  The objective of this paper is to explore how financial big data and machine
learning methods can be applied to model and understand complex financial
products. We focus on residential mortgage backed securities, resMBS, that were
at the heart of the 2008 US financial crisis. The securities are contained
within a prospectus and have a complex payoff structure. Multiple financial
institutions form a supply chain to create the prospectuses. We provide insight
into the performance of the resMBS securities through a series of increasingly
complex models. First, models at the security level directly identify salient
features of resMBS securities that impact their performance. Second, we extend
the model to include prospectus level features. We are the first to demonstrate
that the composition of the prospectus is associated with the performance of
securities. Finally, to develop a deeper understanding of the role of the
supply chain, we use unsupervised probabilistic methods, in particular, dynamic
topics models (DTM), to understand community formation and temporal evolution
along the chain. A comprehensive model provides insight into the impact of DTM
communities on the issuance and evolution of prospectuses, and eventually the
performance of resMBS securities.
