.png){kind=icon}
返回
Uncertainty-Aware Reinforcement Learning for Portfolio Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3494859.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We explored the use of Reinforcement Learning (RL) combined with risk assessment for optimizing investment portfolios. The dynamic nature of trading, compounded by market frictions, the responses of other market participants, and uncertainties, poses challenges to portfolio optimization. The financial market's intricacies make it difficult to model accurately, compounded by regulatory requirements and internal risk policies mandating risk-averse decisions to avoid catastrophic outcomes. To address this, we proposed risk estimation for investor's risk tolerance threshold. Moreover, modern Deep Learning models are adept at approximating complex relationship between abundant data, however, the main drawback we face now a day is generalization of the relationship to the unseen data. Therefore, the epistemic uncertainty can pose risk to the decision making system. This uncertainty is further addressed using a Variational Autoencoder (VAE) to estimate, and Cost Network to backpropogate riskiness through the model to learn actions with safe results. The actions with stable result or lower reward will be avoided due to reward optimization of RL. Consequently, we successfully managed to reduce the risk and uncertainties in the agent testing process. Our risk-constrained RL algorithm demonstrated zero violation of the constraint in the testing phase. This suggests that adopting a risk-averse RL approach could be beneficial for portfolio optimization, particularly for risk-averse investors.
Keyword:
Uncertainty
Portfolios
Optimization
Investment
Deep learning
Bayes methods
Data models
Predictive models
Measurement uncertainty
Decoding
Risk-averse reinforcement learning
portfolio optimization
variational auto encoder
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection具有不确定性量化的贝叶斯自动编码器: 迈向可信赖的异常检测
OAAI
OAAI分享收藏Social Network Analysis Metrics and Their Application in Microbiological Network Studies社交网络分析指标及其在微生物网络研究中的应用
PREAI
分享收藏A prescriptive Dirichlet power allocation policy with deep reinforcement learning一种基于深度强化学习的规定性Dirichlet功率分配策略OAAI
OAAI分享收藏
查看更多内容