https://wiki.umiacs.umd.edu/clip/ngfci/api.php?action=feedcontributions&user=Louiqa&feedformat=atomngfci - User contributions [en]2024-03-29T15:40:01ZUser contributionsMediaWiki 1.39.6https://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=497ResMBS2016-05-23T04:14:35Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities started becoming very<br />
popular in 2002. The issued securities reached a peak in 2006 and then started to decline in 2007 <br />
and came to an abrupt end in 2008. <br />
<br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsWmJXSTM0TEFLa1E]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]<br />
<br />
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary<br />
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.<br />
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains<br />
Zheng Xu and Louiqa Raschid, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsWWJ4b0RiMWFaSlk]<br />
<br />
If you want the gory details of the tools on the IBM System T platform that were developed ...<br />
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from <br />
Unstructured Documents<br />
Zheng Xu (University of Maryland) and Douglas Burdick (IBM) and Louiqa Raschid (University of Maryland)<br />
[http://arxiv.org/abs/1602.04427]<br />
<br />
The dataset is available for research.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=496ResMBS2016-05-23T04:12:27Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities started becoming very<br />
popular in 2002. The issued securities reached a peak in 2006 and then started to decline in 2007 <br />
and came to an abrupt end in 2008. <br />
<br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsWmJXSTM0TEFLa1E]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]<br />
<br />
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary<br />
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.<br />
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains<br />
Zheng Xu and Louiqa Raschid, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsYUFIVlQ3QTVHQW8]<br />
<br />
If you want the gory details of the tools on the IBM System T platform that were developed ...<br />
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from <br />
Unstructured Documents<br />
Zheng Xu (University of Maryland) and Douglas Burdick (IBM) and Louiqa Raschid (University of Maryland)<br />
[http://arxiv.org/abs/1602.04427]<br />
<br />
The dataset is available for research.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=495KarshaVizAn2016-05-19T02:09:16Z<p>Louiqa: </p>
<hr />
<div>[[Media:[[Media:Example.ogg]]]] The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn [http://karsha.umiacs.umd.edu/karshaCEP/Testing.html] [http://karsha.umiacs.umd.edu/karshaCEP/PRC_analysis.html]<br />
<br />
Monthly Drawdowns [http://karsha.umiacs.umd.edu/Drawdowns/]<br />
Karsha Explorer short paper [[file:KarshaExplorer.pdf]]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=494KarshaVizAn2016-05-19T02:06:48Z<p>Louiqa: </p>
<hr />
<div>[[Media:[[Media:Example.ogg]]]] The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn [http://karsha.umiacs.umd.edu/karshaCEP/Testing.html] [http://karsha.umiacs.umd.edu/karshaCEP/PRC_analysis.html]<br />
<br />
Monthly Drawdowns [http://karsha.umiacs.umd.edu/Drawdowns/]<br />
Karsha Explorer short paper</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=File:KarshaExplorer.pdf&diff=493File:KarshaExplorer.pdf2016-05-19T02:05:13Z<p>Louiqa: </p>
<hr />
<div></div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=492KarshaVizAn2016-05-19T02:04:54Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn [http://karsha.umiacs.umd.edu/karshaCEP/Testing.html] [http://karsha.umiacs.umd.edu/karshaCEP/PRC_analysis.html]<br />
<br />
Monthly Drawdowns [http://karsha.umiacs.umd.edu/Drawdowns/]<br />
Karsha Explorer short paper [[file:KarshaExplorer.pdf]]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=491KarshaVizAn2016-05-19T02:04:25Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn [http://karsha.umiacs.umd.edu/karshaCEP/Testing.html] [http://karsha.umiacs.umd.edu/karshaCEP/PRC_analysis.html]<br />
<br />
Monthly Drawdowns [http://karsha.umiacs.umd.edu/Drawdowns/]<br />
Karsha Explorer short paper [[KarshaExplorer.pdf]]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=490KarshaVizAn2016-05-19T02:02:50Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn [http://karsha.umiacs.umd.edu/karshaCEP/Testing.html] [http://karsha.umiacs.umd.edu/karshaCEP/PRC_analysis.html]<br />
<br />
Monthly Drawdowns [http://karsha.umiacs.umd.edu/Drawdowns/]<br />
Karsha Explorer short paper [[Media:Example.ogg]]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=489KarshaVizAn2016-05-19T00:50:45Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn [http://karsha.umiacs.umd.edu/karshaCEP/Testing.html] <br />
[http://karsha.umiacs.umd.edu/karshaCEP/PRC_analysis.html]<br />
<br />
Monthly Drawdowns [http://karsha.umiacs.umd.edu/Drawdowns/]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=488KarshaVizAn2016-05-19T00:49:05Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn [http://karsha.umiacs.umd.edu/karshaCEP/Testing.html]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=487KarshaVizAn2016-05-19T00:48:46Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Locally Important Maxima and Minima LIMx and LIMn http://karsha.umiacs.umd.edu/karshaCEP/Testing.html</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=486KarshaVizAn2016-05-19T00:47:50Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.<br />
<br />
Link</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=485KarshaVizAn2016-05-19T00:47:34Z<p>Louiqa: </p>
<hr />
<div> The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. <br />
The user can filter the monthly data to highlight equities that experience significant variations. <br />
The tool will allow the user to compare signals from individual equities that have experienced <br />
