LEET:股票市场预测与长期情绪变化增强的时间模型
Honglin Liao1, Jiacheng Huang1, Yong Tang2
1Maynooth International Engineering College, Fuzhou University, Fuzhou, Fujian Province, China.
PeerJ. Computer science
|April 25, 2024
概括
这项研究引入了长期情绪变化增强时间分析 (LEET),以更准确地预测股价. LEET有效地纳入了长期的市场情绪,优于现有的深度学习模型.
科学领域:
- * 计算金融和人工智能.
- * 金融市场分析和预测.
背景情况:
- * 股价预测对投资者来说至关重要,但往往忽视了市场情绪.
- * 现有的深度学习模型在大型数据集中的长期依赖性和情绪分析方面扎.
- * 忽视长期市场情绪可能导致不准确的股票价格预测.
研究的目的:
- * 提出一种新的技术,即长期情绪变化增强时间分析 (LEET),用于改进股票价格预测.
- *有效地将长期市场情绪纳入预测模型.
- *通过解决当前深度学习方法的局限性,提高股票价格预测的精度.
主要方法:
- * 开发LEET方法,结合两种情绪指数估计方法:指数加权情绪分析 (EWSA) 和加权平均情绪分析 (WASA).
- * 实现了带有 ProbAttention 和旋转位置编码的变压器架构,以捕捉长期的情绪依赖.
- *使用标准普尔500 (SP500) 和富时100指数验证LEET方法.
主要成果:
- *与大多数深度学习架构相比,LEET方法在股票价格预测方面表现优越.
- *实验结果在现实世界的数据集证实了整合长期市场情绪的有效性.
- * 提出的情绪分析技术 (EWSA和WASA) 成功提取了相关的市场情绪指数.
结论:
- * 通过整合长期市场情绪,LEET技术在股票价格预测方面取得了重大进展.
- * 具有 ProbAttention 和旋转位置编码的变压器架构有效地捕捉了长期的市场动态.
- *LEET提供了一种更强大,更准确的股票市场预测方法,比现有的方法更高效.
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