The ‘High-frequency Analytics Laboratory’ will serve as a unique infrastructure for conducting frontier analytical and computational analysis of the high-frequency data emerging from in the domains of finance, social networks (social media) and other sub-domains within social sciences. The Lab will foster and serve as a focal point for bringing together Information Scientists and Applied Mathematicians to work with Business and Social Scientists to conduct cutting-edge interdisciplinary research in Finance and Social sciences, in general.

In the last decades, securities trading experienced significant changes and more and more stages in the trading process were automated by incorporating electronic systems. The securities trading landscape is characterized by fragmentation among trading venues and competition for order flow, different market access models and a significant market share of automated trading technologies like algorithmic trading (AT) and high-frequency trading (HFT). While AT is mostly associated with the execution of client orders, HFT relates to the implementation of proprietary trading strategies by technologically advanced market participants. HFT is often seen as a subgroup of AT, however, both AT and HFT enable market participants to dramatically speed up the reception of market data, internal calculation procedures, order submission and reception of execution confirmations. Currently, regulators around the globe are discussing whether there is a need for regulatory intervention in HFT activities. Ironically during this period of recession, from 2008 onwards there has been a tremendous increase in the trading volumes of HFT strategies. The flash crash of 2010 stands a testimony to such an increase in the adoption of HFT strategies, and is driven by sophisticated technology on all layers of trading value chain.

With HFT changing the landscape of financial markets, there is a growing demand for understanding the impact of such developments, particularly after the 2010 flash crash, from both policy makers and the general public. Such a development in the financial market has broader implications not only to the academic research on the implications of these HFT strategies but also to the way financial economics and finance, in general, are being taught in the Universities. Students seldom get a thorough understanding of how the market is actually organized, or the ‘microstructure’ of the financial market, because clearly, as we see now in this Crisis, the dynamics of order-flow in the markets, which are revolutionized by new technologies, has completely changed the time-scale of decision making in both political decision making and policy making at both macro level and individual market level. Such a fundamental change in time-scale has very important implications for understanding volatility in the financial markets, and contagion of volatility from one asset class to another asset class.

This fundamental change warrants a different approach than conventional approach in the study of financial markets – even minute-by-minute data would not help to understand these dynamics because one-minute data is an average of multitude of orders that are executed at 1/10 of a second! In such a case, one requires high-frequency data to truly understand the functioning of the financial markets and their implications for broader issues of regulation and policy.

The analysis of high-frequency financial data requires an interdisciplinary expertise from the disciplines of Engineering, Information Science and Technology, Applied Mathematics, Economics and Accountancy and Finance, and such an endeavour will enhance the already existing expertise of applied non-linear dynamics within the School of Business and Economics. This Lab is a unique research infrastructure within the auspices of the Whitaker Institute to foster interdisciplinary thinking in both research and teaching.

Funded by

NUI Galway Registrar’s Strategic Fund