skfolio.descriptor.ReturnOnAssets#

class skfolio.descriptor.ReturnOnAssets[source]#

Return on assets (ROA) descriptor.

Computes the ratio of trailing twelve-month net income to total assets:

\[\text{ROA}(t) = \frac{\text{net\_income\_ttm}(t)}{\text{total\_assets}(t)}\]

Return on assets measures how efficiently a firm converts its asset base into earnings. Higher values indicate greater profitability per unit of capital deployed [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 assets.
`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

ReturnOnEquity: Profitability per unit of equity.
AssetTurnover: Sales generated per unit of total assets.

Notes

Net income can be negative, so \(ROA\) can be negative. Negative values distinguish profitable from unprofitable firms.

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 ReturnOnAssets
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = ReturnOnAssets()
>>> return_on_assets = descriptor.fit_transform(X)

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

Compute return on assets.

Parameters:

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

Returns:

return_on_assetsndarray of shape (n_observations, n_assets): Net income 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.