Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting

Predicting crude oil prices is critical due to their significant impact on the global economy. While numerous studies have focused on time series forecasting and machine learning methods to predict crude oil spot prices, the use of futures prices segmented by the maturity terms as predictors is comparatively underexplored. This study explored a deep learning approach to investigate using futures prices across different maturities to predict spot prices. Specifically, this paper analyzes the predictive power of one-, two-, three-, six-, and 12-month crude oil futures contracts. This study employs multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN), to forecast crude oil spot prices. This research investigates the performance of these machine learning models when exploring the relationship between futures and spot prices of crude oil. In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms. • Extensive hyperparameter tuning to enhance the interpretability of the machine learning models, thereby contributing to the field of Explainable Artificial Intelligence (XAI). • Exploring a deep learning approach to investigate using futures prices across different maturities to predict spot prices with one-, two-, three-, six-, and 12-month crude oil futures contracts. • Employing multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN). • Systematically showing results demonstrating predictive power in terms of XAI between spot prices, futures prices with different maturities, and machine learning algorithms.