Distinct from Sharpe and Treynor, which measure return against a risk-free rate — the information ratio measures excess return specifically against a chosen benchmark.
How it works
Portfolio return minus benchmark return, divided by tracking error (the volatility of that excess return), gives the information ratio.
What this does not include
This takes tracking error as a direct input — calculating it from raw historical returns requires a full return history and standard deviation calculation across many periods, not computed here.
How to use this calculator
- Enter portfolio return, benchmark return, and tracking error.
A worked example
A portfolio returning 12% against a 9% benchmark, with a 4% tracking error: information ratio = (12 − 9) ÷ 4 = 0.75 — a positive figure showing consistent outperformance relative to the risk taken to achieve it.
What the variables mean
| Variable | Meaning |
|---|---|
| Portfolio return | The portfolio’s actual return over the period |
| Benchmark return | The comparison index’s return over the same period |
| Tracking error | Volatility of the difference between portfolio and benchmark returns |
Edge cases worth knowing
A higher information ratio means more consistent outperformance, not just bigger outperformance. Two managers beating the benchmark by the same amount can have very different ratios if one does it more erratically than the other.
Zero tracking error makes the ratio undefined — a portfolio that never deviates from its benchmark has nothing to divide the excess return by.
Frequently asked questions
What does the information ratio measure that Sharpe doesn’t?
Consistency of outperformance against a specific, relevant benchmark, rather than absolute risk-adjusted return against a risk-free rate.
What’s a good information ratio?
Values above 0.5 are commonly considered good, and above 1.0 exceptional, for an actively managed strategy, though standards vary across asset classes and strategies.
Why does tracking error matter?
A manager who beats the benchmark inconsistently (high tracking error) is penalized relative to one who beats it by the same average amount but consistently (low tracking error).
