While I doubt that they were aware of it, when Franco and Leah Modigliani developed their risk-adjusted return measure, M-squared, they were extending an idea first promulgated by Peter Dietz in his 1966 thesis, from which we obtained the notion of time-weighting and the Dietz return formulas.
Peter recognized that return without risk didn’t show the full picture. But, if we are comparing two managers or a manager with his benchmark, even when risk is shown it’s difficult to draw any conclusions when the two return and risk measures are different. For example, if Manager A has a return of 3.00% and the benchmark has a return of 2.95%, and the manager’s standard deviation is 1.02% vs. 0.98% for the benchmark, what can we conclude? We must somehow bring these numbers together.
Dietz felt that if the portfolio and benchmark had the same return, then we can compare their risks, or vice versa, but as long as they were different we had a problem. Well, since then we’ve seen the development of numerous risk-adjusted measures that are able to handle this situation.
Franco and Leah, however, implemented Dietz’s idea, so to speak, by equalizing the risk measures so that we end up with a simple comparison of returns. Theirs is the most intuitive of all the risk-adjusted measures and the one I champion the most.
To learn more about risk-adjusted returns, join us on August 19 for our next webinar. And, to learn more about M-squared, I suggest you read my article: “M-squared: A Double-take on Three Approaches to a Primary Risk Measure,” The Journal of Performance Measurement, Summer 2007.