skfolio.descriptor.ShareholderYield#

class skfolio.descriptor.ShareholderYield[source]#

Shareholder yield descriptor.

Computes net cash returned to common shareholders through dividends and share repurchases as a fraction of market capitalization:

\[\text{shareholder\_yield}(t) = \frac{\text{dividends\_ttm}(t) + \text{net\_buybacks\_ttm}(t)} {\text{market\_cap}(t)}\]

Dividend yield alone misses a large share of corporate payout. Since the 1990s, share repurchases have overtaken dividends as the dominant mechanism for returning cash to shareholders. Shareholder yield captures the total payout: a company paying 0% dividends but buying back 5% of its equity annually has a positive payout yield that pure dividend yield scores as zero [1].

High shareholder yield identifies firms that return substantial capital. Empirically, shareholder yield subsumes much of the stand-alone dividend yield premium and provides a stronger value/payout signal [2].

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 shareholder yield 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

DividendToPrice: Dividend-only yield (trailing).

Notes

dividends_ttm should contain positive cash dividends paid on common shares only, excluding preferred dividends.

net_buybacks_ttm should equal net share repurchases, defined as repurchases minus issuances, over the trailing twelve months. Positive values increase shareholder yield and negative values represent net issuance. Some data vendors provide net equity issuance from the cash flow statement instead, with the opposite sign convention. In that case, net_buybacks_ttm = -net_equity_issuance_ttm.

This descriptor uses aggregate quantities divided by market_cap, consistent with DividendToPrice and other value descriptors.

References

[1]

“On the importance of measuring payout yield: implications for empirical asset pricing” The Journal of Finance. Boudoukh, Michaely, Richardson & Roberts (2007).

[2]

“Dividends, share repurchases, and the substitution hypothesis” The Journal of Finance. Grullon & Michaely (2002).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import ShareholderYield
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = ShareholderYield()
>>> shareholder_yield = descriptor.fit_transform(X)

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

Compute shareholder yield ratios.

Parameters:

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

Returns:

shareholder_yieldndarray of shape (n_observations, n_assets): Shareholder yield 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.