significant variations with the contemporaneous moves in the S&P 500 index for that month. <br />
These combinations of extreme moves in individual equities and the market, together with turnover<br />
and other features are the basis to create pattern templates.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=KarshaVizAn&diff=484KarshaVizAn2016-05-19T00:46:39Z<p>Louiqa: Created page with "The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. The user can filter the monthly data to highlight equities that experience s..."</p>
<hr />
<div>The Karsha Explorer tool can visualize monthly price variations for equities in the S&P 500 Index. The user can filter the monthly data to highlight equities that experience significant variations. The tool will allow the user to compare signals from individual equities that have experienced significant variations with the contemporaneous moves in the S&P 500 index for that month. These combinations of extreme moves in individual equities and the market, together with turnover and other features are the basis to create pattern templates.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=483CorpDebtRep2016-05-06T01:32:18Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt. A description of the Corporate Debt Repository project is available here [https://drive.google.com/open?id=0BzTeYQSh4QTsa2hPQjdCcWJ6VXc].<br />
<br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
<br />
* Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
* Guarantees, covenants, corporate events, etc.<br />
* Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
<br />
Current resources, e.g., Bloomberg, will typically identify the existence of a type of covenant and provide a link to the text on that page but the resource will not process the language of the covenant, identify events, etc.<br />
<br />
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=482CorpDebtRep2016-05-06T01:31:52Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt.<br />
<br />
A description of the Corporate Debt Repository project is available here [https://drive.google.com/open?id=0BzTeYQSh4QTsa2hPQjdCcWJ6VXc].<br />
<br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
<br />
* Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
* Guarantees, covenants, corporate events, etc.<br />
* Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
<br />
Current resources, e.g., Bloomberg, will typically identify the existence of a type of covenant and provide a link to the text on that page but the resource will not process the language of the covenant, identify events, etc.<br />
<br />
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=481CorpDebtRep2016-05-06T01:28:59Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt.<br />
<br />
A description of the Corporate Debt Repository project is available here [https://drive.google.com/open?id=0BzTeYQSh4QTsa2hPQjdCcWJ6VXc].<br />
<br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
<br />
* Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
* Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
* Guarantees, covenants, corporate events, etc.<br />
<br />
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=480CorpDebtRep2016-05-03T23:03:57Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt.<br />
<br />
A description of the Corporate Debt Repository project is available here [https://drive.google.com/open?id=0BzTeYQSh4QTsa2hPQjdCcWJ6VXc].<br />
<br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
<br />
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=479CorpDebtRep2016-05-03T23:03:20Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt.<br />
<br />
A description of the Corporate Debt Repository project is available here [https://drive.google.com/open?id=0BzTeYQSh4QTsa2hPQjdCcWJ6VXc].<br />
<br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
<br />
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=478CorpDebtRep2016-05-03T23:02:58Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt.<br />
<br />
A description of the Corporate Debt Repository project is available here <br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsa2hPQjdCcWJ6VXc]<br />
<br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
<br />
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=477CorpDebtRep2016-05-03T22:43:03Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt. <br />
<br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
<br />
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=476CorpDebtRep2016-05-03T22:41:45Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. <br />
<br />
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, <br />
and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered <br />
around US corporate debt. <br />
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry <br />
Business Ontology (EDM Council 2016):<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
Impact on financial stability and monitoring<br />
The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=475CorpDebtRep2016-05-03T22:40:05Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt repository <br />
that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships <br />
among financial institutions, and specific details about a corporation and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products.<br />
<br />
The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered around US corporate debt. The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
Impact on financial stability and monitoring<br />
The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=474CorpDebtRep2016-05-03T22:38:17Z<p>Louiqa: </p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt <br />
repository that will provide precise yet detailed insights into the corporate debt eco-system, <br />
capturing both relationships among financial institutions, and specific details about a corporation <br />
