SLSTM-Based Covariance Prediction and Its Application in Hierarchical Risk Parity Asset Allocation
收藏DataCite Commons2025-04-27 更新2025-04-16 收录
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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.
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Science Data Bank
创建时间：
2025-02-24
