skfolio.descriptor.CashFlowToAssets#

class skfolio.descriptor.CashFlowToAssets[source]#

Cash flow to assets descriptor.

Computes the ratio of trailing twelve-month operating cash flow to total assets:

\[\text{cash\_flow\_to\_assets}(t) = \frac{\text{operating\_cash\_flow\_ttm}(t)}{\text{total\_assets}(t)}\]

Cash flow to assets measures cash-based profitability: how much cash a firm generates from operations per unit of assets. Unlike net income-based measures (ReturnOnAssets), operating cash flow is less directly affected by accrual accounting choices [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 cash flow to 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

ReturnOnAssets: Net income-based profitability per unit of assets.
CashFlowToPrice: Cash flow normalized by market cap (value signal).

Notes

Operating cash flow can be negative, so this descriptor can take negative values.

References

[1]

“Accruals, cash flows, and operating profitability in the cross section of stock returns” Journal of Financial Economics. Ball, Gerakos, Linnainmaa & Nikolaev (2016).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import CashFlowToAssets
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = CashFlowToAssets()
>>> cash_flow_to_assets = descriptor.fit_transform(X)

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

Compute cash flow to assets.

Parameters:

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

Returns:

cash_flow_to_assetsndarray of shape (n_observations, n_assets): Operating cash flow 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.