对股票资产管理的混合深度学习模型的研究
Yuanzhi Huo1, Mengjie Jin2, Sicong You3
1College of Information Engineering, HAUST, Luoyang, Henan, China.
PeerJ. Computer science
|December 16, 2024
概括
本研究介绍了一种混合深度学习模型,将长短期记忆 (LSTM) 网络和强化学习 (RL) 结合起来,用于同时预测和交易股票价格. 由人工智能驱动的方法将股票资产提高1.05倍,证明了有效的投资管理.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 金融预测 金融预测
背景情况:
- 由于市场波动,开发利的股票交易策略是复杂的.
- 现有的人工智能方法,如用于预测的LSTM和用于交易的RL,通常是孤立使用的.
- 需要集成的人工智能解决方案来同时预测股票价格和自动交易.
研究的目的:
- 提出一种新的混合深度学习模型,用于整合股票价格预测和交易.
- 通过减少市场不确定性和最大限度地提高股票资产来加强资产管理.
- 利用人工智能实现更有效,更低风险的投资策略.
主要方法:
- 一种混合模型,集成长期短期记忆 (LSTM) 网络进行价格预测和强化学习 (RL) 进行交易控制.
- 改进了LSTM模型,以预测趋势理解的平均定向指数 (ADX).
- 纳入特定约束来帮助资产管理和降低风险.
主要成果:
- 混合模型在预测股票价格波动时实现了0.94的平均R平方值.
- 综合方法,包括ADX预测和约束,增加了股票资产的1.05倍初始金额.
- 证明了成功的同时预测和交易,优于单独的AI应用程序.
结论:
- 拟议的混合LSTM-RL模型为同时预测和交易股票价格提供了一个强大的解决方案.
- 整合ADX和约束可以显著改善资产管理,降低风险并最大限度地提高回报.
- 这种人工智能驱动的方法代表了自动化股票交易和投资管理的重大进步.
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