Sectoral inter-dependencies drive the loss of structural balance in signed financial networks
Kartik Dahake, Abhijit Chakraborty
Abstract
Signed graphs provide an effective architecture for portraying a system in which cooperation and conflict coexist. Emerging from the concept of balance in psychological sciences, they have found applications across several domains. Financial markets are one such example that can be modeled using signed networks, where assets exhibit correlations in price movements. During periods of systemic risk, such a signed financial network shows a loss of balance, which has been consistently demonstrated. Here, we explore how this structural imbalance is distributed across scales within the financial network, revealing its mesoscopic origin. Adopting the framework of structural balance theory, we use a measure of polarization based on triadic motifs to investigate the distribution of structural imbalance across varying sectoral scales. We analyze the temporal evolution of global polarization and its sectoral constituents using longitudinal data derived from the S&P 500 index. By decomposing global polarization into intra-sectoral and inter-sectoral constituents, we show that structural imbalance arises predominantly from interactions between sectors rather than within them during periods marked by systemic risk. We employ randomization protocols to confirm that observed imbalance configurations are statistically significant and not artifacts of lower-order interactions. We derive a regression equation demonstrating that the variance in global polarization is well explained by macroeconomic variables, indicating that low levels of global polarization during economic crises are driven by compounding pressures from supply chain disruptions and inflation uncertainty. Collectively, these findings provide a quantitative framework for understanding how localized sectoral conflicts propagate across the financial network and contribute to large-scale structural instability during periods of economic crisis.
Read the AI summary and key takeaways for traders on WOBR Quant Research.