混合深度学习模型与多种分类投资者情绪预测中国领先股票的价格
Hongli Niu1, Qiaoying Pan1,2, Kunliang Xu1
1School of Economics and Management, University of Science and Technology Beijing, Beijing, China.
PloS one
|November 27, 2023
概括
这项研究通过将深度学习与新的投资者情绪数据集集成,增强了中国A股股价预测. 整合情绪分析显著提高了行业领导者的预测准确度.
科学领域:
- * 计算金融学
- * 人工智能 * 人工智能
- * 行为金融学
背景情况:
- * 股票价格预测是一个复杂的挑战,特别是在像中国A股这样的动态市场.
- *现有的模型往往缺乏强大的方法来纳入细微的投资者情绪.
- *了解投资者心理对于准确的金融市场预测至关重要.
研究的目的:
- * 预测中国五个主要A股行业的突出股价.
- * 根据现代心理学,开发和使用一套新的多分类投资者情绪数据集.
- *评估情绪分析对股票价格预测准确性的影响.
主要方法:
- * 基于四种心理情绪类型,为中国股市构建了一个独特的情绪多分类数据集.
- *应用双向长期短期记忆与注意力 (Bi-LSTM-ATT) 模型来分析情绪异质性.
- *利用卷积神经网络-Bi-LSTM-ATT (CNN-Bi-LSTM-ATT) 模型来评估情绪对股价预测的影响.
主要成果:
- * 该研究成功构建了针对中国股票市场的全面投资者情绪数据集.
- *Bi-LSTM-ATT模型有效地捕捉和分析了不同部门的情绪变化.
- *将情绪指标集成到CNN-Bi-LSTM-ATT模型中显著提高了股票价格预测的准确性.
结论:
- * 深度学习模型与强大的投资者情绪数据集相结合,为股票价格预测提供了强大的方法.
- * 开发的情绪数据集为分析金融市场投资者行为提供了有价值的框架.
- * 纳入心理情绪分析是提高股票市场预测精度的关键因素.
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