The Effects of Algorithmic Trading on Security Market Quality

Author(s):  
Michael J. Aitken ◽  
Angelo Aspris ◽  
Sean Foley ◽  
Frederick H. deB. Harris
Author(s):  
Ritesh Kumar Dubey ◽  
A. Sarath Babu ◽  
Rajneesh Ranjan Jha ◽  
Urvashi Varma

2020 ◽  
Vol 6 (2) ◽  
pp. 270-305
Author(s):  
Clara Martins Pereira

Abstract Trading in modern equity markets has come to be dominated by machines and algorithms. However, there is significant concern over the impact of algorithmic trading on market quality and a number of jurisdictions have moved to address the risks associated with this new type of trading. The European Union has been no exception to this trend. This article argues that while the European Union algorithmic trading regime is often perceived as a tough response to the challenges inherent in machine trading, it has one crucial shortcoming: it does not regulate the simpler, basic execution algorithms used in automated order routers. Yet the same risk generally associated with algorithmic trading activity also arises, in particular, from the use of these basic execution algorithms—as was made evident by the trading glitch that led to the fall of United States securities trader Knight Capital in 2012. Indeed, such risk could even be amplified by the lack of sophistication of these simpler execution algorithms. It is thus proposed that the European Union should amend the objective scope of its algorithmic trading regime by expanding the definition of algorithmic trading under the Markets in Financial Instruments Directive (MiFID II) to include all execution algorithms, regardless of their complexity.


2020 ◽  
Author(s):  
John Paul Broussard ◽  
Andrei L. Nikiforov ◽  
Sergey Osmekhin

2019 ◽  
Vol 12 (2) ◽  
pp. 68 ◽  
Author(s):  
Purba Mukerji ◽  
Christine Chung ◽  
Timothy Walsh ◽  
Bo Xiong

In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly analyze AT behavior to connect AT trading strategies to specific outcomes in the market; (2) measure the impact of AT on market quality; and (3) test the sensitivity of our findings to variations in market conditions and possible future events of interest. Examples of such variations and future events are the level of market uncertainty and the degree of algorithmic versus human trading. Our results show that liquidity increases initially as AT rises to about 10% share of the market; beyond this point, liquidity increases only marginally. Statistical arbitrage appears to lead to significant deviation from fundamentals. Our results can facilitate market oversight and provide hypotheses for future empirical work charting the path for developing countries where AT is still at a nascent stage.


Author(s):  
Michael J. Aitken ◽  
Drew Harris ◽  
Frederick H. deB. Harris

Author(s):  
Matthew R. Lyle ◽  
James P. Naughton ◽  
Brian M. Weller

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