eventstudy.Multiple.results

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

Give event study result 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

AAR and AAR’s variance, CAAR and CAAR’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 on an aggregate of events (Apple Inc. 10-K form releases) imported from a csv, with specific number of decimal for each column:

>>> events = es.Multiple.from_csv(
...     'AAPL_10K.csv',
...     es.Single.FamaFrench_3factor,
...     event_window = (-5,+5),
...     date_format = '%d/%m/%Y'
... )
>>> events.results(decimals = [3,5,3,5,2,2])

	AAR	Variance AAR	CAAR	Variance CAAR	T-stat	P-value
-5	-0	3e-05	-0.0	3e-05	-0.09	0.47
-4	-0.002	3e-05	-0.003	5e-05	-0.35	0.36
-3	0.009	3e-05	0.007	8e-05	0.79	0.22
-2	0.003	3e-05	0.01	0.0001	1.03	0.15
-1	0.008	3e-05	0.018 *	0.00013	1.61	0.05
0	-0	3e-05	0.018 *	0.00015	1.46	0.07
1	-0.006	3e-05	0.012	0.00018	0.88	0.19
2	0.006	3e-05	0.017	0.0002	1.22	0.11
3	0	3e-05	0.018	0.00023	1.17	0.12
4	-0.007	3e-05	0.011	0.00025	0.69	0.24
5	0.001	3e-05	0.012	0.00028	0.72	0.24

Note

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