Coefficient of Variation (CV)
Calculate the Coefficient of Variation (CV) of an investment portfolio or asset’s returns for a given period based on the daily historical returns.
The Coefficient of Variation is the ratio of the standard deviation to the mean of returns, which normalizes dispersion relative to the average return. This makes it useful for comparing the relative volatility of assets with different average returns, which a raw standard deviation cannot do.
Also known as: relative standard deviation.
Coefficient of Variation (CV) in Python
Install the Finance Toolkit with:
pip install financetoolkit -U
Then call get_coefficient_of_variation as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")
toolkit.risk.get_coefficient_of_variation(period="yearly")
Which returns:
| Date | AMZN | TSLA | Benchmark |
|---|---|---|---|
| 2021 | 73.121 | 15.7477 | 8.3938 |
| 2022 | -14.1417 | -12.7544 | -20.4791 |
| 2023 | 8.0356 | 10.0506 | 9.1833 |
| 2024 | 10.9398 | 14.7623 | 9.1557 |
| 2025 | 49.9543 | 32.8037 | 18.0122 |
| 2026 | 31.132 | -163.952 | 11.047 |
Parameters
The function accepts the following parameters:
- period (str, optional): The data frequency for returns (weekly, monthly, quarterly, or yearly). Defaults to “quarterly” if the Toolkit is initialized with quarterly=True, otherwise “yearly”.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
- growth (bool, optional): Whether to calculate the growth of the CV values over time. 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.