计算机科学与探索2026，Vol.20Issue(6)：1746-1768,23.DOI:10.3778/j.issn.1673-9418.2507069
基于多元函数型聚类和深度强化学习的最优资产配置策略
Optimal Asset Allocation Strategy Based on Multivariate Functional Clustering and Deep Reinforcement Learning
摘要
Abstract
This paper proposes a novel optimal asset allocation method by comprehensively using multivariate functional clustering,deep learning,and deep reinforcement learning techniques.Firstly,the multivariate functional clustering method is used to cluster the available stock assets in the trading pool.Secondly,the LSTM-Non-stationary Transformer model with time series feature extraction capabilities is applied to predict the return of each stock.Next,based on the return prediction information,high-quality assets are selected from each clustered stock pool.Finally,dynamic optimal asset allocation models are constructed using three deep reinforcement learning methods:advantage actor-critic,proximal policy optimization,and soft actor-critic,based on the representative assets selected from each stock pool and their prediction information.This asset allocation strategy is implemented using the constituent stocks of the Shanghai Stock Exchange 50 Index and the Nasdaq 100 Index.The empirical results show that asset screening through functional clustering can effectively reduce the impact of non-systematic risk on asset allocation performance.By incorporating return prediction information into the deep reinforcement learning models,the profitability of asset allocation can be significantly improved,and robust,sustainable returns can be achieved.Furthermore,compared with the maximum Sharpe ratio model,the equal-weight model,and the market index,the proposed model demonstrates superior investment performance.关键词
多元函数型聚类/半非负矩阵分解/LSTM-非平稳Transformer模型/深度强化学习/资产配置优化Key words
multivariate functional clustering/semi-nonnegative matrix factorization/LSTM-non-stationary Transformer model/deep reinforcement learning/asset allocation optimization分类
信息技术与安全科学引用本文复制引用
孙景云,贺哲,姚晓红..基于多元函数型聚类和深度强化学习的最优资产配置策略[J].计算机科学与探索,2026,20(6):1746-1768,23.基金项目
国家自然科学基金(72061020) （72061020）
甘肃省自然科学基金(25JRRA979) （25JRRA979）
甘肃省"飞天学者"特聘教授项目 （）
兰州财经大学金融统计科研融合团队项目(XKKYRHTD202304) （XKKYRHTD202304）
甘肃省科技重大专项计划(24ZDWA007). This work was supported by the National Natural Science Foundation of China(72061020),the Natural Science Foundation of Gansu Province(25JRRA979),the Gansu Province"Feitian Scholar"Distinguished Professor Program,the Project of Financial Statistics Research Integration Team of Lanzhou University of Finance and Economics(XKKYRHTD202304),and the Gansu Provincial Major Science and Technology Special Project(24ZDWA007). （24ZDWA007）
