SHARPE RATIO VS. INFORMATION RATIO: LIES, DAMN LIES AND STATISTICS By Janus Portfolio Construction Services Team
For advisors familiar with our Portfolio Construction Services reports, you know the wealth of data and statistics available for review. For those unfamiliar, our reports range between 38 and 40 pages of comprehensive portfolio analysis. Here on the team, like any great rock ’n’ roll band, we play the hits when we are reviewing reports with clients. We focus our time on aggregate, model-level observations and then move to data that concerns the underlying constituent managers.
The time we spend on aggregate observations contains many of the stats with which most advisors are familiar. It is our view that no one statistic alone should be used to judge a portfolio. And since that makes sense intuitively, the question becomes which stats should we look at together?
Two of the stats we share with advisors as related are the Sharpe ratio and information ratio. Many advisors are well versed in the Sharpe ratio as a measure of risk-adjusted returns; therefore, they put a lot of emphasis on the statistic when judging their own portfolios. One of the challenges for the Sharpe ratio, given the current market environment of low interest rates, is it uses a risk-free rate of return in its numerator. Since the risk-free rate has been close to zero over the last five years, maybe the Sharpe ratio shouldn’t be the “be all, end all” stat for judging a portfolio’s performance. So in PCS, we pair our Sharpe ratio diagnosis with the information ratio.
Fewer investors are familiar with the information ratio and its uses, but it is fairly straightforward. Mathematically, the information ratio is ER/TE where ER equals excess return and TE equals tracking error – which is the standard deviation of the difference between the returns of the portfolio and those of the index. Colloquially, the information ratio is a measure of the consistency with which a model generates alpha (performance above a given index on a risk-adjusted basis) over a set time period.
If one steps back and thinks about their model from a 20,000-foot. level, the more consistently the model generates alpha (as measured by the information ratio) the more outperformance we should see over that same set time period. For us, pairing the information ratio with the Sharpe ratio in our diagnosis allows us not only to measure the risk-adjusted return of an overall model but also the consistency with which the model generates risk-adjusted outperformance (alpha), which is just as important for wealth creation long term. Understanding these two stats can provide us a well-rounded view of an aggregate portfolio and enable us to more effectively monitor our performance as we seek to generate risk-adjusted returns and consistently generate alpha over time for our clients.
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