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.

Nonbank Lenders as Global Shock Absorbers: Evidence from US Monetary Policy Spillovers

“…Compustat In order to study firm-level real effects, we match DealScan to borrowerlevel financial statements from Compustat North America and Compustat Global using the updated link provided by Chava and Roberts (2008). We extend this link using a matching algorithm based on firm names, countries, and SIC codes, following an approach similar to Cohen et al (2021).…”