Download - Defense and Aerospace
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A REPORT ON
“Event Study of Contract Win And Earnings
release in Defense/Aerospace Industry”
By
Gaurav Kadian
07BS1431
METRICS4 ANALYTICS PRIVATE LIMITED
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A Report
On
Event Study of Contract Win and Earnings release in
Defense/Aerospace Industry
By
Gaurav Kadian
A Report Submitted in Partial Fulfillment of the Requirements of MBA
Program
Distribution List:
Mr. Anjaneyulu Marempudi (Founder & CEO, Metrics4 Analytics)
Mr. Rajeev Gupta (Director - Research & Analytics, Metrics4 Analytics)
Mr. Sanjay Banka (Director - Research, Metrics4 Analytics)
Dr. S.V. Seshaiah (Associate Dean, ICFAI Hyderabad)
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ABSTRACT
This study investigates quarterly earnings releases and contract win announcements of a firm to
test for the existence of an information effect, the impact of an event on the announcing firm’s
stock returns, and to estimate its magnitude also.
This study investigates the affect of the event, contract win, on the stock returns of the concerned
firm. Exhaustive list of all the financial parameters were considered for the purpose of analysis
and the data was collected through online database and websites. For the purpose of this event
study, impact of the event was considered on the firm’s stock returns and three variable, i.e.
contract size, contract size as a percentage of market capital and new & follow up contracts, were
studied. The three variables were studied to understand the variance in the stock returns for the
contract win announcement.
This study also investigates the impact of quarterly earnings releases for the Aerospace/Defense
Products & Services industry on the firm’s stock returns. Study was conducted to analyze
whether the year-over-year increase/decrease in the earnings had a more profound effect on the
stock returns or the quarter-over-quarter increase/decrease in earnings had a more profound
effect. For this event study, four years quarterly earnings releases data was analyzed from 2004
to 2008, and earning releases were studied for arriving at a conclusive results.
For this study, hypothesis was developed and analyzed as to how the events might affect stock
price volatility and various statistical methods were also used to draw conclusion regarding the
trends from preceding and succeeding the event.
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Table of Contents
Page No.
1) Abstract......................................................................................................................... ......... 3
2) Objective................................................................................................................................ 5-6
3) Methodology .........................................................................................................................7-14
3.1) Methodology for Contract Wins.....................................................7-11
3.2) Methodology for Earnings Release................................................12-14
4) Results and findings for Contract Wins...............................................................................15-20
4.1) Results for Contract Wins..............................................................15-18
4.2) Finding for Contract Wins.............................................................19-20
5) Results and findings for Earnings Releases........................................................................ 21-23
5.1) Results for Earnings Releases........................................................21-22
5.2) Findings for Earnings Releases......................................................23
9) Conclusion .............................................................................................................................24
11) References ........................................................................................................................25-26
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Objective
The objective of the project was to analyze the impact of events on stock returns. To understand
how stock returns are affected by different events in different manner.
The main aim was to become more scientific about understanding the stock market and particular
industry or even particular returns. All in all, the main objective was to become apt in various
aspects of finance and to gain as much knowledge as possible to become a better student in this
huge ocean of financial studies.
The project was directed towards understanding the effect of Contract Win announcements on
stock prices of companies belonging to Aerospace/Defense Industry.
Further, the project was aimed to understand whether the year-over-year (YoY) quarterly
earnings results comparison has more effect on stock prices, or quarter-over-quarter (QoQ)
quarterly earnings results comparison.
The main objective of this study was to investigate the effect of Contract win announcement by
firms of the Aerospace/Defense Industry. Tests were carried out first to determine expected
returns and then calculate abnormal returns for the event days, the event window (+/-4 days).
After calculating abnormal returns, Z-values were arrived at to ascertain whether the Z-value
shoe significant values or not.
For the purpose of Earnings release, study was carried out to check which one had a more
profound effect on stock returns (positive or negative), among the increase/decrease in quarterly
earnings result, YoY, (i.e. increase/decrease in both Net income and Operating income for that
quarter) from last year quarter, or the increase/decrease from sequential quarter, QoQ or
preceding quarter.
