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.

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“…A significant part of enterprises recently received a refusal to issue a loan, which may be due to a very small number of monitoring and requirements for collateral. In general, BEEPS data indicate that the share of SMEs suffering from credit restrictions in Kazakhstan was about 67 %, compared with 54,4 % in other CIS countries and 35,7 % in 8 countries of OECD (Cohen, 2018).…”