Working Paper

The U.S. Syndicated Loan Market: Matching Data


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.

Keywords: Bank Credit; Syndicated Loans; Probabilistic Matching; Company Level Matching; Loan Level Matching;

JEL Classification: C55; C88; E44; G21;

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File(s): File format is text/plain https://doi.org/10.18651/RWP2018-09
Description: https://doi.org/10.18651/RWP2018-09

Authors

Bibliographic Information

Provider: Federal Reserve Bank of Kansas City

Part of Series: Research Working Paper

Publication Date: 2018-12-03

Number: RWP 18-9

Pages: 24 pages