Download Event Study Dissertation

Event Study Dissertation
An event studies examines the average stock market reaction to a particular stock
market event, by averaging across the same events in different companies, or at
different times in the same company.
The event could be a macroeconomic event such as an interest rate announcement, or
company specific events such as earnings announcements, directors trading
announcements, merger announcements, right issue announcements etc
To conduct an event study:
1. Decide upon the type of event. Events could be earnings/dividend
announcements. rights issue or other seasoned equity offering announcement,
merger announcement, investment announcement, executive remuneration
package etc
2. Determine the precise date/time of the event announcement (THE EVENT
DATE) for a sample of companies. This date is referred to as date 0. The
number of companies or events should be at least 100. Identifying the precise
event date affects the power of the test. To some extent the event may be
anticipated, and this will weaken the power of the event study, since stock
prices will have already reacted to some of the event before it has been
announced. Brown and Warner (1980, JFE). Sometimes in daily event studies,
the two-day event date is considered to be the event date itself and the
previous day
3. Collect stock price data on this sample of firms which have had a surprise
announcement (THE EVENT). You may collect intra-day, daily or monthly
data, depending on whether you are examining the short-run or long-run
effects of the announcement. Determine the time period to be studied around
the announcement date (EVENT PERIOD). Convert sample period to event
time (for example -20, -19, . . . ., -1, 0, +1, . . . . . +29, +30)
Stock Price
for
company j
Day=20
Day=0
Day=+1
0
Date with respect
to Event day=0
4. For each firm j in the sample compute the actual daily/monthly return Rjt
5. For each firm in the sample compute the abnormal return, by deducting the
expected return from the actual:
ARjt = Rjt - E(Rjt)
where the expected return is estimated from an appropriate asset pricing
model: CAPM, APT, Market Model etc. The asset pricing model is normally
estimated using data in a pre-event estimation period.
6. For each event date compute average abnormal return across companies in the
sample. Test whether this average abnormal return is statistically significant
from zero.
7. Cumulated abnormal returns to obtain a simple picture of the cumulative
average abnormal return - measure average abnormal stock price movements.
Undertake a statistical test of the null hypothesis of zero-abnormal cumulative
returns.
8. Examine results
9. The dissertation should include a chapter:
a. On previous literature that has examined the stock market reaction to
this same event.
b. Explaining the methodology of event studies, and the specific event
study that you have undertaken. This chapter should also include a
statement of the hypothesis that you are testing.
c. Explaining the sample selection and data
d. Results
Ian Tonks
20/06/07
Problems
1. What is the appropriate benchmark?
- mean asset return, market adjusted (0,1), CAPM, market model, APT,
factor models?
- matched portfolios [Loughran, T. and J.R. Ritter (1995)]?
- You should choose a number of different models for the expected returns
benchmark. I would suggest CAPM, Market Model, and Zero-one model,
or get more sophisticated and try 3-factor model, size adjusted benchmark
model!
- Model 1: CAPM
E(Rit) – rf = i[ERM – rf]
- Model 2: Market Model E(Rit) = i + iRM + it
- Model 3: Zero-One
E(Rit) = RM
- The CAPM and Market model will need to be estimated over some preevent period.
2. Cross-section dependence in returns
- Alternative testing using “Crude Dependence Adjustment”, (Brown &
Warner (1980)
3. Thin trading
- Scholes-Williams (1987)
- Dimson (1987)
4. Event Clustering
5. Non-stationarity of parameters induced by the event
- IPO’s
6. Biases in long-term CARs and Buy-and-Hold Returns
Kothari and Warner (1997) and Barber and Lyon (1997) emphasise that in
long-run event studies CARs are baised, and the appropriate yardstick is Buy-and
Hold returns
Frequently Asked Questions
> 1. The cross- sectional dependence: does it mean that the return to one
> security might depend on the return of another security, which is in the
> different section from it? For example, the return of a car company's stock
> might depend on the return of an oil company.
Exactly: so that if you have two car companies in your sample,
representing two separate events, since they are both car companies
their returns may nt be independent. In which case you need to allow
for this when undertaking a significance test. The Brown and Warner
paper suggests a way to allow for this
>
> 2.
non-synchronous trading: does it mean the return on a security and the
> return on the market index are each measured over a different trading
> interval? What is the meaning of trading interval? Could you give me some
> examples about the trading interval?
The trading interval could be one day, one weeks, two weeks etc.
Meaning that returns are defined as the price at the end of the week
minus the price at the beginning of the week relative to the price at
the beginning of the week (plus any dividends paid during that week.
