 -16.2 1977 47 35 74 . 5 18 . 6 32 . 0 1978 20 18 90 . 0 45.2 53 . 3 1979 11 11 100 . 0 39 . 9 42 . 5 1980 9 6 66.7 17 . 3 34.5 1981 6 5 83 . 3 25 . 0 33.9 1982 3 2 66.7 -6.5 8.8 1983 5 1 20 . 0 -38 . 6 -22.9 1986 3 3 100 . 0 63 . 7 61.8 a Number of sample firms initiating dividends in that year. b Number of sample firms with positive percentage variance changes, i.e. variance decreases. 0 Sample firms with positive percentage variance changes as a percentage of sample firms initiating dividends in that year .

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75 the sample of dividend initiating firms through a moving window of 24 months. From 1/70 to 12/72, VarM is the monthly returns variance of the equally-weighted NYSE/AMEX market index. From 1/73 to 12/86, VarM is a weighted average of the monthly returns variance of the equally— weighted NYSE/AMEX and NASDAQ market indices . VarM m = wVarjjYgg/^gx p, + (1-w) Var NASDAQ(m The weight w corresponds to the proportional representation of NYSE/AMEX firms in the sample (w = 247/332) . The monthly variances of the market indices are calculated in the same way as for individual firms. Figure 3 shows the wide variations in returns variance of the sample. The returns variance is particularly high in the years 1973 to 1976. The sample variance behaves similarly to the wider market. Therefore, variations in return variance are not peculiar to the sample of dividend initiating firms. Figure 4 plots the monthly variance and the distribution of firms initiating dividends, expressed as a percentage of the total sample (% INIT) . This figure illustrates the effect of clustering in the sample. The large proportion of firms initiating dividends between 1975 and 1977 follows a period of above average returns variance. This potentially could explain the reported decrease in returns variance and quarterly earnings price revaluations.

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76 10 X10 7001 7101 7201 7301 7401 7501 7001 7701 7801 7901 8001 8101 8201 8301 8401 8501 8001 VAR VarM Figure 3 Monthly Returns Variance of an Index of Dividend Initiating Firms (VAR) and a Weighted Market Index (VarM) . Figure 5 plots the monthly variance and the 3-month T-bill rate. The source for the 3 month T-bill rate is the Interest Rates: Money and Capital Market Rates tables from the Federal Reserve Bulletin . The figure shows that the increase in returns variance in the middle seventies appears to be preceded by increases in interest rates. This is not to say that interest rates have caused the changes in returns

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77 volatility. They could both be reactions some other macroeconomic event, such as the oil shock of 1973. Figure 4 Monthly Returns Variance of an Index of Dividend Initiating Firms (VAR) and the Distribution of Firms Initiating Dividends (% INIT) 1 Values on the vertical axis are in percentages. Â’ Multiply values on the vertical axis by 10~ 3 .

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78 Figure 5 Monthly Returns Variance of an Index of Dividend Initiating Firms (VAR) and the 3-Month Treasury Bill Rate (T-Bill) a Source: Federal Reserve Bulletin . Values on the vertical axis are in percentages. b Multiply values on the vertical axis by 10~ 3 . The general conclusion that can be drawn from the above analysis is that variance decreases seemingly associated with dividend initiation are not caused by information related factors. Variance decreases do not reduce the degree of negative autocorrelation that would indicate reductions in

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79 noise trading or bid-ask spreads. Neither do they systematically occur following dividend initiation. The variance decreases appear to be occur mostly in the middle seventies when a large proportion of dividend initiations coincide with periods of low interest rates preceded by periods of high interest rates. Variance decreases could therefore be driven by changes in interest rates or other macro-economic events. The evidence in this and the previous chapter tends to reject the hypothesis that there are long run information effects associated with dividend initiation.

