<a id="skfolio-descriptor-assetsgrowthrate"></a>

# skfolio.descriptor.AssetsGrowthRate

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### *class* skfolio.descriptor.AssetsGrowthRate(lag=252)

Asset growth rate descriptor.

Computes period-over-period growth in total assets:

$$
\text{assets\_growth}(t)
= \frac{\text{total\_assets}(t)}
       {\text{total\_assets}(t - \text{lag})} - 1
$$

The first `lag` observations are NaN because no lagged history is available.

`total_assets` must contain non-missing finite non-negative values. NaNs are
allowed as missing observations and propagate when either the current or lagged
value is missing. Zero values are allowed and a zero lagged value makes the growth
rate undefined and produces NaN.

Asset growth is commonly used as an investment or balance-sheet expansion signal.
Firms with rapid asset growth tend to earn lower future returns [[1]](#r8145a2769a60-1).

This is a convenience subclass of [`GrowthRate`](https://skfolio.org/generated/skfolio.descriptor.GrowthRate.html.md#skfolio.descriptor.GrowthRate) with `field="total_assets"`.

* **Parameters:**
  **lag** *int, default=252*
  : Number of observations to look back. The interpretation depends on the data
    frequency: `lag=12` means 1 year for monthly data, `lag=252` for daily data,
    `lag=4` for quarterly data.
* **Attributes:**
  **n_assets_** *int*
  : Number of assets seen during fitting.

  **asset_names_** *ndarray of shape (n_assets,)*
  : Asset names seen during fitting.

  **growth_rate_** *ndarray of shape (n_assets,)*
  : Last asset growth value for each asset.

### Methods

| [`fit_transform`](#skfolio.descriptor.AssetsGrowthRate.fit_transform)(X[, y])         | Compute simple growth rates of the configured field.   |
|--------------------------------------------------------------------------------|--------------------------------------------------------|
| [`get_metadata_routing`](#skfolio.descriptor.AssetsGrowthRate.get_metadata_routing)()        | Get metadata routing of this object.                   |
| [`get_params`](#skfolio.descriptor.AssetsGrowthRate.get_params)([deep])            | Get parameters for this estimator.                     |
| [`partial_fit_transform`](#skfolio.descriptor.AssetsGrowthRate.partial_fit_transform)(X[, y]) | Compute simple growth rates of the configured field.   |
| [`set_params`](#skfolio.descriptor.AssetsGrowthRate.set_params)(\*\*params)        | Set the parameters of this estimator.                  |

#### SEE ALSO
[`GrowthRate`](https://skfolio.org/generated/skfolio.descriptor.GrowthRate.html.md#skfolio.descriptor.GrowthRate)
: Generic period-over-period growth rate descriptor.

### References

* <a id='r8145a2769a60-1'>**[1]**</a> “Asset growth and the cross-section of stock returns” The Journal of Finance. Cooper, M. J., Gulen, H., & Schill, M. J. (2008).

### Examples

```pycon
>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import AssetsGrowthRate
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = AssetsGrowthRate(lag=252)
>>> assets_growth_rate = descriptor.fit_transform(X)
```

<a id="skfolio.descriptor.AssetsGrowthRate.fit_transform"></a>

#### fit_transform(X, y=None, \*\*fit_params)

Compute simple growth rates of the configured field.

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing the `field` characteristic configured at
    construction.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **growth_rate** *ndarray of shape (n_observations, n_assets)*
  : Period-over-period growth rate for each observation and asset.

<a id="skfolio.descriptor.AssetsGrowthRate.get_metadata_routing"></a>

#### 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.

<a id="skfolio.descriptor.AssetsGrowthRate.get_params"></a>

#### 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.

<a id="skfolio.descriptor.AssetsGrowthRate.partial_fit_transform"></a>

#### partial_fit_transform(X, y=None, \*\*fit_params)

Compute simple growth rates of the configured field.

This method supports online updates by continuing from the current fitted state.
Use `fit_transform` to start from a clean state.

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing the `field` characteristic configured at construction.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **growth_rate** *ndarray of shape (n_observations, n_assets)*
  : Period-over-period growth rate for each observation and asset.

<a id="skfolio.descriptor.AssetsGrowthRate.set_params"></a>

#### 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:**
  **\*\*params** *dict*
  : Estimator parameters.
* **Returns:**
  **self** *estimator instance*
  : Estimator instance.