and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products.<br />
Intellectual merit<br />
The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered around US corporate debt. The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
Impact on financial stability and monitoring<br />
The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=CorpDebtRep&diff=473CorpDebtRep2016-05-03T22:36:47Z<p>Louiqa: Created page with "Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activ..."</p>
<hr />
<div>Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures (Whited 1992; Hennessey and Whited 2005, 2007; Guedes and Oppler 1996; Chen et al 2007; Adrian et al 2015), or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.<br />
This research will address the “data science for finance” challenges of creating a public CorpDebt <br />
repository that will provide precise yet detailed insights into the corporate debt eco-system, <br />
capturing both relationships among financial institutions, and specific details about a corporation <br />
and its debt structure. <br />
<br />
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products.<br />
Intellectual merit<br />
The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers. We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered around US corporate debt. The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):<br />
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc.<br />
Amount and timing of new issuances, debt structure, seniority, cash flow, etc.<br />
Guarantees, covenants, corporate events, etc.<br />
Impact on financial stability and monitoring<br />
The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=472ResMBS2016-05-03T22:32:51Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities started becoming very<br />
popular in 2002. The issued securities reached a peak in 2006 and then started to decline in 2007 <br />
and came to an abrupt end in 2008. <br />
<br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]<br />
<br />
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary<br />
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.<br />
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains<br />
Zheng Xu and Louiqa Raschid, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsYUFIVlQ3QTVHQW8]<br />
<br />
If you want the gory details of the tools on the IBM System T platform that were developed ...<br />
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from <br />
Unstructured Documents<br />
Zheng Xu (University of Maryland) and Douglas Burdick (IBM) and Louiqa Raschid (University of Maryland)<br />
[http://arxiv.org/abs/1602.04427]<br />
<br />
The dataset is available for research.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=471ResMBS2016-04-13T03:20:46Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities were first created in 2002. <br />
They reached a peak in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. <br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]<br />
<br />
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary<br />
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.<br />
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains<br />
Zheng Xu and Louiqa Raschid, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsYUFIVlQ3QTVHQW8]<br />
<br />
If you want the gory details of the tools on the IBM System T platform that were developed ...<br />
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from <br />
Unstructured Documents<br />
Zheng Xu (University of Maryland) and Douglas Burdick (IBM) and Louiqa Raschid (University of Maryland)<br />
[http://arxiv.org/abs/1602.04427]<br />
<br />
The dataset is available for research.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=470ResMBS2016-04-13T03:20:25Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities were first created in 2002. <br />
They reached a peak in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. <br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]<br />
<br />
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary<br />
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.<br />
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains<br />
Zheng Xu and Louiqa Raschid, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsYUFIVlQ3QTVHQW8]<br />
<br />
If you want the gory details of the tools on the IBM System T platform that were developed ...<br />
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents<br />
Zheng Xu (University of Maryland) and Douglas Burdick (IBM) and Louiqa Raschid (University of Maryland)<br />
[http://arxiv.org/abs/1602.04427]<br />
<br />
The dataset is available for research.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=469ResMBS2016-04-13T03:00:21Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities were first created in 2002. <br />
They reached a peak in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. <br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]<br />
<br />
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary<br />
of FIs and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.<br />
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains<br />
Zheng Xu and Louiqa Raschid, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsYUFIVlQ3QTVHQW8]<br />
<br />
The dataset is available for research.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=468ResMBS2016-04-13T02:59:39Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities were first created in 2002. <br />
They reached a peak in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. <br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]<br />
<br />
We used a topic modeling approach to develop a model FI-Comm where a topic is defined over a vocabulary of FIs <br />
and a model Role-FI-Comm where a topic is defined over a vocabulary of Role-FI pairs.<br />
Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains<br />
