Designing an efficient Deep Dyna Q based VARMAx Model for Prediction of Real-Time changes in Stock Value Patterns

In the intricate complexities of modern financial markets, the capability to predict stock values with high accuracy stands as a cornerstone for investors and analysts alike. Existing methods still struggle with the complex dynamics of markets, especially in adapting to stock fluctuations' multifaceted nature. In response to these limitations, the proposed work introduces a ground-breaking approach that integrates the robust forecasting capabilities of VARMAx with the adaptive ability of Deep Dyna Q algorithms. The rationale for this integration is rooted in the quest to enhance the responsiveness and accuracy of stock value predictions. The model's performance was evaluated using datasets from the Indian and USA markets. It showed significant improvements in precision, accuracy, recall, AUC, and specificity, as well as a substantial reduction in response time. These enhancements are crucial for financial decision-making, where accuracy and timeliness are essential.