Modeling Financial Products and Their Supply Chains

Abstract

The objective of this paper is to explore how novel financial datasets and machine learning methods can be applied to model and understand financial products. We focus on residential mortgage backed securities, resMBS, which were at the heart of the 2008 US financial crisis. These securities are contained within a prospectus and have a complex waterfall payoff structure. Multiple financial institutions form a supply chain to create the prospectuses. To model this supply chain, we use unsupervised probabilistic methods, particularly dynamic topics models (DTM), to extract a set of features reflecting community (topic) formation and temporal evolution along the chain. We then provide insight into the performance of the resMBS securities and the impact of the supply chain communities through a series of increasingly comprehensive models. First, models at the security level directly identify salient features of resMBS securities that impact their performance. We then extend the model to include prospectus level features and demonstrate that the composition of the prospectus is significant. Our model also shows that communities along the supply chain that are associated with the generation of the prospectuses and securities have an impact on performance. We are the first to show that toxic communities that are closely linked to financial institutions that played a key role in the subprime crisis can increase the risk of failure of resMBS securities. History: Olivia Sheng served as the senior editor for this article. Funding: This research was partially supported by National Science Foundation [Grant CNS1305368] and National Institute of Standards and Technology [Grant 70NANB15H194]. Data Ethics & Reproducibility Note: No data ethics considerations are foreseen related to this article. The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.8845455.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2020.0006 ).