Switching Horses Mid-Race: How to know when riding different betas creates alpha
| Aug 10th, 2010 | Filed under: Guest Posts, Performance, Analytics & Metrics, Today's Post | By: Guest |
|
When you think about it, the idea of an investment benchmark is somewhat philosophical. Although many hedge fund managers profess to deliver uncorrelated “absolute” returns – not relative returns – a large portion of them compare their results to equity markets. Why? Not necessarily because they manage to that bogey, but because hedge fund allocations are often made by diverting capital from equity allocations. In other words, equities are the most likely alternative. Today’s guest contributor says that the choice of benchmark can make or break a “horse-switching” manager.
To continue reading this article please login (at the right) or click here to learn more about accessing our archives.
Very interesting and thorough post – thanks. I completely agree with the concept of reducing the manager compensation based on their Information Coefficient. And this concept gives useful leads to evaluate the merits of dynamic asset allocations.
One question regarding your calculation of the Information Ratio, as you mention it is based upon geometric returns, do you have an exact formula for the ratio calculation. Or the underlying concept of how it is calculated.
Not wanting to use arithmetic returns myself, I have devised a formula for a (beta-adjusted) “Geometric Information Ratio”, mostly using common sense and would be interested in checking how it compares with your methodology.
The formula for “my” Geometric Information Ratio can be found here:
http://www.automated-trading-system.com/geometric-information-ratio/
hope you find it interesting – and thanks in advance for any feedback.
The methodology presented here is similar to using random portfolios for performance measurement. That’s explained, for instance, at:
http://www.portfolioprobe.com/about/random-portfolios-in-finance/
I suspect that the “inertia benchmark” is a better performance measure than what is almost always done now, but I agree that it has problems. There is a blog post on that at
http://www.portfolioprobe.com/2010/08/19/a-performance-step-beyond-economists-hubris/
Patrick
There are some similarities. One difference is certainly the underlying benchmark distribution. This methodology encompasses an active benchmark as opposed to a passive and this may be quite different and would require further work to verify. I believe methodology becomes a random portfolio methodology in the limit case of continuous switching with degrees of freedom = all combinatorials of assets. In that sense your random portfolio methodology is the unconstrained special case of my methodology.
Regards,
Eric