eventstudy.Multiple.from_list

classmethod Multiple.from_list(event_list: list, event_study_model, event_window: tuple = (-10, 10), estimation_size: int = 300, buffer_size: int = 30, *, keep_model: bool = False, ignore_errors: bool = True)

Compute an aggregate of event studies from a list containing each event’s parameters.

Parameters

• event_list (list) – List containing dictionaries specifing each event’s parameters (see example for more details).

• event_study_model – Function returning an eventstudy.Single class instance. For example, eventstudy.Single.market_model() (a custom functions can be created).

• event_window (tuple, optional) – Event window specification (T2,T3), by default (-10, +10). A tuple of two integers, representing the start and the end of the event window. Classically, the event-window starts before the event and ends after the event. For example, event_window = (-2,+20) means that the event-period starts 2 periods before the event and ends 20 periods after.

• estimation_size (int, optional) – Size of the estimation for the modelisation of returns [T0,T1], by default 300

• buffer_size (int, optional) – Size of the buffer window [T1,T2], by default 30

• keep_model (bool, optional) – If true the model used to compute each single event study will be stored in memory. They will be accessible through the class attributes eventStudy.Multiple.singles[n].model, by default False

• ignore_errors (bool, optional) – If true, errors during the computation of single event studies will be ignored. In this case, these events will be removed from the computation. However, a warning message will be displayed after the computation to warn for errors. Errors can also be accessed using print(eventstudy.Multiple.error_report()). If false, the computation will be stopped by any error encounter during the computation of single event studies, by default True

See also

from_text(), from_csv()

Example

>>> list = [
...     {'event_date': np.datetime64("2018-11-05"), 'security_ticker': 'AAPL'},
...     {'event_date': np.datetime64("2017-11-03"), 'security_ticker': 'AAPL'},
...     {'event_date': np.datetime64("2016-10-26"), 'security_ticker': 'AAPL'},
...     {'event_date': np.datetime64("2015-10-28"), 'security_ticker': 'AAPL'},
... ]
>>> agg = eventstudy.Multiple.from_list(
...     text = list,
...     event_study_model = eventstudy.Single.FamaFrench_3factor,
...     event_window = (-5,+10),
... )