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

Author(s):  
Conac Pierre-Henri

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.

2017 ◽  
Vol 32 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Wendy L. Currie ◽  
Jonathan J. M. Seddon

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.


2021 ◽  
Author(s):  
Rabeea Sadaf ◽  
Orla McCullagh ◽  
Barry Sheehan ◽  
Colette Grey ◽  
Erin King ◽  
...  

2017 ◽  
Vol 72 (3) ◽  
pp. 967-998 ◽  
Author(s):  
ANDREI KIRILENKO ◽  
ALBERT S. KYLE ◽  
MEHRDAD SAMADI ◽  
TUGKAN TUZUN

Author(s):  
Chris Rose

<p class="MsoNormal" style="text-align: justify; line-height: normal; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt;">On May 6th., 2010, the Dow fell about a thousand points in a half hour and Wall Street lost $800 billion of value. Some claim that it was just an isolated incident and there was nothing nefarious but with the majority of trading being done by electronic exchanges and with the increase in High Frequency Trading, evidence is emerging that the crash just might have been a case of deliberate manipulations of the market.</span></p>


Sign in / Sign up

Export Citation Format

Share Document