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Conducting an Event Study Without a Macro-Enabled SpreadsheetGo to Yahoo! Finance (finance.). In the search bar where it says “Quote Lookup”, enter the ticker symbol for a stock that was added to the S&P 500. This will take you to Yahoo! Finance’s summary page for that stock. Click on the tab titled “Historical Data” and under “Time Period”, enter the “End Date” that is 11 trading days prior to the date the stock was added to the S&P500. The “Start Date” should be 101 trading days prior to the date you entered for the end date. “Frequency” should already be set to “Daily”, so you can now click on “Apply”. Download your data to Excel by clicking on the “Download Data” button. A spreadsheet with all the data ordered from the end date to the start date will appear. Select the first date (probably in cell A2) and then have Excel sort your data from oldest to newest. The only columns we will be concerned with are “Date” and “Adjusted Close”. The adjusted close is the closing price of the stock on that day adjusted for stock splits, dividends, etc. Using it rather than the actual closing price will allow you to accurately calculate returns. If you have 101 daily adjusted closing prices for your stock, you will be able to calculate 100 daily returns. In Excel, calculate each return as the natural log of the later price divided by the earlier price [=ln(G3/G2) for example] if you want continuously compounded daily returns. You should calculate each return as the later price divided by the earlier price minus one [=(G3/G2)-1 for example] if you want to use daily compounding. Either is ok for this exercise as long as you are consistent throughout the entire exercise. Go back to Yahoo! Finance and do the same thing for the S&P 500 Index (symbol ^GSPC). Be sure to use the same dates. You will now have daily returns for your stock and daily returns for the S&P 500 Index for the same 100 days. Place the 100 returns for your stock and the 100 returns for the S&P 500 in adjacent columns in your spreadsheet and make sure they are properly aligned.If you want to use Excel’s regression function (found under data analysis), you may – or, you can find the alpha and beta estimates that we are looking for with the “Intercept” and “Slope” functions in Excel. In either case, the 100 returns for the stock you selected will be the “known ys” and the 100 returns for the S&P 500 will be the “known xs”. Go back to Yahoo! Finance and download to a spreadsheet, the data for your stock (and for the S&P 500) for the eleven trading days prior to the day your stock was added to the S&P 500 Index, the day it was added, and the ten trading days after it was added (12 trading days total). As you did earlier, use these 12 days of adjusted daily closing prices to calculate 11 days of daily returns. This should give you returns for the ten days prior to the event date, the event date, and the ten days after the event date. This is our event window.For each day in the event window, in Excel, multiply the beta estimate you calculated above times the return for the S&P 500 for that day and then add your alpha estimate. This is the “risk-adjusted expected return” for the stock for that day. Based on that stock’s sensitivity to the S&P 500 during the prior 100 days, this is the return we would expect the stock to have on that day if nothing unusual happened to it. Calculate the expected return for your stock for each of the 21 days in the event window.For each day in the event window, subtract your stock’s risk-adjusted expected return from its actual return for that day. This is its “abnormal” return for that day. We interpret this as the return that (perhaps) was influenced by the event we are looking at.For each of the 21 days in the event window, calculate the “cumulative abnormal return” (CAR) for your stock. For day 1, the CAR is the day 1 abnormal return. For day 2, the CAR is the day 1 abnormal return plus the day 2 abnormal return. For day 3, the CAR is the sum of the abnormal returns for days 1-3. For day 4, the CAR is the sum of the abnormal returns for days 1-4, etc.Select additional stocks that were added to the S&P 500 Index and repeat each of the above steps with each of them. There is no specific number of stocks that you should select. The number of stocks that you select is your “sample size”. Generally, a larger sample size is better – but you need to consider how much time it takes you to collect a larger sample size.Once you have CARs for each of the event days for each stock in your sample, find the average (arithmetic mean) CAR for each day in the event window for your sample of stocks. Calculate the standard deviation of the CARs for each day as well.A t-statistic tells you how many standard errors a test statistic is from the null hypothesis that you want to test. In our case, the null hypothesis is that nothing unusual is happening to these stocks during the event window (meaning that the CARs are zero). So for each day in the event window, you should divide the average CARs you calculated by the standard error for that day. The standard error is the standard deviation (for that day) for your sample divided by the square root of the sample size. Thus, a larger sample (all else equal) results in a smaller standard error and a larger t-statistic.Researchers generally consider a t-statistic with an absolute value greater than 2.0 to indicate that there is only a small chance that there is nothing going on here (that the CARs are actually zero for the entire population of stocks that were added to the S&P 500). If the t-stat is greater than 2.0, we fail to reject our hypothesis that being added to the S&P 500 is (on average) causing a stock’s return to be different from what it otherwise would have been. ................
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