TY - JOUR T1 - The Cross Section of Commodity Returns: <em>A Nonparametric Approach</em> JF - The Journal of Financial Data Science DO - 10.3905/jfds.2020.1.034 SP - jfds.2020.1.034 AU - Clemens Struck AU - Enoch Cheng Y1 - 2020/06/18 UR - https://pm-research.com/content/early/2020/06/17/jfds.2020.1.034.abstract N2 - To what extent are financial market returns predictable? Standard approaches to asset pricing make strong parametric assumptions that undermine their return-predicting ability. The authors employ tree-based methods to overcome these limitations and attempt to approximate an upper bound for the predictability of returns in commodities futures markets. Out of sample, they find that up to 3.74% of 1-month returns are predictable—more than a 10-fold increase from standard approaches. The findings hint at the importance multiway interactions and nonlinearities acquire in the data; they imply that new factors should be tested on their ability to add explanatory power to an ensemble of existing factors.TOPICS: Futures and forward contracts, commoditiesKey Findings• Standard approaches to asset pricing make strong parametric assumptions that undermine their return-predicting ability.• The authors employ tree-based methods to overcome these limitations and estimate the predictability of returns in commodities futures markets.• Out of sample, they find that up to 3.74% of 1-month returns are predictable—more than a 10-fold increase from standard approaches. ER -