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 firms 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 firms 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 firms 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 ContentsPage No. 1) Abstract......................................................................................................................... ......... 3 2) Objective................................................................................................................................ 5-6 3) Methodology .........................................................................................................................7-14 3.1) 3.2) Methodology for Contract Wins.....................................................7-11 Methodology for Earnings Release................................................12-14

4) Results and findings for Contract Wins...............................................................................15-20 4.1) 4.2) Results for Contract Wins..............................................................15-18 Finding for Contract Wins.............................................................19-20

5) Results and findings for Earnings Releases........................................................................ 21-23 5.1) 5.2) Results for Earnings Releases........................................................21-22 Findings for Earnings Releases......................................................23

9) Conclusion .............................................................................................................................24 11) References ........................................................................................................................25-26

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ObjectiveThe 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 criterias, 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.7

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 4.00 3.00 2.00 1.00 0.00 -1.00 -2.00 -3.00 -4.00

Date 39511.00 39510.00 39507.00 39506.00 39505.00 39504.00 39503.00 39500.00 39346.00

Stock Closing 22.65 22.85 21.65 22.65 23.28 23.41 22.74 22.95 23.02

S&P Stock Closing Returns S&P Returns 1423.57 -1.84 -1.55 1408.66 -2.97 -0.84 1403.04 0.26 -2.20 1403.58 3.62 0.52 1388.28 -0.88 -0.34 1397.68 5.54 0.05 1392.57 -4.42 -2.71 1418.26 -2.71 -0.89 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:

Model

a

Unstandardized Coeff. S tandardized Coefficients S t ig. S Error Beta td. B 1 (Constant) 0.230727069 0.168326 1.370714 0.173066 VAR00002 1.161888797 0.121666 0.660261852 9.549855 2.34E-16 Dependent Variable: VAR00001 S tock 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 window4.00 3.00 2.00 1.00 0.00 -1.00 -2.00 -3.00 -4.00

Date39511.00 39510.00 39507.00 39506.00 39505.00 39504.00 39503.00 39500.00 39346.00

Abnormal Expectd Rtn.= Stock Returns = Stock expected returns = Alpha actual Rtn. Stock S&P + (Beta* S&P returns) + Expectd Rtn. (C Returns Returns Std err D)-1.84 -2.97 0.26 3.62 -0.88 5.54 -4.42 -2.71 -0.56 -1.55 -0.84 -2.20 0.52 -0.34 0.05 -2.71 -0.89 -0.09 0.17 4.57 1.06 -0.52 -0.93 -0.62 0.67 2.18 3.18 -0.62 -1.87 -0.73 1.87 -1.78 -0.44 0.19 2.36 -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 thats 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 criterias. 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 thats 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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ResultsHypotheses 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 -0.17 -0.08 -0.30 0.01 0.28 -0.10 -0.33 -0.03 -0.07

S TD 1.27 1.33 1.53 1.43 1.55 1.38 1.45 1.50 1.55

S TD err 0.11 0.12 0.13 0.12 0.13 0.12 0.13 0.13 0.13

E NT DAY VE Z-S core -1.55 4.00 -0.72 3.00 -2.29 2.00 0.05 1.00 2.06 0.00 -0.85 -1.00 -2.66 -2.00 -0.21 -3.00 -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)16

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 zeroCoefficients(a) Model Unstandardized Coefficients B 1.00 X Y Z -0.15 0.00 0.00 0.15 Standardized Coefficients t Std. Error Beta 0.00 0.04 0.17 -3.06 -0.01 0.28 1.94 0.00 0.99 0.78 0.06 Sig.

Model Summary R Adjusted R Square Square Model 1.00 0.03 Std. Error of the Estimate 0.42

ANOVA(b) Model Sum of Mean Squares df Square F 1.00 0.67 0.22 22.88 0.18 23.54 Sig. 1.25 0.29

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.R Adjusted Std. Error of Square R Square the Estimate 0.12 -0.01 2.27df R egres ion s R idual es Total Mean S quare F 3.00 3.34 129.00 5.18 132.00 S ig. 0.65 0.59

R

(C ons tant) VAR 00006 VAR 00007 VAR 00008

S oefficients t Uns tandardized C tandardized C oeffic ients S td. E rror Beta 0.27 0.00 -0.03 0.03 0.11 0.41 0.10

S ig. -0.24 -0.25 0.83 1.12 0.81 0.80 0.41 0.26

3) CAR +/-2 day returns as dependent variable

Model Summary RS quare Model 1.00

0.04

Adjusted R S quareS Error of the Estimate td. 0.02 2.65

Model

S of S um quares df 36.16 1.00 903.64 939.79

Mean S quare F 3.00 12.05 129.00 7.00 132.00

S ig. 1.72 0.17

Model

Unstandardized Coefficients B S Error td. -0.87 1.00 0.00 -0.01 1.07

S tandardized Coefficients S t ig. Beta 0.31 -2.80 0.00 0.03 0.25 0.04 -0.02 -0.14 0.48 0.20 2.25

