skfolio.descriptor.DebtToAssets#

class skfolio.descriptor.DebtToAssets[source]#

Debt-to-assets ratio descriptor.

Computes the ratio of total debt to total assets:

\[\text{debt\_to\_assets}(t) = \frac{\text{total\_debt}(t)}{\text{total\_assets}(t)}\]

Debt-to-assets is the most widely used leverage descriptor in equity risk models. It measures the proportion of a firm’s asset base financed by debt. Higher values indicate greater reliance on debt financing and, all else equal, a smaller equity cushion to absorb losses, increasing the firm’s vulnerability to earnings shocks, adverse financing conditions and credit deterioration [1].

The ratio is naturally bounded between 0 (no debt) and approximately 1 (assets fully debt-financed), though it can exceed 1 when accumulated losses erode equity below zero, making total liabilities exceed total assets.

NaNs are allowed as missing observations and propagate to the output. Non-missing total_debt values must be finite. Non-missing total_assets values must be finite and strictly positive.

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`(X[, y])	Compute debt-to-assets ratios.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Stateless class delegation to `fit_transform`.
`set_params`(**params)	Set the parameters of this estimator.

See also

BookLeverage: Leverage as a fraction of total book capital.
MarketLeverage: Leverage as a fraction of total market capital.

References

[1]

“Debt/equity ratio and expected common stock returns: empirical evidence” The Journal of Finance. Bhandari, L. C. (1988).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import DebtToAssets
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = DebtToAssets()
>>> debt_to_assets = descriptor.fit_transform(X)

fit_transform(X, y=None, **fit_params)[source]#

Compute debt-to-assets ratios.

Parameters:

XAssetPanel: Input panel containing total_debt and total_assets.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

debt_to_assetsndarray of shape (n_observations, n_assets): Debt-to-assets ratio for each observation and asset.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: 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 <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.