系统管理学报2026，Vol.35Issue(1)：233-246,14.DOI:10.3969/j.issn2097-4558.2026.01.017
端到端框架下基于LSTM与在线修正的适应性投资组合策略
Adaptive Investment Portfolio Strategy Based on LSTM and Online Modification Under an End-to-End Framework
摘要
Abstract
Deep learning exhibits powerful capabilities for handling long-sequence information and modeling intricate relationships.This paper,utilizing a many to many long short-term memory(M2M-LSTM)network,investigates portfolio strategies under an end-to-end framework.First,within the end-to-end deep learning framework,it constructs a portfolio strategy by integrating a M2M-LSTM neural network with a sliding window technique.Then,using a fixed historical window uniform constant rebalancing strategy as a benchmark,it assesses and adjusts the recent performance of the neural network-based strategy online to mitigate concept drift.Finally,it aggregates adjusted strategies from multiple historical windows to a robust portfolio strategy.Numerical analysis based on domestic and international market data indicate that the proposed strategy outperforms comparison strategies in terms of robustness,profitability,and sensitivity to transaction costs.关键词
投资组合/端到端学习/多对多长短期记忆网络/在线修正/概念漂移Key words
portfolio/end-to-end learning/many to many long short-term(M2M-LSTM)memory networks/online modification/concept drift分类
管理科学引用本文复制引用
刘悦,张永,黎嘉豪,王晓辉..端到端框架下基于LSTM与在线修正的适应性投资组合策略[J].系统管理学报,2026,35(1):233-246,14.基金项目
国家自然科学基金资助项目(72371080,72101183) （72371080,72101183）
广东省基础与应用基础研究基金资助项目(2024A1515012670,2023A1515012840) （2024A1515012670,2023A1515012840）
广州市基础与应用基础研究专题(SL2024A04J02640) （SL2024A04J02640）
