Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-23T04:16:35.592Z Has data issue: false hasContentIssue false
  • English
  • Français

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. [email protected]
Sanket Korgaonkar
Affiliation:
University of Virginia McIntire School of Commerce [email protected]
Lily Shen
Affiliation:
Clemson University Wilbur O. and Ann Powers College of Business [email protected]
*
[email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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 , Volume 59 , Issue 4 , June 2024 , pp. 1692 - 1725
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.

References

Agarwal, S.; Chang, Y.; and Yavas, A.. “Adverse Selection in Mortgage Securitization.” Journal of Financial Economics, 105 (2012), 640–660.10.1016/j.jfineco.2012.05.004CrossRefGoogle Scholar
Agarwal, S., and Hauswald, R.. “Distance and Private Information in Lending.” Review of Financial Studies, 23 (2010), 2757–2788.10.1093/rfs/hhq001CrossRefGoogle Scholar
Ambrose, B., and Sanders, A.. “Commercial Mortgage-Backed Securities: Prepayment and Default.” Journal of Real Estate Finance and Economics, 26 (2003), 179–196.10.1023/A:1022978708728CrossRefGoogle Scholar
Ambrose, B. W.; LaCour-Little, M.; and Sanders, A. B.. “Does Regulatory Capital Arbitrage, Reputation, or Asymmetric Information Drive Securitization?” Journal of Financial Services Research, 28 (2005), 113–133.10.1007/s10693-005-4358-2CrossRefGoogle Scholar
Ambrose, B. W.; Sanders, A. B.; and Yavas, A.. “Servicers and Mortgage-Backed Securities Default: Theory and Evidence.” Real Estate Economics, 44 (2016), 462–489.10.1111/1540-6229.12099CrossRefGoogle Scholar
An, X.; Deng, Y.; and Gabriel, S. A.. “Asymmetric Information, Adverse Selection, and the Pricing of CMBS.” Journal of Financial Economics, 100 (2011), 304–325.10.1016/j.jfineco.2010.12.002CrossRefGoogle Scholar
An, X.; Deng, Y.; Nichols, J. B.; and Sanders, A. B.. “What is Subordination About? Credit Risk and Subordination Levels in Commercial Mortgage-Backed Securities (CMBS).” Journal of Real Estate Finance and Economics, 51 (2015), 231–253.10.1007/s11146-014-9480-1CrossRefGoogle Scholar
An, X.; Deng, Y.; and Sanders, A. B.. “Subordination Levels in Structured Financing.” In Handbook of Financial Intermediation and Banking, Thakor, A. V. and Boot, A. W. A., eds. Amsterdam, The Netherlands: North-Holland (2008), 41–60.10.1016/B978-044451558-2.50006-4CrossRefGoogle Scholar
Begley, T. A., and Purnanandam, A.. “Design of Financial Securities: Empirical Evidence from Private-Label RMBS Deals.” Review of Financial Studies, 30 (2017), 120–161.10.1093/rfs/hhw056CrossRefGoogle Scholar
Bellstam, G.; Bhagat, S.; and Cookson, J. A.. “A Text-Based Analysis of Corporate Innovation.” Management Science, 67 (2020), 3985–4642.Google Scholar
Black, L.; Krainer, J.; and Nichols, J.. “From Origination to Renegotiation: A Comparison of Portfolio and Securitized Commercial Real Estate Loans.” Journal of Real Estate Finance and Economics, 55 (2017), 1–31.10.1007/s11146-016-9548-1CrossRefGoogle Scholar
Boudoukh, J.; Feldman, R.; Kogan, S.; and Richardson, M.. “Information, Trading, and Volatility: Evidence from Firm-Specific News.” Review of Financial Studies, 32 (2018), 992–1033.10.1093/rfs/hhy083CrossRefGoogle Scholar
Bougheas, S. “Pooling, Tranching, and Credit Expansion.” Oxford Economic Papers, 66 (2014), 557–579.10.1093/oep/gpt029CrossRefGoogle Scholar
