skfolio.descriptor.CashFlowToPrice#

class skfolio.descriptor.CashFlowToPrice[source]#

Cash-flow-to-price ratio descriptor.

Computes the ratio of trailing twelve-month operating cash flow to market capitalization:

\[\text{cash\_flow\_to\_price}(t) = \frac{\text{operating\_cash\_flow\_ttm}(t)}{\text{market\_cap}(t)}\]

Operating cash flow measures cash generated by a firm’s core business after working-capital adjustments. A high ratio identifies firms generating substantial cash relative to their market capitalization, providing a value signal that is less directly affected by accrual accounting choices than earnings-based measures [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 price.
`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

CashFlowToAssets: Operating cash flow normalized by total assets.
BookToPrice: Common equity normalized by market capitalization.

Notes

Non-missing market_cap values must be finite and strictly positive. Operating cash flow can be negative, so this descriptor can take negative values.

This descriptor uses aggregate quantities (total operating cash flow divided by total market capitalization) rather than per-share quantities (cash flow per share divided by price). The two are mathematically equivalent:

\[\frac{\text{operating\_cash\_flow\_ttm}}{\text{market\_cap}} = \frac{\text{cash\_flow\_per\_share}}{\text{price}}\]

The aggregate form is preferred because it avoids subtle split-adjustment mismatches between numerator and denominator. Aggregate fundamentals are the primary form from data providers; per-share quantities are derived from them.

References

[1]

“Contrarian investment, extrapolation, and risk” The Journal of Finance. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import CashFlowToPrice
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = CashFlowToPrice()
>>> cash_flow_to_price = descriptor.fit_transform(X)

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

Compute cash flow to price.

Parameters:

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

Returns:

cash_flow_to_pricendarray of shape (n_observations, n_assets): Cash-flow-to-price ratio 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.