Readers' Choice Awards
2025
Whispers in the Oil Market: Exploring Sentiment and Uncertainty Insights
By Luigi Gifuni, Ph.D., Assistant Professor, University of Strathclyde, U.K.
This paper develops a set of innovative text-based indices capturing oil market sentiment and oil price uncertainty. The textual analysis includes over 6 million news items, spanning from January 1982 to June 2021. The evidence shows that sentiment indicators readily react to economic and geopolitical events impacting oil prices, thereby enabling said indicators to accurately predict the price of oil. In contrast, uncertainty measures have inherent weaknesses thus yielding unreliable oil price forecasts. This research results in a novel and robust text indicator that offers valuable insights into predicting the intricate dynamics of crude oil prices, particularly excelling in short-term forecasts and during periods of economic recessions.
40 Classic Crude Oil Trades: Real-Life Examples of Innovative Trading
By Owain Johnson, Global Head of Research and Product Development, CME Group, U.K.
The day-to-day world of crude oil traders is not usually open to outsiders. Few non-specialists appreciate how oil traders approach the markets, what their backgrounds are, and how they make money. This article briefly summarizes a book which brings the oil trading world to vivid life. The book, in turn, introduces the reader to 40 real-life trades or strategies that were carried out by named market participants. The 40 chapters cover different geographies and different crude oil markets, providing an unparalleled insight into how crude oil traders work and think, as well as insight into how the markets have developed over time.
2024
Predictability of Commodity Futures Returns with Machine Learning Models
By Shirui Wang, Ph.D., Independent Researcher, China; and Tianyang Zhang, Ph.D., Wenlan School of Business, Zhongnan University of Economics and Law, China
The authors use prevailing machine learning models to investigate the predictability of futures returns in 22 commodities with commodity-specific and macroeconomic factors as predictors. Out-of-sample prediction errors for the majority of futures contracts are lowered compared with those obtained by the baseline models of AR(1) and forecast combinations. Using Shapley values to explain feature importance, we identify dominant predictors for each commodity. A long–short portfolio strategy based on monthly Light Gradient-boosting Machine predictions outperforms the benchmark linear models in terms of annual return, Sharpe ratio, and maximum drawdown.
2023
Convenience Yield Risk
By Marcel Prokopczuk, Ph.D., Professor of Finance and Commodity Markets, School of Economics and Management, Leibniz University Hannover, Germany and Editorial Board Member, Commodity Insights Digest; Lazaros Symeonidis, Ph.D., Senior Lecturer in Finance, Essex Business School, University of Essex, U.K. and Editorial Board Member, Commodity Insights Digest; Chardin Wese Simen, Ph.D., Professor of Finance, Management School, University of Liverpool, U.K. and Editorial Board Member, Commodity Insights Digest; and Robert Wichmann, Ph.D., Senior Associate, UniCredit Bank AG, Germany
In this paper, the authors develop a framework to quantify the convenience yield risk (CYR) inherent to each commodity futures market. Implementing the paper’s approach, the authors document that their novel CYR measure is informative about future commodity returns. In panel regressions, the CYR predicts future returns with a positive sign. Economically, a strategy that opens long positions in commodity markets with a higher than median CYR signal and sells the remaining commodities yields an average return of 6.93% per year. The performance of the CYR strategy cannot be explained by exposure to existing commodity strategies or other variables that capture changes in the investment opportunity set.
Dr. Chardin Wese Simen, Professor in Finance, University of Liverpool (U.K.); and Member of the CID Editorial Board.