Information Ratio Calculator
The formula
What the information ratio tells you
The active manager's Sharpe: how much return above the benchmark per unit of deviation from it. Grinold and Kahn made it the core of quantitative portfolio construction — everything about a strategy is traded off through this single number.
Calculation: IR = active return ÷ tracking error. Enter your inputs and every substitution is shown below the result.
Reading the result
Sustained IR > 0.5 places a manager in roughly the top decile; the very best systematic strategies in liquid markets hover around 1.5–2.5 and anything above ~3 over long periods deserves forensic scrutiny.
Rules of thumb: ~0 no value added · 0.3–0.5 respectable and common at top quant firms · > 0.7 exceptional · > 1.5 verify the data
Limitations and best practice
Short samples and illiquid assets (private equity, some bond funds) inflate IR through stale pricing that suppresses apparent tracking error. Also IR measures consistency, not the size of the prize — a tiny IR on huge assets beats a big IR on a million dollars.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
What is a realistic Information Ratio?
IR vs Sharpe for choosing a fund?
Source & methodology
Grinold, R. & Kahn, J. (1999) Active Portfolio Management; Grinold (1989) "The fundamental law of active management".
Information Ratio: the number, the method and the caveats
You are here because you need a specific number, and you want to see how it was produced before you rely on it. Searchers land on an Information Ratio page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
Return and risk arrive as a package in Information Ratio. A result of 0.83 means little until you ask what variance sits behind it, how it compares with the boring alternative (the companion tools above compute exactly that comparison), and whether the inputs assume the good scenario or the expected one.
Every investing metric in Investing & Markets is a bet about the future priced in today’s language. Information Ratio translates that bet: IR = active return ÷ tracking error. The translation is mechanical and reliable; the judgement is deciding whether the inputs you feed it are honest forecasts or wishful numbers.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here information ratio = 0.83, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Tracking error first, at ≈11.1 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
The Information Ratio formula, explained
The engine implements a single stated model: IR = active return ÷ tracking error. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Active (excess) return vs benchmark (%) (example value 3) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
Tracking error (%) (example value 3) is a measured number in the unit named by the label; the formula does not convert for you. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
A fully worked Information Ratio example
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: Active (excess) return vs benchmark (%), Tracking error (%). The computed information ratio comes out at 0.83, which the interpretation band labels “Strong”.
- IR = 2.5% ÷ 3% = 0.83
- Fundamental law check: with breadth of ~100 independent bets per year, an average skill of 0.025 per bet yields IR ≈ 0.025×√100 = 0.25
Active return — 2.50 %. Compare it against the main result of 0.83 before drawing conclusions.
Tracking error — 3.00 %. Compare it against the main result of 0.83 before drawing conclusions.
Active return an IR of 0.5 implies — 1.50 %. Compare it against the main result of 0.83 before drawing conclusions.
Active return needed for IR 1 — 3.00 %. Compare it against the main result of 0.83 before drawing conclusions.
Overwrite any input above and every line of this example updates with it — the substitution is live, not a screenshot of somebody else’s numbers.
The Information Ratio corridor: your result across a realistic band
The honest version of any estimate is a corridor, and this one is cheap to produce: Information Ratio was recomputed at 5 points along the dominant input, with every other field left untouched.
| Tracking error setting | Information ratio |
|---|---|
| 80 % of the example value | 1.04 |
| 90 % of the example value | 0.93 |
| example inputs | 0.83 |
| 110 % of the example value | 0.76 |
| 120 % of the example value | 0.69 |
The corridor runs from 0.69 to 1.04 across that band — which is the range worth planning inside rather than the single 0.83 point estimate. Reproduce it in the form above by editing Tracking error alone; the substitution steps will follow your figures exactly.
What actually moves your Information Ratio
This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the information ratio was recalculated from scratch. It is the fastest honest answer to “what matters most in Information Ratio?” — and it is specific to these defaults, because sensitivity moves with the starting point.
- Tracking error +10 % → Information Ratio = 0.76 · −10 % → 0.93 (the information ratio shifts ≈11.1 %)
- Active (excess) return vs benchmark: ten percent up gives 0.92, ten percent down gives 0.75 — ≈10.0 % of the information ratio either way
Tracking error is the dominant lever here: ten percent either way moves the result by about 11.1 %, from 0.76 to 0.93. Spend your verification effort there first, and treat Active (excess) return vs benchmark as the second priority (≈10.0 % per ten percent). Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
What an Information Ratio of 0.83 means in practice
Nobody acts on a bare figure — they act on a figure with a comparison attached. The verdict attached to the default calculation — “Strong” — is one band on a ladder your own figure may sit somewhere else on. Before treating the band as fact, note its width: ten percent either way on Tracking error already spans 0.76 to 0.93. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.
Where Information Ratio sits next to similar measures: this tool answers “how much is the Information Ratio”, while the related calculators answer “what do I do about it”. Reading the result alone tends to over-weight a single snapshot; pairing it with the companion tools above turns the number into a decision.
Common mistakes with Information Ratio (and the fix)
Short samples and illiquid assets (private equity, some bond funds) inflate IR through stale pricing that suppresses apparent tracking error. Also IR measures consistency, not the size of the prize — a tiny IR on huge assets beats a big IR on a million dollars. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reading Information Ratio as a promise rather than a conditional. Every number here is of the form “if these inputs, then this result”; when an input moves, the honest response is to recompute rather than to defend the old figure.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Tracking error first, everything else second.
A final trap is presentation: reporting Information Ratio to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
A quick check for any Information Ratio figure
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Note the date and the inputs beside the number so the figure can be audited later.
- Re-verify Tracking error against a source document — ten percent of error there moves the headline by ≈11.1 %.
