Information Ratio
The Information Ratio (IR) is a financial metric that assesses the risk-adjusted performance of a portfolio or investment strategy relative to a benchmark index. It quantifies how much active return (the return above the benchmark) the portfolio generates per unit of tracking error, the volatility of that active return. The Information Ratio is commonly used by portfolio managers, financial analysts, and investors to evaluate the skill of a portfolio manager in generating returns beyond what would be expected based on the risk taken.
- IR > 0: A positive Information Ratio indicates that the portfolio has generated excess returns compared to the benchmark, suggesting that the portfolio manager has added value.
- IR = 0: An Information Ratio of zero implies that the portfolio’s excess return is in line with the benchmark, meaning the portfolio manager has not added or lost value relative to the benchmark.
- IR < 0: A negative Information Ratio suggests that the portfolio has underperformed the benchmark, potentially indicating that the portfolio manager has detracted value.
The formula is as follows:
\[\text{Information Ratio} (\text{IR}) = \operatorname{Mean}(\text{Portfolio Return} - \text{Benchmark Return}) / \text{Tracking Error}\]By default it is computed from the daily returns within each period and is not annualized: multiply by SQRT(252) to compare it with the annualized figures in the literature, where an IR of 0.5 is considered good. With rolling, the returns of period itself are used instead.
See definition: https://en.wikipedia.org/wiki/Information_ratio
Also known as: active return per risk.
Information Ratio in Python or via the MCP
Install the Finance Toolkit with:
pip install financetoolkit -U
Then call get_information_ratio as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(
["XLK", "XLE", "XLV"],
api_key="FINANCIAL_MODELING_PREP_KEY",
start_date="2021-01-01",
end_date="2025-12-31",
)
toolkit.performance.get_information_ratio()
Or, after setting up the Finance Toolkit MCP, ask:
What was the information ratio of XLK, XLE and XLV against the S&P 500, measured on daily returns, in each year from 2021 to 2025?
Which returns:
| Date | XLK | XLE | XLV |
|---|---|---|---|
| 2021 | 0.0377 | 0.047 | -0.0204 |
| 2022 | -0.0557 | 0.1406 | 0.0776 |
| 2023 | 0.147 | -0.0655 | -0.1316 |
| 2024 | -0.0053 | -0.0549 | -0.1084 |
| 2025 | 0.042 | -0.0246 | -0.0114 |
Parameters
get_information_ratio accepts the following parameters.
| Parameter | Type | Description | Example |
|---|---|---|---|
period | str | The period to use for the calculation. Defaults to “quarterly” if the Toolkit is initialised with quarterly=True, otherwise “yearly”. | "quarterly" |
rolling | int | The rolling window size to use for the calculation. If set, the Information Ratio is calculated over a rolling window of this many periods across the full return history instead of per period. Defaults to None. | 4 |
rounding | int | None | The number of decimals to round the results to. Defaults to None, which uses the rounding of the Toolkit (4 unless set otherwise). | 2 |
growth | bool | Whether to calculate the growth of the ratios. Defaults to False. | True |
lag | int | list[int] | The lag to use for the growth calculation. Defaults to 1. | 4 |
standardize | bool | Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False. | True |