skfolio.descriptor.ShortInterest#

class skfolio.descriptor.ShortInterest[source]#

Short interest descriptor.

Computes the ratio of shares sold short to common shares outstanding:

\[\text{short\_interest}(t) = \frac{\text{short\_interest}(t)} {\text{adj\_shares\_outstanding}(t)}\]

Short interest measures the fraction of common shares outstanding that have been borrowed and sold short. High values indicate stronger bearish positioning and may proxy for informed negative sentiment [1] [2].

Parameters:

None

Attributes:

n_assets_int: Number of assets seen during fitting.
asset_names_ndarray of shape (n_assets,): Asset names seen during fitting.

Methods

`fit_transform`(X[, y])	Compute short interest.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Stateless class delegation to `fit_transform`.
`set_params`(**params)	Set the parameters of this estimator.

See also

DaysToCover: EWMA-smoothed days to cover (short interest / volume).

Notes

short_interest is the number of shares held short. Non-missing values must be finite and non-negative.

adj_shares_outstanding is common shares outstanding. Non-missing values must be finite and strictly positive.

Both fields must use the same split-adjustment basis.

References

[1]

“An investigation of the informational role of short interest in the Nasdaq market” The Journal of Finance. Desai, H., Ramesh, K., Thiagarajan, S. R., & Balachandran, B. V. (2002).

[2]

“Short interest and aggregate stock returns” Journal of Financial Economics. Rapach, D. E., Ringgenberg, M. C., & Zhou, G. (2016).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import ShortInterest
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = ShortInterest()
>>> short_interest = descriptor.fit_transform(X)

fit_transform(X, y=None, **fit_params)[source]#

Compute short interest.

Parameters:

XAssetPanel: Input panel containing short_interest and adj_shares_outstanding.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

short_interestndarray of shape (n_observations, n_assets): Short interest divided by adjusted shares outstanding for each observation and asset.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

partial_fit_transform(X, y=None, **fit_params)#: Stateless class delegation to fit_transform.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.