A study of forecasting stocks price by using deep Reinforcement Learning

Abstract

Financial investors are so concerned now about the future of the stock market and how the market will behave next decade because the world economy is now in an alarming condition which leads to losses in the stock market. That is why traders want to know a little bit about the future forecast of the stock market. So, in this paper, we approach a bit to predict the stock market using the deep reinforcement learning method. Traditional methods for stock price prediction often rely on statistical models or technical indicators, which may struggle to capture the non-linear patterns and sudden shifts in stock prices. In recent years, deep reinforcement learning (DRL) has emerged as a promising approach for predicting stock prices, as it can learn complex patterns from raw data and make decisions based on sequential actions. n this study, we propose a novel framework for stock price prediction using DRL. The framework incorporates a deep neural network as a function approximator, which is trained using the Q-learning algorithm to learn optimal actions for buying, selling, or holding stocks. The neural network takes historical stock price data as input and outputs Qvalues, which represent the expected rewards for different actions at each time step. The best course of action to pursue in each market state is then determined using the Q-values. We performed a sensitivity analysis to investigate the effects of various network designs and hyperparameters on the effectiveness of our DRL-based strategy. We found that the choice of hyperparameters, such as learning rate and exploration rate, had a significant impact on the performance, and tuning these hyperparameters could further improve the prediction accuracy. Our experimental results showed that our DRLbased approach outperformed the traditional methods in terms of predicting stock prices, with higher accuracy and lower prediction errors.