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

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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.