事件研究法命令

在做事件研究法，前两天发了求助帖https://www.360docs.net/doc/a91748654.html,/thread-1192259-1-1.html，问题是想把每个事件日的数据与距事件日最近的财务数据（如EPS）对应起来

今天终于把问题解决了，基本的想法是用date命令把事件日以及财务数据公布日的天数列出来，然后进行区分，借鉴了网站上教的事件研究法处理初始数据的方法（详见https://www.360docs.net/doc/a91748654.html,/online_help/stats_packages/stata/eventstudydataprep.html）想来还是把用到的命令贴出来，希望对童鞋们有用~

use 用来做每个减持事件日对应最近的EPS的.dta,clear

sort stock_code event_date

by stock_code event_date:gen n=_n

keep if n==1

save eventdates

by stock_code: gen eventcount=_N

drop n

by stock_code: keep if _n==1

by stock_code: gen eventcount=_N

drop n

by stock_code: keep if _n==1

sort stock_code

keep stock_code eventcount

save eventcount

use 只有EPS每季度的数据.dta, clear

sort stock_code

merge m:m stock_code using eventcount

use 只有EPS每季度的数据.dta, clear

sort stock_code

merge m:m stock_code using eventcount

keep if _merge==3

drop _merge

expand eventcount

drop eventcount

sort stock_code accounting_period

by stock_code accounting_period: gen set=_n

sort stock_code set accounting_period

save stockdata2

use eventdates, clear

drop n

by stock_code: gen set=_n

sort stock_code set

save eventdates2

use stockdata2, clear

merge m:m stock_code set using eventdates2

keep if _merge==3

drop _merge

egen group_id = group( stock_code set)

save 处理好的每个减持公告日对应了全部的EPS use 处理好的每个减持公告日对应了全部的EPS.dta gen date1=date( event_date,"YMD")

gen date2=date( accounting_period,"YMD")

gen dif1= date1- date2

sort group_id accounting_period

by group_id:egen dif2=min( dif1) if dif1>=0

keep if dif1== dif2

save 已经把每个减持公告日对应了距离最近的每股收益数据

use 做子市场反应的起点.dta,clear

drop set volume percent group_id p target

merge m:m stock_code event_date using 已经把每个减持公告日对应了距离最近的每股收益数据.dta

keep if _merge==3

drop set group_id dif1 dif2 _merge

drop date1 date2

sort stock_code event_date trading_date

egen group_id=group( stock_code event_date)

save 做不同水平EPS的基础数据

Data Preparation for Event Studies using Stata

Preparing your own data

You may have downloaded datasets for an event study, or created ones by entering data into excel sheets. Usually people have two files, one for stock returns, and the other for your event of interest. In this example, we start with two data sets, one called eventdates and the other called

stockdata. In the eventdates data, we have company id (company_id) and the date of event (event_date) as variables. In the stock data, we have matching company id (company_id), stock return date (date), stock return (ret), and market return value (market_return).

If a set of observations for each company can be matched to a single event date, the study will be much simpler. In some situations one may wish to examine more than one event date for each company. In multiple observations per company, it is necessary to create a duplicate set of observations for each event date/company combination. You need a full set of stock observations to examine each event. If you are not sure how many events per company you have on your dataset, we recommend that you go through this exercise to check the number of events per company.

If you already know that you have only one event per company, you may skip the instruction below, merge the eventdate and stockdata data files and go to the Event Study with Stata page.

Using example data

Alternatively, you may try the commands in our event studies example using our sample data set. There are two data sets: one called eventdates, that contain event information, and the other called stockdata. The events, in this example, are merger announcement dates for 2007 obtained from SDC Platinum. The stock return data for 2007 were obtained from CRSP daily stock.

The computer you are using Stata needs to be connected to the internet for this download to work. Please be patient with the download. You can see a counter on the bottom of Stata window which shows how much percent of the file has been downloaded. Once you finish the download, save the data on your computer or where you have write permission, like thesis folder in your H drive. Make sure that you have enough space in your drive to save these data files.

set memory 200m

use https://www.360docs.net/doc/a91748654.html,/sampleData/eventdates.dta /* about 11k */

save H:/thesis/eventdates

use https://www.360docs.net/doc/a91748654.html,/sampleData/stockdata.dta, clear /* about 90m */

save H:/thesis/stockdata

Combining event and stock data

First, set memory to a large enough size so that you can do the rest of the operations below. We will be creating some variables and possibly duplicating cases, so the dataset can get VERY BIG. To check how much memory you have allocated, the command is query, and to check how big your file is, the command is describe.

