Federal Reserve Bank of Kansas City
Research Working Paper
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 at https://github.com/seunglee98/fedmatch.
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, Federal Reserve Bank of Kansas City, Research Working Paper RWP 18-9, 03 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
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
Keywords: Bank Credit; Syndicated Loans; Probabilistic Matching; Company Level Matching; Loan Level Matching
This item with handle RePEc:fip:fedkrw:rwp18-09
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