Information in Financial Contracts: Evidence from Securitization Agreements
用机器学习分析商业抵押贷款支持证券的合同文本，发现合同条款差异很大，且与贷款和债券的后续表现相关，说明研究复杂金融证券时应审查整个合同。
Abstract 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.