Deep LSTM and LSTM-Attention Q-learning based reinforcement learning in oil and gas sector prediction

Abstract

Accurate prediction of stock market trends and movements holds great significance in the financial industry as it enables investors, traders, and decision-makers to make informed choices and optimize their investment strategies. In the context of the oil and gas sector, where stock prices are influenced by complex market dynamics and various external factors, reliable predictions are essential for effective decision-making and risk management. This study proposes Deep Long Short-Term Memory Q-Learning (DLQL) and Deep Long Short-Term Memory Attention Q-Learning (DLAQL) models and state-of-the-art Long Short-Term Memory (LSTM) for predicting stock prices in the oil and gas sector. The study utilizes historical stock price data of Cenovus Energy Inc. (CVE), MPLX LP (MPLX), Cheniere Energy Inc. (LNG), and Suncor Energy Inc. (SU) to create and validate these models. The research employs the Markov Decision Process (MDP) framework, a widely-used reinforcement learning technique, to train the deep LSTM Q-Learning and deep LSTM Attention Q-Learning models. This framework allows the models to learn optimal policies based on historical data, enabling them to make accurate predictions and adapt to changing market conditions. The findings of this study reveal that the proposed DLQL and DLAQL perform excellently well in terms of prediction accuracy in the oil and gas sector. The inclusion of attention mechanisms in the DLAQL model further enhances its performance by allowing it to focus on important features and capture relevant information. The results of this research underscore the potential of deep LSTM Q-Learning and deep LSTM Attention Q-Learning models in stock market prediction within the oil and gas sector. The application of these models can lead to improved decision-making, enhanced risk management, and increased profitability for market participants. Further exploration and refinement of these models, along with the incorporation of additional variables and market indicators, can contribute to the development of more sophisticated prediction models in the future. Overall, this study contributes to the advancement of stock market prediction techniques, specifically in the oil and gas sector, by introducing and evaluating the efficacy of deep LSTM Q-Learning and deep LSTM Attention Q-Learning models. The findings highlight the importance of accurate stock market predictions and demonstrate the potential benefits of leveraging these models within the MDP framework to support decision-making and risk management in the dynamic and competitive oil and gas industry.