Multimodal Stock Price Prediction

ArXiv ID: 2502.05186 “View on arXiv”

Authors: Unknown

Abstract

In an era where financial markets are heavily influenced by many static and dynamic factors, it has become increasingly critical to carefully integrate diverse data sources with machine learning for accurate stock price prediction. This paper explores a multimodal machine learning approach for stock price prediction by combining data from diverse sources, including traditional financial metrics, tweets, and news articles. We capture real-time market dynamics and investor mood through sentiment analysis on these textual data using both ChatGPT-4o and FinBERT models. We look at how these integrated data streams augment predictions made with a standard Long Short-Term Memory (LSTM model) to illustrate the extent of performance gains. Our study’s results indicate that incorporating the mentioned data sources considerably increases the forecast effectiveness of the reference model by up to 5%. We also provide insights into the individual and combined predictive capacities of these modalities, highlighting the substantial impact of incorporating sentiment analysis from tweets and news articles. This research offers a systematic and effective framework for applying multimodal data analytics techniques in financial time series forecasting that provides a new view for investors to leverage data for decision-making.

Keywords: Multimodal machine learning, Sentiment analysis, Long Short-Term Memory (LSTM), Stock price prediction, Natural Language Processing

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper employs standard deep learning architectures (LSTM, FinBERT) without advanced mathematical derivations, but demonstrates rigorous empirical testing with diverse real-world data sources, multiple models, and clear performance metrics.

  flowchart TD
    A["Research Goal:<br>Accurate Stock Price Prediction"] --> B["Data Collection & Preprocessing"]
    B --> C{"Multimodal Data Sources"}
    C --> D["Traditional Financial Metrics"]
    C --> E["Social Media Tweets"]
    C --> F["News Articles"]
    
    D --> G["Computational Processing"]
    E --> G
    F --> G
    
    subgraph G ["LSTM Model Pipeline"]
        G1["Sentiment Analysis<br>ChatGPT-4o & FinBERT"] --> G2["Feature Integration"]
        G2 --> G3["Long Short-Term Memory<br>LSTM Network"]
        G3 --> G4["Price Prediction Output"]
    end
    
    G4 --> H["Key Findings & Outcomes"]
    
    H --> I["5% Performance Gain<br>vs. Baseline LSTM"]
    H --> J["Senti-mental Analysis<br>Significantly Enhances Accuracy"]
    H --> K["Effective Framework for<br>Multimodal Financial Analytics"]