基于市场情绪双重定的SSE预测
Lei Yang1, Bo Gan1, Xueyan Niu1
1Department of Business Administration, Shandong Labor Vocational and Technical College, Jinan, China.
PloS one
|December 26, 2025
概括
本研究引入了一种新的双重市场情绪定模型,用于精确预测上海证券交易所 (SSE) 指数. 先进的CNN2D-ABiLSTM模型达到90%以上的准确性,超过传统方法.
科学领域:
- * 金融计量经济学和计算金融学.
- * 机器学习在定量交易中的应用.
- * 行为金融和市场心理学.
背景情况:
- * 定偏差是金融市场中显著的认知效应.
- *现有的预测模型往往无法有效地将市场动态与投资者情绪相结合.
- *上海证券交易所 (SSE) 呈现出受情绪影响的复杂动态.
研究的目的:
- * 开发一种新的预测模型,将双重市场情绪定与先进的深度学习相结合.
- *为了提高上海证券交易所 (SSE) 指数预测的精度和交付时间.
- *通过CNN2D进行空间特征提取与通过CNN1D进行时间特征提取的有效性进行评估.
主要方法:
- * 开发一种双重市场情绪定多变量特征矩阵,使用SSE指数极端和情绪数据.
- * 实施一个市场情绪双 CNN2D-ABiLSTM (MSD-CNN2D-ABiLSTM) 模型.
- *利用CNN2D进行空间特征提取,BiLSTM用于时间特征集成,以及注意力机制.
主要成果:
- * 实现了超过90%的预测准确性和R2值>95%的预测准确性,预测SSE指数的预测时间为1到2天.
- * 证明有效的预测性能,可提前10个交易日.
- *CNN2D的局部空间特征提取比CNN1D的短期时间特征具有优越的预测优势.
结论:
- * 拟议的MSD-CNN2D-ABiLSTM模型为金融市场预测提供了显著的进步.
- *双和深度学习集成提供了强大而准确的SSE指数预测.
- *空间特征提取对于在预测中捕捉复杂的市场结构至关重要.
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