Understanding the complexity of futures markets investing in China: evidence from deep learning techniques

Zhenya Liu, Nawazish Mirza, Rongyu You and Yaosong Zhan
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Zhenya Liu: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
Nawazish Mirza: Excelia Group | La Rochelle Business School
Rongyu You: Renmin University of China = Université Renmin de Chine
Yaosong Zhan: NSYSU - National Sun Yat-sen University

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Abstract: We examine the effectiveness of deep learning models in implementing the time-series momentum strategy in the Chinese futures market. Our empirical analysis shows that the long short-term memory (LSTM) model performs better than other machine learning methods in terms of profitability and risk management. Importantly, incorporating the Sharpe ratio into model training significantly increases returns while decreasing risks. Additionally, our findings indicate that considering momentum turning points and combining short- and long-term predictions further enhances the performance of the LSTM model.

Date: 2024-10-04
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Published in Annals of Operations Research, 2024, ⟨10.1007/s10479-024-06277-x⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04972026

DOI: 10.1007/s10479-024-06277-x

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