A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting

TL;DR

This paper introduces a hierarchical Conv-LSTM and LLM integrated model that combines time-series analysis with textual data understanding to enhance stock market prediction accuracy.

Contribution

It presents a novel two-level neural network that integrates Conv-LSTM with a Large Language Model for holistic stock forecasting.

Findings

Improved prediction accuracy over traditional models

Effective integration of textual sentiment analysis

Holistic approach enhances stock advising quality

Abstract

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.