skfolio.descriptor.ReturnOnEquity#

class skfolio.descriptor.ReturnOnEquity[source]#

Return on equity (ROE) descriptor.

Computes the ratio of trailing twelve-month net income to common shareholders’ equity:

\[\text{ROE}(t) = \frac{\text{net\_income\_ttm}(t)}{\text{book\_equity}(t)}\]

Return on equity measures profitability from the common shareholders’ perspective: how much profit a firm generates per unit of common equity capital. Stocks with high \(ROE\) tend to earn higher average returns [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 return on equity.
`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

ReturnOnAssets: Profitability per unit of total assets.

Notes

When book_equity <= 0 (e.g., firms with accumulated deficits or heavy share buybacks), the ratio does not represent an interpretable return on equity. These observations are masked to NaN.

References

[1]

“A five-factor asset pricing model” Journal of Financial Economics. Fama, E. F., & French, K. R. (2015).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import ReturnOnEquity
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = ReturnOnEquity()
>>> return_on_equity = descriptor.fit_transform(X)

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

Compute return on equity.

Parameters:

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

Returns:

return_on_equityndarray of shape (n_observations, n_assets): Net income divided by book equity 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.