Algorithmic Trading, High-frequency Trading: Implications for MiFID II and Market Abuse Regulation (MAR) in the EU

2021 ◽  
Author(s):  
Rabeea Sadaf ◽  
Orla McCullagh ◽  
Barry Sheehan ◽  
Colette Grey ◽  
Erin King ◽  
...  
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Algorithmic Trading ◽  
Market Abuse ◽  
The Eu

Part III Trading, 17 Algorithmic Trading and High-Frequency Trading (HFT)

2017 ◽  
Author(s):  
Conac Pierre-Henri
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Algorithmic Trading ◽  
Technical Approach ◽  
Great Cost ◽  
Scarce Resources ◽  
Flash Crash ◽  
Market Abuse ◽  
Front Running ◽  
Do So

This chapter analyses the MiFID II rules on algorithmic trading (AT), including high-frequency trading (HFT). The author argues that AT raises serious issues of volatility and systemic risk, and HFT issues of systematic front-running of investors. However, opinions are divided on the benefits and risks of these techniques, especially HFT. MiFID II takes a technical approach mostly focused on prevention of a repeat of the 2010 ‘Flash Crash’ with provisions on market abuse. The ESMA 2012 Guidelines remain the most effective regulation to frame the development of HFT, able to tackle market developments with relative speed. However, with implementation of the directive still far away, prosecution of market abuse among HFT traders by legislators and supervisors could lead to a de facto ban of HFT in some Member States. However, the author argues that supervisors would need to allocate scarce resources to it, at great cost, and only the most motivated supervisors will do so.

The Regulatory, Technology and Market ‘Dark Arts Trilogy’ of High Frequency Trading: A Research Agenda

2017 ◽  
Vol 32 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Wendy L. Currie ◽  
Jonathan J. M. Seddon
Keyword(s):  
Information Systems ◽  
Financial Markets ◽  
Empirical Data ◽  
High Frequency ◽  
Research Agenda ◽  
High Frequency Trading ◽  
Algorithmic Trading ◽  
Flash Crash ◽  
Financial Trading ◽  
Dow Jones Industrial Average

Computerization has transformed financial markets with high frequency trading displacing human activity with proprietary algorithms to lower latency, reduce intermediary costs, enhance liquidity and increase transaction speed. Following the “Flash Crash” of 2010 which saw the Dow Jones Industrial Average plunge 1000 points within minutes, high frequency trading has come under the radar of multi-jurisdictional regulators. Combining a review of the extant literature on high frequency trading with empirical data from interviews with financial traders, computer experts and regulators, we develop concepts of regulatory adaptation, technology asymmetry and market ambiguity to illustrate the ‘dark art’ of high frequency trading. Findings show high frequency trading is a multi-faceted, complex and secretive practice. It is implicated in market events, but correlation does not imply causation, as isolating causal mechanisms from interconnected automated financial trading is highly challenging for regulators who seek to monitor algorithmic trading across multiple jurisdictions. This article provides information systems researchers with a set of conceptual tools for analysing high frequency trading.

High Frequency Trading and Circuit Breakers in the EU

2018 ◽  
pp. 308-331
Author(s):  
Steffen Kern ◽  
Giuseppe Loiacono
Keyword(s):  
High Frequency ◽  
Financial Stability ◽  
Investor Protection ◽  
Legal Framework ◽  
Regulatory Framework ◽  
High Frequency Trading ◽  
Circuit Breakers ◽  
Market Order ◽  
Securities Trading ◽  
The Eu

This chapter reviews the fundamental workings of the EU regulatory framework and its implications for high frequency trading (HFT) and the latest findings on the market realities in the EU. Over the last decade, securities trading landscapes have undergone significant change, with the emergence of HFT being one of the most important developments in this context. At the same time, the EU has made landmark legislative advances with the aim of increasing investor protection, market order, and financial stability, and of containing risks in those areas. As the new MiFID2 legal framework takes effect, a wealth of new data and evidence will become available in coming years that will improve understanding of HFT patterns, the effectiveness of circuit breakers, and their optimal calibration.

European Legal Framework for Algorithmic and High Frequency Trading (Mifid 2 and MAR)

2018 ◽  
Vol 9 (1) ◽  
pp. 146-153 ◽  
Author(s):  
Tilen ČUK ◽  
Arnaud VAN WAEYENBERGE
Keyword(s):  
Mathematical Modelling ◽  
Systemic Risk ◽  
High Frequency ◽  
Financial Innovation ◽  
Legal Framework ◽  
Computer Hardware ◽  
High Frequency Trading ◽  
Computer Algorithms ◽  
Early Assessment ◽  
Market Abuse

AbstractAlgorithmic and high frequency trading use computer algorithms to execute strategies and the confluence of trends in computer hardware, programming, mathematical modelling, and financial innovation have pushed the limits of trading speed to unprecedented levels. Algorithms are fast and automatically spread disruptions through the financial system. Over the last decade, the ensuing systemic risk called for new regulations. This article attempts an early assessment of the new European legal framework (Mifid 2 and Market Abuse Regime) intended to tackle the technological risks of the modern trading paradigm.

scholarly journals On studying algorithms ethnographically: Making sense of objects of ignorance

Organization ◽  
2018 ◽  
Vol 26 (4) ◽  
pp. 598-617 ◽  
Author(s):  
Ann-Christina Lange ◽  
Marc Lenglet ◽  
Robert Seyfert
Keyword(s):  
Decision Making ◽  
High Frequency ◽  
Organizational Politics ◽  
High Frequency Trading ◽  
Algorithmic Trading ◽  
Michel Serres ◽  
Making Sense ◽  
Dynamic Relationship ◽  
Organizational Ethnography ◽  
Automated Decision Making

In this article, we make sense of financial algorithms as new objects of concern for organizational ethnography. We conceive of algorithms as ‘objects of ignorance’ jeopardizing traditional ethnography from the perspective of its categories and methods. We investigate the organizational politics taking place within high-frequency trading – a sub-field of algorithmic trading where automated decision-making without human direction has reached a peak, and show that financial algorithms raise particular epistemic and methodological challenges for practitioners and ethnographers alike. Consequently, we develop a typology for various interpretations of algorithms as ethnographic objects, accounting for their structural ignorance and shedding light on a continuum of the changing human-machine/trader-algorithm relation. To this end, we use the concepts of ‘quasi-object’ and ‘quasi-subject’ as developed by Michel Serres, and make the point that in order to study financial algorithms ethnographically, we need to think anew the dynamic relationship they embody, and acknowledge their constitutive heterogeneity.

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