基于行为的深度强化学习,以优化投资组合,避免损失和过度自信
Atefe Charkhestani1, Akbar Esfahanipour2
1Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.
Scientific reports
|January 27, 2026
概括
本研究介绍了一种基于行为的深度强化学习 (DRL) 框架,用于算法交易. 整合投资者偏见,如损失厌恶和过度自信,可以提高动态市场的交易绩效和稳定性.
科学领域:
- 计算金融是指计算金融.
- 行为金融是行为金融.
- 机器学习 机器学习
背景情况:
- 传统的算法交易模型通常假定理性投资者,忽视现实世界的行为影响.
- 深度强化学习 (DRL) 提供了适应性交易策略,但通常缺乏行为考虑.
- 投资者的异质性和市场动态需要更复杂的投资组合优化框架.
研究的目的:
- 开发一个新的DRL框架,整合关键的行为偏见 (损失厌恶,过度自信) 来进行算法投资组合优化.
- 在非固定金融市场中增强交易策略的适应性和稳定性.
- 根据加密货币和股票市场的既定基准来评估框架的表现.
主要方法:
- 开发了一种基于行为信息的DRL演员批判架构,其中包括损失厌恶和过度自信.
- 实施了适应性定位大小和投资者异质性的取决于制度的偏差值.
- 利用TimesNet进行市场制度预测,以动态切换行为模型 (不利于损失,过度自信,中立).
- 采用严格的步行前进评估协议进行现实的样本外绩效评估.
主要成果:
- 集成的BBAPT架构 (TimesNet + 行为DRL) 始终优于基准策略 (中性RL,马科维茨,同等权重).
- 在加密货币市场 (2018-2024) 中实现了优异的风险调整表现.
- 在股票市场 (道斯,2008-2024) 提供了更好的风险回报结果,即使有时间变化的组成部分.
结论:
- 将行为金融原则嵌入DRL显著提高交易系统的稳定性,适应性和风险调整回报.
- 以行为为基础的DRL为下一代算法交易提供了有希望的基础,特别是在波动的,非静止的金融环境中.
- 拟议的框架证明了将心理因素纳入人工智能驱动的财务决策的实际实用性.
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