Brogaard, J., and Zareei, A.. “Machine Learning and the Stock Market.” Journal of Financial and Quantitative Analysis, 58 (2023), 1431–1472.10.1017/S0022109023000571CrossRefGoogle Scholar
Buehlmaier, M. M. M., and Whited, T. M.. “Are Financial Constraints Priced? Evidence from Textual Analysis.” Review of Financial Studies, 31 (2018), 2693–2728.10.1093/rfs/hhy007CrossRefGoogle Scholar
Buschbom, S. L.; Kau, J. B.; Keenan, D. C.; and Lyubimov, C.. “Delinquencies, Default and Borrowers’ Strategic Behavior Toward the Modification of Commercial Mortgages.” Real Estate Economics, 49 (2021), 936–967.10.1111/1540-6229.12237CrossRefGoogle Scholar
Chen, J., and Deng, Y.. “Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans.” Journal of Real Estate Finance and Economics, 46 (2013), 609–632.10.1007/s11146-012-9374-zCrossRefGoogle Scholar
Chen, M. A.; Wu, Q.; and Yang, B.. “How Valuable is FinTech Innovation?” Review of Financial Studies, 32 (2019), 2062–2106.10.1093/rfs/hhy130CrossRefGoogle Scholar
Cong, L. W.; Liang, T.; Yang, B.; and Zhang, X.. “Analyzing Textual Information at Scale.” In Information for Efficient Decision Making Big Data, Blockchain and Relevance, Balachandran, K. R., ed. Singapore: World Scientific Publishing Co. Pte. Ltd., (2020), 239–271.10.1142/9789811220470_0010CrossRefGoogle Scholar
Cordell, L.; Roberts, M. R.; and Schwert, M.. “CLO Performance.” NBER Working Paper No. 29410 (2020).10.21799/frbp.wp.2020.48CrossRefGoogle Scholar
DeMarzo, P. “The Pooling and Tranching of Securities: A Model of Informed Intermediation.” Review of Financial Studies, 18 (2005), 1–35.10.1093/rfs/hhi008CrossRefGoogle Scholar
DeMarzo, P., and Duffie, D.. “A Liquidity-Based Model of Security Design.” Econometrica, 67 (1999), 65–99.10.1111/1468-0262.00004CrossRefGoogle Scholar
Demiroglu, C., and James, C.. “How Important is Having Skin in the Game? Originator-Sponsor Affiliation and Losses on Mortgage-Backed Securities.” Review of Financial Studies, 25 (2012), 3217–3258.10.1093/rfs/hhs095CrossRefGoogle Scholar
DiMatteo, L. A., and Morant, B. D.. “Contract in Context and Contract as Context.” Wake Forest Law Review, 45 (2010), 549.Google Scholar
Epstein, R. A. “Toward a Revitalization of the Contract Clause.” University of Chicago Law Review, 51 (1984), 703.10.2307/1599484CrossRefGoogle Scholar
Fuster, A.; Goldsmith-Pinkham, P.; Ramadorai, T.; and Walther, A.. “Predictably Unequal? The Effects of Machine Learning on Credit Markets.” Journal of Finance, 77 (2022), 5–47.10.1111/jofi.13090CrossRefGoogle Scholar
Gaur, V.; Seshadri, S.; and Subrahmanyam, M. G.. “Securitization and Real Investment in Incomplete Markets.” Management Science, 57 (2011), 2180–2196.10.1287/mnsc.1100.1155CrossRefGoogle Scholar
Guo, G., and Wu, H.-M.. “A Study on Risk Retention Regulation in Asset Securitization Process.” Journal of Banking & Finance, 45 (2014), 61–71.10.1016/j.jbankfin.2013.12.027CrossRefGoogle Scholar
Hartman-Glaser, B. “Reputation and Signaling in Asset Sales.” Journal of Financial Economics, 125 (2017), 245–265.10.1016/j.jfineco.2017.05.009CrossRefGoogle Scholar
Huang, A. G.; Tan, H.; and Wermers, R.. “Institutional Trading Around Corporate News: Evidence from Textual Analysis.” Review of Financial Studies, 33 (2020), 4627–4675.10.1093/rfs/hhz136CrossRefGoogle Scholar
