Evaluating Trend‐Based Strategies in Chinese Commodity Futures Markets

ABSTRACT We examine the performance of trend‐based strategies in Chinese commodity futures markets, using a data set of 64 commodity futures from 2003 to 2023. We find that TSMOM strategies generate high returns. The performance is not driven by specific sample periods or individual commodity futures. TSMOM factors also exhibit strong explanatory power for other commodity portfolios, including newly developed trend‐based strategies. Decomposition analysis reveals key drivers, including the autocovariance of commodity returns, independent predictive effects, the distribution of volatility‐managed weights, and a non‐positive risk‐return relationship. We develop four enhanced strategies to improve returns and risk control. All strategies remain robustly profitable after accounting for transaction costs.

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Publication Details

Journal
Journal of Futures Markets
Published
2025-09-10
DOI
https://doi.org/10.1002/fut.70033
Citations
1
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
3.13
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article

Evaluating Trend‐Based Strategies in Chinese Commodity Futures Markets

Xiaojian Yu, Yiran Zheng, Donald D. Lien, Xili Zhang
1 citations
Journal of Futures Markets
Market Dynamics and Volatility
3.13
article

Evaluating Trend‐Based Strategies in Chinese Commodity Futures Markets

Xiaojian Yu, Yiran Zheng, Donald D. Lien, Xili Zhang
article en
1 citations

Abstract

ABSTRACT We examine the performance of trend‐based strategies in Chinese commodity futures markets, using a data set of 64 commodity futures from 2003 to 2023. We find that TSMOM strategies generate high returns. The performance is not driven by specific sample periods or individual commodity futures. TSMOM factors also exhibit strong explanatory power for other commodity portfolios, including newly developed trend‐based strategies. Decomposition analysis reveals key drivers, including the autocovariance of commodity returns, independent predictive effects, the distribution of volatility‐managed weights, and a non‐positive risk‐return relationship. We develop four enhanced strategies to improve returns and risk control. All strategies remain robustly profitable after accounting for transaction costs.

Journal of Futures MarketsVol. 45(12)
Zhejiang University of Science and Technology (CN), Guangdong University Of Finances and Economics (CN), Communication University of Zhejiang (CN), Guangdong University of Finance (CN), The University of Texas at San Antonio (US), Zhejiang University (CN), South China University of Technology (CN)
Openalex Percentile: Top 7%
Market Dynamics and Volatility
3.13
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