skfolio.descriptor.MarketLeverage#

class skfolio.descriptor.MarketLeverage[source]#

Market leverage descriptor.

Computes the proportion of total capital (at market value) financed by debt:

\[\text{market\_leverage}(t) = \frac{\text{total\_debt}(t)} {\text{total\_debt}(t) + \text{market\_cap}(t)}\]

Market leverage blends accounting data (total debt) with market data (market capitalization). Unlike BookLeverage, the denominator updates daily with the stock price, making it more responsive to changes in the firm’s risk profile. When a stock drops sharply, market leverage rises immediately, capturing the increased financial risk before any accounting restatement [1].

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

When total_debt is non-negative and given that market_cap is non-negative by construction, the ratio is bounded in \([0, 1)\). This makes it the most numerically well-behaved of the leverage descriptors, requiring no special treatment for negative-equity firms.

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 market leverage 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

DebtToAssets: Leverage relative to total assets.
BookLeverage: Leverage as a fraction of total book capital.

References

[1]

“Capital structure decisions: which factors are reliably important?” Financial Management. Frank, M. Z., & Goyal, V. K. (2009).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import MarketLeverage
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = MarketLeverage()
>>> market_leverage = descriptor.fit_transform(X)

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

Compute market leverage ratios.

Parameters:

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

Returns:

market_leveragendarray of shape (n_observations, n_assets): Market leverage 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.