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

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===Overview===
 
===Overview===
  
The promise of Big Data, linked data and social data is the availability of large scale yet granular datasets to support modeling of complex ecosystems reflecting cyber-human decision making.  While complex data-driven models have emerged for climate modeling or systems biology, there has been less activity in macro-modeling with multiple heterogeneous economic or financial datasets. Such analytics requires dealing with multiple heterogeneous streams of data, each of which can be high in volume and variety and reflect varying degrees of veracity.  The advent of Big Data infrastructures and analytical tools can support the required integration across these data sources, as well as macro-modeling with diverse datasets, and can potentially lead to the exploration of complex financial and economic ecosystems.
+
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''''.
 +
[https://ir.nist.gov/dsfin/]
 +
Financial big data and FINTECH applications are in the vanguard of
 +
activities around the deployment of Open Knowledge Networks.
 +
[http://ichs.ucsf.edu/open-knowledge-network/]
 +
 
 +
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.
  
The financial world is a closely interlinked Web of financial entities and networks, supply chains and financial ecosystems. Financial analysts, regulators and academic researchers recognize they must address the unprecedented and unfamiliar challenges of monitoring, integrating, and analyzing such networks and ecosystems at scale. A researcher would have to process multiple heterogeneous data streams, extract relevant information, clean it, integrate information from distinct streams, perform entity resolution, and aggregate data before they can even begin their analysis.  Doing all of this creates a high barrier for financial and economic data science at scale. The benefits of addressing these challenges are immense and may result in improved tools for regulators to monitor financial systems or to set economic or fiscal policy. Additional benefits may include fundamentally new designs of market mechanisms, new ways to reach consumers, and new ways to exploit the wisdom of the crowds.
 
  
The DSMM 2017 workshop will explore the challenges of data science for macro-modeling with financial and/or economic datasets. The workshop will also showcase the '''Financial Entity Identification and Information Integration (FEIII) at Scale Challenge'''.  [https://ir.nist.gov/dsfin/]
 
  
 
Proceedings of ACM DSMM 2014 are available here: http://dl.acm.org/citation.cfm?id=2630729. The Proceedings of ACM DSMM 2016 are available here: http://dl.acm.org/citation.cfm?id=2951894
 
Proceedings of ACM DSMM 2014 are available here: http://dl.acm.org/citation.cfm?id=2630729. The Proceedings of ACM DSMM 2016 are available here: http://dl.acm.org/citation.cfm?id=2951894

Revision as of 02:34, 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]

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.


Proceedings of ACM DSMM 2014 are available here: http://dl.acm.org/citation.cfm?id=2630729. The Proceedings of ACM DSMM 2016 are available here: http://dl.acm.org/citation.cfm?id=2951894