A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting
This addresses stock forecasting for financial analysts by integrating multiple data sources, but it appears incremental as it combines existing methods like Conv-LSTM and LLMs.
The paper tackled stock market prediction by proposing a hierarchical Conv-LSTM and LLM integrated model to analyze time-series and textual data, achieving improved prediction accuracy with contextually rich advising, though no concrete numbers were provided.
The financial domain presents a complex environment for stock market prediction, characterized by volatile patterns and the influence of multifaceted data sources. Traditional models have leveraged either Convolutional Neural Networks (CNN) for spatial feature extraction or Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, with limited integration of external textual data. This paper proposes a novel Two-Level Conv-LSTM Neural Network integrated with a Large Language Model (LLM) for comprehensive stock advising. The model harnesses the strengths of Conv-LSTM for analyzing time-series data and LLM for processing and understanding textual information from financial news, social media, and reports. In the first level, convolutional layers are employed to identify local patterns in historical stock prices and technical indicators, followed by LSTM layers to capture the temporal dynamics. The second level integrates the output with an LLM that analyzes sentiment and contextual information from textual data, providing a holistic view of market conditions. The combined approach aims to improve prediction accuracy and provide contextually rich stock advising.