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

CI-P：开发下一代社区金融网络基础设施，用于监控和建模金融生态系统以及管理系统性风险

• 批准号: 1305368
• 负责人: Louiqa Raschid
• 金额: $ 9.98万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1305368&HistoricalAwards=false
• 关键词: CI; Developing; Next; Generation; Community

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.

迫切需要由数据驱动和提供信息的金融生态系统模型。 不幸的是，目前的金融网络基础设施严重限制了市场参与者、监管机构和研究人员获得数据。 由于缺乏本体和标准，监管机构的数据收集权力受到限制。 除了这些限制之外，还有一个固有的挑战，即处理复杂的财务信息，并满足对异构生态系统建模所需的多样化和复杂的分析。对于计算机科学家来说，一个核心要求是数据的可用性-作为范例以及测试和基准。 虽然某些类型的数据很容易获得，但许多其他重要类型的财务数据是专有的，并且通常不可用于计算研究社区。 社区基础设施的创建可以大大满足这一需求，从而使计算机科学研究能够在金融数据科学的新领域中发挥作用。下一代社区金融网络基础设施和金融数据科学框架的影响将是巨大的。 应用计算技术、BIGDATA和关联数据以及社交媒体将产生越来越多的协同作用，以解决困难的建模和监测问题。 这可能会为监管机构带来更好的工具，以及对市场机制、建议、评级等的全新设计。在教育前沿，金融数据科学应该培养新一代的多学科学者，他们将把计算解决方案与金融、经济、数学和统计学的理论、模型和方法相结合。