- Confirm the example values are plausible for your own situation before replacing them.
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Confirm the answer against one independent method or published benchmark for Information Ratio.
Step by step: getting a trustworthy Information Ratio out of this tool
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for Information Ratio takes a minute to learn and applies to every input above.
Step 1 — collect, then type. Everything this tool asks for is listed above (active (excess) return vs benchmark (%), tracking error (%)). Pull each figure from something you can point at — paperwork, a dashboard, a label — before touching a field. Guesswork at the input stage cannot be repaired at the interpretation stage. The test of a good estimate is not that it feels right but that it changes what you do.
Step 2 — start from the case you can defend. Enter pessimistic-but-honest figures first; the optimistic run is a ceiling, not a plan. A conclusion that holds on conservative inputs holds everywhere. A calculation you cannot explain to somebody else is a calculation you cannot defend.
Step 3 — measure what deserves measuring. The ranked levers above tell you where a careful figure pays off. Everything else can stay approximate, which is a licence to stop hunting for data you do not need. Estimates improve most when the worst case is written down in ink.
Step 4 — compute a corridor, not a point. Three runs — worst, likely, best — turn Information Ratio from a snapshot into a range. Plan against the pessimistic end and treat the optimistic end as the upside story. Numbers are cheap to produce and expensive to retract; the extra minute is the cheapest insurance available.
Step 5 — write the one-line memo. “Information ratio = 0.83 on this date, from these inputs.” That line is the entire deliverable; everything else on this page is how you earned the right to write it. The fastest sanity test is to guess the answer first and see how far the calculation disagrees.
The real-world conversations Information Ratio settles
Most Portfolio Management disagreements are arithmetic disagreements in disguise. When Information ratio of 0.83 sits on the table with its working shown, the conversation stops being about who sounds more confident and starts being about which input is wrong. One decimal place more than your data supports is one decimal place of fiction.
The audience for this page is people who need one trustworthy number before their next decision. In professional Portfolio Management practice the pattern is always the same — screen with Information Ratio, then invest slow analysis only in whatever survives the screen. The point of transparency is that a reader can disagree with a specific line rather than with you.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. A figure nobody can reproduce is a figure nobody will act on twice.
People also ask about Information Ratio
What affects Information Ratio the most?
Measured on this engine, Tracking error is the dominant lever: moving it ±10 % shifts the information ratio across 0.76 to 0.93 — about 11.1 % of the headline, with Active (excess) return vs benchmark next at ≈10.0 %. The ranked list in the sensitivity section is computed live, not asserted from a template.
Which matters more: active (excess) return vs benchmark or tracking error?
By the numbers, Tracking error: a ten-percent change in it moves the result ≈11.1 %, versus ≈10.0 % for Active (excess) return vs benchmark. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Information Ratio?
There is no honest universal average for Information Ratio, but there is a reference point: the defaults on this page produce 0.83 (Strong). Replace the inputs with your own and compare — that comparison is the useful meaning of “typical”.
How do I calculate Information Ratio by hand?
Three steps: write the formula (IR = active return ÷ tracking error), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about Information Ratio than any number of calculator runs.
What happens to Information Ratio if tracking error changes by 10 %?
On these inputs a ±10 % change in tracking error takes Information Ratio to 0.76 against the 0.83 example — around 11.1 % of the headline. The same test on every other field is in the sensitivity section.
Is the Information Ratio result of 0.83 good or bad?
Neither, on its own — the label “Strong” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Tracking error alone moves it by ≈11.1 %.
How often should I check Information Ratio?
Whenever an input changes by more than a few percent, and otherwise on the rhythm of your decisions — before a purchase, a review, a renewal. Information Ratio is a snapshot, not a subscription; stale snapshots are the most common silent error in Investing & Markets planning.
Can I predict future returns with Information Ratio?
No tool predicts; Information Ratio computes implications. Use it to ask "if these inputs hold, what follows" — then stress the assumptions.
What is a realistic Information Ratio?
For most mutual funds and hedge strategies, 0.1–0.4. The legendary Medallion-style figures are outliers by decades, not the bar to expect.
IR vs Sharpe for choosing a fund?
Benchmark-tracking mandate → IR. Absolute-return or blended holdings → Sharpe. They answer different questions.
Is my data private on this page?
Yes — computation happens locally in your browser, with no account, no tracking of your inputs and nothing sent to a server. The printed formula is the whole story of what happens to your figures.
What the labels on this page mean
- Active return
- 2.50 % in the default Information Ratio calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Tracking error
- 3.00 % on the example inputs — a companion figure printed beside the headline of Information Ratio so that no result is read in isolation.
- Active return an IR of 0.5 implies
- 1.50 % on the example inputs — a companion figure printed beside the headline of Information Ratio so that no result is read in isolation.
- Information ratio
- The primary output of this tool: 0.83 for the default inputs, computed exactly from IR = active return ÷ tracking error.
- Worked example
- Every substitution in order, from the example inputs to 0.83 — the audit trail for the headline figure.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Strong”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Information Ratio moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
The model is published arithmetic implemented exactly as written: IR = active return ÷ tracking error. Source of record: Grinold, R. & Kahn, J. (1999) Active Portfolio Management; Grinold (1989) "The fundamental law of active management". Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
Nothing here replaces a qualified professional, and nothing here is approximate either — the arithmetic is exact, and the honesty is in the inputs you supply. What that buys you is reproducibility — two people, two browsers, the same arithmetic, and a printed trail from inputs to result that either of them can audit.
After the number: what to do with it
Save the number together with its inputs and today’s date; an Information Ratio figure without context is unusable the moment an input changes.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.