Social Networks and the Propagation of Household Shocks to Asset Prices

Paper: arXiv 2611.01540

Authors: Faisal Quaiyyum

Abstract

When can local household disturbances move credit prices? I develop a financially weighted propagation operator on the social network. In a conditional household-intermediary equilibrium, its projection determines the household-demand component of the excess bond premium. Because responses decay at different rates across network patterns and channels, a location’s relative financial importance generically changes with the horizon, so no single centrality measure summarizes exposure across horizons. Household measurements recover this projection exactly when they span independently measured demand sensitivities. Empirically, social exposure to storm-damaged counties is negatively associated with spending weeks later in counties outside the recorded footprints. In simulations on the observed network under the model’s diffusion law, the operator predicts the propagated credit-premium component of held-out storms with about 30 percent less error than direct social exposure.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a highly complex mathematical framework for social network propagation, coupled with rigorous empirical testing using real-world storm data and simulations. Its novelty lies in integrating financially weighted propagation operators into asset pricing, offering a fresh perspective on household shock transmission.

Research Flowchart

  flowchart TD
    A[Research Goal: When can local household disturbances move credit prices?] --> B{Methodology: Develop Financially Weighted Propagation Operator};
    B --> C[Data/Inputs: Social Network, Household-Intermediary Equilibrium Model, Storm-Damaged Counties (Empirical), Held-out Storms (Simulations)];
    C --> D{Computational Process: Project Operator to Determine Household-Demand Component of Excess Bond Premium};
    D --> E{Computational Process: Simulate Diffusion Law on Observed Network};
    E --> F[Key Finding 1: Location's relative financial importance changes with horizon; no single centrality measure suffices];
    F --> G[Key Finding 2: Social exposure to storm-damaged counties negatively associated with spending weeks later outside footprints];
    G --> H[Key Finding 3: Operator predicts propagated credit-premium component with ~30% less error than direct social exposure];