TY - JOUR
T1 - Information in Financial Contracts
T2 - Evidence from Securitization Agreements
AU - Ambrose, Brent W.
AU - Han, Yiqiang
AU - Korgaonkar, Sanket
AU - Shen, Lily
N1 - Publisher Copyright:
© The Author(s), 2023.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - 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.
AB - 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.
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U2 - 10.1017/S0022109023000509
DO - 10.1017/S0022109023000509
M3 - Article
AN - SCOPUS:85153934100
SN - 0022-1090
VL - 59
SP - 1692
EP - 1725
JO - Journal of Financial and Quantitative Analysis
JF - Journal of Financial and Quantitative Analysis
IS - 4
ER -