skfolio.descriptor.ForwardEarningsToPrice#

class skfolio.descriptor.ForwardEarningsToPrice[source]#

Forward earnings-to-price ratio descriptor.

Computes the ratio of consensus NTM earnings per share to split-adjusted close price:

\[\text{forward\_earnings\_to\_price}(t) = \frac{\text{eps\_ntm}(t)}{\text{adj\_close}(t)}\]

Forward earnings-to-price reflects consensus expectations of future profitability relative to the current price [1]. Because it incorporates analyst forecasts rather than trailing accounting data, it captures forward-looking value and is less affected by stale or one-off items in historical earnings. A high ratio identifies firms expected to generate strong earnings relative to their price.

Unlike the other value descriptors which use aggregate fundamentals divided by market_cap, this descriptor uses per-share quantities (eps_ntm / adj_close). Consensus estimates from data providers are delivered as per-share forecasts, making per-share the primary form. eps_ntm should use the same split-adjustment basis as adj_close.

The aggregate equivalent is:

\[\frac{\text{eps\_ntm}}{\text{adj\_close}} = \frac{\text{eps\_ntm} \times \text{shares\_out}} {\text{adj\_close} \times \text{shares\_out}} = \frac{\text{earnings\_ntm}}{\text{market\_cap}}\]

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 forward earnings-to-price ratios.
`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.

References

[1]

“Expectations and share prices” Management Science. Elton, E. J., Gruber, M. J., & Gultekin, M. (1981).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import ForwardEarningsToPrice
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = ForwardEarningsToPrice()
>>> forward_earnings_to_price = descriptor.fit_transform(X)

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

Compute forward earnings-to-price ratios.

Parameters:

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

Returns:

forward_earnings_to_pricendarray of shape (n_observations, n_assets): Forward earnings-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.