Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies.

ABSTRACT: The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing

SUBMITTER: Peng W 

PROVIDER: S-EPMC11239051 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature