DSMM 2016 - International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets

The DSMM 2016 workshop will explore the challenges of data science for macro-modeling with financial and/or economic datasets. The workshop will also showcase a planned multi-year Financial Entity Identification and Information Integration (FEIII) at Scale Challenge.
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. However, fully realizing this promise requires successful integration of heterogeneous data from a wide variety of data sources. 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. Two trends are increasing opportunities for such macro-modeling of financial and economic ecosystems. First, public financial data is becoming increasingly available from a variety of sources, including WRDS’s CRSP, SEC EDGAR, and the Federal Reserve’s FRED. Second, Big Data infrastructures and analytical tools to support the required integration across these data sources are becoming increasingly available. Thus, an exploration of the data science challenges involved in such macro-modeling with financial and economic data is timely. Economists have had a successful history of using longitudinal datasets (US Census Bureau, Department of Labor, World Bank, etc.) to drive econometric and statistical research in finance and economics. However, such analyses fail to completely address the compelling need to analyze complex ecosystems and supply chains in their entirety. 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. Clearly, we have a classic big data challenge.
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 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.
Targeted Audience: We expect attendees with an interest in information integration, data mining, knowledge representation, network and visual analytics, stream data processing, etc. The DSMM 2014 workshop, in conjunction with SIGMOD 2014, attracted a diverse group of researchers from databases, data modeling, finance, math/stat and economics. Proceedings of ACM DSMM 2014 are available here: http://dl.acm.org/citation.cfm?id=2630729