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

ArXiv ID: 2410.12807 “View on arXiv”

Authors: Unknown

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.

Keywords: Conv-LSTM, Large Language Model, stock market prediction, sentiment analysis, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper presents a novel, complex deep learning architecture (Conv-LSTM + LLM) with some mathematical notation and gating mechanisms, indicating moderate-to-high math complexity. However, the provided excerpt and summary lack any details on implementation, backtesting, datasets, or performance metrics, suggesting low empirical rigor and limited readiness for direct trading deployment.

  flowchart TD
    A["Research Goal: Improve Stock Prediction via<br>Holistic Data Integration"] --> B
    
    subgraph B ["Data Collection & Preprocessing"]
        direction LR
        B1["Historical Stock Prices<br>& Technical Indicators"]
        B2["Textual Data<br>News, Social Media, Reports"]
    end

    B --> C["Level 1: Two-Level Conv-LSTM"]
    C --> C1["Convolutional Layers<br>Extract Spatial Features"]
    C1 --> C2["LSTM Layers<br>Capture Temporal Dependencies"]

    B --> D["Level 2: LLM Integration"]
    D --> D1["Sentiment Analysis<br>& Contextual Understanding"]

    C2 --> E["Feature Fusion & Prediction"]
    D1 --> E

    E --> F["Key Outcomes"]
    F --> F1["Enhanced Prediction Accuracy"]
    F --> F2["Contextually Rich Stock Advising"]