A Hybrid Deep Reinforcement Learning Framework for Stock Market Prediction

Predicting and trading stock values is an intriguing and intricate area of study that keeps getting more and more attention. Generating quick and accurate decisions is essential for generating money in the stock market. Advanced predictive algorithms, made possible by technological advancements, are changing the way investors approach their work. A key component of this shift is the increasing use of automated decision-making processes driven by historical data analysis, especially in the context of individual stocks. Researchers have recently focused on deep reinforcement learning algorithms, which have shown encouraging results and could lead to better stock market forecasts. This paper presents a groundbreaking method that differs from the current models that are based on ANN and LSTM algorithms. Precisely for stock market anticipation, this research constructs a revolutionary architecture by merging ANN, LSTM, and NLP approaches with the deep-Q-network(DQN). This cutting-edge architecture relies on a plethora of stock market data, particularly that pertaining to gold stocks. Extensive dataset validation of the generated model's predictive prowess demonstrates its capacity to estimate the initial stock rate for the upcoming day. In the end, this research adds new dimensions to the field of stock market prediction and provides significant insights.