Prophetic markets: Multi-modal deep learning redefines stock market predictions

Stock market prediction is a vital resource for investors and financial experts looking to reduce risk and maximize profits. In this article, we propose a novel multi-modal deep learning-based method to improve predictions of stock market trends. This study uses an integrated framework of state-of-the-art machine learning techniques, deep learning with LLM, and RAG. We use data retrieval techniques, such as web scraping, to download financial reports containing corporate balance sheets. Further, our method includes real-time sentiment analysis in order to understand the investors’ current mentality which can be crucial in determining the market movements. Technical analysis uses deep learning models to interpret important indicators such as RSI, MACD, CCI, and Stochastic oscillators to uncover underlying patterns and correlations in stock trends. Our experiments demonstrate that this integrated strategy performs better than conventional single-dimensional techniques. We are combining data from corporate financial, sentiment analysis, and technical factors as well as active news sentiment analysis to improve the accuracy and reliability of market predictions by demonstrating the performance of a combined set of data sources. This work draws attention to the importance of the multifaceted integration of data by demonstrating RAG benefits for accurate financial data retrieval and multi-modal analysis in the practical applications of stock markets. The results demonstrate how these new approaches can be combined for profit maximization with better decision-making and risk reduction in financial markets.