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 and Martin Sicilian
Additional contact information
Melanie Friedrichs: https://www.financialresearch.gov/research-staff/details/melanie-friedrichs/
W. Blake Marsh: https://www.kansascityfed.org/research-staff/w-blake-marsh/

No 2018-085, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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.

Keywords: Bank credit; Company level matching; Loan level matching; Probabilistic matching; Syndicated loans (search for similar items in EconPapers)
JEL-codes: C55 C88 E44 G21 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2018-12-07
New Economics Papers: this item is included in nep-big and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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https://www.federalreserve.gov/econres/feds/files/2018085pap.pdf (application/pdf)

Related works:
Working Paper: The U.S. Syndicated Loan Market: Matching Data (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2018-85

DOI: 10.17016/FEDS.2018.085

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