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基于模型的强化学习与非高斯环境动态及其对投资组合优化应用
Huifang Huang1, Ting Gao2, Pengbo Li2
1School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China.
Chaos (Woodbury, N.Y.)
|August 10, 2023
概括
本研究介绍了一种新的AI交易策略,使用重尾保护复杂金融市场的规范化流动. 该方法提高了投资组合的优化,降低了风险,在市场波动期间表现优于现有的方法.
科学领域:
- 量化金融 量化金融
- 人工智能的人工智能
- 金融工程是金融工程.
背景情况:
- 金融市场表现出复杂的动态,包括突然的转变和隐藏的因果因素,挑战传统的基于AI的算法交易.
- 精确模拟高维联合概率对于强大的投资组合优化至关重要.
研究的目的:
- 开发一个人工智能驱动的算法交易策略,有效模拟复杂的金融市场.
- 通过在基于模型的强化学习框架内采用重尾保护正常化流程来增强投资组合优化.
主要方法:
- 利用重尾保护正常化流量来模拟高维联合概率.
- 实施了基于模型的强化学习框架,用于算法交易.
- 在道,纳斯达克和标普市场的股票上进行了实验.
主要成果:
- 拟议的方法在测试市场的投资组合优化方面表现出卓越的表现.
- 这种方法有效地减轻了COVID-19大流行期间的损失,显示了较低的最大 drawdown.
- 分析证实了算法的融合,并确定了优化的有效模式.
结论:
- 在AI交易中,重尾保护正常化流提供了一个强大的解决方案,用于模拟复杂的金融环境.
- 基于模型的强化学习框架为适应性和弹性投资组合优化提供了一个强大的工具.
- 该研究强调了先进的人工智能技术在应对金融市场不确定性方面的潜力.
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