skfolio.descriptor.GrossProfitability#

class skfolio.descriptor.GrossProfitability[source]#

Gross profitability descriptor.

Computes the ratio of gross profit to total assets:

\[\text{gross\_profitability}(t) = \frac{\text{sales\_ttm}(t) - \text{cost\_of\_revenue\_ttm}(t)} {\text{total\_assets}(t)}\]

Gross profitability captures a firm’s ability to generate profit from its asset base before operating expenses, interest and taxes. It is less affected by financing, tax and accrual accounting choices than net income-based ratios.

Profitable firms typically earn significantly higher returns than unprofitable ones [1].

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 gross profitability.
`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

GrossMargin: Gross profit normalized by sales.
ReturnOnAssets: Net income normalized by total assets.

Notes

cost_of_revenue_ttm (trailing twelve months) should be reported as a positive number representing the cost. The descriptor computes sales_ttm - cost_of_revenue_ttm to obtain gross profit.

References

[1]

“The other side of value: The gross profitability premium” Journal of Financial Economics. Novy-Marx, R. (2013).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import GrossProfitability
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = GrossProfitability()
>>> gross_profitability = descriptor.fit_transform(X)

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

Compute gross profitability.

Parameters:

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

Returns:

gross_profitabilityndarray of shape (n_observations, n_assets): Gross profit divided by total assets 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.