基于深度q网络的股票投资组合配置风险感知方法

Jacopo Fior, Luca Cagliero
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引用次数: 1

摘要

强化学习技术在股票投资组合的主动配置方面显示出巨大的潜力。然而,最先进的解决方案显示出有限的稳定性和对波动的市场条件相当高的敏感性。为了解决这些问题,本文提出了一种基于深度q学习网络的新的风险感知方法。它利用分位数回归dqn来减轻潜在的市场风险,并利用动作分支架构来有效地处理高维库存空间。此外,它还引入了噪声扰动到网络的权重,旨在自调整每个输入维度的探索程度。基于对道琼斯30指数股票进行的为期三年的实证模拟,所提出的系统在累积回报、稳定性和夏普比率方面优于最先进的RL解决方案。
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A risk-aware approach to stock portfolio allocation based on Deep Q-Networks
Reinforcement Learning techniques have shown a great potential in the active allocation of stock portfolios. However, state-of-the-art solutions show limited stability and fairly high sensitivity to volatile market conditions. To tackle these issues, this paper presents a new risk-aware approach based on Deep Q-learning Networks. It leverages Quantile Regression DQNs to mitigate the underlying market risks and an action branching architecture to effectively handle high-dimensional stock spaces. Furthermore, it also introduces noise perturbations to the network’s weights aimed at self-tuning the degree of exploration for each input dimension. Based on the empirical simulations, which were carried out on the Dow Jones-30 stocks over a three-year period, the proposed system performs better than state-of-the-art RL solutions in terms of cumulative return, stability, and sharpe ratio.
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