Zheng Xu and Louiqa Raschid, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsYUFIVlQ3QTVHQW8]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=467ResMBS2016-04-13T02:53:33Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities were first created in 2002. <br />
They reached a peak in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. <br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]<br />
<br />
The networks described in the paper can be viewed here.<br />
FI clusters [http://dsfin.umiacs.umd.edu/FI-network/4roles/]<br />
FI clusters based on FC-FC similarity [http://dsfin.umiacs.umd.edu/FC-network/4roles/]<br />
FI-FC bipartite graph [http://pattaran.umiacs.umd.edu/clusters/resmbs-4roles]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=466ResMBS2016-04-13T02:49:12Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities were first created in 2002. <br />
They reached a peak in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. <br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick, IBM<br />
Soham De and Louiqa Raschid and Mingchao Shao and Zheng Xu and Elena Zotkina, University of Maryland<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=465ResMBS2016-04-13T02:47:52Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US <br />
residential mortgage backed securities filed with the SEC. These securities were first created in 2002. <br />
They reached a peak in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. <br />
We extracted the "financial supply chain" comprising "financial institutions" (FI) and the role (Role) <br />
that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering <br />
analysis on the graph.<br />
resMBS: Constructing a Financial Supply Chain from Prospecti<br />
Doug Burdick<br />
IBM<br />
drburdic@us.ibm.com<br />
Soham De<br />
University of Maryland<br />
sohamde@cs.umd.edu<br />
Louiqa Raschid<br />
University of Maryland<br />
louiqa@umiacs.umd.edu<br />
Mingchao Shao<br />
University of Maryland<br />
shao@cs.umd.edu<br />
Zheng Xu<br />
University of Maryland<br />
xuzh@cs.umd.edu<br />
Elena Zotkina<br />
University of Maryland<br />
ezotkina@umiacs.umd.edu<br />
[https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=464ResMBS2016-04-13T02:45:47Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for <br />
US residential <br />
mortgage backed securities filed with the SEC. These securities were first created in 2002. They reached a peak <br />
in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. We extracted the "financial supply <br />
chain" comprising "financial institutions" (FI) and the role (Role) that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering analysis on<br />
the graph.<br />
[[Media:https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=463ResMBS2016-04-13T02:45:02Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US residential <br />
mortgage backed securities filed with the SEC. These securities were first created in 2002. They reached a peak <br />
in 2006 and then started to decline in 2007 and came to an abrupt end in 2008. We extracted the "financial supply <br />
chain" comprising "financial institutions" (FI) and the role (Role) that they play on a financial contract (FC).<br />
<br />
The following paper provides an overview of how the dataset was created and some preliminary clustering analysis on<br />
the graph.<br />
[[Media:https://drive.google.com/open?id=0BzTeYQSh4QTsdzRxbGhKSDVpblE]]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=462ResMBS2016-04-13T02:36:33Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS is a graph / dataset that has been extracted from the contents of financial prospecti for US residential <br />
mortgage backed securities filed with the SEC. These securities were first created in 2002. They 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).</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=461ResMBS2016-04-13T02:33:08Z<p>Louiqa: </p>
<hr />
<div><br />
resMBS</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=ResMBS&diff=460ResMBS2016-04-13T02:30:18Z<p>Louiqa: Created page with "resMBS"</p>
<hr />
<div>resMBS</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=Next_Generational_Financial_Cyberinfrastucture_Workshop&diff=459Next Generational Financial Cyberinfrastucture Workshop2014-11-24T01:55:15Z<p>Louiqa: /* DSfin Financial BIGDATA Challenge */</p>
<hr />
<div>'''<br />
==Overview==<br />
The Great Recession of 2008 and the continuing reverberations in the Eurozone have highlighted <br />
significant limitations in the ability of regulators and analysts/researchers to monitor and <br />
model the national and global financial ecosystem. This includes the lack of financial <br />
cyberinfrastructure to ingest and process numerous streams of financial transactions, as well as <br />
the accompanying data streams of economic activity, in real time. Also absent are open standards<br />
and shared semantics so that this data can be used to populate models of individual markets, <br />
financial networks and the interconnected ecosystem representing the global financial system. <br />
The most important challenge is the need to develop computational research frameworks, models <br />
and methods, in the spirit of past efforts to identify computational grand challenges in a diversity <br />
of data intensive domains including the biomedical sciences, health information management, <br />
climate change, etc. The next generation of financial cyberinfrastructure must provide a platform <br />
that can transform our current approaches to monitoring and regulating systemic risk. <br />
<br />
Organized under the auspices of the [http://www.rhsmith.umd.edu/cfp/ University of Maryland's Center for Financial Policy], <br />
the goal of this workshop (and related activities) is to work closely with federal regulatory <br />
agencies, academic research communities in computer science, finance, economics and <br />
