A Unified Hybrid Deep Learning Framework for Stock Market Forecasting Using Transformers, Reinforcement Learning, and Sentiment Intelligence

Abstract

Forecasting stock prices remains a highly challenging problem due to the volatile, nonlinear, and unpredictable nature of financial markets. Traditional forecasting models often fail to effectively integrate real-time data, capture nuanced sentiment, or adapt to rapidly changing market dynamics. To address these limitations, this paper proposes the Intelli Fusion Adaptive Decision Engine (IADE), a comprehensive hybrid framework that unifies multiple advanced AI technologies, including Deep Q-Learning (DQN), the Prophet time-series algorithm, Bidirectional Encoder Representations from Transformers (BERT), Adaptive Resonance Theory Neural Networks (ART-NN), and Transformer-based architectures with attention mechanisms. IADE is designed to improve user accessibility, enhance real-time forecasting accuracy, increase sentiment analysis precision, and enable adaptive predictive behavior. Experimental results demonstrate that the proposed system substantially improves forecasting performance and strengthens decision-making effectiveness in highly volatile financial environments.

Introduction

The text presents a comprehensive review of stock market prediction and introduces a novel hybrid AI framework called the IntelliFusion Adaptive Decision Engine (IADE). Stock market forecasting is critical but highly challenging due to volatility, non-linearity, economic factors, geopolitical events, and investor sentiment. Traditional statistical methods such as ARIMA are limited in handling these complexities, leading to the growing adoption of machine learning (ML), deep learning (DL), reinforcement learning (RL), and sentiment analysis.

The literature review highlights significant advances in stock prediction using LSTM, BiLSTM, Transformers, SVM, ensemble models, reinforcement learning, and sentiment-aware systems. Studies show that combining quantitative market data with qualitative sources like news and social media improves prediction accuracy. Hybrid and graph-based models further enhance performance, though challenges remain in scalability, generalization, real-time processing, and domain knowledge integration.

To address these gaps, the proposed IADE framework integrates deep learning (LSTM, Transformers), reinforcement learning (DQN), time-series modeling (Prophet), ART neural networks, and advanced sentiment analysis (BERT, TF-IDF, ensembles). IADE is designed to process real-time, multi-source data, adapt to rapid market changes, and provide user-friendly visualizations for investors and policymakers. Its modular architecture includes data acquisition and preprocessing, a prediction engine, a sentiment analysis unit, model integration via ensemble learning, and an interactive visualization layer.

The methodology emphasizes robust data cleaning, normalization, feature extraction, adaptive learning, and ensemble optimization to minimize prediction error. Experimental results demonstrate substantial performance improvements over baseline models, including higher sentiment accuracy, lower prediction errors, better trend capture, improved reinforcement learning rewards, faster UI response, and significantly higher overall forecast accuracy.

Conclusion

The IntelliFusion Adaptive Decision Engine (IADE) represents a transformative advancement in stock market forecasting by seamlessly integrating cutting-edge AI and machine learning (ML) techniques within a cohesive and user-friendly framework. This innovative approach effectively addresses critical challenges, including enhancing forecasting accuracy, ensuring efficient real-time data processing, refining sentiment analysis precision, and adapting to rapidly changing market dynamics.IADE excels in its ability to process and analyze diverse data sources in real-time, offering a robust platform for extracting actionable insights. Coupled with its intuitive user interface, the framework empowers users—ranging from investors to policymakers—to make well-informed and timely financial decisions. By prioritizing both technical sophistication and practical usability, IADE bridges the gap between complex analytical models and their real-world applications. Looking ahead, future developments aim to expand IADE\'s applicability to international and emerging markets, broadening its scope and relevance across diverse financial environments. Additional enhancements will include incorporating new data sources, such as economic indicators and geopolitical events, to provide a more comprehensive understanding of market drivers. Efforts will also focus on optimizing computational efficiency through algorithmic improvements and distributed computing, ensuring scalability and faster processing times. Furthermore, integrating explainable AI (XAI) into IADE will provide transparent, interpretable predictions, fostering greater user trust and understanding of the underlying decision-making processes. These advancements position IADE as a pioneering solution poised to revolutionize stock market forecasting and financial decision-making.

References

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Copyright

Copyright © 2025 Mahesh Nannepagu, Dr. D. Bujji Babu , Dr. Ch. Bindu Madhuri . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.