Calculate the Graham Number, a conservative estimate of a stock’s fair value based on its earnings and book value, as devised by Benjamin Graham.

The Graham Number is intended as an upper bound on the price a defensive investor should pay for a stock. It is most meaningful for stable, profitable companies with positive book value - for companies with negative earnings or negative book value the result is not meaningful (the square root of a negative number is undefined and will show up as NaN).

The formula is as follows:

\[\text{Graham Number} = \sqrt{22.5 x \text{Earnings per Share} x \text{Book Value per Share}}\]

Also known as: Graham fair value.

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Calculate the Graham Number in Python

The Graham Number is available in the Models module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_graham_number as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_graham_number()

Which returns:

  2021 2022 2023 2024 2025
AAPL 21.7378 20.662 23.2901 22.4927 28.7292
TSLA 18.1054 32.3757 41.7451 30.9185 23.7345

Parameters

get_graham_number accepts the following parameters:

  • diluted (bool, optional): Whether to use diluted shares in the calculation. Defaults to True.
  • trailing (int | None, optional): The trailing period to use for the calculation. Defaults to None.
  • rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.
  • standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.

The Models module page introduces the module, and the sidebar lists all of its functions.

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