skfolio.descriptor.EarningsToPrice#

class skfolio.descriptor.EarningsToPrice[source]#

Earnings-to-price ratio descriptor.

Computes the ratio of trailing twelve-month net income to market capitalization:

\[\text{earnings\_to\_price}(t) = \frac{\text{net\_income\_ttm}(t)}{\text{market\_cap}(t)}\]

This is the inverse of the price-to-earnings (P/E) ratio and measures how much profit a firm generates per unit of market value. A high ratio identifies firms with strong current profitability relative to their price [1]. Unlike BookToPrice, which is based on the balance sheet, this descriptor is based on the income statement, capturing a distinct dimension of value.

This descriptor can be negative for loss-making firms, which is economically meaningful (unlike P/E, which becomes uninterpretable for negative earnings).

net_income_ttm should represent net income available to common shareholders when the data source distinguishes common and preferred claims. This is consistent with market_cap, which reflects common equity.

This descriptor uses aggregate quantities (total net income divided by total market capitalization) rather than per-share quantities (earnings per share divided by price). The two are mathematically equivalent when EPS and price use the same split-adjustment basis:

\[\frac{\text{net\_income\_ttm}}{\text{market\_cap}} = \frac{\text{eps\_ttm}}{\text{price}}\]

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.

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 trailing 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]

“Investment performance of common stocks in relation to their price-earnings ratios: A test of the efficient market hypothesis” The Journal of Finance. Basu, S. (1977).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import EarningsToPrice
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = EarningsToPrice()
>>> earnings_to_price = descriptor.fit_transform(X)

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

Compute trailing earnings-to-price ratios.

Parameters:

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

Returns:

earnings_to_pricendarray of shape (n_observations, n_assets): 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.