other related disciplines, the financial industry and the computing industry. <br />
<br />
The broader impact will include the following: <br />
* A blueprint for the next generation financial cyberinfrastructure. <br />
* A computational research framework with models and methods that are needed to transform <br />
the monitoring and regulation of systemic risk.<br />
* Best practices for the software industry and a robust regulatory framework.<br />
* Multi-disciplinary Ph.D. level curriculum and doctoral dissertation challenges.<br />
<br />
==DSfin Financial Entity Identification and Information Integration (FEIII) Challenge==<br />
<br />
* CLOSED CHALLENGES (with ground truth for evaluation)<br />
** Matching between reference resources, e.g., LEI and SEC Central Index Key [http://www.sec.gov/edgar/searchedgar/cik.htm SEC CIK].<br />
<br />
<br />
* [OPEN CHALLENGES] <br />
** Resolving mentions in a document collection.<br />
** Creating a Linked Data resource over some public dataset.<br />
** Extending NAICS to create a more relevant classification for the financial activities of organizations. The challenge is to classify companies.<br />
<br />
==DSfin Consortium - Meeting Notes and Agenda==<br />
* January meeting<br />
IBM Presentation<br />
Berkeley BIDS [[Media:BIDS.pdf]]<br />
Contract Aggregation Framework<br />
* Friday March 21 Meeting Agenda <br />
Berkeley BIDS Project: Complex contracts, e.g., mortgage backed securities. <br />
Discussion on Indices.<br />
DSfin / IBM MIDAS / LEI / Financial BIGDATA Challenge<br />
<br />
* TO DO <br />
NSF DIBBS [http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBS April 9]<br />
NSF BIGDATA [http://www.nsf.gov/cise/news/bigdata.jsp NSF BIGDATA June 9]<br />
<br />
* ACTION ITEMS from March<br />
Paulo and Sekar and Louiqa to identify relevant tasks for a Berkeley BIDS Summer Internship<br />
- Summary page<br />
- Contract structure<br />
<br />
==Workshop Sponsor==<br />
[http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1237476 National Science Foundation, Division of Information & Intelligent Systems]<br />
<br />
==Organizers==<br />
<br />
Organizers<br />
* Louiqa Raschid, Professor, University of Maryland<br />
* H. V. Jagadish, Bernard A Galler Professor, University of Michigan<br />
* Michelle Lui, Assistant Director, Center for Financial Policy, University of Maryland<br />
<br />
Advisory Committee and/or Report Writing Committee ''sponsored by the Computing Research Association / Computing Community Consortium'' [http://www.cra.org/ccc/]<br />
<br />
* Mike Bennett, EDM Council<br />
* Phil Bernstein, Microsoft<br />
* Andrea Cali, Oxford Man Institute of Quantitative Finance and University of London<br />
* Benjamin Grosof<br />
* A. “Pete” Kyle, Charles E. Smith Chair in Finance, University of Maryland<br />
* Joe Langsam, Committee to Establish the NIF; formerly of Morgan Stanley<br />
* Leora Morgenstern, Technical Fellow and Senior Scientist, SAIC<br />
* David Newman, Vice President for Enterprise Architecture, Wells Fargo<br />
* Frank Olken<br />
* Rachel Pottinger, University of British Columbia<br />
* Chester Spatt, Pamela R. and Kenneth B. Dunn Professor of Finance, Carnegie Mellon University<br />
* Lemma Senbet, William E. Mayer Chair in Finance and Director, Center for Financial Policy, University of Maryland<br />
* Nancy Wallace, Professor, University of California<br />
* Michael Wellman, University of Michigan<br />
<br />
==Participants==<br />
For a list of participants [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Participants click here]<br />
<br />
==Background Reading==<br />
For a list of articles [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/BackgroundReading click here]<br />
<br />
==Agenda==<br />
For the agenda [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Agenda click here]<br />
<br />
==Research Challenges==<br />
For the challenges [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/ResChall click here]<br />
<br />
== Logistics ==<br />
<br />
<br />
====When and Where====<br />
<br />
July 19-20, 2012<br />
<br />
9am-5pm<br />
<br />
[http://www.executiveboard.com/exbd-resources/content/waterview/index.html Waterview Conference Center]<br />
<br />
1919 North Lynn Street<br />
<br />
Arlington, VA 22209<br />
<br />
24th Floor<br />
<br />
For more information regarding workshop location, accommodations, and travel, [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Logistics click here]<br />
<br />
==Sloan Foundation Call for Proposals Related to LEI==<br />
<br />
The Alfred P. Sloan Foundation<br />
[http://www.rhsmith.umd.edu/doit/docs/SloanLEIRequestforProposals.pdf]<br />
<br />
== Report and Dissemination ==<br />
<br />
[http://www.cccblog.org/2012/07/31/a-workshop-on-next-generational-financial-cyberinfrastructure/ CRA CCC Blog]<br />
<br />
Report [[File:NSF-NextGen.pdf]]<br />
<br />
==ISWC 2012 Tutorial==<br />
<br />
For details about the tutorial [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/ISWC2012Tutorial click here]<br />
<br />
==Karsha ==<br />
<br />
===Pariksha Document Annotation and Semantic Search===<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/KarshaDASS click here]<br />
<br />
==CRI CI-P and CI Proposal==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/CRI-CI-P click here]<br />
<br />
==DIBBs Proposal==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/DIBBs click here]<br />
<br />
==UMIACS-Smith Financial Cyberinfrastructure Srping 2013 Seminar Series==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Spring2013FCISeminar click here]<br />
<br />
==DTCC==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/DTCC click here]<br />
<br />
==Meeting Notes==<br />
<br />
November 27<br />
* Lemma will invite Dick Berner for a campus meeting possibly with the President and Provost.<br />
Dick may also be interested in presenting a talk about the OFR?<br />
* Louiqa will prepare a 1 page description to motivate a cluster-hire.<br />
