融合新闻、微博与财务数据，用大模型提升股价预测准确率5%。
Multimodal Stock Price Prediction
- 用LSTM结合财务数据、微博和新闻文本进行多模态预测。
- 引入ChatGPT-4o和FinBERT做情感分析，使预测准确率提升最高达5%。
- 适合关注金融量化和舆情分析的投资者与研究者参考。
在金融市场的静态与动态因素日益复杂的时代，精准的股价预测愈发依赖于将多元数据源与机器学习有效结合。本文提出一种多模态机器学习方法，整合传统财务指标、社交媒体推文及新闻文章数据。通过ChatGPT-4o与FinBERT模型对文本数据进行情感分析，捕捉实时市场动态与投资者情绪。实验表明，该方法显著提升了标准长短期记忆（LSTM）模型的预测性能，最大可提高5%。研究还揭示了各模态的独立与协同预测能力，强调了来自微博和新闻的情感分析对预测效果具有显著贡献。本工作为金融时间序列预测提供了系统化的多模态数据分析框架，为投资者决策提供新视角。
原文摘要 · Abstract (English)
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.
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