Jacob, D., and Fabozzi, F.. “The Impact of Structuring on CMBS Class Performance - Complex Interactions Between Collateral and Structure.” Journal of Portfolio Management, 29 (2003), 76–86.Google Scholar
Kruger, S. “The Effect of Mortgage Securitization on Foreclosure and Modification.” Journal of Financial Economics, 129 (2018), 586–607.10.1016/j.jfineco.2018.01.008CrossRefGoogle Scholar
Le, Q., and Mikolov, T.. “Distributed Representations of Sentences and Documents.” Proceedings of Machine Learning Research, 32 (2014), 1188–1196.Google Scholar
Li, K.; Mai, F.; Shen, R.; and Yan, X.. “Measuring Corporate Culture Using Machine Learning.” Review of Financial Studies, 34 (2021), 3265–3315.10.1093/rfs/hhaa079CrossRefGoogle Scholar
Liu, C. H.; Nowak, A. D.; and Smith, P. S.. “Asymmetric or Incomplete Information About Asset Values?” Review of Financial Studies, 33 (2020), 2898–2936.10.1093/rfs/hhz096CrossRefGoogle Scholar
Liu, P., and Quan, D.. “Foreclosure of Securitized Commercial Mortgages-A Model of the Special Servicer.” Journal of Real Estate Finance and Economics, 46 (2013), 321–338.10.1007/s11146-011-9337-9CrossRefGoogle Scholar
Loughran, T., and McDonald, B.. “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks.” Journal of Finance, 66 (2011), 35–65.10.1111/j.1540-6261.2010.01625.xCrossRefGoogle Scholar
Mooradian, R. M., and Pichler, P.. “Servicer Contracts and the Design of Mortgage-Backed Security Pools.” Real Estate Economics, 46 (2018), 698–738.10.1111/1540-6229.12188CrossRefGoogle Scholar
Seslen, T., and Wheaton, W. C.. “Contemporaneous Loan Stress and Termination Risk in the CMBS Pool: How “Ruthless” is Default?” Real Estate Economics, 38 (2010), 225–255.10.1111/j.1540-6229.2010.00266.xCrossRefGoogle Scholar
Shen, L., and Ross, S.. “Information Value of Property Description: A Machine Learning Approach.” Journal of Urban Economics, 121 (2021), 103299.10.1016/j.jue.2020.103299CrossRefGoogle Scholar
Shen, L., and Springer, T. M.. “The Odd One Out? The Impact of Property Uniqueness on Selling Time and Selling Price.” Journal of Housing Research, 31 (2022), 220–240.10.1080/10527001.2022.2078530CrossRefGoogle Scholar
Shen, L., and Wilkoff, S.. “Cleanliness is Next to Income: The Impact of COVID-19 on Short-Term Rentals.” Journal of Regional Science, 62 (2022), 799–829.10.1111/jors.12581CrossRefGoogle Scholar
SIFMA. “Capital Markets Fact Book 2019.” Available at https://www.sifma.org/wp-content/uploads/2019/09/2019-Capital-Markets-Fact-Book-SIFMA.pdf (2019).Google Scholar
Tetlock, P. C. “Giving Content to Investor Sentiment: The Role of Media in the Stock Market.” Journal of Finance, 62 (2007), 1139–1168.10.1111/j.1540-6261.2007.01232.xCrossRefGoogle Scholar
Titman, S., and Tsyplakov, S.. “Originator Performance, CMBS Structures, and the Risk of Commercial Mortgages.” Review of Financial Studies, 23 (2010), 3558–3594.10.1093/rfs/hhq055CrossRefGoogle Scholar
Wooldridge, J. M. Introductory Econometrics: A Modern Approach. Boston, MA: Cengage Learning (2015).Google Scholar
Yildirim, Y. “Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring.” Journal of Real Estate Finance and Economics, 37 (2008), 93–111.10.1007/s11146-007-9046-6CrossRefGoogle Scholar
Supplementary material: File

Ambrose et al. supplementary material

Ambrose et al. supplementary material
Download Ambrose et al. supplementary material(File)
File 2.4 MB