0.01 0.80 0.89 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 dont 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 significantt 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 S DV T S D err T Z -value 0.00 1.36 0.16 0.00 0.09 1.26 0.15 0.64 0.05 1.42 0.17 0.32 0.01 1.22 0.14 0.10 0.21 1.97 0.23 0.90 0.18 2.02 0.23 0.78 -0.51 1.84 0.21 -2.39 -0.41 2.00 0.23 -1.76 -1.03 4.51 0.52 -1.97 0.24 2.32 0.27 0.88 0.27 2.19 0.25 1.04 0.32 1.43 0.17 1.90 -0.13 1.22 0.14 -0.91 -0.39 1.40 0.16 -2.40 -0.25 1.41 0.16 -1.50 0.18 1.00 0.12 1.56 0.08 1.24 0.14 0.59

DAY 8 DAY 7 DAY 6 DAY 5 DAY 4 DAY 3 DAY 2 DAY 1 DAY 0 DAY -1 DAY -2 DAY -3 DAY -4 DAY -5 DAY -6 DAY -7 DAY -7

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For YoY earnings increase and QoQ earnings also increased.

ME AN S TDV S TDV err Z VALUE -0.06 1.30 0.09 -0.64 -0.17 1.75 0.13 -1.37 0.03 1.32 0.10 0.28 0.05 1.46 0.11 0.47 -0.07 1.67 0.12 -0.59 -0.32 1.52 0.11 -2.89 0.18 1.69 0.12 1.47 0.39 2.45 0.18 2.20 1.52 4.52 0.33 4.63 0.33 2.17 0.16 2.10 0.05 1.54 0.11 0.45 0.13 1.40 0.10 1.29 -0.16 1.26 0.09 -1.77 -0.02 1.31 0.10 -0.26 0.08 1.48 0.11 0.76 0.01 1.78 0.13 0.11 -0.19 1.57 0.11 -1.70

DAY DAY 8 DAY 7 DAY 6 DAY 5 DAY 4 DAY 3 DAY 2 DAY 1 DAY 0 DAY -1 DAY -2 DAY -3 DAY -4 DAY -5 DAY -6 DAY -7 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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ConclusionThis 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--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 Dayzero for contract win will result in positive mean abnormal returns, though very less mean returns can be expected.

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ReferencesBooks Business Research Methods (ICFAI Publication) Damadoran Gujrati. Basic Econometrics

Research Studies Wong Shou Woon Introduction to the Event Study Methodology Singapore Management University http://www.smueye.com/resources/Introduction%20to%20the%20Event%20Study%20Metho dology.pdf Rohitha Goonatilake, Susantha Herath,The Volatility of the Stock Market and News International Research Journal of Finance and Economics, Issue 11 http://www.eurojournals.com/irjfe11%20rohit.pdf Jess C. Beltz, , Share Price Performance and Observable Factors University of Michigan Business School and James S. Moore, Department of Management and Marketing, Indiana University - Purdue University at Fort Wayne http://webuser.bus.umich.edu/jcbeltz/observable4.doc Christopher B Branston and Nicolaas Groenewold, Investment and Share Prices: Fundamental versus Speculative Components Department of Economics, University of Western Australia, Crawley, Australia http://www.biz.uwa.edu.au/home/research/discussionworking_papers/economics/2003?f=151 073 Thomas SchusterPrice Effects of Economic and Non-Economic Publications in the News Media Institute for Communication and Media Studies Leipzig University http://129.3.20.41/eps/fin/papers/0305/0305009.pdf

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Elaine Henry Market Reaction to Verbal Components of Earnings Press Releases: Event Study Using a Predictive Algorithm Journal Of Emerging Technologies In Accounting Vol. 3 2006, University of Miami http://papers.ssrn.com/sol3/papers.cfm?abstract_id=958749 Adam T. Samson Vindication of the Event Driven Investment Strategy Dmitry Krivin, Robert Patton, Erica Rose, and David Tabak Determination of the

Appropriate Event Window Length in Individual Stock Event Studies http://papers.ssrn.com/sol3/papers.cfm?abstract_id=466161 David Luckham A Short History of Complex Event Processing http://complexevents.com/wpcontent/uploads/2008/02/1-a-short-history-of-cep-part-1.pdf Lisa A. Kramer Alternative Methods for Robust Analysis in Event Study Applications C.F. Lee, ed., Volume 8, 109-132, Elsevier Science Ltd., 2001 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=278109 Roselinde Kessels, Bradley Jones, Peter Goos, and Martina Vandebroek An efficient algorithm for constructing Bayesian optimal choice designs http://papers.ssrn.com/sol3/papers.cfm?abstract_id=968620 Event Studies: Practical Issues and Solutions- CLIFF'S NOTES http://research.cliffordang.com/event_study.pdf

Websites: www.yahoofinance.com www.s&p.com www.ssrn.com www.eventvestor.com

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