black littermam -- my brief notes

http://www.blacklitterman.org/what.html is a brief description.

First, forget about optimizer, capm, MeanVariance, or views. Let's first get a grip on Bayesian inference. Given an unfair coin, we are trying to estimate the Pr(tail) by tossing it over and over. There's uncertainty (a pr distribution) about the mean.

Updating estimates -- Now we can formulate the problem as how to update our estimate of expected return when we get some special insight (like insider news). Such an insight is called a subjective "view", in contrast to the public information about the securities. The updated estimates must be numbers, not some vague preference.

Optimizer -- Once we get the updated estimates, they go into a regular portfolio allocation optimizer.

Problems of MV -- concentration. Small change in the input (returns, covariance etc.) leading to drastic re-alloations.

The investor is uncertain in their estimates (prior and views), and expresses them as distributions of the unknown mean about the estimated mean. As a result, the posterior estimate is also a distribution.