The Geometry of Financial Institutions -Wasserstein Clustering of Financial Data
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View abstract on PubMed
Summary
This summary is machine-generated.Regulators can now map financial institutions using optimal transport theory. This method assesses credit data as probability distributions, revealing institutional similarities and clusters for better oversight.
Area Of Science
- Quantitative finance
- Computational statistics
- Financial regulation
Background
- Financial institutions generate vast, granular data for regulatory oversight.
- Condensing this data into a map of institutional similarity is challenging.
- Missing data further complicates regulatory analysis.
Purpose Of The Study
- To develop a method for mapping the financial landscape.
- To identify clusters and outliers among financial institutions.
- To quantify the similarity and distance between institutions.
Main Methods
- Interpreting financial credit data as probability distributions.
- Applying optimal transport theory to measure distances between distributions.
- Utilizing a variant of Lloyd's algorithm with generalized Wasserstein barycenters.
Main Results
- Construction of a metric space for financial institutions.
- Development of a quantitative approach to assess institutional similarity.
- Enabling the identification of clusters and outliers in the banking sector.
Conclusions
- The proposed method effectively maps the banking landscape.
- Optimal transport provides a robust framework for financial data analysis.
- This facilitates enhanced regulatory supervision and risk assessment.
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