融合社交媒体情绪与蜡烛图数据，用CNN-LSTM预测股价
Stock Price Prediction using Multi-Faceted Information based on Deep Recurrent Neural Networks
- 用CNN提取短期价格特征，LSTM捕捉长期依赖
- 结合推特情绪分类与蜡烛图，提升预测准确率
- 适合量化交易与金融风控场景使用
准确预测股市走势对投资决策和组合管理至关重要，有助于财富增长与风险控制。本研究提出一种新方法，通过整合卷积神经网络（CNN）与长短期记忆网络（LSTM），结合社交媒体数据的情绪分析与蜡烛图数据（价格）进行股价预测。该方法包含两大核心：社交媒体情绪分析与蜡烛图数据分析。通过融合推特情绪与蜡烛图信息，实现对市场趋势与模式的更细致、更精准分析，从而提升股价预测效果。此外，采用随机森林算法对推文进行正负情感分类，实现对市场情绪的更精细评估。利用CNN提取短期特征，LSTM建模长期依赖，二者协同实现对市场动态的全面分析，显著提高预测准确性。
原文摘要 · Abstract (English)
Accurate prediction of stock market trends is crucial for informed investment decisions and effective portfolio management, ultimately leading to enhanced wealth creation and risk mitigation. This study proposes a novel approach for predicting stock prices in the stock market by integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, using sentiment analysis of social network data and candlestick data (price). The proposed methodology consists of two primary components: sentiment analysis of social network and candlestick data. By amalgamating candlestick data with insights gleaned from Twitter, this approach facilitates a more detailed and accurate examination of market trends and patterns, ultimately leading to more effective stock price predictions. Additionally, a Random Forest algorithm is used to classify tweets as either positive or negative, allowing for a more subtle and informed assessment of market sentiment. This study uses CNN and LSTM networks to predict stock prices. The CNN extracts short-term features, while the LSTM models long-term dependencies. The integration of both networks enables a more comprehensive analysis of market trends and patterns, leading to more accurate stock price predictions.
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