Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis

Pavan Kumar Nagula and Christos Alexakis
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Pavan Kumar Nagula: ESC [Rennes] - ESC Rennes School of Business
Christos Alexakis: ESC [Rennes] - ESC Rennes School of Business

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Abstract: Recently, many researchers have shown keen interest in natural gas price prediction using machine learning and hybrid architectures. Our research forecasts natural gas future prices with different hybrid machine learning models using over a hundred technical indicators. The hybrid deep cross‐network model outperformed the single‐stage deep cross‐network regression and hybrid support vector machine models with 33% and 46% lower mean absolute error and 22% and 1.2 times better directional hit rate during 11 months of turbulent market circumstances due to the Russia–Ukraine crisis. The hybrid deep cross‐network model is 14, 5, and 6 times more profitable than the hybrid support vector machine, the benchmark passive buy‐and‐hold strategy, and the single‐stage deep cross‐network regression models. The hybrid deep cross‐network model is resilient during low‐ and high‐volatility periods. Deep cross‐network algorithm technical indicator interactions are more statistically significant than support vector machine polynomial kernel interactions. Energy traders and policymakers can exploit our findings.

Keywords: deep cross-networks; Energy markets; hybrid architecture; Machine learning; Natural gas market; Technical indicators (search for similar items in EconPapers)
Date: 2025-01-05
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Published in Journal of Forecasting, 2025, ⟨10.1002/for.3250⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05105115

DOI: 10.1002/for.3250

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