Paper: SSRN 4422754

Abstract

After the run on Silicon Valley Bank (SVB) in March 2023, U.S. regional banks entered a period of significant distress. We quantify social media’s role in this

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses extensive Twitter data and robust econometric specifications (e.g., regression analyses with numerous controls, specification curves) to link social media exposure to bank run outcomes, demonstrating high empirical rigor. The mathematical content is relatively light, focusing on regression models and standard financial metrics rather than advanced theoretical derivations.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Quantify social media's role<br>in SVB bank run"] --> B["Key Methodology<br>High-frequency data analysis"]
  B --> C["Data / Inputs<br>Social media volume & sentiment<br>Bank stock prices & CDS spreads"]
  C --> D["Computational Process<br>Causal inference & time-series<br>regression models"]
  D --> E["Key Findings<br>1. Social media predicts withdrawals<br>2. Amplifies deposit flight<br>3. Material impact on bank stability"]