# skfolio.descriptor.EbitdaToEnterpriseValue
### *class* skfolio.descriptor.EbitdaToEnterpriseValue
EBITDA-to-enterprise-value ratio descriptor.
Computes the ratio of trailing twelve-month EBITDA to enterprise value:
$$
\text{ebitda\_to\_enterprise\_value}(t) =
\frac{\text{ebitda\_ttm}(t)}{\text{enterprise\_value}(t)}
$$
Enterprise value adjusts for capital structure by adding debt and subtracting cash
and equivalents from market capitalization:
$$
EV = \text{market\_cap} + \text{total\_debt} - \text{cash\_and\_equivalents}
$$
EBITDA measures operating profitability before financing, taxes and non-cash
charges. A high ratio identifies firms generating strong operating income relative
to their total firm value, regardless of how they are financed. The corresponding
enterprise multiple has been studied as a predictor of average stock returns [[1]](#rf7da3061f712-1).
This is the inverse of the conventional EV/EBITDA multiple. It provides a valuation
measure that is comparable across firms with different leverage, unlike price-based
ratios which only reflect equity value.
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.
* **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.EbitdaToEnterpriseValue.fit_transform)(X[, y]) | Compute EBITDA-to-enterprise-value ratios. |
|--------------------------------------------------------------------------------|------------------------------------------------|
| [`get_metadata_routing`](#skfolio.descriptor.EbitdaToEnterpriseValue.get_metadata_routing)() | Get metadata routing of this object. |
| [`get_params`](#skfolio.descriptor.EbitdaToEnterpriseValue.get_params)([deep]) | Get parameters for this estimator. |
| [`partial_fit_transform`](#skfolio.descriptor.EbitdaToEnterpriseValue.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. |
| [`set_params`](#skfolio.descriptor.EbitdaToEnterpriseValue.set_params)(\*\*params) | Set the parameters of this estimator. |
### 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 EbitdaToEnterpriseValue
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = EbitdaToEnterpriseValue()
>>> ebitda_to_enterprise_value = descriptor.fit_transform(X)
```
#### fit_transform(X, y=None, \*\*fit_params)
Compute EBITDA-to-enterprise-value ratios.
* **Parameters:**
**X** *AssetPanel*
: Input panel containing `ebitda_ttm` and `enterprise_value`.
**y** *None*
: Ignored. Present for compatibility with scikit-learn’s API.
**\*\*fit_params** *dict*
: Additional fit parameters. Ignored.
* **Returns:**
**ebitda_to_enterprise_value** *ndarray of shape (n_observations, n_assets)*
: EBITDA 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.