Brown Warner

7/29/2019 Brown Warner

1/20

Siraprapa Watakit

5502310013

MEASURING SECURITY

PRICE ERFORMANCE

Brown and Warner [1980]


2/20

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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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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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 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 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)

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Brown Warner