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So, this study tries to find whether to take last year quarter results as a benchmark for comparing
the current quarter results, or one should take sequential quarter, last quarter for comparing
current quarter results.
Same method was used for arriving at abnormal returns for the earnings event study also. Four
year quarterly results, from 2004 to 2008, for more than 20 firms of Aerospace/Defense industry,
were studied and further segregated into two categories. The first category was taken as the case
when the quarterly results in which earnings results increased from last year quarter ( YoY Up),
but current quarter earnings decreased when compared with the results of preceding quarter or
last quarter (QoQ Down).
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Methodology
The event study methodology is used to investigate the effect of an event on a specific dependant
variable. The dependent variable in the study is the stock price daily returns of the company. The
study analyzes the changes in stock price daily returns over and above the normal returns of the
stock, over a period of time (event window).
The event study methodology seeks to determine whether there is an abnormal stock price effect
associated with the event –Contract wins and Earnings releases, and infer the significance of the
event.
Index or reference chosen is Standard & Poor 500 Composite Index (S&P500). S&P500 index is
a broad measure of the US equity market, which reflects the performance of the 500 largest
quoted companies in the US. The index had market capitalization of $11.94 trillion on March 31,
2008. The index has on aggregate given a return of 15.45% since 2003 and portrays 9.70% per
annum as risk.
Methodology for Contract Win
As discussed earlier, to do a basic single event study analysis, one has to firstly decide on few
criteria’s, example:
Period of data to be studied.
Estimation window.
Event window.
Model to be applied and procedure.
Hypothesis to be tested.
Tests to be carried out.
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Period of data to be studied
The period of event study is of one year, from 2007 to 2008. One year data is studied as the data
is readily available in the database of Metrics4, along with the trade date and contract size. And
as the data for this period was covered for more than twenty firms of Aerospace/Defense Product
& Services Industry, the numbers of data points were 133.
Event window
The event window is the time span in which all relevant information needed to assess outcomes
linked to the event being examined. The event window can be from day t – x‟ day to t + y‟ and
is of length y + x + 1 days. For this event study analysis +4 to -4 is the event window.
Estimation window
The estimation window, also called pre-event period, is usually a time span before the event
period. The estimation period is the period where you measure the normal relationship between
the stock of interest and the variables in your model. One should have a 50 observation
estimation period. However, anything more than a 100 observations should be sufficient. Most
researchers choose anywhere from 200 to 250 observations, where approximately 250 daily
observations is one calendar year. For this study 120 days pre-event period was taken as
estimation window.
Model to be applied
Market model or single index model was chosen for this event study. The market model says that
the return on a stock depends on the return on the market portfolio (S&P 500) and the extent of
the security's responsiveness as measured by beta (calculated in the regression analysis). The
return also depends on conditions that are unique to the firm. The market model can be graphed
as a line fitted to a plot of asset returns against returns on the market portfolio.
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The procedure for Market Model was applied as follows:
Step 1:
Firstly calculated the stock returns for the stock closing price and S&P 500 Index values for the
estimation window., which is 120 days prior to event window of +4/-4 days. Table below gives
an example how step 1 is carried out
Event window Date
Stock Closing
S&P Closing
Stock Returns S&P Returns
4.00 39511.00 22.65 1423.57 -1.84 -1.553.00 39510.00 22.85 1408.66 -2.97 -0.842.00 39507.00 21.65 1403.04 0.26 -2.201.00 39506.00 22.65 1403.58 3.62 0.520.00 39505.00 23.28 1388.28 -0.88 -0.34
-1.00 39504.00 23.41 1397.68 5.54 0.05-2.00 39503.00 22.74 1392.57 -4.42 -2.71-3.00 39500.00 22.95 1418.26 -2.71 -0.89-4.00 39346.00 23.02 1388.28 -0.56 -0.09
Step 2:
After calculating returns for the estimation window and event window. The stock returns were
taken as dependent variable, S&P 500 returns are taken as independent variable and regression is
run on SPSS, which will give a table as shown below:
ModelUnstandardized Coeff. Standardized Coefficientst Sig.B Std. Error Beta
1 (Constant) 0.230727069 0.168326 1.370714 0.173066VAR00002 1.161888797 0.121666 0.660261852 9.549855 2.34E-16
a Dependent Variable: VAR00001Stock returns
Alpha= 0.230727
Beta= 1.61888
Std Err.= .168
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Then these values were used in the equation given below for calculating Stock expected returns
for the event window.
