eventstudy.Single.results

Single.results(asterisks: bool = True, decimals=3)

Return event study’s results in a table format.

Parameters

• asterisks (bool, optional) – Add asterisks to CAR value based on significance of p-value, by default True

• decimals (int or list, optional) – Round the value with the number of decimal specified, by default 3. decimals can either be an integer, in this case all value will be round at the same decimals, or a list of 6 decimals, in this case each columns will be round based on its respective number of decimal.

Note

When asterisks is set as True, CAR’s are converted to string type. To make further computation on CARs possible set asterisks to False.

Returns

AR and AR’s variance, CAR and CAR’s variance, T-stat and P-value, for each T in the event window.

Return type

pandas.DataFrame

Note

The function return a fully working pandas DataFrame. All pandas method can be used on it, especially exporting method (to_csv, to_excel,…)

Example

Get results of a market model event study, with specific number of decimal for each column:

>>> event = EventStudy.market_model(
...     security_ticker = 'AAPL',
...     market_ticker = 'SPY',
...     event_date = np.datetime64('2007-01-09'),
...     event_window = (-5,+5)
... )
>>> event.results(decimals = [3,5,3,5,2,2])

	AR	Variance AR	CAR	Variance CAR	T-stat	P-value
-5	-0.053	0.00048	-0.053 **	0.00048	-2.42	0.01
-4	0.012	0.00048	-0.041 *	0.00096	-1.33	0.09
-3	-0.013	0.00048	-0.055 *	0.00144	-1.43	0.08
-2	0.004	0.00048	-0.051	0.00192	-1.15	0.13
-1	0	0.00048	-0.051	0.00241	-1.03	0.15
0	-0.077	0.00048	-0.128 **	0.00289	-2.37	0.01
1	-0.039	0.00048	-0.167 ***	0.00337	-2.88	0
2	0.027	0.00048	-0.14 **	0.00385	-2.26	0.01
3	0.024	0.00048	-0.116 **	0.00433	-1.77	0.04
4	-0.024	0.00048	-0.14 **	0.00481	-2.02	0.02
5	0.023	0.00048	-0.117 *	0.00529	-1.61	0.05

Note

Significance level: *** at 99%, ** at 95%, * at 90%