Information in Financial Contracts: Evidence from Securitization Agreements

Abstract

We introduce a novel application of machine learning to compare Pooling and Servicing Agreements (PSAs) that govern commercial mortgage-backed securities (CMBS). 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.

Keywords: Machine Learning, Neural Network, Financial Contracts, CMBS, Mortgages, Asymmetric Information

JEL Classification: C45, D82, G20, G21, G30, G34