Stock expected returns = Alpha + (Beta*S&P returns) + Std err
Step 3:
After that the abnormal returns for the stock during event window were calculated by the
difference of Expected returns from Actual stock returns. The table below shows how abnormal
returns are calculated for the event window.
A Abnormal Rtn. = Actual stock Rtn.- Expected Rtn
A B C D
Event window Date
Stock Returns
S&P Returns
Expectd Rtn.= Stock expected returns = Alpha + (Beta*S&P returns) + Std err
Abnormal Returns = Stock actual Rtn. - Expectd Rtn. (C-D)
4.00 39511.00 -1.84 -1.55 0.17 -0.623.00 39510.00 -2.97 -0.84 4.57 -1.872.00 39507.00 0.26 -2.20 1.06 -0.731.00 39506.00 3.62 0.52 -0.52 1.870.00 39505.00 -0.88 -0.34 -0.93 -1.78
-1.00 39504.00 5.54 0.05 -0.62 -0.44-2.00 39503.00 -4.42 -2.71 0.67 0.19-3.00 39500.00 -2.71 -0.89 2.18 2.36-4.00 39346.00 -0.56 -0.09 3.18 -4.11
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Hypothesis to be tested:
The Null hypotheses is that: there is no significant change in the actual stock returns from the
expected returns of the stock.
Testing procedure
After calculations of abnormal returns for each day of event window for all the sample, average
abnormal return of the entire event window, post event average returns, pre event average
returns, CAR (cumulative average returns) for ( + 1,-1) day, CAR (+2,-2) and CAR (+3,-3) of
events is calculated.
Now Z Test is applied on sample for each of these calculated values and compared with desired
results. The results of Z Test are attached in the appendices; significance of the results is
discussed separately. Since the sample size is more than 30, we can comfortably apply Z test for
each of sample. The results are enclosed in appendices.
The abnormal return for each day during the event window is calculated and that’s why one can
easily verify the significance on each day of event window. But for this study four different
time span were selected and done all the analysis based on these time period CAR (+ 1,-1) day,
CAR (+2,-2) and CAR (+3,-3) and CAR (0 Day).
Taking the study further the study included the following data for the whole sample of 133
events.
Contract size
Contract size as a percentage of Market Capital
New or follow up contract, as dummy variable
Multiple-regression analysis were carried, taking Abnormal returns for CAR (+ 1,-1) day, CAR
(+2,-2), CAR (+3,-3) and Event Day abnormal returns dependent variables and Contract size,
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Contract size as a percentage of Market Capital, and New or follow up contract, as independent
variables.
Dummy variable Analysis were also used to verify the results, if the new or follow up contract
have an impact on the abnormal returns win.
Methodology for Earnings Releases
The methodology for earnings releases event study was based on lot of criteria’s. As the purpose
was to determine whether the earnings had a positive impact on stock returns when Net income
and Operating both have increased, but have decreased from the preceding quarter. And to
determine the stock returns when both YoY and QoQ earnings results are positive.
The methodology is based on:
Event definition
Event window
Estimation window
Hypotheses
Test procedure
Event definition
The event is earning release, and the event study is to test whether the stock returns
increased/decreased on YoY comparison of earnings results or QoQ results comparison, and
which is having a more profound effect on stock returns, if any.
More than 20 Firms from Aerospace/defense industry were studied for 4 years quarterly results,
from 2004 to 2008, and YoY and QoQ comparison were carried out. 74 events data points were
covered when both net income and operating income increased YoY but decreased QoQ, and
190 events data points were studied when both YoY and QoQ showed positive earnings results.
