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
{"title":"美国银团贷款市场:匹配数据","authors":"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","doi":"10.17016/FEDS.2018.085","DOIUrl":null,"url":null,"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.","PeriodicalId":278071,"journal":{"name":"Board of Governors: Finance & Economics Discussion Series (Topic)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"The U.S. Syndicated Loan Market: Matching Data\",\"authors\":\"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\",\"doi\":\"10.17016/FEDS.2018.085\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":278071,\"journal\":{\"name\":\"Board of Governors: Finance & Economics Discussion Series (Topic)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Board of Governors: Finance & Economics Discussion Series (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17016/FEDS.2018.085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Board of Governors: Finance & Economics Discussion Series (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17016/FEDS.2018.085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.