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The U.S. syndicated loan market: Matching data

Author

Listed:
  • 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

Abstract

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.

Suggested Citation

  • 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, 2021. "The U.S. syndicated loan market: Matching data," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(4), pages 695-723, December.
    • Handle: RePEc:bla:jfnres:v:44:y:2021:i:4:p:695-723
    DOI: 10.1111/jfir.12261
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