You are correct that non-synchronous trading means that returns on the
index and the company might have been computed over different trading
intervals
>
> 3. time-series properties of data: does it mean that data obtained from
> different days might depend on each other? For example, that the excess
> return on the event day will have effect on the returns on the following
> days. Can this kind of dependence be called serial dependence?
Time series properties referrs to the properties of the distribution
that you collect by looking at observations over time. So if you have
collected a sample of daily returns, then this sample will be a
time-series sample and will have time series properties. ie if you
compute the mean return from the sample, this will be a time-series mean
>
> 4. What is the meaning of 'stationarity of daily variance'?
It will probably be the case that if you compute the standard deviation
of daily returns of a company over the period Jan-Jun2002, and
calculate it again over the periof July-Dec2002, that the two estimated
standard deviations are different. If they are different then we would
say the distribution of daily returns is non-=stationary. On the
otherhand if the two standard deviations were pretty close together we
might say that the returns distribution is staionarry over time.
>
> 5. Can 'event day' be two days? I am confused about the Brown and Warner
> 's(1985) tests that abnormal performance can be introduced for one day in
> the interval (-5,+5), with each day having an equal probability of being
> selected. To my understanding, that the event day is just the day that the
> announcement is announced and public get to know the news on the day. I
> think it is fixed on that day. How can we just select one day from the
> event period. Could you give me some explanations?
As I mentioned in my lectures, sometimes we do not know the time of an
announcement, say that if the announcement occured after te market
closed, then it would not be incorporated into todays return even
though the announcement was on the same date. By calculating two-day
returns, you do not need to worry about wherther the time of the
announcement was on or after the day of the announcement.
>
> 6. what do the values of kurtosis and skewness mean?
They are additional properties of the distribution of returns.
First moment of the returns is the mean,
Second moment is the standard deviation
Third moment is the skewness
Fourth moment is the kurtosis
etc
References to Methodology of Event Studies:
Boehmer, E., J. Musumeci, and A. Poulsen, 1991, “Event-Study Methodology under
Conditions of Event-Induced Variance,” Journal of Financial Economics, 30, 253–
272.
Binder, J. J., 1998, “The Event Study Methodology Since 1969,” Review of
Quantitative Finance and Accounting, 11, 111–137.
Brown, S. and J.B. Warner (1980), “Measuring security price performance”, Journal
of Financial Economics, 8, 205-258.
Brown, S. and J. Warner (1985), “Using Daily Stock Returns (The Case of Event
Studies”, Journal of Financial Economics, vol. 14, 3-31.
Campbell, J.Y., A. Lo and C. MacKinlay (1997) The Econometrics of Financial
Markets, (Princeton University Press)
Dodd, P. and Warner, J.B. (1983), 'On corporate governance: a study of proxy
contests', Journal of Financial Economics , 11, pp 401-38.
Loughran, T. and J.R. Ritter (1995) “The new issues puzzle”, Journal of Finance, 50,
23-51.
Strong, N. (1992), ‘Modelling Abnormal Returns: A Review Article’, Journal of
Business Finance and Accounting, June, pp. 533-553.
Thompson, R. (1985), “Conditioning the returns-generating process of firm-specific
events: a discussion of event study methods”, Journal of Financial and Quantitative
Analysis, 20, 151-168.
Thin Trading Adjustments
Dimson, E. (1979) "Risk measurement when shares are subject to infrequent trading",
Journal of Financial Economics, vol. 7, 197-226.
Fowler, J. and C.H. Rorke (1983) "Risk measurement when shares are subject to
infrequent trading", Journal of Financial Economics, vol. 12, 279-283.
Scholes, M. S. and J. T. Williams (1977) “Estimating betas from Nonsynchronous
Data”, Journal of Financial Economics, vol. 5, 309-327.
References to Long-Run Returns Methodology
Barber, B.M. and J.D. Lyon (1997), “Detecting long-run abnormal stock returns: the
empirical power and specification of test statistics”, Journal of Financial Economics,
43, 341-372.
Kothari, S.P. and J.B. Warner (1997), “Measuring long-horizon security price
performance”, Journal of Financial Economics, 43, 301-339.
Fama, E. (1998), “Market efficiency, long-term returns and behavioural finance”,
Journal of Financial Economics, 49, 283-`306.
Lyon, J. Barber, B. and C.-L. Tsai (1998) “Improved methods for tests of long-run
abnormal stock returns”, Forthcoming in Journal of Finance.