Actions

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

From datascience

No edit summary
(5 intermediate revisions by the same user not shown)
Line 2: Line 2:
===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
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/]


Proceedings of ACM DSMM 2014 are available here: http://dl.acm.org/citation.cfm?id=2630729.
Past proceedings are available here: [https://dl.acm.org/citation.cfm?id=3077240]
The Proceedings of ACM DSMM 2016 are available here: http://dl.acm.org/citation.cfm?id=2951894
[https://dl.acm.org/citation.cfm?id=2951894] [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. 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.


Two trends are providing data-rich opportunities for macro-modeling of
We expect attendees with an interest in information integration, data mining, knowledge representation, network and visual analytics, stream data processing, etc. to participate.
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

Revision as of 05:07, 11 January 2018

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]

Past proceedings are available here: [3] [4] [5].

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. 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. to participate.