Geolocation and SEC Investigations: A New Perspective on Market Moves

Monday, 16 September 2024, 11:00

Geolocation data can reveal significant insights into when firms are under investigation by the SEC. By tracking smartphone data, researchers have uncovered patterns suggesting potential insider trading risks linked to SEC visits. The findings indicate that frequent SEC engagements may precede notable stock declines, emphasizing the relevance of geolocation in regulatory contexts.
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Geolocation and SEC Investigations: A New Perspective on Market Moves

Geolocation Insights: A New Approach to SEC Monitoring

Geolocation data has emerged as a powerful tool for revealing critical information about potentially impending investigations by the Securities and Exchange Commission (SEC). Researchers from multiple U.S. universities analyzed smartphone data across 26 metropolitan areas to identify patterns in SEC visits, linking them to company headquarters during business hours.

Understanding SEC Visits through Geolocation

  • The researchers monitored devices frequently present in SEC offices.
  • They discovered that many visits to firms occur outside formal investigations.
  • For companies historically under SEC scrutiny, visits increased significantly.

“We cannot observe any one individual,” stated Steven Irlbeck, assistant professor of finance. “What we can do is aggregate to understand patterns.”

Implications of SEC Engagements

  1. Firms that receive visits from the SEC tend to experience stock price declines.
  2. On average, stock prices drop between 1.4% and 1.94% following an SEC visit.
  3. Surprisingly, insider trading tendencies may decrease immediately surrounding SEC visits.

However, the data indicates that those who act on this information may avoid losses, particularly if they make trades before an investigation announcement can squash stock prices.


This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.


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