Modeling Financial Products and Their Supply Chains

Margrét Vilborg Bjarnadóttir () and Louiqa Raschid ()
Additional contact information
Margrét Vilborg Bjarnadóttir: University of Maryland, College Park, Maryland 20742
Louiqa Raschid: University of Maryland, College Park, Maryland 20742

INFORMS Joural on Data Science, 2023, vol. 2, issue 2, 138-160

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.

Keywords: latent Dirichlet allocation (LDA); topic models; probabilistic model; financial supply chain; mortgage-backed securities; financial communities; subprime crisis; 2008 U.S. financial crisis (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/ijds.2020.0006 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:2:y:2023:i:2:p:138-160

Access Statistics for this article

More articles in INFORMS Joural on Data Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().