CI-P: Developing the Next Generation of Community Financial CyberInfrastructure for Monitoring and Modeling Financial Eco-Systems and for Managing Systemic Risk

University Of Maryland, College Park, College Park MD

Investigators

Abstract

There is an urgent need for models of financial ecosystems that are driven and informed by data. Unfortunately, current financial cyberinfrastructures severely restrict the availability of data to market participants, regulators and researchers. There are constraints on the data collection authority of regulators that are exacerbated by the lack of ontologies and standards. Beyond these limitations is the inherent challenge of dealing with the complexity of financial information and meeting the diverse and sophisticated analyses required to model heterogeneous ecosystems. For computer scientists to get engaged, a central requirement is the availability of data -- as exemplar and for testing and benchmarking. While some types of data are easily available, many other important types of financial data are proprietary and generally unavailable to the computing research community. The creation of a community infrastructure can go a long way toward meeting this need and hence enabling computer science research in a new domain of data science for finance. The impact of the next generation of community financial cyberinfrastructure and a framework of data science for finance will be significant. There will be increasing synergy from applying computational technology, BIGDATA and Linked Data, and social media, to address difficult modeling and monitoring problems. This may result in improved tools for regulators, as well as fundamentally new designs of market mechanisms, recommendations, ratings, etc. On the educational frontier, data science for finance should nurture a new generation of multi-disciplinary scholars who will blend computational solutions with theories, models and methodologies from finance, economics, mathematics and statistics.