The Information Ratio (IR), also known as the Information Coefficient, 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 excess return the portfolio generates for each unit of tracking error (volatility of tracking error). 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:

\[\text{Information Ratio} (\text{IR}) = (\text{Portfolio's Excess Return} - \text{Benchmark's Excess Return}) / \text{Tracking Error}\]

See definition: https://en.wikipedia.org/wiki/Information_ratio

Also known as: active return per risk.

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the Information Ratio for you. Just ask in plain English.

Calculate the Information Ratio in Python

The Information Ratio is available in the Performance module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_information_ratio as shown below.

from financetoolkit import Toolkit

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

toolkit.performance.get_information_ratio()

Which returns:

Date AAPL TSLA
2021 0.0253 0.0381
2022 -0.0212 -0.0739
2023 0.0833 0.0817
2024 0.0231 0.0499
2025 -0.0106 0.0164
2026 0.0269 -0.0641

Parameters

get_information_ratio accepts the following parameters:

  • period (str, optional): The period to use for the calculation. Defaults to “quarterly” if the Toolkit is initialised with quarterly=True, otherwise “yearly”.
  • rolling (int, optional): 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.
  • 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 ratios. Defaults to False.
  • lag (int | str, 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.
Ulcer Performance Index (UPI) Calmar Ratio Sterling Ratio Burke Ratio M2 Ratio Tracking Error Upside Capture Ratio Downside Capture Ratio Win Rate Kappa Ratio Omega Ratio Gain-to-Pain Ratio

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

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