Information ratio in mutual funds

The information ratio (IR) is a risk-adjusted performance measure that quantifies a mutual fund manager’s ability to generate consistent excess returns (active returns) above a benchmark, per unit of active risk taken (tracking error). It combines the concepts of alpha and tracking error into a single efficiency measure, rewarding managers who generate high excess returns consistently rather than those who occasionally produce large excess returns with high variability.

The information ratio is particularly relevant in the Indian context as it distinguishes skilled active managers from those who appear to generate alpha simply by taking concentrated sector or stock bets.

Formula

\[ \text{IR} = \frac{\overline{R_p - R_b}}{\text{TE}} = \frac{\bar{d}}{\sigma_d} \]

Where:

The information ratio is closely related to the Sharpe ratio: it is, in effect, the Sharpe ratio of the active return series, using the benchmark rather than the risk-free rate as the hurdle.

Worked example

An actively managed large-cap fund vs Nifty 100 TRI over 36 months:

• Average monthly active return (fund − benchmark): +0.20%
• Standard deviation of monthly active returns: 1.80%

Annualised active return: \(0.20% \times 12 = 2.40%\) Annualised tracking error: \(1.80% \times \sqrt{12} = 6.24%\)

\[ \text{IR} = \frac{2.40%}{6.24%} = 0.38 \]

This manager generates 0.38 units of excess return per unit of active risk, above zero, indicating skill, but below the 0.50 threshold that many institutional investors consider the minimum for a compelling active management case.

Interpretation benchmarks

A benchmark-matching (index) fund has an information ratio of zero by construction (no active return, near-zero tracking error, the ratio is indeterminate at the limit, but approaches zero).

IR and the tracking error relationship

The IR captures the return earned per unit of tracking error. Two managers with the same alpha may have very different IRs:

Manager A generates the same alpha as Manager B but with far lower deviation from the benchmark, indicating more disciplined, consistent active positioning. Manager B’s higher tracking error may reflect a concentrated or style-tilted portfolio that happened to work in the measurement period.

Optimal tracking error and the fundamental law of active management

Grinold and Kahn’s fundamental law of active management (1994) formalises the relationship between the information ratio, manager skill, and breadth:

\[ \text{IR} \approx \text{IC} \times \sqrt{N} \]

Where:

This law implies that a manager can improve the IR either by being more skilled (higher IC) or by making more independent bets (higher N). An Indian large-cap manager with a universe of ~100 stocks can improve N by making more granular active weight decisions. An ELSS fund manager restricted to 80% equity is further constrained.

The law also implies an optimal tracking error for a given IC, managers should not take so much active risk that tracking error swamps their IC.

Information ratio vs Sharpe ratio vs Jensen’s alpha

IR for index funds and closet indexers

A true index fund targeting zero tracking error will have an information ratio of zero or close to it (zero active return, minimal tracking error). More revealing is the IR of “closet indexers”, active funds that charge active management fees but hold portfolios very similar to the benchmark (low tracking error, low or no alpha). These funds typically have IRs near zero or negative because they generate minimal alpha with minimal active risk.

SEBI has noted the closet indexing problem. Funds with a tracking error below 2 per cent against their benchmark while charging active management TER are increasingly scrutinised by SEBI and investor advocates. The information ratio directly identifies such funds: high fee, low tracking error, near-zero IR.

Limitations

• Requires long data series: Reliable IR estimation requires at least 36–60 months of data. With 36 monthly observations and typical tracking error of 4–6 per cent, the standard error of IR is approximately 0.2–0.3, meaning an observed IR of 0.40 is not statistically different from zero at the 95% confidence level.
• Sensitive to benchmark choice: A fund benchmarked to an easy-to-beat index (e.g., a multi-cap fund using Nifty 50 as benchmark instead of Nifty 500) will show an inflated active return and IR. SEBI’s category rationalisation of 2017 mandated appropriate benchmarks, reducing this problem, but benchmark gaming persists.
• TER drag reduces IR: Every percentage point of TER reduces the fund’s net active return without changing tracking error, systematically reducing the IR. Direct plan investors receive a meaningfully higher IR than regular plan investors in the same fund.

See also

• Tracking error in index funds
• Alpha (Jensen’s alpha) in mutual funds
• Sharpe ratio in mutual funds
• Treynor ratio
• Total expense ratio
• Total return index benchmarking
• Mutual fund

References

1. Treynor, J. L. and Black, F. (1973). “How to Use Security Analysis to Improve Portfolio Selection.” Journal of Business, 46(1), 66–86.
2. Grinold, R. and Kahn, R. (1994). Active Portfolio Management. McGraw-Hill.
3. Goodwin, T. H. (1998). “The Information Ratio.” Financial Analysts Journal, 54(4), 34–43.
4. AMFI, Scheme factsheet disclosures, amfiindia.com.
5. PrimeInvestor, Information ratio analysis for Indian mutual funds, primeinvestor.in.