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Event window:
Event window was considered to be +/-8 days from the event date of earning release. Formal
earnings announcements by the company are easily identified. However, the information may
have been leaked out and market participants may have already been trading on such
information. Therefore have taken this into account and determine a number of days x prior to
the event date to start the event window.
Estimation window:
Estimation window was taken as 180 days, of which 90 were taken as post event window and 90
days were taken as pre event window. One should have a minimum 50 observation estimation
period. However, anything more than a 100 observations should be sufficient. Most researchers
choose anywhere from 200 to 250 observations, where approximately 250 daily observations is
one calendar year.
Hypotheses
The null hypotheses was taken as that there is no significant change in actual; stock returns from
expected returns.
Market Model
Market model or single index model was chosen for this event study. The market model says that
the return on a stock depends on the return on the market portfolio (S&P 500) and the extent of
the security's responsiveness as measured by beta (calculated in the regression analysis). The
return also depends on conditions that are unique to the firm. The market model can be graphed
as a line fitted to a plot of asset returns against returns on the market portfolio
Testing procedure
Firstly 74 events data points were studied for earning results when results are positive YoY, but
the results are negative QoQ. Then, 190 events data points were studied when earnings results
are positive both YoY and QoQ. After calculations of abnormal returns for each day of event
window for all the sample, average abnormal return of the entire event window, post event
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average returns, pre event average returns, CAR (cumulative average returns) for ( + 1,-1) day,
CAR (+2,-2) and CAR (+3,-3) of events was calculated.
Z Test was applied on sample for each of these calculated values and compared with desired
results. The results of Z Test are attached in the appendices; significance of the results is
discussed separately. Since the sample size is more than 30, we can comfortably apply Z test for
each of sample. The results are enclosed in appendices.
The abnormal return for each day during the event window is calculated and that’s why one can
easily verify the significance on each day of event window. But for this study four different
time span were selected and done all the analysis based on these time period CAR (+ 1,-1) day,
CAR (+2,-2) and CAR (+3,-3) and CAR (0 Day).
Same was done for both data sets of 74 events data points and 190 events data points.
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Results
Hypotheses were tested for both contract win and earnings release events, and even multiple
regression analysis was carried out on contract win.
Results for Contract Win
Results were calculated for 133 events of contract win for Aerospace/defense industry for period
2007 to 2008 . To analyze the event, hypothesis Z test was used.
The values Mean returns, standard deviation and standard error for the 133 events were
calculated and the same are attached in the appendices.
These results were used to calculate the estimated values in the event window of +/-4 days, and
after calculating mean abnormal returns for event days, abnormal returns were calculated by
using the standard deviation, mean and Standard errors to calculate the Z score.
Results included, Avg. abnormal returns, CAR (+1,-1), CAR (+2,-2), CAR (+3,-3) and event day
abnormal returns for the event.
The results for all 133 events gave the following values:
MeanR STD STD err Z-Score EVENT DAY-0.17 1.27 0.11 -1.55 4.00-0.08 1.33 0.12 -0.72 3.00-0.30 1.53 0.13 -2.29 2.000.01 1.43 0.12 0.05 1.000.28 1.55 0.13 2.06 0.00
-0.10 1.38 0.12 -0.85 -1.00-0.33 1.45 0.13 -2.66 -2.00-0.03 1.50 0.13 -0.21 -3.00-0.07 1.55 0.13 -0.53 -4.00
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The charts below represent the Z scores and average returns expected during the time span selected. Chart 1 shows
the Z score for the five time span selected. On X axis we have the time span and Y axis shows the Z score value
The chart below depict the average returns an investor can expect if he invests for this time duration. Chart 2 has
time span on X axis and Average returns during this time period on Y axis
D is the event day. ( Day zero)
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Multiple regression results
1) Average abnormal return as the dependent variable and independent variables as X,Y and Z.
For dependent variable of CAR on Event day or day zero
Coefficients(a)
ModelUnstandardized Coefficients
Standardized Coefficients t Sig.
