用情绪分析增强GRU模型，提升股市预测准确率
GRUvader: Sentiment-Informed Stock Market Prediction
- 结合词典法情绪特征与GRU网络进行股价预测
- 情绪指标与股价变动存在显著相关性
- AI增强模型优于传统独立模型，适合金融预测研究者
由于全球经济不稳定、高波动性和金融市场的复杂性，股票价格预测极具挑战。本研究对比了多种机器学习算法在股市预测中的表现，并进一步考察了情绪分析指标对股价预测的影响。结果表明：首先，采用基于词典的情绪分析方法提取情感特征，验证了情绪指标与股价变动之间的相关性；其次，提出GRUvader——一种优化的门控循环单元网络，用于股票市场预测。研究发现，独立模型表现不佳，而基于人工智能增强的系统表现更优，因此本文对后一类系统提出了进一步改进建议。
原文摘要 · Abstract (English)
Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction of stock prices. Our results were two-fold. Firstly, we used a lexicon-based sentiment analysis approach to identify sentiment features, thus evidencing the correlation between the sentiment indicator and stock price movement. Secondly, we proposed the use of GRUvader, an optimal gated recurrent unit network, for stock market prediction. Our findings suggest that stand-alone models struggled compared with AI-enhanced models. Thus, our paper makes further recommendations on latter systems.
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