Design Hybrid Model To Improve Stock Selection And Market Timing By Integrating Deep Learning And Sentiment Analysis

In the big data age, the financial industry produces enormous volumes of both structured and unstructured data from sources including social media platforms, stock prices, and financial news. However, typical statistical models’ ability to accurately choose stocks and time the market is limited by the dynamic and turbulent character of financial markets. In order to improve stock prediction performance, this research suggests a unique hybrid model that combines sentiment analysis with deep learning. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are combined with sophisticated transformer-based Natural Language Processing (NLP) architectures, such as BERT and RoBERTa, to extract sentiment signals from unstructured textual data and provide reliable time-series forecasting. The proposed framework includes a thorough pipeline for preparing data, which includes technical indicators and normalization for structured financial data and tokenization, vectorization, and feature engineering for sentiment data. By combining sentiment data with past stock trends, the algorithm generates more precise Buy, Sell, and Hold recommendations, greatly enhancing stock selection and market timing. According to experimental findings, the suggested hybrid model performs better than baseline deep learning models (LSTM, GRU, BERT+LSTM, RoBERTa+GRU) and conventional models (ARIMA, GARCH), obtaining higher Sharpe Ratio and prediction accuracy. This work offers practical insights for researchers and investors seeking to make data-driven decisions in unpredictable market settings, in addition to advancing AI-driven financial forecasts.