Concept

Information Ratio

Information Ratio is a Performance, Backtesting & Validation concept. First implementations are in the build queue: the write-up leads, the indicators follow.

What is the information ratio?

The information ratio (IR) measures benchmark-relative skill: it divides a strategy's active return, its return minus the benchmark's, by the tracking error, the standard deviation of that same difference. Where the Sharpe ratio asks how much reward a strategy earns per unit of total volatility over cash, the information ratio asks how much outperformance it earns per unit of deviation from its benchmark.

The measure exists because beating a benchmark by taking wildly different risk is easy, while beating it consistently with controlled deviation is hard. A high IR means the departures from the benchmark were productive and steady rather than lucky and erratic. In institutional settings it is the canonical score for active managers, and it connects to the fundamental law of active management, which links IR to the product of skill per decision and the number of independent decisions taken.

Traders care because the same logic applies whenever a strategy claims to improve on a passive alternative: a stock system should be judged against buy-and-hold of its index, a crypto rotation system against holding BTC or a market-cap basket. Reporting absolute returns while ignoring what the benchmark did over the same window is one of the most common ways backtests flatter themselves, a failure of benchmark comparison discipline.

How it's calculated

The information ratio is active return divided by the variability of active return.

active return_t = R_p,t - R_b,t
IR = mean(active return) / tracking error
tracking error = standard deviation of active return
R_p,t: portfolio or strategy return in period t
R_b,t: benchmark return in period t
tracking error: sample standard deviation of the period-by-period active returns

Annualize by multiplying a per-period IR by the square root of periods per year (e.g. sqrt(12) for monthly data).

Some definitions use regression alpha over residual risk instead of raw active return over tracking error; results differ when the strategy's beta is far from 1.

How traders use it

  • Active-management evaluation: institutions rank managers by IR because it rewards consistent outperformance rather than outperformance bought with large benchmark deviations; values near 0.5 sustained over years are commonly considered good, and above 1.0 exceptional.
  • Backtest honesty: retail system developers compute IR against buy-and-hold to test whether the strategy's complexity actually adds anything beyond owning the underlying market, particularly in long-biased equity and crypto systems.
  • Signal comparison: when several signals or models target the same market, comparing IRs against the common benchmark identifies which one converts deviation into outperformance most efficiently, complementing alpha estimates.
  • Limitations: the IR is entirely benchmark-dependent, so a mismatched benchmark renders it meaningless, and like the Sharpe ratio it assumes reasonably well-behaved return distributions and enough observations for the mean estimate to be trustworthy.

Information ratio vs. related concepts

Sharpe Ratio: Sharpe uses the risk-free rate as the reference and total volatility as risk; the IR uses a market benchmark as the reference and tracking error as risk. Sharpe scores the strategy in isolation, IR scores it as an active bet against an alternative.

Alpha: Alpha is the size of the benchmark-adjusted excess return; the information ratio is that excess scaled by its own variability. A strategy can have positive alpha but a poor IR if the outperformance arrives erratically.

Relative Strength Comparative: A comparative RS line charts the ratio of an instrument to its benchmark for visual analysis; the information ratio is the statistical summary of the same relationship, one number instead of a curve.

Related concepts · Return/risk metrics

Sharpe Ratio1CAGR0Sortino Ratio0Calmar Ratio0Extended Risk-adjusted Ratios0Drawdown Statistics0Martin Ratio0SQN0Significance of Returns0

Concept family

Performance, Backtesting & Validation

30 concepts mapped · 30 in the Library

Information Ratio FAQ

What is a good information ratio?

Rules of thumb from institutional practice call roughly 0.5 good and 1.0 excellent when sustained over multiple years. Short backtests routinely print far higher values that regress sharply out of sample.

How do I choose the right benchmark?

Use the passive alternative an investor would actually hold instead of the strategy: the traded market's index for equity systems, BTC or a broad basket for crypto. Choosing a weak benchmark inflates the IR and defeats its purpose.

Can the information ratio be negative?

Yes, whenever average active return is negative, meaning the strategy underperformed its benchmark. A negative IR over a meaningful sample is a strong argument for holding the benchmark instead.

Is the information ratio just Sharpe with a different baseline?

Structurally yes: both are a mean excess return over its standard deviation. The practical difference is what question they answer, absolute reward for risk versus skill relative to an investable alternative, and a strategy can rank well on one and poorly on the other.

Build Information Ratio your way.

Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.