One of my formative physics memories is watching Prof. Donald H. Perkins derive the Rutherford Scattering cross-section formula on half a blackboard in about five minutes. His derivation was based on dimensional arguments and physical principles, and came just one week after my graduate class in Oxford had derived the very same formula from first priciples, which took several hours and pages of dense algebra. Prof. Perkins class was about phenomenology, which means it was about what happens in nature and his lesson to me was that Physics is not Applied Mathematics. That what happens in nature is due to the structure of the universe and not because of the way the math works out.

When solving the Markowitz Mean-Variance efficient investment problem one is lead to the portfolio defined by the product of the inverse of the covariance matrix into the vector of the asset return forecasts. So let's follow Prof. Perkins' lead and ask what this equation tells us about the principles of how we should structure a portfolio.

First of all, remember that the covariance matrix is required to be a symmetric positive definite matrix. What this means is that it can be diagonalized by a similarity transformation and that the diagonal terms of the resultant matrix are positive quantities. (A little linear algebra reveals that the transformation matrix is the matrix of eigenvectors of the covariance matrix and the diagonal terms are the associated eigenvalues.)

From a statistical point of view what we have done is rotated into a new coordinate system in which our set of original correlated random variables have been replaced with new variables, each one formed from a linear combination of the original variables, which are all statistically independent. The new, transformed, matrix represents the covariance matrix of these new variables.

Many authors now declare that these new variables are the actualdriving factors behind the variance of our original portfolio and that each asset has a factor loading on to the factors which are the real sources of portfolio variance.

I would not go so far. Mathematically, any symmetric positive definite matrix, whatever its source, can be decomposed in this manner, so I feel unwilling to add any interpetational overhead where it is not neccessary. I am not saying that factor models do not exist, what I'm saying is that all covariance matrices can be diagonalized, with or without the existence of factors, so the fact that a particular matrix can be treated in this manner doesn't actually contain any new information. We will use the common term principal components to refer to the independent variables we have produced, but need to go no further that that.

This philosophical diversion notwithstanding, the mathematics is fairly straightforward. When we transform to the principal components coordinate system we move from a system in which each axis represents a particular asset to one in which each axis represents an independent portfolio. The vector of asset forecasts is similarly transformed into a vector of portfolio forecasts. The payoff is that the covariance matrix has become a trivial diagonal matrix and, even more usefully, the inverse of the covariance matrix is simple a that matrix with the diagonal elements replaced by their reciprocals. The product of the inverse covariance matrix with the forecast vector then becomes a simple vector where each element is the ratio of the forecast to the variance of each component portfolio.

So the big question is why is this the right portfolio? The answer comes when we consider what the expected profit for each component portfolio is. This is just the product of the forecast and the holding which is the ratio of the square of the component forecast to the component variance. This is interesting because it is dimensionless and structure free (by which I mean that the formula is the same for every component independent of the component label). We are diversified because we are treating each component equally --- it's not due to the fancy mathematics, but it is clearly the right answer.