* Louiqa will follow up with the Development Office.<br />
(Morgan Stanley, Bank of America, Nomura)</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=File:Proposal.pdf&diff=458File:Proposal.pdf2014-04-15T22:32:26Z<p>Louiqa: </p>
<hr />
<div></div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=DIBBs&diff=457DIBBs2014-04-15T22:29:45Z<p>Louiqa: </p>
<hr />
<div> NSF Data Infrastructure Building Blocks<br />
[http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBs CFP]<br />
[[Media:Proposal.pdf]] - Proposal submitted April 2014.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=File:Proposal%2BLetters.pdf&diff=456File:Proposal+Letters.pdf2014-04-15T22:29:27Z<p>Louiqa: </p>
<hr />
<div></div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=DIBBs&diff=455DIBBs2014-04-15T22:25:47Z<p>Louiqa: </p>
<hr />
<div> NSF Data Infrastructure Building Blocks<br />
[http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBs CFP]<br />
[[Media:Proposal+Letters.pdf]] - Proposal submitted April 2014.</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=DIBBs&diff=454DIBBs2014-04-15T22:24:42Z<p>Louiqa: </p>
<hr />
<div> NSF Data Infrastructure Building Blocks<br />
[http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBs CFP]<br />
[[Media:]]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=DIBBs&diff=453DIBBs2014-04-15T22:24:13Z<p>Louiqa: </p>
<hr />
<div> NSF Data Infrastructure Building Blocks<br />
[http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBs CFP]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=DIBBs&diff=452DIBBs2014-04-15T22:23:39Z<p>Louiqa: </p>
<hr />
<div>[http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBs CFP]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=DIBBs&diff=451DIBBs2014-04-15T22:23:18Z<p>Louiqa: </p>
<hr />
<div>[[http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm]] NSF DIBBs CFP</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=DIBBs&diff=450DIBBs2014-04-15T22:22:19Z<p>Louiqa: Created page with "[title]http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBs CFP]"</p>
<hr />
<div>[title]http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBs CFP]</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=Next_Generational_Financial_Cyberinfrastucture_Workshop&diff=449Next Generational Financial Cyberinfrastucture Workshop2014-04-15T22:21:05Z<p>Louiqa: /* DIBBs Proposal */</p>
<hr />
<div>'''<br />
==Overview==<br />
The Great Recession of 2008 and the continuing reverberations in the Eurozone have highlighted <br />
significant limitations in the ability of regulators and analysts/researchers to monitor and <br />
model the national and global financial ecosystem. This includes the lack of financial <br />
cyberinfrastructure to ingest and process numerous streams of financial transactions, as well as <br />
the accompanying data streams of economic activity, in real time. Also absent are open standards<br />
and shared semantics so that this data can be used to populate models of individual markets, <br />
financial networks and the interconnected ecosystem representing the global financial system. <br />
The most important challenge is the need to develop computational research frameworks, models <br />
and methods, in the spirit of past efforts to identify computational grand challenges in a diversity <br />
of data intensive domains including the biomedical sciences, health information management, <br />
climate change, etc. The next generation of financial cyberinfrastructure must provide a platform <br />
that can transform our current approaches to monitoring and regulating systemic risk. <br />
<br />
Organized under the auspices of the [http://www.rhsmith.umd.edu/cfp/ University of Maryland's Center for Financial Policy], <br />
the goal of this workshop (and related activities) is to work closely with federal regulatory <br />
agencies, academic research communities in computer science, finance, economics and <br />
other related disciplines, the financial industry and the computing industry. <br />
<br />
The broader impact will include the following: <br />
* A blueprint for the next generation financial cyberinfrastructure. <br />
* A computational research framework with models and methods that are needed to transform <br />
the monitoring and regulation of systemic risk.<br />
* Best practices for the software industry and a robust regulatory framework.<br />
* Multi-disciplinary Ph.D. level curriculum and doctoral dissertation challenges.<br />
<br />
==DSfin Financial BIGDATA Challenge==<br />
<br />
* CLOSED CHALLENGES (with ground truth for evaluation)<br />
** Matching between reference resources, e.g., LEI and SEC Central Index Key [http://www.sec.gov/edgar/searchedgar/cik.htm SEC CIK].<br />
** Extending NAICS to create a more relevant classification for the financial activities of organizations. The challenge is to classify companies.<br />
<br />
* [OPEN CHALLENGES] <br />
** Resolving mentions in a document collection.<br />
** Creating a Linked Data resource over some public dataset.<br />
<br />
==DSfin Consortium - Meeting Notes and Agenda==<br />
* January meeting<br />
IBM Presentation<br />
Berkeley BIDS [[Media:BIDS.pdf]]<br />
Contract Aggregation Framework<br />
* Friday March 21 Meeting Agenda <br />
Berkeley BIDS Project: Complex contracts, e.g., mortgage backed securities. <br />
Discussion on Indices.<br />
DSfin / IBM MIDAS / LEI / Financial BIGDATA Challenge<br />
<br />
* TO DO <br />
NSF DIBBS [http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBS April 9]<br />
NSF BIGDATA [http://www.nsf.gov/cise/news/bigdata.jsp NSF BIGDATA June 9]<br />
<br />
* ACTION ITEMS from March<br />
Paulo and Sekar and Louiqa to identify relevant tasks for a Berkeley BIDS Summer Internship<br />
- Summary page<br />
- Contract structure<br />
<br />
==Workshop Sponsor==<br />
