DSMM: Data Science for Macro-Modeling with Financial and Economic Datasets
Revision as of 02:34, 16 November 2017 by Louiqa
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'.  Financial big data and FINTECH applications are in the vanguard of activities around the deployment of Open Knowledge Networks. 
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.