Mutual Fund Analysis with the Information Ratio

Published: May 12, 2016 at 6:16 am

Last Updated on September 4, 2018

Understanding the notion of a risk-adjusted return requires some maturity to think beyond mere returns. While comparing funds, a fund that offers a lower return with lower risk is better than one with higher return with higher risk.

There are many risk-adjusted measures and one of them is the information ratio.  The information ratio is a measure of how effective the fund manager is in beating the benchmark.

The information ratio has always been part of the mutual fund risk-return analyzer. I started to pay more attention to it when Dr. Uma Shashikant remarked at FB group, Asan Ideas for Wealth in a thread on star ratings: “Information ratio is my key quantitative indicator”.

So that got me curious and I included a new sheet which plots the information ratio for each period in the year-on-year risk-return analyzer

In this post, let us look at what the information is, how it can be interpreted, and what are its limitations. First published Apr 26 2015. Republished now with updated graphs.

What is the information ratio? It is a measure of outperformance per unit risk associated with the outperformance.

How is the Information ratio calculated?

To calculated the information ratio, the follow steps are necessary:

  1. Calculate daily or month returns of the fund and benchmark (I use daily returns) for a given duration.
  2. Calculate the difference between the two. This is known as the excess return (we hope it will be positive!)
  3. Calculate the average of the return difference for the duration. Call this Avg
  4. Calculate the standard deviation of the return difference. Call this Stdev. This is a measure of how much individual excess returns deviate from the average.
  5. Information ratio = Avg/Stdev

Or it is the average excess return divided by volatility associated with the excess return.

In contrast, the Sharpe ratio also calculates excess return per unit average risk but with respect to a fixed risk-free return.

Higher average excess returns and lower volatility (standard deviation) are desirable. So higher the information ratio, the better.  Since the ratio depends on the duration considered, it is difficult to say what value is good.

In general, a high positive value is acceptable.

A negative information ratio implies that the numerator, the average excess return is negative. The denominator, the standard deviation is always positive.

Here is an example:


The top graph is for different investment durations: 1 year to 8 years. The bottom graph represents year-on-year information ratios.

For all investment durations, Quantum Long Term Equity has consistently produced excess returns at minimal risk.For a few years (bottom graph), the fund has not been able to produce excess returns (on average).

However, the information ratio has a flaw. A fund can have negative information ratio and could have still beat the benchmark comfortably or a fund with positive information ratio could have lagged behind the benchmark.

Take the case of HDFC Top 200. The SIP and lump sum returns for different investment durations are plotted below.


Although it does not make for spectacular viewing, HDFC Top 200 has managed to beat BSE 200 (excluding dividends!!) on many occasions.

Yet all the information ratios are negative!


This is misleading as it suggests that HDFC Top 200 never produced excess returns wrt BSE 200 in the past 8 years.

This is because the information ratio considers the arithmetic average of excess returns and ignores the way in which the excess returns can compound and produce alpha.

The so-called geometric information ratio is the way out of this discrepancy.

The geometric information ratio for HDFC Top 200 is computed below


For all investment durations (except the last Y – top graph) the geometric information ratio is positive.The geometric version is a better estimate of risk-adjusted returns.


Download the information ratio calculator (the format is the same as the risk-return analyser but only the returns and information ratio are computed here)

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About the Author Pattabiraman editor freefincalM. Pattabiraman(PhD) is the founder, managing editor and primary author of freefincal. He is an associate professor at the Indian Institute of Technology, Madras. since Aug 2006. Connect with him via Twitter or Linkedin Pattabiraman has co-authored two print-books, You can be rich too with goal-based investing (CNBC TV18) and Gamechanger and seven other free e-books on various topics of money management. He is a patron and co-founder of “Fee-only India” an organisation to promote unbiased, commission-free investment advice. He conducts free money management sessions for corporates and associations on the basis of money management. Previous engagements include World Bank, RBI, BHEL, Asian Paints, Cognizant, Madras Atomic Power Station, Honeywell, Tamil Nadu Investors Association, IIST Alumni Association. For speaking engagements write to pattu [at] freefincal [dot] com
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