BStd. Error Beta
1.00 -0.15 -3.06 0.00X 0.00 0.00 -0.01 0.99Y 0.00 0.04 0.28 0.78Z 0.15 0.17 1.94 0.06
Model Summary
ModelR Square
Adjusted R Square
Std. Error of the Estimate
1.00 0.03 0.42
ANOVA(b)
ModelSum of Squares df
Mean Square F Sig.
1.00 0.67 0.22 1.25 0.2922.88 0.1823.54
Independent Variables :
X- Contract Size
Y- Contract size as % of M.cap
Z- Dummy variable
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2) CAR +/-1 Returns as dependent variable.
RR Square
Adjusted R Square
Std. Error of the Estimate
0.12 -0.01 2.27
df Mean Square F Sig.Regression 3.00 3.34 0.65 0.59Residual 129.00 5.18Total 132.00
Unstandardized CoefficientsStandardized Coefficientst Sig.Std. Error Beta
(Constant) 0.27 -0.24 0.81VAR00006 0.00 -0.03 -0.25 0.80VAR00007 0.03 0.11 0.83 0.41VAR00008 0.41 0.10 1.12 0.26
3) CAR +/-2 day returns as dependent variable
Model SummaryModel R Square Adjusted R SquareStd. Error of the Estimate
1.00 0.04 0.02 2.65
Model Sum of Squares df Mean Square F Sig.1.00 36.16 3.00 12.05 1.72 0.17
903.64 129.00 7.00939.79 132.00
Model Unstandardized Coefficients Standardized Coefficientst Sig.B Std. Error Beta
1.00 -0.87 0.31 -2.80 0.010.00 0.00 0.03 0.25 0.80
-0.01 0.04 -0.02 -0.14 0.891.07 0.48 0.20 2.25 0.03
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Findings
The results for Z- test shows that on day zero (the event day), the Z-value was 2.06 which
means significance of 95% and above. Which means that investor can be atleast 95%
confident of having a return of 0.28% on day zero.
The results also show that avg. abnormal returns for the event window are having a
significant Z-value of -2.46 which is again showing a confidence of 95% and above that
for the event window. The investor can be atleast 95% confident of facing a negative
return of 0.09% for the event window.
Z-value for CAR +/-1 is not significant to arrive at any conclusive results.
Z-value for CAR +/-2 and CAR +/-3 show significant Z-values of -1.98 and -1.88, with
mean abnormal returns of -0.46% and -0.57%, respectively.
Next Study was carried out to find the variables which may be affecting the dependent variable.
To find the relevance of these variables multi variable Cross-Sectional Regression Analysis was
done.
Results also show the relationship between dependent and independent variables.
Independent variables were:
Size of the contract
Size of the contract as a percentage of market capital
Contract from a new supplier or old supplier
Dependent variable for time span of these four categories
CAR (+1,-1), b) CAR (+2,-2), and c) Event day CAR (0)
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Findings for Multiple-regression analysis:
Overall the independent variables don’t explain much of the movement of
dependent variables, as value of R square was 0.03, -0.01 and 0.04 for Event day,
CAR (+1,-1), and CAR (+2,-2) respectively.
Only the independent variable, Dummy variable showed significant values, as the
value of ‘t’ was 1.94 with significance value of 0.06 for CAR (day zero).
Dummy variable also showed significant‘t’ value of 2.25 with significance value
of 0.03 for CAR (+2,-2).
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Results for Earnings:
Results were calculated for events of earnings releases for Aerospace/defense industry for period
2004 to 2004 . To analyze the event, hypothesis Z test was used.
Firstly 74 events data points were studied for earning results when results are positive YoY, but
the results are negative QoQ.
Then, 190 events data points were studied when earnings results are positive both YoY and QoQ.
After calculations of abnormal returns for each day of event window for all the sample, average
abnormal return of the entire event window, post event average returns, pre event average
returns, CAR (cumulative average returns) for ( + 1,-1) day, CAR (+2,-2) and CAR (+3,-3) of
events was calculated.
