skfolio.descriptor.DividendToPrice#

class skfolio.descriptor.DividendToPrice[source]#

Dividend-to-price ratio descriptor.

Computes the ratio of trailing twelve-month common dividends to market: capitalization:

\[\text{dividend\_to\_price}(t) = \frac{\text{dividends\_ttm}(t)}{\text{market\_cap}(t)}\]

Dividend-to-price measures the income yield that shareholders receive relative to the current market price. High-yield stocks tend to be mature, cash-generative businesses, while low-yield stocks are typically growth-oriented or retain earnings for reinvestment [1]. The dividend yield factor captures a distinct dimension of value beyond book or earnings ratios because dividends reflect management’s confidence in sustainable cash flows.

dividends_ttm should contain positive cash dividends paid on common shares only, excluding preferred dividends. This is consistent with market_cap, which reflects common equity.

This descriptor uses aggregate quantities (dividends paid divided by market capitalization) rather than per-share quantities (dividends per share divided by split-adjusted close price). The two are mathematically equivalent when the price and per-share dividend use the same split-adjustment basis:

\[\frac{\text{dividends\_ttm}}{\text{market\_cap}} = \frac{\text{dividends\_ttm} / \text{shares\_out}}{\text{adj\_close}} = \frac{\text{dps\_ttm}}{\text{adj\_close}}\]

The aggregate form is preferred because it avoids subtle split-adjustment mismatches between numerator and denominator. Aggregate fundamentals are the primary form from data providers and per-share quantities are derived from them.

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 trailing dividend-to-price 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

ForwardDividendToPrice: Forward (analyst-predicted) dividend yield.
ShareholderYield: Dividend yield plus net buybacks.

References

[1]

“Common risk factors in the returns on stocks and bonds” Journal of Financial Economics. Fama, E. F., & French, K. R. (1993).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import DividendToPrice
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = DividendToPrice()
>>> dividend_to_price = descriptor.fit_transform(X)

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

Compute trailing dividend-to-price ratios.

Parameters:

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

Returns:

dividend_to_pricendarray of shape (n_observations, n_assets): Dividend-to-price 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.