It is a terrible mistake for investors with long-term horizons – among them, pension funds, college endowments and savings-minded individuals – to measure their investment ‘risk’ by their portfolio’s ratio of bonds to stocks. Often, high-grade bonds in an investment portfolio increase its risk.
~ Warren Buffett, letter to the shareholders of Berkshire Hathaway – February 24, 2018
Before starting, we must define an end goal. Commonly, the initial, singular objective is to maximize performance. This answer is legitimate but raises additional questions.
The first question involves consistency of performance. Certain strategies such as trend-following have desirable risk properties but are intermittent in their returns, while strategies such as option selling may tend to produce consistent returns over most periods but occasionally experience large, sudden draw-downs. Optimizing for performance typically implies that you are optimizing for the average performance over the sample period, but this metric doesn’t account for the year-to-year variability around the average. The importance of consistency depends largely on the time horizons of both the portfolio designer and the investors. Shorter time horizons demand greater consistency of returns.
Another question is that of style, or desired correlation to a benchmark. Alternatively, you may wish to minimize correlation specifically to a particular benchmark. Many portfolio designers seek to replicate the style of trend-followers, yet also improve on the risk-adjusted performance, i.e., they seek “alpha” as well as “beta” (see “Manager lingo,” below). Other portfolios have become popular. For example, an index comprising short-term traders has been developed to reflect a uncorrelated return stream to standard trend-following benchmarks.
Additional and often overlooked objectives include optimizing for various return statistics, including skewness, kurtosis and draw-down measures. Such objectives can be difficult to incorporate into the optimization process accurately. For instance, even though many believe that draw-downs can be bounded a priori and that risk-management methodologies can be separated from the trading program itself, two primary determinants of draw-down magnitude are program style and time. Longer-lived programs generally will have experienced larger peak-to-valley draw-downs, reinforcing the adage: “Your worst draw-down is always ahead of you.” Hence, optimizing for maximum draw-down is an exercise in futility….
via The DNA of a diversified portfolio – Managed Funds – Futures Magazine.
