eventstudy.Multiple.get_CAR_dist

Multiple.get_CAR_dist(decimals=3)

Give CARs’ distribution descriptive statistics in a table format.

Parameters

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.

Returns

CARs’ descriptive statistics

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 CARs’ descriptive statistics of a market model event study on an aggregate of events (Apple Inc. 10-K release) 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.get_CAR_dist(decimals = 4)

	Mean	Variance	Kurtosis	Min	Quantile 25%	Quantile 50%	Quantile 75%	Max
-5	-0	0.001	0.061	-0.052	-0.014	0.001	0.015	0.047
-4	-0.003	0.001	0.247	-0.091	-0.022	0.003	0.015	0.081
-3	0.007	0.002	0.532	-0.082	-0.026	0.006	0.027	0.139
-2	0.01	0.002	-0.025	-0.088	-0.021	0.002	0.033	0.115
-1	0.018	0.003	-0.065	-0.091	-0.012	0.02	0.041	0.138
0	0.018	0.003	-0.724	-0.084	-0.012	0.012	0.057	0.128
1	0.012	0.004	-0.613	-0.076	-0.024	0.003	0.059	0.143
2	0.017	0.005	-0.55	-0.117	-0.026	0.024	0.057	0.156
3	0.018	0.005	0.289	-0.162	-0.032	0.027	0.057	0.17
4	0.011	0.007	2.996	-0.282	-0.039	0.035	0.052	0.178
5	0.012	0.008	1.629	-0.266	-0.05	0.035	0.064	0.174

Note

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