ArticleEntropy (Basel, Switzerland)2020
Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- DisenKGE-DDI: A Knowledge Graph Embedding Framework Based on Disentangled Graph Attention Networks for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Analyzing crises in global financial indices using Recurrent Neural Network based Autoencoder.PloS one · 2025Article
- Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings.Entropy (Basel, Switzerland) · 2024Article
- Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies.PloS one · 2024Article
- Explanatory Change Detection in Financial Markets by Graph-Based Entropy and Inter-Domain Linkage.Entropy (Basel, Switzerland) · 2022Article
- The Linear Relationship Model with LASSO for Studying Stock Networks.Entropy (Basel, Switzerland) · 2022Article
- Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm.Entropy (Basel, Switzerland) · 2022Article
- Spatial Correlation Network and Regional Differences for the Development of Digital Economy in China.Entropy (Basel, Switzerland) · 2021Article
- Structure and dynamics of financial networks by feature ranking method.Scientific reports · 2021Article
- A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis.Entropy (Basel, Switzerland) · 2021Article
- Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
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Abstract
Complex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Information theory is much less discussed for this important topic, though mutual information is able to measure nonlinear pairwise relationship. In this work we propose to use part mutual information for developing stock networks. The path-consistency algorithm is used to filter out redundant relationships. Using the Australian stock market data, we develop four stock relation networks using different orders of part mutual information. Compared with the widely used planar maximally filtered graph (PMFG), we can generate networks with cliques of large size. In addition, the large cliques show consistency with the structure of industrial sectors. We also analyze the connectivity and degree distributions of the generated networks. Analysis results suggest that the proposed method is an effective approach to develop stock relation networks using information theory.
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Registered trials
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