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

目前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取数据的能力。由于缺乏本体论和标准，监管机构的数据收集权限受到了限制。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，并满足建模异质生态系统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，用于测试和基准测试。虽然某些类型的数据很容易获得，但许多其他重要类型的金融数据是专有的，通常对计算研究社区不可用。社区基础设施的创建可以在很大程度上满足这一需求，从而使计算机科学研究成为金融数据科学的新领域。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重大的。应用计算技术、大数据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。这可能会改善监管机构的工具，并从根本上重新设计市场机制、建议、评级等。在教育前沿，金融数据科学应该培养新一代的多学科学者，他们将计算解决方案与金融、经济学、数学和统计学的理论、模型和方法相结合。

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.