The U.S. Syndicated Loan Market: Matching Data

December 7, 2018
By Gregory J. Cohen, Melanie Friedrichs, Kamran Gupta, William Hayes, Seung Jung Lee, Nathan Mislang, Maya Shaton, Martin Sicilian and W. Blake Marsh


Research Working PaperA simple, replicable methodology can help researchers link corporate loan datasets.

We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a tailored approach based on a good understanding of the data can be better in certain dimensions than a more pure machine learning approach. The R package for the company level match can be found on Github at https://github.com/seunglee98/fedmatch.

Download paper

RWP 18-09, December 2018

JEL Classification: C55, C88, E44, G21

Article Citation

  • Cohen, Gregory J., Melanie Friedrichs, Kamran Gupta, William Hayes, Seung Jung Lee, W. Blake Marsh, Nathan Mislang, Maya Shaton, and Martin Sicilian. “The U.S. Syndicated Loan Market: Matching Data.” Federal Reserve Bank of Kansas City, Research Working Paper no. 18-09, December. Available at https://doi.org/10.18651/RWP2018-09