美国银团贷款市场:匹配数据

Gregory J. Cohen, Jacob Dice, M. Friedrichs, Kamran Gupta, William Hayes, Isabel Kitschelt, S. J. Lee, W. Marsh, Nathan Mislang, Maya Shaton, M. Sicilian, Chris Webster
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引用次数: 17

摘要

我们介绍了一个新的软件包,用于确定没有公共标识符的数据集之间的链接。我们将这些方法应用于银团贷款学术研究中常用的三个数据集:Refinitiv LPC DealScan、共享国家信用数据库和标准普尔全球市场情报计算机。我们使用文献中的结果和之前公开的匹配文件对匹配结果进行基准测试。我们发现,通过仔细清理数据并考虑层次关系,公司级别的匹配得到了增强。对于贷款级别匹配,基于对数据的良好理解的定制方法在某些方面可能比更纯粹的机器学习方法更好。公司级别匹配的R包可以在Github上找到https://github.com/seunglee98/fedmatch。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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