# skfolio.descriptor.ReturnOnEquity ### *class* skfolio.descriptor.ReturnOnEquity Return on equity (ROE) descriptor. Computes the ratio of trailing twelve-month net income to common shareholders’ equity: $$ \text{ROE}(t) = \frac{\text{net\_income\_ttm}(t)}{\text{book\_equity}(t)} $$ Return on equity measures profitability from the common shareholders’ perspective: how much profit a firm generates per unit of common equity capital. Stocks with high $ROE$ tend to earn higher average returns [[1]](#r29a2ea881056-1). * **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.ReturnOnEquity.fit_transform)(X[, y]) | Compute return on equity. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.descriptor.ReturnOnEquity.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.descriptor.ReturnOnEquity.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.descriptor.ReturnOnEquity.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.descriptor.ReturnOnEquity.set_params)(\*\*params) | Set the parameters of this estimator. | #### SEE ALSO [`ReturnOnAssets`](https://skfolio.org/generated/skfolio.descriptor.ReturnOnAssets.html.md#skfolio.descriptor.ReturnOnAssets) : Profitability per unit of total assets. ### Notes When `book_equity <= 0` (e.g., firms with accumulated deficits or heavy share buybacks), the ratio does not represent an interpretable return on equity. These observations are masked to NaN. ### References * **[1]** “A five-factor asset pricing model” Journal of Financial Economics. Fama, E. F., & French, K. R. (2015). ### Examples ```pycon >>> from skfolio.datasets import make_synthetic_characteristics >>> from skfolio.descriptor import ReturnOnEquity >>> >>> X = make_synthetic_characteristics() >>> >>> descriptor = ReturnOnEquity() >>> return_on_equity = descriptor.fit_transform(X) ``` #### fit_transform(X, y=None, \*\*fit_params) Compute return on equity. * **Parameters:** **X** *AssetPanel* : Input panel containing `net_income_ttm` and `book_equity`. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. Ignored. * **Returns:** **return_on_equity** *ndarray of shape (n_observations, n_assets)* : Net income divided by book equity 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.