We are in the midst of the 21st and the record-long US government shutdown. So we asked the question, why is there a government shutdown? We decided to dig a little deeper, and answer few questions that arose:
-Does government unification play a role in government shutting down?
-Historically, what happens to Presidents' Job Approval Ratings as they go in and exit out of a shutdown?
-Similarly, What happens to Congressional Approval Ratings as the government exits a shut down?
-Finally, What is the impact of a shutdown on the "economy" (Using S&P 500 stock Prices)?
-S&P 500 Historical Data. Source: Yahoo Finance.Link
-US Party Control 1933-2010 from Southeast Missouri State University. Link
-Shutdowns Dates and Lengths. Link
-Former Presidents' Approval Ratings. Source: The American Presidency Project. Link
-President Trump's Approval Ratings. Source: Gallup. Link
The S&P 500 data we loaded covered daily openings and closings from the date of 1976-07-12 up until 2019-01-11. Only skipping, what we have assumed to be (holidays, weekends and/or other dates stock market was closed).
However, we were only interested in the few days ahead of historical governemnt shutdowns and the few days afterwards, depending on the length of a shutdown and the availability of stock market data for those particular dates.
Therefore a loop over both dataset is necessary to match the dates of interest.
# csv of s&p 500 data from yahoo finance
filepath = "../Input/^GSPC.csv"
# read stock csv
stock_prices_unclean = pd.read_csv(filepath)
# make the index the date to help with cleaning
stock_prices_unclean = stock_prices_unclean.set_index(['Date']
# print first 3 records of stock market dataframe
stock_prices_unclean.head(3)
| Date | Open | High | Low | Close | Adj. Close | Volume |
|---|---|---|---|---|---|---|
| 1976-07-12 | 104.980003 | 106.300003 | 104.739998 | 105.900002 | 105.900002 | 23750000 |
| 1976-07-13 | 105.900002 | 106.779999 | 105.150002 | 105.669998 | 105.669998 | 27550000 |
| 1976-07-14 | 105.669998 | 106.610001 | 105.050003 | 105.949997 | 105.949997 | 23840000 |
As for the government shutdowns data would look something like this: