Source code for skfolio.descriptor._earnings_yield._earnings_to_price
"""Earnings-to-price ratio descriptor."""
# Copyright (c) 2023-2026
# Author: Hugo Delatte <hugo.delatte@skfoliolabs.com>
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import numpy as np
from skfolio.containers import AssetPanel
from skfolio.descriptor._base import BaseDescriptor
from skfolio.typing import FloatArray
from skfolio.utils.stats import safe_divide
from skfolio.utils.validation import validate_asset_panel
[docs]
class EarningsToPrice(BaseDescriptor, stateless=True):
r"""Earnings-to-price ratio descriptor.
Computes the ratio of trailing twelve-month net income to market capitalization:
.. math::
\text{earnings\_to\_price}(t) =
\frac{\text{net\_income\_ttm}(t)}{\text{market\_cap}(t)}
This is the inverse of the price-to-earnings (P/E) ratio and measures how much
profit a firm generates per unit of market value. A high ratio identifies firms with
strong current profitability relative to their price [1]_. Unlike :class:`BookToPrice`,
which is based on the balance sheet, this descriptor is based on the income
statement, capturing a distinct dimension of value.
This descriptor can be negative for loss-making firms, which is economically
meaningful (unlike P/E, which becomes uninterpretable for negative earnings).
`net_income_ttm` should represent net income available to common shareholders when
the data source distinguishes common and preferred claims. This is consistent with
`market_cap`, which reflects common equity.
This descriptor uses aggregate quantities (total net income divided by total market
capitalization) rather than per-share quantities (earnings per share divided by
price). The two are mathematically equivalent when EPS and price use the same
split-adjustment basis:
.. math::
\frac{\text{net\_income\_ttm}}{\text{market\_cap}}
= \frac{\text{eps\_ttm}}{\text{price}}
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. 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.
References
----------
.. [1] "Investment performance of common stocks in relation to their price-earnings
ratios: A test of the efficient market hypothesis" The Journal of Finance.
Basu, S. (1977).
Examples
--------
>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import EarningsToPrice
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = EarningsToPrice()
>>> earnings_to_price = descriptor.fit_transform(X)
"""
