The U.S. syndicated loan market: Matching data

Gregory J. Cohen, Jacob Dice, Melanie Friedrichs, Kamran Gupta, William Hayes, Isabel Kitschelt, Seung Jung Lee, W. Blake Marsh, Nathan Mislang, Maya Shaton, Martin Sicilian, Chris Webster

Research output: Contribution to journalArticlepeer-review


We introduce a new software package for determining linkages between datasets without common identifiers. We apply this to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, S&P Global Market Intelligence Compustat, and National Information Center Structure Data. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. The R package for one of the company-level matches can be found on GitHub and CRAN, which can be considered a general toolkit to match different firm-level datasets with one another.

Original languageAmerican English
Pages (from-to)695-723
Number of pages29
JournalJournal of Financial Research
Issue number4
StatePublished - 1 Dec 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance


Dive into the research topics of 'The U.S. syndicated loan market: Matching data'. Together they form a unique fingerprint.

Cite this