Portfolio construction is largely an exercise in compromise. Further complicating matters is that critical decisions often need to be made based on limited data. Before the 2008 financial crisis, for example, equity-based hedge fund strategies often touted themselves as having low correlation to long-only equity indices. In the low-volatility markets of the mid-2000s, this often was true. But what wasn’t realized at the time is that this was based on a placid global environment. When market conditions became turbulent, a different relationship was exposed: These strategies were all predicated on being short volatility, meaning that any panic would be short-lived in time and bounded in magnitude.
In a short volatility world, mean reversion would restore order soon and, thereby (in a self-fulfilling feedback loop), reward the strategies whose profitability depended on its existence.
In 2008, however, disorder became the rule. As a result, long volatility strategies, such as trend-following, were the only programs to profit systematically because they were the only strategies that were able to short the global economy (see "Diversify with crisis alpha," February 2011).
The designer truly needs to understand the component strategies in the portfolio. But most track records are too short to have experienced the gamut of possible macro-economic conditions. Hence, the portfolio engineer must have sufficient knowledge of the portfolio’s constituent strategies. Once armed with this knowledge, the engineer can make the necessary trade-offs and maximize portfolio fitness.
However, not all trade-offs are created equally. Idiosyncratic risk is a rare example of an objective with a fairly painless trade-off. We define idiosyncratic risk as the potential for performance dispersion by a single program or small set of programs. For instance, within trend-following, most trend-followers’ monthly returns are correlated. There are times, though, where certain trend-followers do significantly better or worse than average. The root causes are often slight differences in time scales, sector allocations and the trade entry/exit directives. Also, many trend-followers have introduced modifications to their systems. As a result, CTAs have added features to their systems (sometimes unknowingly) that are actually counter-trend in nature or at least are non-correlated with the space. By understanding the finer details of the systems under consideration, a portfolio engineer can diversify away this idiosyncratic risk and arrive at a truer representation of the dynamics the portfolio is designed to exploit. The only downside is the cost to implement a sufficiently wide gamut of managers within the same basic style.
But sometimes the trade-off is intractable; mutually exclusive goals are, oftentimes, unknowingly chosen as distinct primary objectives by the portfolio engineers. "Hitting a triple" (below) shows three common objectives: Trend-following de-correlation, risk-adjusted performance and a positive skew of returns. In high-tech product engineering, there are three factors that are highly desirable: Low cost, high reliability and a speedy development time. The historical rule is that one can choose any two of these but never all three simultaneously.
For the portfolio engineer, the three-factor trade-off acts in an analogous manner. Numerous CTAs have proven empirically that solid risk-adjusted performance and good drawdown properties can be achieved with trend-following. In addition, a number of CTAs have achieved low correlations to trend-following along with good risk-adjusted, long-term performance. However, rigorous analysis reveals that such systems periodically experience larger drawdowns than trend-followers, owing to the system architecture needed to remove the trend-following correlation. Finally, systems can be constructed that offer both low correlations to trend-following and also positive return skew. But these systems tend to have very poor (even negative) risk-adjusted performance over long time scales.
Statistical analyses show that markets, on average, exhibit "trendy" behavior over 50-day (and greater) time scales. In other words, prices move more than that predicted by a random walk, even after allowing for the non-Gaussian distribution of daily returns. This explains why trend-following is profitable, but not why markets trend.
Programs exploiting such trends effectively are rowing downstream. By increasing portfolio risk after profits accrue (i.e., cut your losses and let your profits grow), positive return skew and reduced drawdown magnitudes follow as natural consequences. Conversely, constructing a program that is de-correlated to trend-following requires one to selectively take positions that oppose the prevailing long-term price movement. The inherent risk properties of such systems are less optimal; specifically, they tend to lack positive skew of returns and have higher kurtosis on multi-day time scales.