Using commodity trading adviser (CTA) indices as proxies for managed futures trading, the authors offer a series of cross-sectional and time-series empirical tests to assess the pattern of returns and relative performance for this relatively new form of long-term asset class. They note potential biases that can affect analytical results, such as the time period of study, portfolio composition, and independent variables selected for statistical regressions. The time unit of analysis used in conformity with existing academic research (i.e., four years) is described as being a problem for a dynamic investment choice like managed futures because the related statistical averaging potentially obscures the underlying roots of relative CTA benefits.

The statistical expertise the authors exhibit through their numerous regression analyses demonstrates that they are discerning data users. Hence, their assessment of the relative merits of various database sources (CISDM, CSFB, and Barclays) could be useful for practitioners using and/or creating index-based investments. In particular, for the period before 2000—when the databases were not large and diversified enough to reflect the full range of applied futures strategies—CSFB produces disparate and likely misleading results in comparison with other sources.

The authors acknowledge that managed futures traders have a wider range of profit-making opportunities available compared with traditional stock and bond investors. Nonetheless, they note that CTA returns are derived from both manager skill and investment style, as measured relative to an active manager based on a passive algorithm-based benchmark. So, when monitoring CTA managers, it is also possible to derive a quantitatively sound measure of their alphas.

Finally, the research shows that models that incorporate momentum-oriented variables have greater predictive power than those based on dynamic trading; trend following is apparently a more salient characteristic for managed futures. This evidence supports the authors’ assertion that it is important to understand the underlying characteristics of the CTA strategy to draw any meaningful conclusions about its performance.

The authors first examine the statistical reliability of the indices derived from the three major databases that track CTA activity over the period of 1994–2009: the Barclays, CISDM (expanded into asset-weighted and equal-weighted indices), and CSFB databases. Four-year rolling averages of correlations are calculated between these indices and the traditional market factors of the S&P 500 Index, Russell 2000 Index, BarCap U.S. Government Index, and BarCap U.S. High Yield Index to assess the benefits of adding managed futures (low-correlation assets) to traditional portfolios.

The degree to which returns are enhanced is estimated by evaluating seven “long bias” multifactor models that span combinations of the traditional four market factors and the traditional Fama–French equity factors with other factors representing dynamic trading, trend following, and momentum. This process results in tests of statistical significance (t-statistics) and goodness of fit (R²) for regression models with four and eight factors on a four-year rolling basis. Comparison regression models are also investigated to further delineate the effects of trading and momentum in stock, bond, currency, and commodity markets; interest rates and currency typically have the greatest effects in these tests. Overall, the authors emphasize that research must be continually conducted on any investment over time because of the dramatic underlying changes in the regulatory, trading, and investor environments, especially in managed futures.

Because exposure to alternative investments has become prevalent throughout institutional and individual investing, the authors strive to offer timely confirmation of its effects on returns over the period that one of its prominent techniques (managed futures) has been used. Although one of the authors works for the nonprofit foundation that is a key facilitator of the Chartered Alternative Investment Analyst (CAIA) designation, that orientation does not appear to bias the findings and, indeed, may provide the specialist attention to nuance that underpins careful analysis.