Digging into High Frequency Data: Present and Future Risks and Opportunities

在过去十年中，由于交易速度的大幅提高以及随之而来的市场活动的分散，全球股票市场发生了根本性的变化。由此产生的变化引发了投资者、做市商、交易所和监管机构的激烈辩论和审查。该项目的第一个目标是构建，验证和同质化多个现有的数据集，并创建一个跨大西洋证券市场数据库，可以很容易地用于欧洲和美国的研究。第二个目标是分析，计算和建立基于高频数据的模型，以提高我们对电子市场如何运作的理解。该项目将有助于解释数据，理解证券和金融利益相关者之间的全球相互联系，并为理解金融危机和构建有效的金融监管提供新的见解。验证和同质化多个数据集已经提供给该项目的研究人员，并创建一个跨大西洋证券市场数据库（适用于普通股，也适用于债券、期权和期货等其他证券），可以很容易地用于欧洲和美国的研究。主要目标是建立基本的基础设施，以清理和链接美国和欧洲的数据集，使这些数据可供研究团队访问和利用，并提供如何将这些数据合并给其他研究人员和监管机构的知识。这些原始数据不适合分析，首先需要重新创建限价订单簿，同时考虑到每个交易所和其他交易场所的特殊性。目前还没有这样的数据库。该项目的第二个目标是分析，计算和建立基于高频数据的模型，以提高我们对电子市场如何运作的理解。正如过去二十年来连续发生的金融危机所表明的那样，研究和监管方面缺乏经验性金融数据，阻碍了对这些事件的更广泛理解。重要的是要有一个全面的数据，以了解原因，金融传染和金融动荡的后果。该奖项是第四轮挖掘数据挑战赛的一部分，这是一个国际资助机会，旨在促进各国之间的研究合作，并鼓励采用创新方法分析社会科学和人文科学中的大型数据集。美国--研究人员将与芬兰、法国、德国、意大利和英国的学者合作。来实现这个项目的目标。

During the past decade, global equity markets have been fundamentally altered due to the vast increases in the speed of trading and the consequent fragmentation of market activity. The resulting changes have led to intense debate and scrutiny from investors, market makers, exchanges, and regulators. The first objective of this project is to structure, verify and homogenize multiple existing datasets and to create a transatlantic securities markets database that can be easily used for research in Europe and the US. The second objective is to analyze, compute and build models based on high-frequency data to improve our understanding how electronic markets work. This project will help with interpreting the data, understanding global interconnectedness between securities and financial stakeholders, and providing new insights for understanding financial crises and constructing effective financial regulations.The first objective of this project is to structure, verify and homogenize multiple datasets already available to the researchers of the project and to create a transatlantic securities markets database (for common stocks but also for other securities such as bonds, options and futures) that can be easily used for research in Europe and the US. The primary goal is to set up the basic infrastructure to clean up and link the US and European datasets and to make these data accessible and exploitable for the research team and to provide knowledge on how to merge these data to other researchers and regulators. Such data in their raw form are unsuitable for analysis and the limit-order books need to be recreated in the first place, taking into consideration the peculiarities of each exchange and alternative trading venue. At present, no such database exists. The second objective of this project is to analyze, compute and build models based on high frequency data to improve our understanding how electronic markets work. As demonstrated by successive financial crises in the last twenty years, the lack of empirical financial data in research and regulation is a hindrance to the wider understanding of these events. It is important to have a holistic data in order to understand causes, financial contagion, and consequences of financial turbulence. This award was made as part of Round 4 of the Digging Into Data Challenge, an international funding opportunity designed to foster research collaboration across countries and to encourage innovative approaches to analyzing large data sets in the social sciences and humanities. The U.S.-based researchers will collaborate with scholars in Finland, France, Germany, Italy and the U.K. to achieve the goals of this project.