skfolio.descriptor.SalesToEnterpriseValue#

class skfolio.descriptor.SalesToEnterpriseValue[source]#

Sales to enterprise value descriptor.

Computes the ratio of trailing twelve-month sales to enterprise value:

\[\text{sales\_to\_enterprise\_value}(t) = \frac{\text{sales\_ttm}(t)}{\text{enterprise\_value}(t)}\]

This descriptor is a valuation and efficiency measure: it measures how much revenue a firm generates per unit of enterprise value. Unlike AssetTurnover, which normalizes by book assets, enterprise value reflects the market’s assessment of the entire capital structure [1].

A high sales-to-enterprise-value ratio identifies firms that generate substantial revenue relative to their market valuation, combining elements of both value and operational efficiency.

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 sales to enterprise value.
`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

AssetTurnover: Sales normalized by book assets (efficiency).
EbitdaToEnterpriseValue: EBITDA normalized by enterprise value.

Notes

If enterprise_value is not available directly from your data provider, it can be computed as:

\[\text{EV} = \text{market\_cap} + \text{total\_debt} - \text{cash\_and\_equivalents}\]

Non-missing enterprise_value values must be finite. Observations with enterprise_value <= 0 are masked to NaN because the valuation yield is not economically interpretable.

References

[1]

“New evidence on the relation between the enterprise multiple and average stock returns” Journal of Financial and Quantitative Analysis. Loughran, T., & Wellman, J. W. (2011).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import SalesToEnterpriseValue
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = SalesToEnterpriseValue()
>>> sales_to_enterprise_value = descriptor.fit_transform(X)

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

Compute sales to enterprise value.

Parameters:

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

Returns:

sales_to_enterprise_valuendarray of shape (n_observations, n_assets): Sales divided by enterprise value 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.