skfolio.descriptor.AnalystDispersionToPrice#

class skfolio.descriptor.AnalystDispersionToPrice[source]#

Analyst forecast dispersion to price descriptor.

Computes the ratio of analyst earnings forecast dispersion to the split-adjusted close price:

\[\text{analyst\_dispersion\_to\_price}(t) = \frac{\text{eps\_ntm\_std}(t)}{\text{adj\_close}(t)}\]

Higher values indicate greater disagreement among analysts about a firm’s forward earnings relative to its price. Forecast dispersion is a proxy for earnings uncertainty and information asymmetry. Empirically, stocks with high analyst disagreement tend to be overpriced and earn lower future returns [1].

This descriptor uses per-share quantities (standard deviation of per-share EPS forecasts divided by split-adjusted price) because analyst consensus data is natively reported on a per-share basis.

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 analyst earnings dispersion relative to price.
`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

ForwardEarningsToPrice: Level of forward earnings to price.

Notes

eps_ntm_std is the cross-analyst standard deviation of NTM EPS estimates, typically provided by consensus data vendors. It should use the same split-adjustment basis as adj_close.

References

[1]

“Differences of opinion and the cross section of stock returns” The Journal of Finance. Diether, K. B., Malloy, C. J., & Scherbina, A. (2002).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import AnalystDispersionToPrice
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = AnalystDispersionToPrice()
>>> analyst_dispersion_to_price = descriptor.fit_transform(X)

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

Compute analyst earnings dispersion relative to price.

Parameters:

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

Returns:

analyst_dispersion_to_pricendarray of shape (n_observations, n_assets): Standard deviation of forward EPS estimates divided by split-adjusted close 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.