Home About Latest Browse RSS Advanced Search

Federal Reserve Bank of Kansas City
Research Working Paper
The U.S. Syndicated Loan Market: Matching Data
Gregory J. Cohen
Melanie Friedrichs
Kamran Gupta
William Hayes
Seung Jung Lee
W. Blake Marsh
Nathan Mislang
Maya Shaton
Martin Sicilian
Abstract

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 https://doi.org/10.18651/RWP2018-09
Cite this item
Gregory J. Cohen & Melanie Friedrichs & Kamran Gupta & William Hayes & Seung Jung Lee & W. Blake Marsh & Nathan Mislang & Maya Shaton & Martin Sicilian, The U.S. Syndicated Loan Market: Matching Data, Federal Reserve Bank of Kansas City, Research Working Paper RWP 18-9, 03 Dec 2018.
More from this series
JEL Classification:
Subject headings:
Keywords: Bank Credit; Syndicated Loans; Probabilistic Matching; Company Level Matching; Loan Level Matching
For corrections, contact Lu Dayrit ()
Fed-in-Print is the central catalog of publications within the Federal Reserve System. It is managed and hosted by the Economic Research Division, Federal Reserve Bank of St. Louis.

Privacy Legal