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  resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for  
  resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US
  US residential
  residential mortgage backed securities filed with the SEC. These securities started becoming very
mortgage backed securities filed with the SEC. These securities were first created in 2002. They reached a peak  
popular in 2002. The issued securities reached a peak in 2006 and then started to decline in 2007  
in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. We extracted the "financial supply  
and came to an abrupt end in 2008.  
chain" comprising "financial institutions" (FI) and the role (Role) that they play on a financial contract (FC).
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role)  
that they play on a financial contract (FC).


  The following paper provides an overview of how the dataset was created and some preliminary clustering analysis on
  The following paper provides an overview of how the dataset was created and some preliminary clustering  
  the graph.
analysis on the graph.
  [[Media:https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]]
resMBS: Constructing a Financial Supply Chain from Prospecti
Doug Burdick, IBM
  Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland
[https://drive.google.com/open?id=0BzTeYQSh4QTsWmJXSTM0TEFLa1E]
 
The networks described in the paper can be viewed here.
  FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]
 
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains
Zheng Xu and Louiqa Raschid, University of Maryland
[https://drive.google.com/open?id=0BzTeYQSh4QTsWWJ4b0RiMWFaSlk]
 
If you want the gory details of the tools on the IBM System T platform that were developed ...
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from
Unstructured Documents
Zheng Xu (University of Maryland) and Douglas Burdick (IBM) and Louiqa Raschid (University of Maryland)
[http://arxiv.org/abs/1602.04427]
 
The dataset is available for research.

Latest revision as of 04:14, 23 May 2016

resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US 
residential mortgage backed securities filed with the SEC. These securities started becoming very
popular in 2002. The issued securities reached a peak in 2006 and then started to decline in 2007 
and came to an abrupt end in 2008. 

We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) 
that they play on a financial contract (FC).
The following paper provides an overview of how the dataset was created and some preliminary clustering 
analysis on the graph.
resMBS: Constructing a Financial Supply Chain from Prospecti
Doug Burdick, IBM
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland
[1]
The networks described in the paper can be viewed here.
FI clusters [2]
FI clusters based on FC-FC similarity [3]
FI-FC bipartite graph [4]
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains
Zheng Xu and Louiqa Raschid, University of Maryland
[5]
If you want the gory details of the tools on the IBM System T platform that were developed ...
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from 
Unstructured Documents
Zheng Xu (University of Maryland) and Douglas Burdick (IBM) and Louiqa Raschid (University of Maryland)
[6]
The dataset is available for research.