Now, we need to find out how many event dates there are for each company. Use the dataset of event dates and generate a variable that counts the number of event dates per company.

use eventdates, clear

by company_id: gen eventcount=_N

Cut the dataset down to just one observation for each company. Each company observation is associated with the count of event dates for that company. Save this as a new dataset - don't overwrite your dataset of event dates!

by company_id: keep if _n==1

sort company_id

keep company_id eventcount

save eventcount

The next step is to merge the new 'eventcount' dataset with your dataset of stock data.

use stockdata, clear

sort company_id

merge company_id using eventcount

tab _merge

keep if _merge==3

drop _merge

Now use Stata's 'expand' command to create the duplicate observations. The 'eventcount' variable has been merged on to each stock observation, and tells Stata how many copies of that observation are needed. This is where your dataset can get VERY BIG, as we are duplicating the observations to however many counts of event we have per company.

expand eventcount

You need to create a variable that indicates which 'set' of observations

within the company each observation belongs to. Then sort the dataset to prepare for another merge.

drop eventcount

sort company_id date

by company_id date: gen set=_n

sort company_id set

save stockdata2

Back in your original event dates dataset - not the 'eventcount' one! You need to create a matching set variable to identify the different event dates within each company. The final step is to use the set variable to match each event date with a set of stock observations.

use eventdates, clear

by company_id: gen set=_n

sort company_id set

save eventdates2

use stockdata2, clear

merge company_id set using eventdates2

tab _merge

Here, you may have observations where you have the events information but not stock information. You may examine which companies stock information is missing.

list company_id if _merge==2

keep if _merge==3

drop _merge

Finally, create a new variable that groups company_id and set so that you have a unique identifier to use in the rest of your analysis.

egen group_id = group(company_id set)

During the rest of your analysis, use group_id wherever the event study instructions say company_id. You're now ready to return to the Event Study with Stata page.

Event Studies with Stata

An event study is used to examine reactions of the market to events of interest. A simple event study involves the following steps:

?Cleaning the Data and Calculating the Event Window

?Estimating Normal Performance

?Calculating Abnormal and Cumulative Abnormal Returns

?Testing for Significance

?Testing Across All Events

This document is designed to help you conduct event studies using Stata. We assume that you already have data with a date variable, which we call "date", and a company identifier, which we have called "company_id". If you need to prepare your data or want to try out the commands with our sample data, go to data preparation page.

We also assume that you have a basic familiarity with Stata. If you need assistance with Stata commands, you can find out more about it here. Your task will be much easier if you enter the commands in a do file, which is a text file containing a list of Stata commands.

Cleaning the data and Calculating the Event and Estimation Windows

It's likely that you have more observations for each company than you need. It's also possible that you do not have enough for some. Before you can continue, you must make sure that you will be conducting your analyses on the correct observations. To do this, you will need to create a variable, dif, that will count the number of days from the observation to the event date. This can be either calendar days or trading days.

For number of trading days:

sort company_id date

by company_id: gen datenum=_n

by company_id: gen target=datenum if date==event_date

egen td=min(target), by(company_id)

drop target

gen dif=datenum-td

For calendar days:

gen dif=date-event_date

As you can see, calculating the number of trading days is a little trickier than calendar days. For trading days, we first need to create a variable that counts the number of days within each company_id. Then we determine

which observation occurs on the event date. We create a variable with the event date's day number on all of the observations within that company_id. Finally, we simply take the difference between the two, creating a variable, dif, that counts the number of days between each individual observation and the event day. Next, we need to make sure that we have the minimum number of observations before and after the event date, as well as the minimum number of observations before the event window for the estimation window. Let's say we want 2 days before and after the event date (a total of 5 days in the event window) and 30 days for the estimation window. (You can of course change these numbers to suit your analysis.)

by company_id: gen event_window=1 if dif>=-2 & dif<=2

egen count_event_obs=count(event_window), by(company_id)

by company_id: gen estimation_window=1 if dif<-30 & dif>=-60

egen count_est_obs=count(estimation_window), by(company_id)

replace event_window=0 if event_window==.

replace estimation_window=0 if estimation_window==.

