Analyzing Event Statistics in Corporate Finance: Methodologies, Evidences, and Critiques

201 Pages·2015·0.97 MB·English

by  Jau-Lian Jeng

ANALYZINGEVENT STATISTICS IN
CORPORATEFINANCE
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Analyzing Event
Statistics in Corporate Finance
Methodologies, Evidences, and Critiques
Jau-Lian Jeng
ANALYZINGEVENTSTATISTICSINCORPORATEFINANCE
Copyright©Jau-LianJeng,2015.
Allrightsreserved.
Firstpublishedin2015by
PALGRAVEMACMILLAN®
intheUnitedStates—adivisionofSt.Martin’sPressLLC,
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andhascompaniesandrepresentativesthroughouttheworld.
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ISBN:978–1–137–39717–1
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AcataloguerecordofthebookisavailablefromtheBritishLibrary.
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Firstedition:February2015
10 9 8 7 6 5 4 3 2 1
Contents
Preface vii
Acknowledgments xi
Part I Event Study Methodology I
Chapter1
DataCollectioninLong-Run orShort-RunFormat? 3
Chapter2
ModelSpecificationsforNormal(orExpected)Returns 29
Chapter3
CumulativeAbnormalReturns orStructuralChangeTests? 83
Part II Event Study Methodology II
Chapter4
RecursiveEstimationforNormal(orExpected)Returns 121
Chapter5
TimeWillTell!AMethodwithOccupationTimeStatistics 135
Epilogue 169
Notes 173
References 177
Index 185
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Preface
T
he literature on the corporate finance is so abundant that,
ever since the pioneering work of Fama, Fisher, Jesen and Roll
(1969), many research articles are published every year for
event studies. Although many new methodologies are devised,
empiricalcorporatefinanceisstillstrugglingfromcontroversies
of many issues such as proper data collection, time-horizon
decisions, event period determination, and robust statistical
procedures. These issues certainly are not easy tasks for any
researcheroranenthusiasticgraduatestudentineconomicsand
financetoresolvethempromptly.Theaimofthisbookhenceis
notsettoprovideanswerstoalltheseissues,either.Instead,the
intent is to reiterate the necessary cautions in analyses on the
event studies in corporate finance and to provide some alter-
natives. Therefore, this book aims to become a reference for
the researchers, graduate students, and professionals who are
interestedinexploringthepossibleimpactsofcorporatefinance
events rigorously.
Starting from data collection, researchers should be careful
in identifying the issues of interest and the representativeness
of data set. Before anything starts, one can’t be more cautious
than to investigate the intrinsic (time-series/cross-sectional)
properties of the data introduced either from the sampling
schemes or from the nature of the data themselves, particu-
larly associated with financial times series. More importantly,
althoughitistemptingtoexplorealldifferentmodelsorregula-
rities that possibly can explain the financial data, all models or
proposals for these data should provide sufficient verifications
from the foundation of financial economics. Or, even more
viii PREFACE
pleasingly, these models should provide some better ones in
theories or rigors if possible.
The contents of the book starts from Chapter 1 that surveys
(a) the issues of event-oriented constructed data and (b) the
inclusion of firm-specific attributes as variables for empirical
asset pricing models and leads to the importance of model
search for the normal (or expected) returns. The task then is
continuedinChapter2wherediagnostictestsforthenondiver-
sifiability of factor-oriented variables in empirical asset pricing
models (for normal returns) are covered. Although various
specification tests or model selection criteria have been applied
to the empirical asset pricing models, only a few of them
emphasizethepre-requisitethattheseincludedvariablesshould
be nondiversifiable so that separation between normal (or
expected) returns versus abnormal returns can be well stated.
In essence, many empirical research results on corporate event
studies are questioned since the findings can either be the
outcomes of events or model specification errors (in normal
returns), or both. Criticism results since little justification in
(model search for) empirical asset pricing models is done prior
to applications of abnormal returns. Chapter 2 in this book
attempts to provide somealternatives.
Formanystudiesincorporatefinanceorfinancialeconomics,
studies based on abnormal returns are applied in the verifi-
cations of events. Conventional studies either apply the
cumulative abnormal returns or perform the regressions of
abnormal returns onfirm-specific attributes toobtain statistical
tests. Either way, the hypothesis of interest is to verify that
whether the mean of abnormal returns is nonzero within the
eventwindowornot.Thisinturns,showsthatthehypothesisis
equivalent to verification of structural changes of means parti-
cularly, in abnormal returns. If empirical asset pricing models
with regressions are applied to obtain both the normal and
abnormal returns, the hypothesis of interest is equivalent to a
structural change of parameter(s) in regressions over the event
window. Given so, it is not difficult to see that the conven-
tional tests such as cumulative abnormal returns (CARs) (with
normalityornot)aresimilartotheCUSUM(cumulativesums)
tests for parameter changes—at least, from the perspective of
hypotheses.
PREFACE ix
Although the asymptotic arguments of CUSUM tests are
mostly based on Brownian motion, the applications and
hypotheses of CUSUM tests coincide with those of CARs
tests in using the cumulative sums of abnormal returns or
residuals from regressions of empirical asset pricing models.
Chapter 3 argues that the deficiency of conventional CARs
tests in event studies is bound to appear since financial time
series almost always contain parameter changes even if there
is no significant event at all. Hence, using CARs tests is not
entirely robust to verify the impacts of events even though
the empirical asset pricing models for normal returns are
selected properly. The same critique applies to the CUSUM-
based (monitoring) tests for parameter changes of empirical
asset pricing models as well. It is hardly conclusive to consider
that parameter changes are solely the results of firm-specific
events. Many financial time series (and the proposed models
in using them) are usually subject to time-varying parametri-
zation.Thesearguments show thatthetests simplybaseonthe
meanchanges ofabnormal returns arenotsufficient enoughto
tell whether the impacts from the events are significant or not.
Some alternatives must be provided to investigate the strength
ofimpactsfromeventswithoutincurringthesimilaritybetween
CARs tests and CUSUM tests for structural changes.
Chapter 4 is prepared as a prerequisite for Chapter 5.
Given the model identified in model search, the possible time-
varying coefficients are taken into account. The intuition is,
if the capital market adjustments are successfully efficient, the
updating mechanism should possibly incorporate concurrent
information into the normal (or expected) returns. Likewise,
in forming the normal (or expected) returns, the recursive
estimation should be introduced to accommodate the time-
changing expectations of the market. Notice that these
updating procedures apply only the systematic (nondiver-
sifiable) information provided from the model(s) identified
earlier. Therefore, the residuals resulted from the time-varying
coefficientmodelsofnormal(orexpected)returnsapproximate
closer to the abnormal returns as planned. Loosely speaking,
this approach provides a better classification between normal
and abnormal returns. In addition, the recursive algorithms
applied do not assume some particular estimation periods or,