Local and Global Balance in Financial Correlation Networks: an Application to Investment Decisions

ArXiv ID: 2512.10606 “View on arXiv”

Authors: Paolo Bartesaghi, Rosanna Grassi, Pierpaolo Uberti

Abstract

The global balance is a well-known indicator of the behavior of a signed network. Recent literature has introduced the concept of local balance as a measure of the contribution of a single node to the overall balance of the network. In the present research, we investigate the potential of using deviations of local balance from global balance as a criterion for selecting outperforming assets. The underlying idea is that, during financial crises, most assets in the investment universe behave similarly: losses are severe and widespread, and the global balance of the correlation-based signed network reaches its maximum value. Under such circumstances, standard diversification (mainly related to portfolio size) is unable to reduce risk or limit losses. Therefore, it may be useful to concentrate portfolio exposures on the few assets - if such assets exist-that behave differently from the rest of the market. We argue that these assets are those for which the local balance strongly departs from the global balance of the underlying signed network. The paper supports this hypothesis through an application using real financial data. The results, in both descriptive and predictive contexts, confirm the proposed intuition.

Keywords: Signed Networks, Local Balance, Network Topology, Correlation Structure, Asset Selection, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper applies advanced mathematical concepts like network theory, matrix exponentials, and eigenvalue analysis for a financial application, and it is empirically supported with real financial data and a multi-dataset rolling-window backtest, including sensitivity analysis and performance metrics.

  flowchart TD
    A["Research Goal<br>Identify assets that deviate from market consensus<br>during crises for improved diversification"] --> B["Data Input<br>Financial Correlation Networks<br>Signed correlation matrices"]
    B --> C["Key Methodology<br>Compute Global Balance<br>Compute Node-Level Local Balance"]
    C --> D["Core Analysis<br>Calculate Local-Global Balance Deviation"]
    D --> E{"Filter"}
    E -- "Assets with<br>Significant Deviation" --> F["Portfolio Selection<br>Concentrate on unique assets"]
    E -- "Standard Assets" --> G["Outcome: Risk Limitation<br>Resilience during<br>market crashes"]
    F --> G