End-to-End Hybrid Stock Market Prediction: Transformer-Based Enhanced Recurrent Neural Networks with FinBERT Integration

In time series stock forecasting tasks, Long Short-Term Memory (LSTM) networks have been extensively utilized due to their proficiency in modeling sequential data. However, LSTM-based methods often struggle to capture long-range dependencies inherent in complex datasets. To address this limitation, Transformer-based models, such as Informer, have been introduced, leveraging attention mechanisms to better capture these long-term dependencies. Nevertheless, these models introduce increased computational complexity and encounter challenges in effectively modeling local dependencies. Additionally, stock market dynamics are heavily influenced by global financial news, necessitating the integration of sentiment data to enhance prediction accuracy. In this paper, we approach stock price prediction as a time series forecasting task by developing a hybrid model that integrates traditional time series forecasting methods (LSTM), attention mechanisms (Informer), and natural language processing techniques (FinBERT). Utilizing preprocessed historical stock price data from the Dow Jones Industrial Average and sentiment data from The Wall Street Journal, we conducted comparative experiments to demonstrate the superior performance of our hybrid model in predicting stock prices.