We perform the longest study of long-run reversal in commodity returns. Using a unique dataset of 52 agricultural, industrial, and energy commodities, we examine the price behavior for the years 1265 to 2017. The findings reveal a strong and robust long-run reversal effect. The returns of the past one to three years negatively predict subsequent performance in the cross-section of returns. The effect is robust to extensive subsample and subperiod analysis, and not driven by statistical biases, extreme events, or macroeconomic risks. Our findings support the explanation that the long-term reversal originates from supply and demand adjustments following price changes. Finally, the phenomenon is elevated in more volatile commodities and in periods of high return dispersion.

We investigate the investability of commodity risk premia in China. Previously documented standard momentum, carry and basis-momentum factors are not investable due to the unique liquidity patterns along the futures curves in China. However, dynamic rolling and strategic portfolio weights significantly boost the investment capacity of such premia without compromising its statistical and economic significance. Meanwhile, style integration delivers enhanced performance and improved opportunity sets. Furthermore, the observed investable premia are robust to execution lags, stop-loss, illiquidity, sub-period specifications, seasonality and transaction costs. They also offer portfolio diversification for investors. Finally, investable commodity premia in China reveal strong predictive ability with global real economic growth.

The growth in commodity-related investments has sparked interest in the performance of momentum strategies in these markets. This paper introduces a behavioral proxy of the 52-week high and low momentum that explains a significant proportion of the variation of conventional momentum returns after controlling for commodity specific risk factors. Our findings show that the 52-week high strategy generates significant profits after accounting for transaction costs. We report that the 52-week high strategy is a better predictor of returns than conventional momentum. Our findings suggest that term structure and hedging pressure risk factors provide only a partial explanation of the results.

This paper examines profitable trading strategies that jointly exploit momentum and reversal signals in commodity futures. While the single-sort momentum strategies returns 11.14% per annum, on average, a consistent reversal pattern of momentum profits is pronounced from 12 to 30 months after portfolio formation. Combining the observed reversal pattern with the momentum signal, our double-sort strategy returns 20.24% per annum, which significantly outperforms single-sort strategies. The proposed strategy is robust to seasonality effects and sample adjustments in commodity futures. The profitability of the double-sort strategy cannot be explained by standard risk factors, term structure, market volatility, investor sentiment, data-mining or transaction costs, but appears to be related to global funding liquidity. As a consequence, the double-sort strategy in commodity futures may be employed as a portfolio diversification tool.

Our understanding of the long-term return behavior and portfolio characteristics of public infrastructure investments is limited by a relatively short history of empirical data. We re-construct U.S. listed infrastructure index returns by mapping their monthly performance to received systematic and industry risk factors from 1927 through 2010. Our findings reveal that the infrastructure returns in recent years may understate the tail-risk that investors could experience over the long-term, however, this tail-risk is commensurate with holding a broad portfolio of U.S. stocks. For mean-variance and mean-CVaR investors, we report the benefits of holding public infrastructure assets in investment portfolios.

The Nelson-Siegel framework is employed to model the term structure of commodity futures prices. Exploiting the information embedded in the level, slope and curvature parameters, we develop novel investment strategies that assume short-term continuation of recent parallel, slope or butterfly movements of futures curves. Systematic strategies based on the change in the slope generate significant profits that are unrelated to previously documented risk factors and can survive reasonable transaction costs. Further analysis demonstrates that the profitability of the slope strategy increases with investor sentiment and is in part a compensation for the drawdowns incurred during economic slowdowns. The profitability can also be magnified through timing and persists under alternative specifications of the Nelson-Siegel model.