An Empirical Assessment of Artificial Intelligence-Based Deep Reinforced Learning in Automatic Stock Trading


  • Joon Soo Yoo Department of Public Administration, Hallym Polytechnic University 48, Janghak-gil, Dong-myeon, Chuncheon-si, Gangwon-do, Republic of Korea


Stock trading, deep reinforcement learning, artificial intelligence, efficiency


Stock trading is the process of buying and selling stocks to boost financial returns. The secret to effective stock trading is making the proper trading decisions at the right moments or developing a competent trading plan. In a lot of recent studies, machine learning (ML) approaches have been utilised to predict stock movements or prices in order to conduct stock trading. This research aims to examine the possibilities of deep reinforcement learning powered by Artificial Intelligence (AI) for enhancing the precision and efficiency of automated stock trading systems. It analyses the difficulties in performing automated stock trading and suggests a brand-new Deep Reinforcement Learning (DRL)method to overcome them. To forecast stock prices and make trading decisions, the suggested method combines a deep neural network and a Reinforcement Learning (RL) algorithm. Based on actual stock data, experiments are run to assess how well the suggested technique performs. The outcomes demonstrate that the suggested technique beats current trading strategies and can generate large increases in profitability.


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How to Cite

Yoo, J. S. . (2023). An Empirical Assessment of Artificial Intelligence-Based Deep Reinforced Learning in Automatic Stock Trading. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 470–476. Retrieved from



Research Article