Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
The U.S. Syndicated Loan Market : Matching Data
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.
Cite this item
Gregory J. Cohen & Melanie Friedrichs & Kamran Gupta & William Hayes & Seung Jung Lee & W. Blake Marsh & Nathan Mislang & Maya Shaton & Martin Sicilian, The U.S. Syndicated Loan Market : Matching Data, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2018-085, 07 Dec 2018.
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
Keywords: Bank credit ; Company level matching ; Loan level matching ; Probabilistic matching ; Syndicated loans
This item with handle RePEc:fip:fedgfe:2018-85
is also listed on EconPapers
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