[http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1237476 National Science Foundation, Division of Information & Intelligent Systems]<br />
<br />
==Organizers==<br />
<br />
Organizers<br />
* Louiqa Raschid, Professor, University of Maryland<br />
* H. V. Jagadish, Bernard A Galler Professor, University of Michigan<br />
* Michelle Lui, Assistant Director, Center for Financial Policy, University of Maryland<br />
<br />
Advisory Committee and/or Report Writing Committee ''sponsored by the Computing Research Association / Computing Community Consortium'' [http://www.cra.org/ccc/]<br />
<br />
* Mike Bennett, EDM Council<br />
* Phil Bernstein, Microsoft<br />
* Andrea Cali, Oxford Man Institute of Quantitative Finance and University of London<br />
* Benjamin Grosof<br />
* A. “Pete” Kyle, Charles E. Smith Chair in Finance, University of Maryland<br />
* Joe Langsam, Committee to Establish the NIF; formerly of Morgan Stanley<br />
* Leora Morgenstern, Technical Fellow and Senior Scientist, SAIC<br />
* David Newman, Vice President for Enterprise Architecture, Wells Fargo<br />
* Frank Olken<br />
* Rachel Pottinger, University of British Columbia<br />
* Chester Spatt, Pamela R. and Kenneth B. Dunn Professor of Finance, Carnegie Mellon University<br />
* Lemma Senbet, William E. Mayer Chair in Finance and Director, Center for Financial Policy, University of Maryland<br />
* Nancy Wallace, Professor, University of California<br />
* Michael Wellman, University of Michigan<br />
<br />
==Participants==<br />
For a list of participants [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Participants click here]<br />
<br />
==Background Reading==<br />
For a list of articles [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/BackgroundReading click here]<br />
<br />
==Agenda==<br />
For the agenda [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Agenda click here]<br />
<br />
==Research Challenges==<br />
For the challenges [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/ResChall click here]<br />
<br />
== Logistics ==<br />
<br />
<br />
====When and Where====<br />
<br />
July 19-20, 2012<br />
<br />
9am-5pm<br />
<br />
[http://www.executiveboard.com/exbd-resources/content/waterview/index.html Waterview Conference Center]<br />
<br />
1919 North Lynn Street<br />
<br />
Arlington, VA 22209<br />
<br />
24th Floor<br />
<br />
For more information regarding workshop location, accommodations, and travel, [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Logistics click here]<br />
<br />
==Sloan Foundation Call for Proposals Related to LEI==<br />
<br />
The Alfred P. Sloan Foundation<br />
[http://www.rhsmith.umd.edu/doit/docs/SloanLEIRequestforProposals.pdf]<br />
<br />
== Report and Dissemination ==<br />
<br />
[http://www.cccblog.org/2012/07/31/a-workshop-on-next-generational-financial-cyberinfrastructure/ CRA CCC Blog]<br />
<br />
Report [[File:NSF-NextGen.pdf]]<br />
<br />
==ISWC 2012 Tutorial==<br />
<br />
For details about the tutorial [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/ISWC2012Tutorial click here]<br />
<br />
==Karsha ==<br />
<br />
===Pariksha Document Annotation and Semantic Search===<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/KarshaDASS click here]<br />
<br />
==CRI CI-P and CI Proposal==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/CRI-CI-P click here]<br />
<br />
==DIBBs Proposal==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/DIBBs click here]<br />
<br />
==UMIACS-Smith Financial Cyberinfrastructure Srping 2013 Seminar Series==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Spring2013FCISeminar click here]<br />
<br />
==DTCC==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/DTCC click here]<br />
<br />
==Meeting Notes==<br />
<br />
November 27<br />
* Lemma will invite Dick Berner for a campus meeting possibly with the President and Provost.<br />
Dick may also be interested in presenting a talk about the OFR?<br />
* Louiqa will prepare a 1 page description to motivate a cluster-hire.<br />
* Louiqa will follow up with the Development Office.<br />
(Morgan Stanley, Bank of America, Nomura)</div>Louiqahttps://wiki.umiacs.umd.edu/clip/ngfci/index.php?title=Next_Generational_Financial_Cyberinfrastucture_Workshop&diff=448Next Generational Financial Cyberinfrastucture Workshop2014-04-15T22:20:15Z<p>Louiqa: /* CRI CI-P and CI Proposal */</p>
<hr />
<div>'''<br />
==Overview==<br />
The Great Recession of 2008 and the continuing reverberations in the Eurozone have highlighted <br />
significant limitations in the ability of regulators and analysts/researchers to monitor and <br />
model the national and global financial ecosystem. This includes the lack of financial <br />
cyberinfrastructure to ingest and process numerous streams of financial transactions, as well as <br />
the accompanying data streams of economic activity, in real time. Also absent are open standards<br />
and shared semantics so that this data can be used to populate models of individual markets, <br />
financial networks and the interconnected ecosystem representing the global financial system. <br />
The most important challenge is the need to develop computational research frameworks, models <br />
and methods, in the spirit of past efforts to identify computational grand challenges in a diversity <br />
of data intensive domains including the biomedical sciences, health information management, <br />
climate change, etc. The next generation of financial cyberinfrastructure must provide a platform <br />
that can transform our current approaches to monitoring and regulating systemic risk. <br />
<br />
Organized under the auspices of the [http://www.rhsmith.umd.edu/cfp/ University of Maryland's Center for Financial Policy], <br />
the goal of this workshop (and related activities) is to work closely with federal regulatory <br />
agencies, academic research communities in computer science, finance, economics and <br />
