How did standard deviation distort the manager's performance? Part of the answer lies in the two hatched areas at the tails of the curves in Chart 2. Forcing symmetry on the manager's returns creates "phantom" poor returns in the left tail of the distribution. Standard deviation needs these artificial values to offset the disproportionate number of good returns achieved by the manager on the upside. Similarly, some of the manager's best returns from the right tail must go unrecognized. This is shown in the hatched area in the right tail.
Given that non-normality in investment returns is so prevalent and that it can affect some very important investment decisions, what is the best way to measure these asymmetrical risk patterns? The answer is Post-Modern Portfolio Theory, the method used in the preceding example. PMPT, which is gaining acceptance with institutional investors worldwide, replaces standard deviation with downside risk, which differentiates between upside and downside variability. In so doing, it treats as risky only those returns that have fallen below some target or benchmark return. Downside risk works equally well with both normal and non-normal distributions.
With the advent of increasingly powerful computer technology, a new generation of analytical tools is available to investors. These tools have everyday applications in performance measurement and asset allocation for investments with both normal and non-normal return patterns. Their most important benefits to investors are more accurate and better information for making better investment decisions.