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Information in Financial Contracts: Evidence from Securitization Agreements

Published online by Cambridge University Press:  18 April 2023

Brent W. Ambrose Open the ORCID record for Brent W. Ambrose [Opens in a new window] ,
Yiqiang Han ,
Sanket Korgaonkar Open the ORCID record for Sanket Korgaonkar [Opens in a new window]  and
Lily Shen
Brent W. Ambrose*
Affiliation:
The Pennsylvania State University Smeal College of Business
Yiqiang Han
Affiliation:
Roku Inc. hanyiqiang@gmail.com
Sanket Korgaonkar
Affiliation:
University of Virginia McIntire School of Commerce sanketk@virginia.edu
Lily Shen
Affiliation:
Clemson University Wilbur O. and Ann Powers College of Business yannans@g.clemson.edu
*
bwa10@psu.edu (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

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We introduce a novel application of machine learning to compare pooling and servicing agreements (PSAs) that govern commercial mortgage-backed securities. In contrast to the view that the PSA is largely boilerplate text, we document substantial variation across PSAs, both within- and across-underwriters and over time. A part of this variation is driven by differences in loan collateral across deals. Additionally, we find that differences in PSAs are correlated with ex post loan and bond performance. Collectively, our analysis suggests the importance of examining the entire governing document, rather than specific components, when analyzing complex financial securities.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 34
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank Larry Cordell, Ed Coulson, Thies Lindenthal, Yildiray Yildirim, Jiro Yoshida, and the seminar participants at the MIT Center for Real Estate, Baruch College, the Federal Reserve Bank of Philadelphia, the Federal Reserve Bank of Atlanta, Louisiana State University, Santa Clara University, Renmin University, St. Gallen University, University of Texas at Arlington, the 2019 University of Cambridge Real Estate Finance and Investment Symposium, and the 2020 Allied Social Science Association for their helpful comments and suggestions. We thank the Penn State Borrelli Institute for Real Estate Studies for providing access to the Trepp database. We thank the Machine Learning Creative Inquiry Team (James Buba, Emma Isheim, Max Koch, Sean Larkin, Jack Lillig, Jack Maggard, Megan Quinan, and Meredith Quinan) for excellent research assistance. Computations for this research were performed on The Pennsylvania State University’s Institute for Computational and Data Sciences’ Roar supercomputer. We affirm that we have no material financial interests related to this research.

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