TD-HCN:一种趋势驱动的超图卷积网络,用于股票回报预测.
Lexin Fang1, Tianlong Zhao2, Junlei Yu1
1School of Software, Shandong University, Jinan 250101, China.
概括
这项研究引入了一种新的趋势驱动超图卷积网络 (TD-HCN) 用于股票回报预测. TD-HCN有效地捕捉了复杂的,动态的库存关系,优于现有的方法.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 由于其动态,复杂和非线性性质,库存数据分析具有挑战性.
- 现有的基于图表的方法难以捕捉更高阶和动态的股票关系.
- 这种限制阻碍了股票回报预测模型的性能.
研究的目的:
- 提出一种新的趋势驱动的超图卷积网络 (TD-HCN) 用于股票回报预测.
- 整合多种类型的股票数据 (价格,行业,wiki关系) 以改善分析.
- 加强地方动态和全球静态关系的识别和利用.
主要方法:
- 开发了一个趋势驱动的超图卷积网络 (TD-HCN).
- 采用先前受约束的关系学习 (PCRL) 模型来发现潜在的高阶关系.
- 采用了一种带有双重注意力模块的解代表性学习 (DRL) 机制来捕捉动态趋势.
主要成果:
- 在纳斯达克和纽约证券交易所数据集上,TD-HCN的表现始终超过了最先进的方法.
- 在股票回报预测方面取得了重大改进.
- 在学习动态股票关系和捕捉趋势变化方面表现出有效性.
结论:
- 拟议的TD-HCN模型为股票回报预测提供了一个强大而有效的方法.
- 综合多样化的数据和先进的深度学习技术可以更好地捕捉复杂的股票市场动态.
- 在分析和预测股市趋势方面,TD-HCN提供了显著的进步.
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