EventVestor's Issue Equity dataset is now available in Pipeline. The dataset includes events and announcements covering secondary equity issues by companies. The dataset can be imported in the IDE or in research with this statement:
from quantopian.pipeline.data.eventvestor import IssueEquityAnnouncements
The dataset is implemented by querying the same data source that backs
issue_equity dataset in the interactive API, filtering for just events whose
issue_stage starts with "Ann" (the underlying data contains various misspellings and modifications of "Announcement"). The exposed columns of the new dataset are
announcement_date. The first three columns are unchanged from the interactive API, and the last column maps to
asof_date in the interactive API.
In addition, we've added a new
relabel method to the
Classifier base class, which allows you to efficiently apply a mapping function over string data. The intended use for this method is to allow you to apply custom logic to normalize messy string data. In the attached notebook,
relabel is used to clean the
issue_unit column by checking entries against a list of regular expressions.
For the Issue Equity dataset, there is a new built-in classifier,
NormalizedIssueUnits which can be used as a shortcut specifically for a cleaned version of the
issue_units field. The
NormalizedIssueUnits classifier can be imported with:
Now that the Issue Equity dataset is available in pipeline, you can include it in a strategy that gets evaluated for an allocation. If you think you've found something interesting in research, try putting it into an algorithm and run it through a backtest. And if you need an idea to get you started, this paper found that firms issue more equity than debt before periods of low returns. The paper is a bit old but it could be interesting to see if the conclusion still holds up.