# skfolio.descriptor.AnalystDispersionToPrice ### *class* skfolio.descriptor.AnalystDispersionToPrice 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]](#r85cdca767597-1). 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`](#skfolio.descriptor.AnalystDispersionToPrice.fit_transform)(X[, y]) | Compute analyst earnings dispersion relative to price. | |--------------------------------------------------------------------------------|----------------------------------------------------------| | [`get_metadata_routing`](#skfolio.descriptor.AnalystDispersionToPrice.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.descriptor.AnalystDispersionToPrice.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.descriptor.AnalystDispersionToPrice.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.descriptor.AnalystDispersionToPrice.set_params)(\*\*params) | Set the parameters of this estimator. | #### SEE ALSO [`ForwardEarningsToPrice`](https://skfolio.org/generated/skfolio.descriptor.ForwardEarningsToPrice.html.md#skfolio.descriptor.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 ```pycon >>> 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) Compute analyst earnings dispersion relative to price. * **Parameters:** **X** *AssetPanel* : Input panel containing `eps_ntm_std` and `adj_close`. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. Ignored. * **Returns:** **analyst_dispersion_to_price** *ndarray 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](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.