ICIR: What the Mean and Variation of IC Can Tell You

IC measures the relationship between a feature and future returns on an evaluation date. ICIR summarizes the IC series by dividing its mean by its standard deviation. It helps describe average direction relative to day-to-day variation, while discarding some information in the original series.

Specify whether the input is IC or Rank IC, the period, the treatment of invalid dates and the standard-deviation convention. This article explains mean/std without annualization. Compare other reported values only after checking whether their definitions include additional scaling.

Take fictional daily IC values 0.1, 0.2 and 0.3. Their mean is 0.2. For sample standard deviation, divide the squared-deviation sum 0.02 by n−1=2 and take the square root, obtaining 0.1. ICIR is therefore 0.2 / 0.1 = 2.

Using population standard deviation gives √(0.02/3) ≈ 0.08165 and a ratio around 2.45. The convention changes the result for the same observations. Do not apply a universal pass threshold to this three-day illustration or treat it as proof of a useful trading rule.

If every valid IC is identical, standard deviation is zero and the ordinary ratio is undefined. A very small denominator can also create a large ratio. Read the mean, standard deviation and valid-day count separately; a blank or infinite display is not automatically favorable evidence.

When invalid dates are omitted, preserve their reasons. Aggregates with few valid dates and many valid dates do not have identical precision. Inspect daily plots and yearly summaries to see whether a short interval dominates the overall mean.

When multi-session future returns are evaluated every day, neighboring labels can share price intervals. For a fictional three-session horizon, today's and tomorrow's labels can contain overlapping observations. Additional rows do not necessarily provide equally many independent pieces of information.

The mean/std ratio is therefore not, by itself, a calibrated significance test. Statistical inference needs separate methods that address time dependence and sample design. Do not infer the probability of chance or certainty about the future from ICIR alone.

ICIR describes a series of evaluation correlations. It does not measure transaction costs, position sizes, fills or losses. Two designs with similar ICIR can have different trading feasibility. A Sharpe ratio calculated from returns has different inputs and meaning.

Public Demo 2 explicitly treats ICIR as a descriptive mean/std quantity. Read it alongside daily IC, coverage, invalid reasons and yearly descriptive statistics. Your deliverable should explain the convention and sample size as well as the reported ratio.

Read Information coefficient · Sharpe ratio · Out-of-sample testing to place this topic alongside other research checks. Confirm documented operations and limits in the official GUI research courses. Use documentation for your selected build; if public main differs from your environment, defer unverified claims.

This article teaches research procedures. Numerical examples are fictional teaching illustrations, not actual investment performance. It does not recommend securities or trades or guarantee future returns. Historical public courses do not establish complete historical information availability. Disclaimer

Explore the factors learning path

• What Is a Factor? Comparing Security Characteristics with Numbers
• What Is IC? Reading the Relationship Between Factors and Future Returns
• What Is Quantile Analysis? Evaluating Factors by Groups