The procedure for determining the event and estimation windows is the same. First we create a variable that equals 1 if the observation is within the specified days. Second, we create another variable that counts how many observations, within each company_id, has a 1 assigned to it. Finally, we replace all the missing values with zeroes, creating a dummy variable. You can now determine which companies do not have a sufficient number of observations.

tab company_id if count_event_obs<5

tab company_id if count_est_obs<30

The "tab" will produce a list of company_ids that do not have enough observations within the event and estimation windows, as well as the total number of observations for those company_ids. To eliminate these companies:

drop if count_event_obs < 5

drop if count_est_obs < 30

You should make sure the dataset has been saved under a different name before dropping any observations!

At this point you can also drop some variables you won't need any longer: count_event_obs and count_est_obs.

Estimating Normal Performance

Now we are at the point where we can actually start an analysis. First we need a way to estimate Normal Performance. To do this, we will run a seperate regression for each company using the data within the estimation window and save the alphas (the intercept) and betas (the coefficient of the independent variable). We will later use these saved regression equations to predict normal performance during the event window.

Note that return, the dependent variable in our regression, is simply the CRSP variable for a given stock's return, while the independent variable vretd that we use to predict ret is the value-weighted return of an index for whatever exchange the stock trades on. Use the equivalent variables for your dataset.

set more off /* this command just keeps stata from pausing after each screen of output */

gen predicted_return=.

egen id=group(company_id)

/* for multiple event dates, use: egen id = group(group_id) */ forvalues i=1(1)N { /*note: replace N with the highest value of id */

l id company_id if id==`i' & dif==0

reg ret market_return if id==`i' & estimation_window==1

predict p if id==`i'

replace predicted_return = p if id==`i' & event_window==1

drop p

}

Here, we created a variable "id" that numbers the companies from 1 to however many there are. The N is the number of company-event combinations that have complete data. This process iterates over the companies, runs a regression in the estimation window for each, and then uses that regression to predict a 'normal' return in the event window.

Abnormal and Cumulative Abnormal Returns

We can now calculate the abnormal and cumulative abnormal returns for our data. The daily abnormal return is computed by subtracting the predicted normal return from the actual return for each day in the event window. The sum of the abnormal returns over the event window is the cumulative abnormal return.

sort id date

gen abnormal_return=ret-predicted_return if event_window==1

by id: egen cumulative_abnormal_return = sum(abnormal_return)

Here we simply calculate the abnormal return for each observation in the event window. Then we set the cumulative abnormal return equal to the sum of the abnormal returns for each company.

Testing for Significance

We are going to compute a test statistic, test, to check whether the average abnormal return for each stock is statistically different from zero.*

TEST= ((ΣAR)/N) / (AR_SD/sqrt(N))

where AR is the abnormal return and AR_SD is the abnormal return standard deviation. If the absolute value of test is greater than 1.96, then the average abnormal return for that stock is significantly different from zero at the 5% level. The value of 1.96 comes from the standard normal distribution with a mean of 0 and a standard deviation of 1. 95% of the distribution is between ±1.96.

sort id date

by id: egen ar_sd = sd(abnormal_return)

gen test =(1/sqrt(number of days in event window)) *

( cumulative_abnormal_return /ar_sd)

list company_id cumulative_abnormal_return test if dif==0

Note: this test uses the sample standard deviation. A less conservative alternative is to use the population standard deviation. To derive this from the sample standard deviation produced by Stata, multiply ar_sd by the square root of n-1/n; in our example, by the square root of 4/5.

This will output the results of your event study into an Excel-readable spreadsheet file:

outsheet company_id event_date cumulative_abnormal_return test using stats.csv if dif==0, comma names

Testing Across All Events

Instead of, or in addition to, looking at the average abnormal return for each company, you probably want to calculate the cumulative abnormal for all companies treated as a group. Here's the code for that:

reg cumulative_abnormal_return if dif==0, robust

The P-value on the constant from this regression will give you the significance of the cumulative abnormal return across all companies. This test preferable to a t-test because it allows you to use robust standard errors.

Further Reading

Capital Market Responses To Environmental Performance In Developing Countries, section t hree: Event Study Methodology, working paper from the World Bank

Note

Thank you to Kim Baum for useful feedback