Difference between revisions of "DSMM: Data Science for Macro-Modeling with Financial and Economic Datasets"

(Overview)
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===Overview===
 
===Overview===
  
DSMM 2018 will explore the challenges of macro-modeling with financial
+
DSMM 2018 will explore the challenges of macro-modeling with financial and economic datasets. The workshop will also showcase the '''Financial Entity Identification and Information Integration (FEIII) Challenge'''.
and economic datasets. The workshop will also showcase the '''Financial
 
Entity Identification and Information Integration (FEIII) Challenge'''.
 
 
[https://ir.nist.gov/dsfin/]
 
[https://ir.nist.gov/dsfin/]
Financial big data and FINTECH applications are in the vanguard of
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Financial big data and FINTECH applications are in the vanguard of activities around the deployment of Open Knowledge Networks.
activities around the deployment of Open Knowledge Networks.
 
 
[http://ichs.ucsf.edu/open-knowledge-network/]
 
[http://ichs.ucsf.edu/open-knowledge-network/]
  
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Proceedings of ACM DSMM 2014 are available here: [https://dl.acm.org/citation.cfm?id=2630729].
 
Proceedings of ACM DSMM 2014 are available here: [https://dl.acm.org/citation.cfm?id=2630729].
  
IMPORTANT DATES[EDIT]
 
  
 +
Two trends are providing data-rich opportunities for macro-modeling of financial and economic ecosystems. First, public data is becoming increasingly available from a variety of sources (CRSP, EDGAR, FRED). Economists have used longitudinal datasets (US Census, Labor, etc.) to drive econometric and statistical research.  The advent of Big Data infrastructures and analytical tools can support the required information fusion, as well as macro-modeling with diverse datasets, and can potentially lead to the exploration of complex financial and economic ecosystems.  Although integrating datasets may pose technical and policy/privacy challenges, the potential benefits are immense. For example, social media data often contains features that could enhance macroeconomic statistics derived from traditional survey-driven datasets. The resulting enriched datasets could explore hypotheses with a different focus or level of granularity.
  
Two trends are providing data-rich opportunities for macro-modeling of
+
The financial world is a closely interlinked Web of financial entities and networks, supply chains and ecosystems. Analysts, regulators and researchers must address the challenges of monitoring, integrating, and analyzing at scaleTechnical challenges include entity identification; entity classification; learning relationships among entities. The benefits of addressing these challenges may result in improved tools for regulators to monitor financial systems or to set fiscal policyAdditional benefits may include fundamentally new designs of market mechanisms, new ways to reach consumers and to exploit the wisdom of the crowds.
financial and economic ecosystems. First, public data is becoming
 
increasingly available from a variety of sources (CRSP, EDGAR, FRED).
 
Economists have used longitudinal datasets (US Census, Labor, etc.)
 
to drive econometric and statistical research.  The advent of Big Data
 
infrastructures and analytical tools can support the required
 
information fusion, as well as macro-modeling with diverse datasets,
 
and can potentially lead to the exploration of complex financial and
 
economic ecosystemsAlthough integrating datasets may pose technical
 
and policy/privacy challenges, the potential benefits are immense.
 
For example, social media data often contains features that could enhance
 
macroeconomic statistics derived from traditional survey-driven datasets.
 
The resulting enriched datasets could explore hypotheses with a different
 
focus or level of granularity.
 
  
The financial world is a closely interlinked Web of financial entities
+
We expect attendees with an interest in information integration, data mining, knowledge representation, network and visual analytics, stream data processing, etc.
and networks, supply chains and ecosystems. Analysts, regulators and
 
researchers must address the challenges of monitoring, integrating, and
 
analyzing at scale.  Technical challenges include entity identification;
 
entity classification; learning relationships among entities. The benefits
 
of addressing these challenges may result in improved tools for
 
regulators to monitor financial systems or to set fiscal policy.  Additional
 
benefits may include fundamentally new designs of market mechanisms, new
 
ways to reach consumers and to exploit the wisdom of the crowds.
 
 
 
We expect attendees with an interest in information integration, data
 
mining, knowledge representation, network and visual analytics, stream
 
data processing, etc.
 

Revision as of 03:09, 16 November 2017

Overview

DSMM 2018 will explore the challenges of macro-modeling with financial and economic datasets. The workshop will also showcase the Financial Entity Identification and Information Integration (FEIII) Challenge. [1] Financial big data and FINTECH applications are in the vanguard of activities around the deployment of Open Knowledge Networks. [2]

The Proceedings of ACM DSMM 2016 are available here: [3]. Proceedings of ACM DSMM 2014 are available here: [4].


Two trends are providing data-rich opportunities for macro-modeling of financial and economic ecosystems. First, public data is becoming increasingly available from a variety of sources (CRSP, EDGAR, FRED). Economists have used longitudinal datasets (US Census, Labor, etc.) to drive econometric and statistical research. The advent of Big Data infrastructures and analytical tools can support the required information fusion, as well as macro-modeling with diverse datasets, and can potentially lead to the exploration of complex financial and economic ecosystems. Although integrating datasets may pose technical and policy/privacy challenges, the potential benefits are immense. For example, social media data often contains features that could enhance macroeconomic statistics derived from traditional survey-driven datasets. The resulting enriched datasets could explore hypotheses with a different focus or level of granularity.

The financial world is a closely interlinked Web of financial entities and networks, supply chains and ecosystems. Analysts, regulators and researchers must address the challenges of monitoring, integrating, and analyzing at scale. Technical challenges include entity identification; entity classification; learning relationships among entities. The benefits of addressing these challenges may result in improved tools for regulators to monitor financial systems or to set fiscal policy. Additional benefits may include fundamentally new designs of market mechanisms, new ways to reach consumers and to exploit the wisdom of the crowds.

We expect attendees with an interest in information integration, data mining, knowledge representation, network and visual analytics, stream data processing, etc.