other related disciplines, the financial industry and the computing industry. <br />
<br />
The broader impact will include the following: <br />
* A blueprint for the next generation financial cyberinfrastructure. <br />
* A computational research framework with models and methods that are needed to transform <br />
the monitoring and regulation of systemic risk.<br />
* Best practices for the software industry and a robust regulatory framework.<br />
* Multi-disciplinary Ph.D. level curriculum and doctoral dissertation challenges.<br />
<br />
==DSfin Financial BIGDATA Challenge==<br />
<br />
* CLOSED CHALLENGES (with ground truth for evaluation)<br />
** Matching between reference resources, e.g., LEI and SEC Central Index Key [http://www.sec.gov/edgar/searchedgar/cik.htm SEC CIK].<br />
** Extending NAICS to create a more relevant classification for the financial activities of organizations. The challenge is to classify companies.<br />
<br />
* [OPEN CHALLENGES] <br />
** Resolving mentions in a document collection.<br />
** Creating a Linked Data resource over some public dataset.<br />
<br />
==DSfin Consortium - Meeting Notes and Agenda==<br />
* January meeting<br />
IBM Presentation<br />
Berkeley BIDS [[Media:BIDS.pdf]]<br />
Contract Aggregation Framework<br />
* Friday March 21 Meeting Agenda <br />
Berkeley BIDS Project: Complex contracts, e.g., mortgage backed securities. <br />
Discussion on Indices.<br />
DSfin / IBM MIDAS / LEI / Financial BIGDATA Challenge<br />
<br />
* TO DO <br />
NSF DIBBS [http://www.nsf.gov/pubs/2014/nsf14530/nsf14530.htm NSF DIBBS April 9]<br />
NSF BIGDATA [http://www.nsf.gov/cise/news/bigdata.jsp NSF BIGDATA June 9]<br />
<br />
* ACTION ITEMS from March<br />
Paulo and Sekar and Louiqa to identify relevant tasks for a Berkeley BIDS Summer Internship<br />
- Summary page<br />
- Contract structure<br />
<br />
==Workshop Sponsor==<br />
[http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1237476 National Science Foundation, Division of Information & Intelligent Systems]<br />
<br />
==Organizers==<br />
<br />
Organizers<br />
* Louiqa Raschid, Professor, University of Maryland<br />
* H. V. Jagadish, Bernard A Galler Professor, University of Michigan<br />
* Michelle Lui, Assistant Director, Center for Financial Policy, University of Maryland<br />
<br />
Advisory Committee and/or Report Writing Committee ''sponsored by the Computing Research Association / Computing Community Consortium'' [http://www.cra.org/ccc/]<br />
<br />
* Mike Bennett, EDM Council<br />
* Phil Bernstein, Microsoft<br />
* Andrea Cali, Oxford Man Institute of Quantitative Finance and University of London<br />
* Benjamin Grosof<br />
* A. “Pete” Kyle, Charles E. Smith Chair in Finance, University of Maryland<br />
* Joe Langsam, Committee to Establish the NIF; formerly of Morgan Stanley<br />
* Leora Morgenstern, Technical Fellow and Senior Scientist, SAIC<br />
* David Newman, Vice President for Enterprise Architecture, Wells Fargo<br />
* Frank Olken<br />
* Rachel Pottinger, University of British Columbia<br />
* Chester Spatt, Pamela R. and Kenneth B. Dunn Professor of Finance, Carnegie Mellon University<br />
* Lemma Senbet, William E. Mayer Chair in Finance and Director, Center for Financial Policy, University of Maryland<br />
* Nancy Wallace, Professor, University of California<br />
* Michael Wellman, University of Michigan<br />
<br />
==Participants==<br />
For a list of participants [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Participants click here]<br />
<br />
==Background Reading==<br />
For a list of articles [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/BackgroundReading click here]<br />
<br />
==Agenda==<br />
For the agenda [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Agenda click here]<br />
<br />
==Research Challenges==<br />
For the challenges [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/ResChall click here]<br />
<br />
== Logistics ==<br />
<br />
<br />
====When and Where====<br />
<br />
July 19-20, 2012<br />
<br />
9am-5pm<br />
<br />
[http://www.executiveboard.com/exbd-resources/content/waterview/index.html Waterview Conference Center]<br />
<br />
1919 North Lynn Street<br />
<br />
Arlington, VA 22209<br />
<br />
24th Floor<br />
<br />
For more information regarding workshop location, accommodations, and travel, [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Logistics click here]<br />
<br />
==Sloan Foundation Call for Proposals Related to LEI==<br />
<br />
The Alfred P. Sloan Foundation<br />
[http://www.rhsmith.umd.edu/doit/docs/SloanLEIRequestforProposals.pdf]<br />
<br />
== Report and Dissemination ==<br />
<br />
[http://www.cccblog.org/2012/07/31/a-workshop-on-next-generational-financial-cyberinfrastructure/ CRA CCC Blog]<br />
<br />
Report [[File:NSF-NextGen.pdf]]<br />
<br />
==ISWC 2012 Tutorial==<br />
<br />
For details about the tutorial [https://wiki.umiacs.umd.edu/clip/ngfci/index.php/ISWC2012Tutorial click here]<br />
<br />
==Karsha ==<br />
<br />
===Pariksha Document Annotation and Semantic Search===<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/KarshaDASS click here]<br />
<br />
==CRI CI-P and CI Proposal==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/CRI-CI-P click here]<br />
<br />
==DIBBs Proposal==<br />
<br />
==UMIACS-Smith Financial Cyberinfrastructure Srping 2013 Seminar Series==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/Spring2013FCISeminar click here]<br />
<br />
==DTCC==<br />
[https://wiki.umiacs.umd.edu/clip/ngfci/index.php/DTCC click here]<br />
<br />
==Meeting Notes==<br />
<br />
November 27<br />
* Lemma will invite Dick Berner for a campus meeting possibly with the President and Provost.<br />
Dick may also be interested in presenting a talk about the OFR?<br />
* Louiqa will prepare a 1 page description to motivate a cluster-hire.<br />
* Louiqa will follow up with the Development Office.<br />
(Morgan Stanley, Bank of America, Nomura)</div>Louiqa