A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity

ArXiv ID: 2405.10584 “View on arXiv”

Authors: Unknown

Abstract

Stock price prediction has always been a difficult task for forecasters. Using cutting-edge deep learning techniques, stock price prediction based on investor sentiment extracted from online forums has become feasible. We propose a novel hybrid deep learning framework for predicting stock prices. The framework leverages the XLNET model to analyze the sentiment conveyed in user posts on online forums, combines these sentiments with the post popularity factor to compute daily group sentiments, and integrates this information with stock technical indicators into an improved BiLSTM-highway model for stock price prediction. Through a series of comparative experiments involving four stocks on the Chinese stock market, it is demonstrated that the hybrid framework effectively predicts stock prices. This study reveals the necessity of analyzing investors’ textual views for stock price prediction.

Keywords: sentiment analysis, XLNET, BiLSTM, technical indicators, stock prediction, Equity

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper employs advanced deep learning models (XLNet, BiLSTM with highway mechanisms) requiring substantial mathematical understanding, but its primary focus is on a practical, data-heavy implementation with specific experimental details and stock data, making it geared toward application.

  flowchart TD
    A["Research Goal: Predict stock prices using investor sentiment from online forums"] --> B["Data Collection"]
    
    subgraph B ["Data Collection"]
        B1["Stock Price Data"]
        B2["Online Forum Posts"]
        B3["Technical Indicators"]
    end

    B --> C["Sentiment Analysis Pipeline"]
    
    subgraph C ["Sentiment Analysis Pipeline"]
        C1["XLNET Model"]
        C2["Post Popularity Factor"]
        C3["Daily Group Sentiment Calculation"]
    end

    C --> D["Hybrid Deep Learning Framework"]
    
    subgraph D ["Hybrid Deep Learning Framework"]
        D1["Input Layer<br>Sentiment + Technical Indicators"]
        D2["Improved BiLSTM-Highway Model"]
    end

    D --> E["Prediction & Validation"]
    
    subgraph E ["Prediction & Validation"]
        E1["Stock Price Prediction"]
        E2["Comparative Experiments<br>4 Chinese Stocks"]
    end

    E --> F["Key Findings"]
    
    subgraph F ["Key Findings"]
        F1["Effective stock price prediction achieved"]
        F2["Sentiment analysis crucial for forecasting"]
        F3["Hybrid framework outperforms baselines"]
    end