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CHAPTER 6 SUMMARY AND CONCLUSIONS It has been suggested that dividends play a role in transmitting information to investors. This hypothesis has motivated the examination of the relationship between dividend initiation and earnings announcements. Previous research has found that the price reactions at earnings announcements are on average lower after dividends have been initiated. This result has been interpreted to support the contention that dividends has conveyed information. Dividend information has preempted information that would otherwise have been conveyed by earnings reports. I contend that this conclusion is invalid. Showing that price reactions have declined merely shows that earnings and dividends information is substitutable. The testable implication of dividends as an information source is whether dividends reduce costly private information acquisition. The appropriate empirical measure is therefore not earnings returns variance alone but standardized returns variance. If non-announcement returns reflects the private information activities of investors, then standardizing announcement returns variance by the surrounding non-announcement returns gives a measure of the importance of the public announcement. 80

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81 If private information acquisition is reduced by having dividends, investors should place greater reliance on the firm's public announcements, both dividends and earnings. In addition, this reliance should be greatest for firms for which information is least readily available. I do not detect any difference in the relative measures of the information content of earnings announcements before and after dividend initiation. This result holds for the total sample of firms as well as when the sample is grouped by proxies of information availability. The information transmission hypothesis is not supported. What is puzzling is that there is a significant reduction in raw price reactions for post-dividend initiation earnings announcements. I contend that this reduction is caused by a general decrease in the volatility of returns which coincides with dividend initiation. Regression analysis provides evidence that the reduction in post-dividends earnings price reactions is related to the variance decrease and not to a reduction in the information content of earnings. It can be argued that an information effect associated with dividend initiation causes the decrease in returns volatility. For example, if the initiation of dividends induces greater consensus among traders, noise trading or bid-ask spreads may be reduced. I test for reductions in variance ratios before and after dividend initiation. I do not find any significant change. Further, one should observe the frequency of variance

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82 declines to be higher after dividend initiation. Tests of the timing of variance changes show no evidence of this. Variance decreases appear with equal frequency before and after dividend initiation. The findings of this study are not supportive of the hypothesis that dividends play an informational role. The search for a valid reason why firms initiate dividend payments must still continue. However, it does provide evidence that the market is sufficiently efficient such that any informational effects from dividends are minor. /

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83 REFERENCES Asquith, P. and D. W. Mullins, 1983, "The Impact of Initiating Dividend Payments on Shareholders' Wealth," Journal of Business 56, 77-96. Atiase, R. , 1985, "Predisclosure Information, Firm Capitalization, and Security Price Behavior Around Earnings Announcements," Journal of Accounting Research 23, 21-36. Bagehot, W. , 1971, "The Only Game in Town,", Financial Analysts Journal 22, 12-14. Black, F. , 1986, "Noise", Journal of Finance 41, 529-543. Beaver, W. H. , 1968, "The Information Content of Annual Earnings Announcements," Empirical Research in Accounting: Selected Studies, Supplement to the Journal of Accounting Research 6, 67-92. Bhattacharya , S., 1979, "Imperfect Information, Dividend Policy, and the 'Bird in the Hand' Fallacy," Bell Journal of Economics 10, 259-270. Bhushan, R. , 1989, "Collection of Information About Publicly Traded Firms: Theory and Evidence," Journal of Accounting and Economics 11, 183-206. Christie, A. A., 1982, "The Stochastic Behavior of Common Stock Variances," Journal of Financial Economics 10, 407432 . Cochrane, J. H., 1988, "How Big is the Random Walk in GNP? , " Journal of Political Economy 96, 893-920. COMPUSTAT . Standard and Poor's Compustat Services, Englewood, Colo . Copeland, T., and D. Galai, 1983, "Information Effects on the Bid-Ask Spread," Journal of Finance 38, 1457-1469. CRSP Daily Stock Master Returns and Index , Center for Research in Security Prices, University of Chicago, Chicago, 111. Demsetz, H. , 1968, "The Cost of Transacting," Quarterly Journal of Economics 82, 33-53.

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84 Diamond, D. W. , and R. E. Verrecchia, 1981, "Informational Aggregation in a Noisy Rational Expectations Economy," Journal of Financial Economics 9, 221-235. Grant, E. B. , 1980, "Market Implications of Differential Amounts Information of Interim Information," Journal of Accounting Research 18, 255-268. Fama, E. F. and K. R. French, 1988, "Permanent and Temporary Components of Stock Prices," Journal of Political Economy 96, 246-273. Federal Reserve Bulletin . Board of Governors of the Federal Reserve System, Washington, D. C. French, K. , and R. Roll, 1986, "Stock Return Variances: The Arrival of Information and the Reaction of Traders," Journal of Financial Economics 17, 5-26. Glosten, L. , and P. Milgrom, 1985, "Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders," Journal of Financial Economics 14, 71-100. Healy, P. , and K. Palepu, 1988, "Earnings Information Conveyed by Dividend Initiations and Omissions," Journal of Financial Economics 21, 149-175. Hellwig, M. F. , 1980, "On the Aggregation of Information in Competitive Markets," Journal of Economic Theory 22, 477498 . Ho, T. , and H. Stoll, 1981, "Optimal Dealer Pricing under Transaction and Return Uncertainty," Journal of Financial Economics 9, 47-74. Hollander, M. and D. A. Wolfe, 1973, Nonparametric Statistical Methods . (New York, NY: Wiley) . Hsu, D. A., 1977, "Tests of Variance Shift at an Unknown Time Point," Applied Statistics 26, 279-284. Hsu, D. A., 1979, "Detecting Shifts of Parameter in Gamma Sequences with Applications to Stock Price and Air Traffic Flow Analysis," Journal of the American Statistical Association 74, 31-40. John, K. and J. Williams, 1985, "Dividends, Dilution and Taxes: A Signalling Equilibrium," Journal of Finance 40, 1053-1070.

