Deep Q-Network-based Stock Market Optimization for improving Stock Trends

The emergence of new technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT) has left a significant impact on several aspects of our lives. These technologies have found their way into various financial domains, including stock markets, banking, and insurance. In this paper, we propose an intelligent system for the stock market that is based on deep Q-learning and aims to enhance its performance. Our proposed system evaluates the stock market by analyzing past and current actions and predicting their outcomes. Based on the evaluation results, the system provides efficient rewards. To estimate the suitable actions and enhance its learning process, the state information is fed into a convolutional neural network (CNN), which estimates the ‘Q-values.’ Our system uses a deep Q-learning algorithm, a type of reinforcement learning that systematically improves the agent's performance by learning from its experience. The results show that our proposed scheme for predicting the stock market outperforms conventional methods. With an accuracy rate of 89%, the proposed system is a highly effective tool for predicting market trends with increased efficiency. The system is capable of analyzing complex patterns and making informed decisions based on past and current market trends. With the help of this intelligent system, investors can make better decisions, reduce risks, and improve the overall performance of the stock market.