Maybe you would be confident to play a game of horse against him or a round of golf, but would you have the same confidence to bet against him in investing?

Ted Seides, CFA, President and Co-CIO at Protégé Partners did just that. He bet Warren Buffett back in 2007 that hedge funds would outpace the S&P 500 over the next ten years. Mr. Buffett has been on the better side of this bet for the past seven years, as the S&P 500 has outpaced hedge funds. Will this continue for the next three years?

Have you ever found yourself searching for an asset class to use in the Black Litterman model, only to find we unfortunately don’t provide the market capitalization for that asset class? Well, just because you don’t have the market cap at your fingertips does NOT mean you can’t utilize the advantages of Black Litterman. Here’s a tip how you can utilize both the Black Litterman and Historical Models to build your efficient frontier and allocation when using asset classes that you don’t have market caps for in Zephyr AllocationADVISOR.

AllocationADVISOR users are frequently puzzled by the results of their analysis if they select managers rather than market indices at the inputs to a mean variance optimization (MVO). The goal of optimizing active managers is the same as it is for optimizing asset classes, that is, to maximize return and minimize risks. However, the results tend to lead to recommendations that no one in their right mind would follow.

Occasionally we will get questions at Zephyr Associates about the viability of including private equity and/or venture capital (PE/VC) into a mean-variance optimization (MVO).  Ultimately it will be up to you whether or not you think it makes sense, but I am of the opinion that a simple, “naïve” allocation is a better approach than trying to optimize PE/VC into the efficient frontier.

Much of this has to do with the nature of the asset class itself.  Data for private equity is hard to come by, and what you cannot measure you cannot model.

Harry Markowitz’s idea of creating “optimal” portfolios that maximize return and minimize risk has been with us for a very long time. For many people, the manifestation of the idea of “optimal” portfolios is the efficient frontier- those portfolios that by mathematical definition have the most return per level of risk or conversely the least amount of risk per level of return.

Bruce Berkowitz’s Fairholme Fund earned big headlines at the close of Q1, as his stock picking prowess was cited as the source of a 31% return for the quarter and presumably put his fund back on track to continue its long term outperformance versus the S&P 500. How do we get under the hood of Fairholme to understand this relatively unusual fund a little better?

One of the problems with traditional Monte Carlo projections is that it is very iterative process. After establishing the investor’s wealth goals and cash flows, a single portfolio with a forecasted return and risk is chosen. The Monte Carlo process is run and thousands of possible outcomes are calculated. The probability of achieving the client’s goals is presented- for that one portfolio. If the probability of that portfolio reaching the goals is unsatisfactory, it’s back to the drawing board and the whole process is repeated for a different portfolio.

A common question people have when using the Black-Litterman model is: How do I add alternative asset classes to my line-up of traditional asset classes? There are arguments both for and against the idea of using alternative asset classes in a Black-Litterman framework. I discussed the topic at our 2007 Annual Client Conference and the presentation is on-line.