Since 1871 the S&P 500 Total Return Index has gone through four prolonged periods of negative real rates of return and declining PE ratios, covering over 70 years. Additionally, bond market real rates of return have often been negative during periods of either deflation, inflation or the current quantitative easing. Active portfolio management can both stabilize portfolio volatility and generate relatively consistent, predictable returns. Commodity Trading Advisors or CTAs, have as a group generated stable predictable returns for over 30 years, using the following broad three strategies: Diversification, quantitative asset allocation (“QAA”) modeling, and; active long/short investing.
The first strategy of broad diversification has become better known by investors over the past decade, as evidenced by the growing allocation to commodities, real estate and external managers that pursue a variety of absolute return strategies uncorrelated to underlying market returns.
The second strategy employed by CTAs is risk management based on QAA models that focus on downside volatility and correlation as opposed to dollar-based asset allocation. QAA seeks to first establish a level of acceptable portfolio downside volatility, and then allocate portfolio resources so underlying investments will contribute in a pre-defined way to the overall portfolio downside volatility target.
Historically, various QAA models have been proposed and, in general, they all seek to optimize return to volatility based on some measure of expected return. A shortcoming of all standard QAA models is they tend to be static whereas markets are dynamic. Average historical returns are also not a good estimator for future current returns. Moreover, optimizing to a static level of risk breaks down in the real world because volatilities are dynamic.
A solution to the problems of static QAA models is to simplify the model at the front end and manage risks dynamically. A simplified asset allocation model only needs to set the baseline by getting the story right, so to speak. Utilizing average downside volatilities and correlations is sufficient. Also, return expectations can be removed from QAA models because various asset classes display remarkably similar long-term Sharpe ratios.
From this basic model, real world risks can be dynamically managed through the tracking error of real results versus model results. For example, limits can be established for downside volatility or VaR, and all portfolio positions can be calibrated downward if these limits are breached in real time
The third CTA strategy of long/short investing seeks to limit losses and maximize profits by being invested on the right side of the market. Underlying this approach is an awareness that market real returns can and have been negative for very long periods of time, and that bear markets should be viewed as opportunities for gains and not as just another risk to be managed. For fund managers with limited ability to go short, long/flat investing using overlay strategies can achieve the same objective of limiting losses and maximizing profits. A safe haven for negative real rates of return is cash.
Although market returns are generally positive over very long periods of time, they are neither stable nor predictable. However, understanding the strategies employed by CTAs can offer valuable insight into available tools to generate stability and predictability in portfolio returns.
Roland P. Austrup is Chief executive Officer and Chief Investment Officer, Integrated Managed futures Corp., a division of Integrated Asset Management.