Source code for skfolio.descriptor._value._book_to_price

"""Book-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 BookToPrice(BaseDescriptor, stateless=True): r"""Book-to-price ratio descriptor. Computes the ratio of common shareholders' equity (book equity) to market capitalization: .. math:: \text{book\_to\_price}(t) = \frac{\text{book\_equity}(t)}{\text{market\_cap}(t)} A high book-to-price ratio identifies stocks trading at a discount relative to their common equity. Historically, cheap stocks (those with high book-to-price ratios) have earned higher average returns than expensive stocks (those with low book-to-price ratios) [1]_. Parameters ---------- None Attributes ---------- n_assets_ : int Number of assets seen during fitting. asset_names_ : ndarray of shape (n_assets,) Asset names seen during fitting. Notes ----- Non-missing `market_cap` values must be finite and strictly positive. Negative `book_equity` values are preserved because they carry information about the firm's balance sheet. This descriptor uses aggregate quantities (common equity divided by total market capitalization) rather than per-share quantities (book value per share divided by price). The two are mathematically equivalent: .. math:: \frac{\text{book\_equity}}{\text{price} \times \text{shares\_out}} = \frac{\text{book\_value\_per\_share}}{\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. See Also -------- SalesToPrice : Sales normalized by market capitalization. CashFlowToPrice : Operating cash flow normalized by market capitalization. References ---------- .. [1] "The cross-section of expected stock returns" The Journal of Finance. Fama, E. F., & French, K. R. (1992). Examples -------- >>> from skfolio.datasets import make_synthetic_characteristics >>> from skfolio.descriptor import BookToPrice >>> >>> X = make_synthetic_characteristics() >>> >>> descriptor = BookToPrice() >>> book_to_price = descriptor.fit_transform(X) """
[docs] def fit_transform(self, X: AssetPanel, y=None, **fit_params) -> FloatArray: """Compute book to price. Parameters ---------- X : AssetPanel Input panel containing `book_equity` and `market_cap`. y : None Ignored. Present for compatibility with scikit-learn's API. **fit_params : dict Additional fit parameters. Ignored. Returns ------- book_to_price : ndarray of shape (n_observations, n_assets) Book-to-price ratio for each observation and asset. """ validate_asset_panel( self, X, required_fields=["book_equity", "market_cap"], finite_or_nan=["book_equity"], strictly_positive_or_nan=["market_cap"], ) return safe_divide(X["book_equity"], X["market_cap"], fill_value=np.nan)