# skfolio.descriptor.SalesToEnterpriseValue ### *class* skfolio.descriptor.SalesToEnterpriseValue Sales to enterprise value descriptor. Computes the ratio of trailing twelve-month sales to enterprise value: $$ \text{sales\_to\_enterprise\_value}(t) = \frac{\text{sales\_ttm}(t)}{\text{enterprise\_value}(t)} $$ This descriptor is a valuation and efficiency measure: it measures how much revenue a firm generates per unit of enterprise value. Unlike [`AssetTurnover`](https://skfolio.org/generated/skfolio.descriptor.AssetTurnover.html.md#skfolio.descriptor.AssetTurnover), which normalizes by book assets, enterprise value reflects the market’s assessment of the entire capital structure [[1]](#r8c06b785cdaa-1). A high sales-to-enterprise-value ratio identifies firms that generate substantial revenue relative to their market valuation, combining elements of both value and operational efficiency. * **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.SalesToEnterpriseValue.fit_transform)(X[, y]) | Compute sales to enterprise value. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.descriptor.SalesToEnterpriseValue.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.descriptor.SalesToEnterpriseValue.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.descriptor.SalesToEnterpriseValue.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.descriptor.SalesToEnterpriseValue.set_params)(\*\*params) | Set the parameters of this estimator. | #### SEE ALSO [`AssetTurnover`](https://skfolio.org/generated/skfolio.descriptor.AssetTurnover.html.md#skfolio.descriptor.AssetTurnover) : Sales normalized by book assets (efficiency). [`EbitdaToEnterpriseValue`](https://skfolio.org/generated/skfolio.descriptor.EbitdaToEnterpriseValue.html.md#skfolio.descriptor.EbitdaToEnterpriseValue) : EBITDA normalized by enterprise value. ### Notes If `enterprise_value` is not available directly from your data provider, it can be computed as: $$ \text{EV} = \text{market\_cap} + \text{total\_debt} - \text{cash\_and\_equivalents} $$ Non-missing `enterprise_value` values must be finite. Observations with `enterprise_value <= 0` are masked to NaN because the valuation yield is not economically interpretable. ### References * **[1]** “New evidence on the relation between the enterprise multiple and average stock returns” Journal of Financial and Quantitative Analysis. Loughran, T., & Wellman, J. W. (2011). ### Examples ```pycon >>> from skfolio.datasets import make_synthetic_characteristics >>> from skfolio.descriptor import SalesToEnterpriseValue >>> >>> X = make_synthetic_characteristics() >>> >>> descriptor = SalesToEnterpriseValue() >>> sales_to_enterprise_value = descriptor.fit_transform(X) ``` #### fit_transform(X, y=None, \*\*fit_params) Compute sales to enterprise value. * **Parameters:** **X** *AssetPanel* : Input panel containing `sales_ttm` and `enterprise_value`. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. Ignored. * **Returns:** **sales_to_enterprise_value** *ndarray of shape (n_observations, n_assets)* : Sales divided by enterprise value 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.