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85 Kaul G. and M. Nimalendran, 1990, "Price Reversals: Bid-Ask Errors of Market Overreaction?," Journal of Financial Economics 28, 67-93. Livingston, M. , 1990, Money and Capital Markets: Financial Instruments and Their Uses . (Englewood Cliffs, N. J.: Prentice-Hall) . Lo, A. W. and A. C. MacKinlay, 1988, "Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies 1 , 41 66 . Lo, A. W. and A. C. MacKinlay, 1989, "The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation," Journal of Econometrics 40, 203-238. Lobo, G. J. and A. A. W. Mahmoud, 1989, "Relationship Between Differential Information and Security Return Variability," Journal of Accounting Research 27, 116-134. Madura, J., 1989, Financial Markets and Institutions . (St. Paul, Minn.: West Publishing Co.). Marsh, T. , and R. Merton, 1987, "Dividend Behavior for the Aggregate Stock Market," Journal of Business 60 , 1 40 . Miller, M. H. and F. Modigliani, 1961, "Dividend Policy, Growth and the Valuation of Shares," Journal of Business 34, 411-433. Miller, M. H. and K. Rock, 1985, "Dividend Policy Under Asymmetric Information," Journal of Finance 40 , 1031 1051 . Richardson, M. and T. Smith, 1991, "Robust Power Calculations with Tests for Serial Correlation in Stock Prices," Working Paper, Rodney L. White Center for Financial Research, The Wharton School, University of Pennsylvania. Ross, S. A., 1976, "The Determination of Financial Structure: The Incentive-Signalling Approach," Bell Journal of Economics Spring, 23-40. Shores, D. , 1990, "The Association Between Interim and Security Returns Surrounding Earnings Announcements," Journal of Accounting Research 28, 164-181. Thompson, R. B. , II, C. Olsen, and J. R. Dietrich, 1987, "Attributes of News About Firms: An Analysis of FirmSpecific News Reported in the Wall Street Journal Index," Journal of Accounting Research 25, 245-274.

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86 Venkatesh, P. C. , 1989, "The Impact of Dividend Initiation on the Information Content of Earnings Announcements and Returns Volatility", Journal of Business 62, 175-197. Verrecchia, R. E. , 1982, "Information Acquisition in a Noisy Rational Expectations Economy," Econometrica 50, 1415-1430. Wall Street Journal Index . Dow Jones and Co. , New York, N. Y. Zeghal, D. , 1984, "Firm Size and the Informational Content of Financial Statements," Journal of Financial and Quantitative Analysis 19, 299-310.

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87 BIOGRAPHICAL SKETCH Thian Soon Wong was born in Johor Bahru, Malaysia, on March 19, 1958. He received a bachelor's degree in electrical engineering from the University of Malaya, Kuala Lumpur, Malaysia, in 1982 and a Master of Business Administration from the University of Florida in 1986. He expects to receive a Doctor of Philosophy degree in finance from the University of Florida in 1991.

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I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and its fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. / '/ / \ c /) f / Robert C. Radcliffe, Chairman Professor of Finance, Insurance, and Real Estate I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and its fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Anand Desai Assistant Professor of Finance, Insurance, and Real Estate I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and its fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Michael D. Ryngae'rt Assistant Professor of Insurance and Real Estate Finance , I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and its fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. This dissertation was submitted to the Graduate Faculty of the Department of Finance, Insurance, and Real Estate in the College of Business Administration and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. Dean, Graduate School