About This Project
Who we are, what we found, and how to get in touch or dig deeper into the research.
The Project
This research was conducted by the Undergraduate AI Society. Our goal was to test whether macroeconomic conditions predict cryptocurrency adoption—using publicly available data and transparent methods.
- Timeline: 2019–2025
- Approach: We combine statistical analysis (correlation, Granger causality) with machine learning comparison. We report both what works and what doesn’t, including the finding that simple Ridge regression outperformed more complex ML models.
Key Findings Summary
- Strong correlation between economic anxiety and adoption (r = 0.67).
- M2 money supply is the primary driver of the relationship.
- 65% directional accuracy is achievable using only macro data (time series cross-validation).
- Simple models (Ridge) outperformed more complex machine learning in out-of-sample tests.
The Team
Viktoriia Lysenko
Team Lead
Amara Zin
Team Member
Raahim Khan
Team Member
Dominik Vrbanek
Team Member
Gloria Mathew
Team Member
Contact & Resources
Disclaimer
This is academic research, not financial advice. Do not make investment decisions based solely on this analysis. Past correlations do not guarantee future results.