[论文解读] Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis

Qishuo Cheng, Le Yang+3 more|arXiv (Cornell University)|Feb 25, 2024

Stock Market Forecasting Methods被引用 17

一句话总结

本文介绍一种 Deep Q-Network 方法用于数字资产投资组合管理，强调相对于基准的改进并通过分布共享约束解决跨资产 Q 值漂移问题。

ABSTRACT

Portfolio management issues have been extensively studied in the field of artificial intelligence in recent years, but existing deep learning-based quantitative trading methods have some areas where they could be improved. First of all, the prediction mode of stocks is singular; often, only one trading expert is trained by a model, and the trading decision is solely based on the prediction results of the model. Secondly, the data source used by the model is relatively simple, and only considers the data of the stock itself, ignoring the impact of the whole market risk on the stock. In this paper, the DQN algorithm is introduced into asset management portfolios in a novel and straightforward way, and the performance greatly exceeds the benchmark, which fully proves the effectiveness of the DRL algorithm in portfolio management. This also inspires us to consider the complexity of financial problems, and the use of algorithms should be fully combined with the problems to adapt. Finally, in this paper, the strategy is implemented by selecting the assets and actions with the largest Q value. Since different assets are trained separately as environments, there may be a phenomenon of Q value drift among different assets (different assets have different Q value distribution areas), which may easily lead to incorrect asset selection. Consider adding constraints so that the Q values of different assets share a Q value distribution to improve results.

研究动机与目标

• Motivate the need for improved deep learning-based portfolio methods beyond single-prediction models.
• Propose integrating market-wide risk factors into asset-level decision making.
• Evaluate the effectiveness of a DRL-based portfolio strategy against benchmarks.
• Identify and mitigate Q-value drift across independently trained asset environments.

提出的方法

• 将 Deep Q-Network (DQN) 算法引入资产管理投资组合。
• 通过选择具有最大 Q 值的资产和行动来实现策略。
• 将不同资产视为独立环境进行独立训练。
• 研究不同资产间的 Q 值分布造成的 Q 值漂移。
• 建议添加约束，使各资产间的 Q 值共享一个分布以提升一致性。

实验结果

研究问题

• RQ1 采用 DRL 基于 DQN 的方法能否在数字资产投资组合管理中击败传统基准？
• RQ2 将每个资产视为一个独立环境如何影响学习动力学和决策质量？
• RQ3 导致跨资产 Q 值漂移的原因是什么，分布共享约束如何缓解？
• RQ4 结合更广泛的市场风险数据是否能提升超越独立资产数据的预测性能？
• RQ5 对 Q 值分布施加哪些约束能带来更稳定、可靠的资产选择？

主要发现

• 基于 DQN 的投资组合管理方法在作者评估中显著优于基准。
• 将资产作为独立环境进行训练揭示了可能影响资产选择的 Q 值漂移问题。
• 通过分布共享约束解决 Q 值分布漂移可以改善结果。
• 研究强调在考虑市场全局风险因素方面应超越单一资产数据的重要性。