MODERN PORTFOLIO THEORY

No view of contemporary analysis can be complete without at least a passing acquaintance with Modern Portfolio Theory—MPT. MPT, simply, is a scientific approach to understanding the market value of a security. While there is very little a company can do, beyond dealing with analytic fundamentals, to influence portfolio analysis using Modern Portfolio Theory, the increasing use of MPT warrants at least a minimal understanding of it. Essentially, Modern Portfolio Theory is predicated on a concept that the degree of investment risk should be measured in terms of potential reward for that risk. But it also takes as its premise the concept that the greater the range of uncertainty about a stock, the greater the risk. Portfolio diversification is not a new idea, nor is any form of spreading risk. The aim here, though, is not merely to diversify, but to do so with a balance of stocks with varying degrees of risk, and therefore varying likelihood of performance, so that the average uncertainty of the total portfolio —and therefore the average of the portfolio’s risk—is diminished in relation to potential return.

For example, in a two-stock portfolio, if both stocks perform in the same way in response to the market itself, there is no real diversification. If, however, each responds differently to market forces, then you do have diversification. But not necessarily the best diversification, unless the potential performance of one effectively hedges, or acts opposite to and offsets, the potential performance of the other.

Measuring potential performance, and thereby potential risk-return, is done with a series of complex mathematical functions, but the basis is still a judgment of fundamental analysis of the elements of a company’s potential. Beyond that, however, portfolio analysis becomes complex.

The aim is to build an efficient portfolio, one in which the balance of potential performance of all the stocks in the portfolio is one of minimum uncertainty. Taken into account are two major elements of risk—the risk in the individual stock and the risk inherent in the market itself, keeping in mind that not all stocks react or perform in the same way in response to the market at any given moment. Using the Standard & Poor’s 500 Stock Price Index as a basis, price fluctuations—the measure of risk used—are broken down into the two risk elements (market and individual stock). The statistical technique, regression analysis, is used to measure the potential risk. A complex mathematical technique, it measures functional relationships between two or more variables, particularly where a variable (such as a price/earnings ratio) is measured against another variable (such as a market index).

Put simply, the term beta is used to indicate the measure of a stock’s volatility, relative to the volatility of the market during the same period. The higher the beta, the higher the volatility; the lower the beta the more stable. A beta of one means that the stock performs exactly as the market does. The term alpha is used to indicate the measure of average rate of return, in the same period, independent of the market return. A portfolio that matches the alpha and beta of the S&P 500 should— and generally does—perform about the same as the S&P Index, and indeed many index funds (funds designed to match the Standard & Poor’s 500) have been started based on the concept. However, there is a serious question in the minds of many professional investors, particularly institutional investors, whether indexed return, rather than one that outperforms the market, is sufficient.

In the several years since the theory was developed by the statistician Dr. Harry M. Markowitz, it has grown in popularity among analysts. But even its strongest advocates warn that it is a theory with a great deal yet to be developed and proven, and more significantly, that it is only one tool of many that should be used by analysts. It does not portend, in the foreseeable future, eliminating all analysts and replacing them with computers.