Biblio

Filters: Author is Raschid, Louiqa  [Clear All Filters]
2021-10-21
Bjarnadottir, Margret, Hunt, Aaron, Raschid, Louiqa.  2018.  Choosing Models to Explore Financial Supply Chain Relationships. Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets.
2017-05-17
Xu, Zheng, Raschid, Louiqa.  2016.  Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains. Proceedings of the Second International Workshop on Data Science for Macro-Modeling. :8:1–8:6.

There is a growing interest in modeling and predicting the behavior of financial systems and supply chains. In this paper, we focus on the the analysis of the resMBS supply chain; it is associated with the US residential mortgage backed securities and subprime mortgages that were critical in the 2008 US financial crisis. We develop models based on financial institutions (FI), and their participation described by their roles (Role) on financial contracts (FC). Our models are based on an intuitive assumption that FIs will form communities within an FC, and FIs within a community are more likely to collaborate with other FIs in that community, and play the same role, in another FC. Inspired by the Latent Dirichlet Allocation (LDA) and topic models, we develop two probabilistic financial community models. In FI-Comm, each FC (document) is a mix of topics where a topic is a distribution over FIs (words). In Role-FI-Comm, each topic is a distribution over Role-FI pairs (words). Experimental results over 5000+ financial prospecti demonstrate the effectiveness of our models.

Burdick, Doug, De, Soham, Raschid, Louiqa, Shao, Mingchao, Xu, Zheng, Zotkina, Elena.  2016.  resMBS: Constructing a Financial Supply Chain from Prospectus. Proceedings of the Second International Workshop on Data Science for Macro-Modeling. :7:1–7:6.

Understanding the behavior of complex financial supply chains is usually difficult due to a lack of data capturing the interactions between financial institutions (FIs) and the roles that they play in financial contracts (FCs). resMBS is an example supply chain corresponding to the US residential mortgage backed securities that were critical in the 2008 US financial crisis. In this paper, we describe the process of creating the resMBS graph dataset from financial prospectus. We use the SystemT rule-based text extraction platform to develop two tools, ORG NER and Dict NER, for named entity recognition of financial institution (FI) names. The resMBS graph comprises a set of FC nodes (each prospectus) and the corresponding FI nodes that are extracted from the prospectus. A Role-FI extractor matches a role keyword such as originator, sponsor or servicer, with FI names. We study the performance of the Role-FI extractor, and ORG NER and Dict NER, in constructing the resMBS dataset. We also present preliminary results of a clustering based analysis to identify financial communities and their evolution in the resMBS financial supply chain.