8/2/2019 007-Paper 1

1/22

8/2/2019 007-Paper 1

2/22

Introduction

This paper focuses on daily stock returns andexcess returns and how the particularcharacteristics of these data affect event study

methodologies for assessing the share priceimpact of firm-specific events.

This paper is actually an extension of eventstudy methodologies used with monthlyreturns by the same writers BROWN andWARNER in 1980.

8/2/2019 007-Paper 1

3/22

Introduction(Continue..)

Previously for monthly returns a simple

methodology based on the market model is

both well-specified and relatively powerful

under a wide variety of conditions, and in

special cases even simpler methods also

perform well.

8/2/2019 007-Paper 1

4/22

Introduction(Continue..)

Daily and monthly data differ in potentially

important respects.

Non-normality

Non-synchronous trading

Variance estimation

8/2/2019 007-Paper 1

5/22

1.Non-Normality

Daily returns are non-normal depart frommonthly data as it was not observed in monthly.

The evidence generally suggests that

distributions of daily returns are fat-tailedrelative to a normal distribution [Fama (1976, p.21)].

Since event studies generally focus on the cross-

sectional sample mean of security excessreturns, this paper examined the small sampleproperties of the mean excess return.

8/2/2019 007-Paper 1

6/22

Non-Normality(Conti..)

The Central Limit Theorem[Billingsley (1979,

pp. 308-319)] guarantees that if the excess

returns in the cross-section of securities are

independent and identically distributed

drawings from finite variance distributions,

the distribution of the sample mean excess

return converges to normality as the numberof securities increases.

8/2/2019 007-Paper 1

7/22

2.Non-synchronous trading

When the return on a security and the return

on the market index are each measured over a

different trading interval, ordinary least

squares (OLS) estimates of market model

parameters are biased and inconsistent.

With daily data, the bias can be severe

(Scholes and Williams (1977, p. 324), Dimson(1979, p. 197)].

8/2/2019 007-Paper 1

8/22

Non-synchronous trading(Conti)

Concerned with that problem, authors of

event studies with daily data have used a

variety of alternative techniques for

parameter estimation [e.g., Gheyara and

Boatsman (1980, p. ill), Holthausen (1981, p.

88)].

8/2/2019 007-Paper 1

9/22

3.Variance Estimation

With both daily and monthly data, estimation

of the variance of the sample mean excess

return is important for tests of statistical

significance.

Time-series properties of daily data.

Cross-sectional dependence of the security-

specific excess returns.

stationary of daily variances.

8/2/2019 007-Paper 1

10/22

Experimental Design

Sample construction

250 samples of 50 securities.

Simple random sampling and somehow

convenient sampling is used.

Hypothetical dates of events are assigned to

selected securities.

Time period is of July 02, 1962 to December 31,1979.

8/2/2019 007-Paper 1

11/22

Experimental design(Conti.)

Sample Construction..

Maximum of 250 daily observations are taken.

For a security to be included in a sample, it must

have at least 30 daily returns in the entire 250 day

period, and no missing return data in the last 20

days.

Estimation Period = 239 days Event Period = 11 days

8/2/2019 007-Paper 1

12/22

Experimental design(Conti.)

Excess return measures

Let Ri,t designate the observed arithmeticreturn for security i at day t. Define A, as the

excess return for security iat day t.

Mean adjusted returns

A i,t= Ri,ti,t

8/2/2019 007-Paper 1

13/22

Experimental design(Conti.)

Excess return measures

While Rm,tis return on CRSP equaly wieghtedindex for day t.

A i,t= Ri,t Rm,t

OLS market model.

A i,t= Ri,t -Rm,tWhere and are OLS values from the estimation periods.

8/2/2019 007-Paper 1

14/22

Summary and Conclusions

Using simulation procedures with actual dailydata, the paper investigates the impact of a

number of potential problems of concern in

the literature. These include Non-normality and the properties of tests

Alternative procedures for market model

parameter estimation Variance estimation: Autocorrelation, cross-

sectional dependence, and variance increases

8/2/2019 007-Paper 1

15/22

Non-normality and the properties of tests

The non-normality of daily returns has no

obvious impact on event study

methodologies.

Although daily excess returns are also highly

non-normal, there is evidence that the mean

excess return in a cross-section of securities

converges to normality as the number ofsample securities increases.

8/2/2019 007-Paper 1

16/22

Non-normality and the properties oftests(Conti.)

Standard parametric tests for significance of

the mean excess return are well-specified.

In samples of only 5 securities, and even when

event days are clustered, the tests typically

have the appropriate probability of Type I

error.

8/2/2019 007-Paper 1

17/22

Non-normality and the properties of tests(Conti.)

As in the case of monthly data, the conclusionthat the methodologies are well-specifiedapplies to excess returns measured in a variety

of ways, including Market Adjusted Returnsand the OLS market model.

With daily data, these two methodologieshave similar power, and, as expected, thepower of each is much greater with daily thanwith monthly data.

8/2/2019 007-Paper 1

18/22

Non-normality and the properties of tests(Conti.)

Market Adjusted Returns and the OLS marketmodel also outperform a simpler MeanAdjusted Returns procedure, which has low

power in cases involving event-date clustering. In addition, exchange-listing is an important

correlate of the power of the various tests,with samples of NYSE securities exhibitingdramatically higher power than AMEXsecurities.

8/2/2019 007-Paper 1

19/22

Alternative procedures for market

model parameter estimation

Procedures other than OLS for estimating the

market model in the presence of non-

synchronous trading convey no clear-cut

benefit in detecting abnormal performance.

The specification and power of the actual tests

for abnormal performance is similar to that

obtained with the OLS market model.

8/2/2019 007-Paper 1

20/22

8/2/2019 007-Paper 1

21/22

Variance estimation (Conti.)

Tests which assume non-zero cross-sectionaldependence are only about half as powerful

and usually no better specified than those

employed assuming independence. Finally, we illustrated how variance increases

can cause hypothesis tests using standard

event study procedures to becomemisspecified.

8/2/2019 007-Paper 1

22/22

Variance estimation (Conti.)

Several procedures to deal with the possibilityof variance increases were outlined.

However, further research is necessary to fully

understand the properties of alternativeprocedures for measuring abnormal

performance in such situations.