The U.S. Syndicated Loan Market: Matching Data

Cohen, Grégory; Friedrichs, Melanie; Gupta, Kamran; Hayes, William; Lee, Seung Jung; Marsh, Blake; Mislang, Nathan; Shaton, Maya; Sicilian, Martin

doi:10.2139/ssrn.3297815

Cited by 5 publications

(7 citation statements)

References 19 publications

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“…Our sample includes 12,013 loans held by at least 3 supervised institutions in the U.S. between 2009Q4 and 2019Q4. We classify lenders as banks, CLOs, Hedge Funds, Mutual Funds, and other financial institutions based on the lender's name using the algorithm described in Cohen et al (2018). Overall, we observe 12,108,437 loan shares (or 295,328 per quarter on average).…”

Section: Datamentioning

confidence: 99%

Ownership Concentration and Performance of Deteriorating Syndicated Loans

Giannetti

Meisenzahl

2021

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Regulation and capital constraints may force banks and collateralized loan obligations (CLOs) to sell deteriorating loans, potentially hampering renegotiation and amplifying the initial negative shock to the borrower. We show that banks and CLOs sell downgraded loans to mutual funds and hedge funds. The reallocation of loan shares favors the syndicate's concentration, increasing lenders' incentives to renegotiate. However, syndicates remain less concentrated when potential buyers experience financial constraints and subsequently loans are less likely to be amended and more likely to be downgraded even further. Our findings indicate that existing regulations may amplify shocks to credit quality during periods of generalized distress in the financial system.

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Section: Datamentioning

confidence: 99%

Ownership Concentration and Performance of Deteriorating Syndicated Loans

Giannetti

Meisenzahl

2021

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“…The Responsibility of Waste Production behaviours dependent on living environments (Hirsch et al, 2020;Mendez et al, 2014), validating names and addresses of transportation and logistic entities (Guermazi et al, 2020), matching observations across financial datasets (Cohen et al, 2018;Burdick et al, 2015), identifying same entities in patent files (Medvedev and Ulanov, 2011;Magnani and Montesi, 2007). Most of them conclude that domain expert knowledge integration improves or would improve matching results (Pilania and Kumaran, 2019;Cohen et al, 2018;Choi et al, 2017;Antoni et al, 2018;Schild, 2016;Mendez et al, 2014;Magnani and Montesi, 2007). This paper further builds on the existing examples of entity matching using standard computational methods to evaluate name similarity and geospatial proximity between potential matches.…”

Section: Related Workmentioning

confidence: 99%

The Responsibility of Waste Production: comparison of European Waste Statistics Regulation and Dutch National Waste Registry

Šileryté¹,

Wandl²,

Timmeren³

2021

Preprint

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The announcement of a new Circular Economy Action Plan as part of the European Green Deal policy has created an urgent need for the reliable information on resource flows to monitor and support the transition. An updated Monitoring Framework is set to rely as much as possible on European Statistics, however at this point there are no changes introduced in supranational statistics regulations. This raises a question whether regulations that have been created before the paradigm shift are still able to supply us with statistics necessary to inform policy makers about current successful practices, remaining barriers, positive and negative impacts of the transition and overall progress towards the set goals. This paper focuses on the Waste Statistics Regulation, specifically the relationship between the types of waste and economic activities which are considered to be the waste producers. Dutch National Waste Registry is used as a case study to compare the guidelines on pan-European waste data collection to the actual waste reports. The task of this publication is dual: first, it presents a computational method to link waste producers to their economic activities using a national Trade Registry. Secondly, it explores to which extent the guidelines available in the Waste Statistics Regulation correspond to the operational reality. An extensive discussion of the results provides insights and recommendations for the future guidelines of waste statistics to support circular economy transition.

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“…We compute loan prices as the average of the bid and ask quote. For the merge procedures we use a fuzzy match algorithm similar to Cohen et al (2018) based on the borrower name and common loan variables.…”

Section: Datamentioning

confidence: 99%

The Myth of the Lead Arranger’s Share

et al. 2020

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We make use of Shared National Credit Program (SNC) data to examine syndicated loans in which the lead arranger retains no stake. We find that the lead arranger sells its entire loan share for 27 percent of term loans and 48 percent of Term B loans, typically shortly after syndication. In contrast to existing asymmetric information theories on the role of the lead share, we find that loans that are sold are less likely to become non-performing in the future. This result is robust to several different measures of loan performance and is reflected in subsequent secondary market prices. We explore syndicated loan underwriting risk as an alternative theory that may help explain this result.

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The U.S. Syndicated Loan Market: Matching Data

Cited by 5 publications

References 19 publications

Ownership Concentration and Performance of Deteriorating Syndicated Loans

Ownership Concentration and Performance of Deteriorating Syndicated Loans

The Responsibility of Waste Production: comparison of European Waste Statistics Regulation and Dutch National Waste Registry

The Myth of the Lead Arranger’s Share

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