ArticleEntropy (Basel, Switzerland)2018
Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market.
Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Data-driven prediction of future purchase behavior in cross-border e-commerce using sequence modeling with PSO-tuned LSTM.PloS one · 2025Article
- The Complexity Behavior of Big and Small Trading Orders in the Chinese Stock Market.Entropy (Basel, Switzerland) · 2023Article
- Spillover Network Features from the Industry Chain View in Multi-Time Scales.Entropy (Basel, Switzerland) · 2022Article
- An Entropy-Based Approach to Measurement of Stock Market Depth.Entropy (Basel, Switzerland) · 2021Article
- Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic.Entropy (Basel, Switzerland) · 2020Article
- Stock Market Volatility and Return Analysis: A Systematic Literature Review.Entropy (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
By introducing net entropy into a stock network, this paper focuses on investigating the impact of network entropy on market returns and trading in the Chinese Growth Enterprise Market (GEM). In this paper, indices of Wu structure entropy (WSE) and SD structure entropy (SDSE) are considered as indicators of network heterogeneity to present market diversification. A series of dynamic financial networks consisting of 1066 daily nets is constructed by applying the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model with a threshold adjustment. Then, we evaluate the quantitative relationships between network entropy indices and market trading-variables and their bilateral information spillover effects by applying the bivariate EGARCH model. There are two main findings in the paper. Firstly, the evidence significantly ensures that both market returns and trading volumes associate negatively with the network entropy indices, which indicates that stock heterogeneity, which is negative with the value of network entropy indices by definition, can help to improve market returns and increase market trading volumes. Secondly, results show significant information transmission between the indicators of network entropy and stock market trading variables.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.