brown warner
TRANSCRIPT
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Siraprapa Watakit
5502310013
MEASURING SECURITY
PRICE ERFORMANCE
Brown and Warner [1980]
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Agenda
Overview of The Paper
Contribution
Event Study In General
Questions and Concerns Experimental Design & Analysis
Conclusion
2
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Overview of The Paper3
The paper mainly focus about the various measurement methods,
models and statistical tests which employed in Event Study
Research
There are quite a number of factor which may lead researcher to
commit Type-I and Type-II errors
Especially when some model/test assumption doesnt hold
The investigation in this paper shows that simple model provides
powerful test results that sometimesoutperform sophisticated
models
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Contribution4
This paper provide a very detailed summary of methods, models
and test tools which are currently widely used in event study
research
The purpose of this paper is not the label best model/tool for event
study but rather give reader factors to be consider when each ofthem is being employed
In order to avoid Type-I/II Errorswrong inference
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What is Event Study?5
ES is a study about events and its effects towards security price
e.g. when company announces news, will stock price
increase(decrease)?
ES provides a direct test in market efficiency
the market absorbs information quickly, there should not be
abnormal returns after the event
Abnormal returnsafter event are inconsistent with market
efficiency
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To do Event Study, we need?6
To perform the ES, we need to know/assess these things
What is normal and abnormal?
When did it happen? certainty/uncertain?
What kind of statistical test tool we should use?
What methodology?
What we should avoid?
H0: No Abnormal Returns Type-I: Reject H0 when H0 is True(false reject)
Type-II Failed to reject H0 when H0 is False(false accept)
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Measuring Performance:
General Consideration7
Define Abnormal i.e. compare ex anteand ex post
Mean Adjusted Return
Market Adjusted Return
Maker and Risk Adjusted
Abnormal performance is an unbiased measurement
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Concerns/Question8
Which model to use?
The complicated/sophisticated one does not necessaryoutperform the simple one
assumption is critically related to the return generating process
and yet critically related to the test tool to test H0 Besides these 3 models, there are plenty other model
Black models, Fama-Macbeth and etc.
Is there other sensitivity factors to the model/test?
Normality, clustering event, equal andvalue weighted index, time?
Roll critiques
there is no way to find market efficient portfolio
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Experimental Design9
Generates 250 samples
Each sample consists of 50 random security at random time
(on average, thereshould be no abnormal performance)
To investigate models/test-tool,
repeat the models/test-tools on the above sample
introduce fake event into the above sample and repeat the
model/test-tools again
0 indicate event date
fake abnormal include 1%,5%,15% and 20% increase in return
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Simulating the methodologies across
samples: Procedure and initial results10
Rejection frequency
with t-tests
Mean Adj.Ret
performs no less
than others
Parametric vs. Non-
Parametric tests
Sign test and Wilcoxon
seems to beproblematic
There is no abnormal return here, we would
expect less or zero #rejection
There is actual 1%, 5% no abnormal return here, we
would expect much of #rejection
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Simulating the methodologies across
samples: Procedure and initial results11
Compare betweenactualandassumeddistribution: even when
there is no abnormal return, the actual distribution is significantly
different than assumed distribution
at 0.05
sig.level, rejectH0: student-t
distribution
The actual
distribtution is
leptokurtic andskewed to the
right
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Simulating the methodologies across
samples: Procedure and initial results12
Different risk adjustment methods: explicitly adjusting systematic
risksdoesnt help increasing the rejection rate
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The used of prior information13
Previous 3 table results are from the setup that assumed
certain event date is known
the direction of abnormal return is known(one tailed test)
But what if it is not?
Since exact date is unknown, we will use event windows
Since direction is unknown we will usetwo-tailedtests
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The used of prior information14
The rejection rate drop
sharply, event for 15%
abnormal return
Shorter windows(-5,5)
gives higher rejection rate
Two tailedgives lesser
rejection rate
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The used of prior information15
The Cumulative Average Residual:
Repeat the same application to each of 250 samples, then
For each event-month, we will have 250 CAR from 250 sample
Trace the fractiles of this 250 CAR in each even-month
Sample#1
month -10
month 0
month 10
AR-10
CAR0
CAR10
21CARs
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CAR Traces /w and /wo abnormal16
From the comparison,no strong distinguish. CAR can appear
significant + or trend event when there isno abnormal return
However, with (-5,5) we can see something
No Abnormal 5% Abnormal(-10,10) 5% Abnormal(-5,5)
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The Effect of Clustering:
Event-month17
Clustering can be a problem because it reduces the power of test
Mean Average Return perform poorly in this case
Clustering may not be random
e.g. group of sample which
are from same industry would
tend of have event at a similar
or same time
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The Effect of Clustering:
Betas18
When securities have higher betas, it can be expected that the power
of the test will be lower when compare to those with smaller beta
smaller fluctuation will be easier to reject
rejection rate is
higher for
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Choice of Market Index19
Previous results use equal weighted index
With value weighted index , the models suffer from reject too
often, except for Market Model Residual
MAR rejects too
often, but MMR is
ok
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Conclusion20
There are many factors to consider when one wants to do event
study i.e. models, tests, assumed distribution, clustering, CAR
random walk trends,choice of index and sample size, in order to
avoid making wrong inference
So far, a simple method Means Adjusted Return perform no lessthan other sophisticated models(Only except event clustering case)