Stock Price Forecasting With Integration of Sectoral Behavior: A Deep Auto-Optimized Multimodal Framework

Abstract

This study proposes a novel deep auto-optimized architecture for stock price forecasting that integrates sectoral behavior with individual stock sentiment to improve predictive accuracy. Traditional stock prediction models often focus solely on individual stock behavior, overlooking the impact of broader sectoral trends. The proposed approach utilizes advanced deep learning models, including gated recurrent units (GRU), bidirectional GRU, long short-term memory (LSTM), and bidirectional LSTM, with their hybrid ensembles. These models are built using the Keras functional API and auto ML network architecture search technology. The current deep auto-optimized multimodal framework incorporates sectoral behavior, significantly improving performance metrics. This research highlights the critical role of integrating sectoral behavior in stock price prediction models.