Anti-correlation network among China A-shares

Paper: arXiv 2404.00028

Abstract

The correlation-based financial networks are studied intensively. However, previous studies ignored the importance of the anti-correlation. This paper is the first to consider the anti-correlation and positive correlation separately, and accordingly construct the weighted temporal anti-correlation and positive correlation networks among stocks listed in the Shanghai and Shenzhen stock exchanges. For both types of networks during the first 24 years of this century, fundamental topological measurements are analyzed systematically. This paper unveils some essential differences in these topological measurements between the anti-correlation and positive correlation networks. It also observes an asymmetry effect between the stock market decline and rise. The methodology proposed in this paper has the potential to reveal significant differences in the topological structure and dynamics of a complex financial system, stock behavior, investment portfolios, and risk management, offering insights that are not visible when all correlations are considered together. More importantly, this paper proposes a new direction for studying complex systems: the anti-correlation network. It is well worth reexamining previous relevant studies using this new methodology.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced network theory concepts (correlation matrices, topological measurements like assortativity and clustering) and formal definitions, indicating high math complexity. It uses a substantial 24-year dataset of China A-shares with robustness checks, but lacks explicit backtesting or trading strategy implementation, placing it in the high-math, high-rigor quadrant.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Isolate and analyze<br>Anti-correlation in<br>China A-share Market"] --> B["Data Input:<br>24 Years of Stock Price Data<br>Shanghai & Shenzhen Exchanges"]
  B --> C["Methodology: Separate Networks<br>Construct Weighted Temporal Networks:<br>Positive Correlation vs Anti-correlation"]
  C --> D["Computational Analysis:<br>Systematic Topological Measurements<br>(e.g., Degree Centrality, Clustering, Path Length)"]
  D --> E{"Key Findings & Outcomes"}
  E --> F["Structural Differences:<br>Essential distinctions between<br>Positive and Anti-correlation networks"]
  E --> G["Market Asymmetry:<br>Asymmetry effect between<br>market decline and rise"]
  E --> H["New Direction:<br>Proposed methodology for<br>Complex Systems & Risk Management"]

Cited References (38)

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3. The multilayer nature of ecological networks (2017) — link
4. The physics of financial networks (2021) — link
5. Tail event driven networks of SIFIs (2019) — link
6. Coupled propagation dynamics on complex networks: A brief review (2024) — link
7. Mapping coupled time-series onto a complex network (2020) — link
8. Explaining Financial Markets in Terms of Complex Systems (2014) — link

0 of 38 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.