Expected Returns (E[Rᵢₜ])  
Serves as the baseline return projection in the absence of M&A events based on a market model, expressed as: 
E[Rᵢₜ] = αi+βi⋅Rₘₜ 
Where,  
E[Rᵢₜ]: Expected return of stock i on day t. 
αᵢ: Intercept, capturing the average return of stock i that is not explained by market movements. βᵢ: Beta of stock i, indicating the sensitivity of the return of stock i to market return. 
Rₘₜ: Return of the market index on day t. 
The above is formula for expected return and so far the code i am done with is below So now for the above calculation i have prices of market Index but the problem is there are 46 companies and some companies work on BSE ltd and some on National India ..........For this i have 2 excel sheet one has companyid with its exchange name and another excel has    BSE ltd		National India	
Exchange Date,Close	,Exchange Date and Close......So first row has BSE ltd and National India names and second row has exchange date and close (price) 
I have to basically do that i have 141 events and for those events i have to do event study on 46 companies ...So the effect of those 141 events on those 46 companies stock market for -5 to +5 event window ........one excel has stock prices of those 46 companies on trading days for the year 2022 and 2023  and one excel of 141 events with their dates ............So i want to do the above can you please help me with code and please explain that codes as well this is for research ....I want that it should pick the date and then pick data of stock prices from the other sheet and properly calculate Expected return and actual return to calculate abnormal return and then cumulative abnormal return ........Then perform T test and below is my hypothesis 
H0: In the short term, M&A events have no significant immediate impact on the stock prices of competitor companies in the technology sector. 
H1: In the short term, M&A events have a significant immediate impact on the stock prices of competitor companies in the technology sector. .........The below is code so far ..........Can you please give me the simplest way to do this with less lines of code with no hussle bussle 
//Load Data
* Import stock prices
import excel using "stock_prices.xlsx", sheet("Sheet1") firstrow clear
* List the first few rows to verify the variable names
list in 1/10
* Display the variable names
describe
* Assuming the variable names are Date, CompanyID, and Price (adjust accordingly)
rename Date stock_date
rename CompanyID company_id
rename Price stock_price
* Convert the date format
gen double stock_date = date(stock_date, "YMD")
format stock_date %td
* Save the imported stock prices data
save stock_prices.dta, replace


* Import events
import excel using "events.xlsx", sheet("Sheet1") firstrow clear
* List the first few rows to verify the variable names
list in 1/10
* Display the variable names
describe
* Assuming the variable names are EventID and EventDate (adjust accordingly)
* Rename variables if necessary
rename EventID event_id
rename EventDate event_date
* Convert the date format if not already in Stata date format
gen double event_date = date(event_date, "YMD")
format event_date %td
* Save the imported events data
save events.dta, replace

* Load Events Data
use events.dta, clear
* Load Stock Prices Data and Merge by Date Range
use stock_prices.dta, clear
cross using events.dta

* Create variable for the Event Window
gen event_window = stock_date - event_date
keep if inrange(event_window, -5, 5)

* Save the merged data with the event window from -5 to +5 days around the event date
save merged_data.dta, replace


* Load the stock prices dataset
use stock_prices.dta, clear
* Encode the company_id from string to numeric
encode company_id, gen(company_id_num)
* Save the changes
save stock_prices.dta, replace
* Repeat for the event-window merged dataset if necessary
use merged_data.dta, clear
encode company_id, gen(company_id_num)
save merged_data.dta, replace

* Load the data with event windows
use merged_data.dta, clear
* Load the dataset
use merged_data.dta, clear

* Count occurrences of stock_date for each company
bysort company_id_num stock_date : gen count = _N
* List occurrences where there is more than one entry on the same stock_date for a company
list company_id_num stock_date if count > 1 in 1/100
* Keep only the first occurrence
    bysort company_id_num stock_date : keep if _n == 1
* Sort and re-run tsset
gsort company_id_num stock_date
tsset company_id_num stock_date
* Assign the company identifier and date as time series properties
tsset company_id_num stock_date

* Generate lagged stock price variable
gen double lag_stock_price = L.stock_price
* Calculate daily returns as percentage change from previous day's price
gen double ret = (stock_price - lag_stock_price) / lag_stock_price
* You could also deal with or examine cases where there may be infinities or missing values due to division by zero
replace ret = 0 if lag_stock_price == 0
drop if missing(ret)  // Or handle missing values as needed for your analysis context
* Exploring the data to ensure returns are calculated correctly
list company_id_num stock_date stock_price lag_stock_price ret in 1/20