基于注意力的动态图表神经网络用于资产定价
Ajim Uddin1, Xinyuan Tao1, Dantong Yu1
1Martin Tuchman School of Management, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, USA.
概括
本研究引入了一种用于资产定价的新图形神经网络模型,通过分析公司网络和市场动态,有效预测股票回报. 该模型提高了投资组合的表现,并捕捉了关键的市场事件.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 公司网络显著影响资产定价.
- 现有的模型往往忽略了动态的公司间关系.
研究的目的:
- 为资产定价开发一种新的图形神经网络 (GNN) 模型.
- 整合动态网络结构和固定的信息,以改善预测.
- 评估模型在回报预测和投资组合优化方面的有效性.
主要方法:
- 利用图表注意力机制来学习动态股票市场网络结构.
- 在学习网络中使用循环卷积神经网络 (CNN) 进行信息扩散.
- 结合图形神经网络与循环和卷积组件,用于端到端的资产定价.
主要成果:
- 拟议的GNN模型有效地预测了股票收益率.
- 在投资组合业绩中观察到显著的改善.
- 该模型在各种测试和模拟数据中表现出强大而持久的性能.
- 学习的动态网络准确地反映了主要的市场事件.
结论:
- 新型GNN模型成功地捕捉了市场网络结构和动态移动.
- 这种方法为投资者和监管机构提供了宝贵的见解.
- 该模型为理解和预测复杂市场环境中的资产价格提供了一个强大的工具.
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