Social Media as a Bank Run Catalyst

Published: 3 November 2025| Version 2 | DOI: 10.17632/7jgcdt2vxd.2
Contributors:
J. Anthony Cookson, Corbin Fox, Javier Gil-Bazo, Juan Imbet, Christoph Schiller

Description

The data in this repository produces the findings in the paper, "Social Media as a Bank Run Catalyst". The repository's main README file is readme_all.html in the main high-level folder. The README file explains in detail how to interpret and use the data in this repository. The repository is structured into two main sub-directories: "./cross_sectional_hourly/", which produces the cross-sectional and hourly results, and "./high_frequency/", which produces the high-frequency results.

Files

Steps to reproduce

For cross-sectional results, as described in readme_all.html: 1) Open R-project file banking_socialmedia.RProj in RStudio. 2) Execute script ./code/00_main.R. Output files will be saved in ./write/tables_out and ./write/figures_out. For high-frequency results, as described in readme_all.html: 1) Run python/run_all.py to construct the data inputs. 2) Run programs in stata/ to generate tables 8 and 9. The program stata/SBRC_01_hf_analysis.do will run them all. Output files are called appropriate names (table8.tex, table9.tex) and are stored in folder tables/

Institutions

  • University of Colorado Boulder
  • Ohio State University
  • Universitat Pompeu Fabra
  • Universite Paris Dauphine
  • Clemson University

Categories

Banking, Finance, Social Media

Licence