The Predictive Power Of Information Ratios

The Predictive Power Of Information Ratios by AlphaBetaWorks Charts

Our earlier work showed that simple performance metrics, such as nominal returns and Sharpe Ratios, revert. Because of this reversion, above-average past performers tend to become below-average and vice versa. This reversion is primarily due to systematic (factor) noise. Consequently, metrics that remove factor effects from performance reveal persistent stock picking skill. Prompted by readers’ questions, we have investigated the predictive power of popular performance metrics. This article reviews the predictive power of information ratios. They offer a large improvement over simple nominal returns, naive alphas, and Sharpe ratios, but still fall short of the most predictive metrics. Over a 3-year window, the predictive power of information ratios for skill evaluation and manager selection is approximately half that of security selection distilled with a statistical equity risk model.

Measuring the Predictive Power of Information Ratios

We analyze portfolios of all institutions that have filed Forms 13F in the past 15 years. This survivorship-free portfolio dataset covers firms that have held as least $100 million in long U.S. assets. Approximately 5,000 portfolios had sufficiently long histories and low turnover to be analyzable.

Mangrove Partners Narrowly Avoids “Extinction-Level Event”

Nathaniel August's Mangrove Partners is having a rough 2020. According to a copy of the hedge fund's August update, a copy of which ValueWalk has been able to review, for the year to August 5, Mangrove Funds have returned -38%. Over the trailing 12-month period, the funds returned -44%. The S&P 500 produced a positive Read More

To measure the persistence of performance metrics over time, we compare metrics measured in two 12-month periods separated by variable delay. One example is the 24-month delay that separates metrics for 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. A 24-month delay of 12-month metrics thus covers a 48-month time window. We use Spearman’s rank correlation coefficient to calculate statistically robust correlations.

Serial Correlation of Information Ratios

The information ratio is similar to the Sharpe ratio, but with a key upgrade: Sharpe ratio evaluates returns relative to the risk-free rate. Information ratio evaluates returns relative to a (presumably appropriate) benchmark. We use the S&P 500 Index as the benchmark, following a common practice. As a benchmark increasingly matches the factor exposures of a portfolio, information ratios converge to the standard score (z-score) of active returns estimated with a capable equity risk model. Due to the more effective handling of systematic risk, the predictive power of information ratios receives a boost.

The chart below shows correlation between 12-month Information Ratios calculated with lags of one to sixty months (1-60 month delay):

13F Equity Portfolios: Serial correlation of Information Ratios

Delay (months) Serial Correlation
1 0.06
6 0.05
12 0.03
18 0.05
24 0.06
30 0.06
36 0.02
42 -0.02
48 -0.06
54 -0.04
60 0.02

Over the 3-year window, the serial correlation (autocorrelation) of Information Ratios is approximately half of the serial correlation of security selection returns provided in the following section. Unlike simple nominal returns and Sharpe ratios, information ratios do not suffer from short-term reversion.

Serial Correlation of Nominal Returns

For comparison, the following chart shows serial correlation of 12-month cumulative nominal returns calculated with 1-60 month lags. As we discussed in prior articles, these revert with an approximately 18-month cycle – so strong past nominal returns are actually predictive of poor short-term future nominal returns:

Information Ratios

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months) Serial Correlation
1 -0.14
6 -0.24
12 -0.33
18 -0.06
24 0.16
30 0.23
36 0.08
42 -0.26
48 -0.44
54 -0.23
60 0.13

Serial Correlation of Security Selection Returns

As we mentioned above, when a benchmark’s factor exposures match those of the portfolio, information ratio is equivalent to the standard score (z-score) of active returns estimated with a capable equity risk model. In practice, however, information ratio is typically calculated relative to a broad benchmark, such as the S&P 500 Index for equity portfolios. Consequently, one would expect the predictive power of information ratios to be lower than the predictive power of security selection returns, properly estimated. For comparison, we provide serial correlation of a security selection metric that uses an equity risk model to control for factor exposures.

To eliminate the disruptive factor effects responsible for performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. Alpha Return is the return a portfolio would have generated if all factor returns had been zero. The following chart shows correlation between 12-month cumulative AlphaReturns calculated with 1-60 month lags:

Information Ratios

13F Equity Portfolios: Serial correlation of Alpha Returns (risk-adjusted returns from security selection)

Delay (months) Serial Correlation
1 0.08
6 0.10
12 0.08
18 0.05
24 0.06
30 0.04
36 -0.02
42 -0.04
48 -0.05
54 -0.04
60 -0.02

The predictive power of Alpha Returns, as measured by their serial correlation of 12-month performance metrics, is approximately twice that of information ratios over a 3-year window (12-month delay between 12-month performance metrics), but the two begin to converge after three years.

For all performance metrics, the above data is aggregate, spanning thousands of portfolios and return windows. Individual firms can overcome the averages; however, the exceptions require especially careful monitoring.


  • The predictive power of information ratios is significantly higher than that of nominal returns and Sharpe ratios.
  • As a benchmark converges to the factor exposures of a portfolio, information ratios converge to the standard score (z-score) of active returns estimated with a capable risk model.
  • Over a 3-year window, the predictive power of information ratios, as commonly calculated, is approximately half that of the security selection return calculated with a predictive equity risk model.

The information herein is not represented or warranted to be accurate, correct, complete or timely.

Past performance is no guarantee of future results.

Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.

Content may not be republished without express written consent.

U.S. Patents Pending

Previous articleUnprecedented Global Bond Bubble Threatens Holders Of Cash
Next articleJensen Quality Growth Fund – Why We Really Like (The Other) Alphabet
AlphaBetaWorks provides risk management, skill evaluation, and predictive performance analytics. Developed by finance and technology veterans, our proprietary platform combines the latest advances in financial risk modeling, data processing, and statistical analysis. Our Risk Analytics are more robust than alternatives and our Skill Analytics are predictive. Risk Analytics AlphaBetaWorks pinpoints risks missed by other offerings and delivers unique insights. AlphaBetaWorks Risk Analytics were developed by investment professionals seeking usability and a deeper understanding of portfolio exposures. Predictive Performance Analytics Starting with robust, proprietary risk models, AlphaBetaWorks adds layers of attribution and statistical analysis. Our Skill Analytics describe a multitude of specific skills that are strongly predictive of future returns for any fund, manager, or analyst with a sufficient sample of investment history. The AlphaBetaWorks Advantage Our Risk and Performance Analytics provide unique insights: For portfolio managers, we identify overlooked exposures, hidden risk clusters, and crowded bets. Managers can focus on risks in areas where they have proven ability to generate excess returns and avoid undesired risks in areas where they do not. For fund allocators, we identify the skills, crowding, and hidden portfolio bets of individual funds and portfolios of funds. Allocators can identify differentiated and skilled managers that are deploying capital in areas of proven expertise – and more importantly, those that are not. Background As finance professionals, we spent the last decade focused on fundamental investment analysis and the study of great (and seemingly great) investment managers. We asked of ourselves: Where are the unintended risks in a portfolio? What is the chance that a manager possesses true investment skill and was not just lucky? Does investment skill persist and is past skill a predictor of future results? There was no product, service, or technology that rigorously and consistently answered these questions. With decades of fundamental investment analysis, risk management, mathematics, and technology expertise, AlphaBetaWorks professionals have developed risk and skill analytics to address these and related questions.