Identifying International Start Dates for Algorithmic Trading and High Frequency Trading

2012 ◽  
Author(s):  
Michael J. Aitken ◽  
Douglas J. Cumming ◽  
Feng Zhan
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Algorithmic Trading ◽  
Start Dates

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.

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.

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.

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

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

High-Frequency Trading

2011 ◽  
Author(s):  
Peter Gomber ◽  
Björn Arndt ◽  
Marco Lutat ◽  
Tim Elko Uhle
Keyword(s):  
High Frequency ◽  
High Frequency Trading
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