Digging into High Frequency Data: Present and Future Risks and Opportunities
Final Report Abstract
The main purpose of this project was to provide the proper appropriate data and the capacity to manage these data to investigate from different perspectives the impact of HFTs on financial markets and the extent by which the resulting market efficiency, stability and ability to serve the real economy and society are affected. The first objective of this project was to structure, verify and homogenize multiple datasets already available to the researchers of the project and create a transatlantic securities markets database that can be easily used for research in Europe and the US. For this purpose we have set up the basic infrastructure to make this data accessible and exploitable for the research team and to provide knowledge on how to merge these data to other researchers and regulators. The researcher in this project have largely benefitted of these facilities and have developed several papers. The second objective was to analyze, compute and build models based on high frequency data to improve our understanding how electronic markets work. On this regards we provided several papers where we document the negative externality of High-Frequency Trading (HFT). The existing literature generally finds that HFTs increase liquidity in stock markets. However, our results suggest that practitioners, academics, and policy makers should carefully consider the cross-asset effects of HFTs activities in equity markets on derivatives market quality. Moreover, the existing literature shows that algorithmic trading (AT) impacts efficiency and liquidity at extremely high frequencies and, therefore, the economic implications of AT for long-term investors are not clear. Our research shows that AT affects corporate investment by increasing investment to price sensitivity. Our analysis suggests that the AT debate should re-center on long-term investors to understand the benefits and costs of AT fully. Finally, we investigated flash crashes and HFTs are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. A subsequent third goal was to create a network of European and US researchers in finance and computational science who collaborate to generate and use very advanced computational tools to analyze and interpret this data for research and policy purposes. We indeed developed collaboration with computational science, in particular UMASS has developed collaboration with C.S. Engineering and the Mathematics Departments at MIT and both UMASS and SAFE are actively collaborating with the Laboratory for Financial Engineering at MIT. Finally, thanks to this project, researchers at LSE have supported the Bank of England in his Fast Markets initiative by advising the Bank of England team working on these subjects as to how to study and research the effects of HFT markets on market efficiency and market costs. Researchers at SAFE has supported ESMA on the empirical analysis of Institutional Order Execution Costs and HFT in the EU Equity Market.
Publications
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“Are High-Frequency Traders Informed?”, Economic Modelling 93 (2020), 365-383
Anagnostidis, P. and Fontaine, P. C. and Varsakelis, C.
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“High-Frequency Trading in the Stock Market and the Costs of Option Market Making” (January 1, 2020)
Nimalendran, Mahendrarajah and Rzayev, Khaladdin and Sagade, Satchit
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“Liquidity commonality and high frequency trading: Evidence from the French stock market”, International Review of Financial Analysis, Volume 69, 2020, 101428
Panagiotis Anagnostidis, Patrice Fontaine
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“Resiliency: Cross-Venue Dynamics with Hawkes Processes” (September 16, 2020). SAFE Working Paper No. 291
Pelizzon, Loriana and Sagade, Satchit and Vozian, Katia
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“A Modern Take on Market Efficiency: The Impact of Trump’s Tweets on Financial Markets” (May 5, 2021). SAFE Working Paper No. 314
Abdi, Farshid and Kormanyos, Emily and Pelizzon, Loriana and Getmansky Sherman, Mila and Simon, Zorka
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“Predicting stock price and spread movements from news”, Proceedings of the 54th Hawaii International Conference on System Sciences (2021)
Wistbacka, Pontus and Rönnqvist, Samuel and Vozian, Katia and Sagade, Satchit
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“Recovery from Fast Crashes: Role of Mutual Funds” (February 11, 2021). SAFE Working Paper No. 227
Jagannathan, Ravi and Pelizzon, Loriana and Schaumburg, Ernst and Getmansky Sherman, Mila and Yuferova, Darya
