# skfolio.descriptor.ForwardEarningsToPrice ### *class* skfolio.descriptor.ForwardEarningsToPrice 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]](#r3dbaa75bd164-1). 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`](#skfolio.descriptor.ForwardEarningsToPrice.fit_transform)(X[, y]) | Compute forward earnings-to-price ratios. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.descriptor.ForwardEarningsToPrice.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.descriptor.ForwardEarningsToPrice.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.descriptor.ForwardEarningsToPrice.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.descriptor.ForwardEarningsToPrice.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 ```pycon >>> 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) Compute forward earnings-to-price ratios. * **Parameters:** **X** *AssetPanel* : Input panel containing `eps_ntm` and `adj_close`. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. Ignored. * **Returns:** **forward_earnings_to_price** *ndarray 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](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing mechanism works. * **Returns:** **routing** *MetadataRequest* : A `MetadataRequest` encapsulating routing information. #### get_params(deep=True) Get parameters for this estimator. * **Parameters:** **deep** *bool, default=True* : If True, will return the parameters for this estimator and contained subobjects that are estimators. * **Returns:** **params** *dict* : 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 `__` so that it’s possible to update each component of a nested object. * **Parameters:** **\*\*params** *dict* : Estimator parameters. * **Returns:** **self** *estimator instance* : Estimator instance.