SLSTM-Based Covariance Prediction and Its Application in Hierarchical Risk Parity Asset Allocation
收藏科学数据银行2025-02-24 更新2026-04-23 收录
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资源简介：
In this paper, we integrate the selective state space model (SSSM) with the long short term memory (LSTM) neural network to construct a selective long short term memory (SLSTM) neural network model. We propose a novel dynamic covariance matrix prediction method and apply it to improve the hierarchical risk parity (HRP) asset allocation strategy.
提供机构：
江西财经大学; 中山大学; Chenlanxi; 华南理工大学
创建时间：
2025-02-22