These results were used to calculate the estimated values in the event window of +/-8 days, and
after calculating mean abnormal returns for event days, abnormal returns were calculated by
using the standard deviation, mean and Standard errors to calculate the Z score.
For YoY increase in Earning Results and QoQ decrease in Earning releases: (74 data points)
Mean STDV STD err Z-value0.00 1.36 0.16 0.00 DAY 80.09 1.26 0.15 0.64 DAY 70.05 1.42 0.17 0.32 DAY 60.01 1.22 0.14 0.10 DAY 50.21 1.97 0.23 0.90 DAY 40.18 2.02 0.23 0.78 DAY 3
-0.51 1.84 0.21 -2.39 DAY 2-0.41 2.00 0.23 -1.76 DAY 1-1.03 4.51 0.52 -1.97 DAY 00.24 2.32 0.27 0.88 DAY -10.27 2.19 0.25 1.04 DAY -20.32 1.43 0.17 1.90 DAY -3
-0.13 1.22 0.14 -0.91 DAY -4-0.39 1.40 0.16 -2.40 DAY -5-0.25 1.41 0.16 -1.50 DAY -60.18 1.00 0.12 1.56 DAY -70.08 1.24 0.14 0.59 DAY -7
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For YoY earnings increase and QoQ earnings also increased.
MEAN STDV STDV err Z VALUE DAY-0.06 1.30 0.09 -0.64 DAY 8-0.17 1.75 0.13 -1.37 DAY 70.03 1.32 0.10 0.28 DAY 60.05 1.46 0.11 0.47 DAY 5
-0.07 1.67 0.12 -0.59 DAY 4-0.32 1.52 0.11 -2.89 DAY 30.18 1.69 0.12 1.47 DAY 20.39 2.45 0.18 2.20 DAY 11.52 4.52 0.33 4.63 DAY 00.33 2.17 0.16 2.10 DAY -10.05 1.54 0.11 0.45 DAY -20.13 1.40 0.10 1.29 DAY -3
-0.16 1.26 0.09 -1.77 DAY -4-0.02 1.31 0.10 -0.26 DAY -50.08 1.48 0.11 0.76 DAY -60.01 1.78 0.13 0.11 DAY -7
-0.19 1.57 0.11 -1.70 DAY -7
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Findings
When YoY was up but QoQ was down, and the Z-values were significant with
negative mean values.
When YoY and QoQ both were up, and the Z-values were significant with positive
mean returns.
WHEN YoY was UP and QoQ was down, Z-values are negative, and When YoY and QoQ
are Both Up then for them positive results
WHEN YoY was UP and QoQ was down, Z-values are negative, and When YoY and QoQ
are Both Up then for them positive results
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Conclusion
This study paper analyses the events for contract wins for the Aerospace/Defense Industry and
also the impact of Earnings releases on the scrip returns. The purpose of this study was to
analyses the impact and underlying trend in contract wins on the stock returns. The results
showed for contracts that z value was significant on day zero only. Indicating a fact that the
impact of contract win is short-lived yet significantly strong, the returns obtained on the other
days are not that significant from the normal returns. Another fact that came to forefront from
this study was that, when YoY (Year over Year) earnings increases and QoQ earnings decreases
the result were significant with negative z values and negative mean abnormal returns. When
YoY earnings increases and QoQ also increase, the z values are significant in these cases with
positive mean abnormal returns. Thus we conclude that QoQ earning results have more impact
on stock returns, as compared to YoY comparison of the earnings releases.
The basic objective was to find out that whether an investor should compare the quarterly
earnings releases vis-à-vis prior year quarter results or one should compare it with prior quarter
results.
For contract win the study established that the results were showing significant Z-value for the
Day-zero, with positive mean returns. So, one can say with more than 95% confidence that Day-
zero for contract win will result in positive mean abnormal returns, though very less mean
returns can be expected.
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References
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Websites:
www.yahoofinance.com
www.s&p.com
www.ssrn.com
www.eventvestor.com
