Modeling Financial Products and their Supply Chains
Margret Bjarnadottir, Louiqa Raschid

TL;DR
This paper applies unsupervised probabilistic models to analyze the complex supply chain and community structures of residential mortgage-backed securities, revealing factors influencing their performance and risks, including toxic communities linked to the 2008 crisis.
Contribution
It introduces a novel application of dynamic topics models to model financial supply chains and identify risk factors, including toxic communities, in resMBS securities.
Findings
Community structures impact security performance
Prospectus composition influences risk levels
Toxic communities increase failure risk
Abstract
The objective of this paper is to explore how financial big data 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 prospectuses. To model this supply chain, we use unsupervised probabilistic methods, particularly dynamic topics models (DTM), to extract a set of features (topics) reflecting community 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 through a series of increasingly comprehensive models. First, models at the security level directly identify salient features of…
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Taxonomy
TopicsBanking stability, regulation, efficiency · Insurance and Financial Risk Management · Housing Market and Economics
