What it computes: the Information Coefficient (IC) is the cross-sectional correlation between a signal's forecasts and the returns that follow. The rank IC (Spearman's ρ) is the conventional measure because it's robust to outliers and only assumes a monotonic relationship; the Pearson IC assumes linearity. New to this? Start with the Information Coefficient guide.
Open source — view or fork the calculator on GitHub (MIT, single dependency-free HTML file). Related tools: Signal Decay Calculator · Backtest Overfitting Simulator · Cointegration & Pairs Trading Simulator · Signal Combination Simulator · Correlation Heatmap · VPIN & the Volume Clock · Signal Skill Explorer.
Predictions and realized returns
Paste two columns — your signal's predictions (forecast scores, any scale) and the realized forward returns for the same names. One value per line; rows are matched in order, so both columns must be the same length.
Frequently asked
- What is the difference between Pearson and rank (Spearman) IC?
- Rank IC correlates the ranks of forecasts and returns: it is robust to outliers, only assumes a monotonic relationship, and is the conventional measure in practice. Pearson IC assumes a linear relationship and is more sensitive to extreme values. Report rank IC unless you specifically need the linear version.
- What do I paste in?
- Two aligned columns for the same names at the same time: your signal's predictions (any scale — only the ordering matters for rank IC) and the realized forward returns that followed. The tool standardises and correlates them. Your data never leaves the browser.
- How much data do I need for the IC to mean anything?
- The t-stat the tool reports (approximately IC times the square root of N minus 2) tells you. With a handful of names an IC is almost meaningless; significance usually needs a few hundred observations, and a single cross-section's IC is far noisier than an IC averaged over many periods (the ICIR).
- What IC should I expect from a usable signal?
- A sustained rank IC of roughly 0.03 to 0.06 is a genuinely useful equity signal. A much higher figure on real out-of-sample data usually warrants a check for look-ahead bias or overfitting. See the Information Coefficient guide for context.
- Is anything stored or uploaded?
- No. The calculation runs entirely in your browser and nothing is sent anywhere. It is also open source on GitHub if you want to verify or self-host it.