High-frequency trading, algorithmic finance and the Flash Crash: reflections on eventalization

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.

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The Flash Crash Of May 2010: Accident Or Market Manipulation?

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v9i1.944 ◽

2011 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Chris Rose

Keyword(s):

High Frequency ◽

High Frequency Trading ◽

Market Manipulation ◽

Font Size ◽

Wall Street ◽

Flash Crash

<p class="MsoNormal" style="text-align: justify; line-height: normal; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="font-family: "Times New Roman","serif"; 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>

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High Frequency Trading, it’s role in the 2007/2009 financial crisis and the 2010 flash crash

Jurnal Perspektif Pembiayaan dan Pembangunan Daerah ◽

10.22437/ppd.v6i4.6154 ◽

2019 ◽

Vol 6 (4) ◽

pp. 429-434

Author(s):

Stanley Kimani Kirika

Keyword(s):

Financial Crisis ◽

High Frequency ◽

High Frequency Trading ◽

Flash Crash

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

10.1093/law/9780198767671.003.0017 ◽

2017 ◽

Cited By ~ 5

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.

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The Flash Crash: The Impact of High Frequency Trading on an Electronic Market

SSRN Electronic Journal ◽

10.2139/ssrn.1686004 ◽

2011 ◽

Cited By ~ 210

Author(s):

Andrei A. Kirilenko ◽

Albert S. Kyle ◽

Mehrdad Samadi ◽

Tugkan Tuzun

Keyword(s):

High Frequency ◽

High Frequency Trading ◽

Electronic Market ◽

Flash Crash ◽

The Impact

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Volume Limit: An Effective Response to the India Flash Crash?

Journal of Financial Regulation ◽

10.1093/jfr/fjz006 ◽

2019 ◽

Vol 5 (2) ◽

pp. 249-255

Author(s):

Viktoria Dalko ◽

Michael H Wang

Keyword(s):

Stock Market ◽

High Frequency ◽

Securities Regulation ◽

High Frequency Trading ◽

Negative Consequences ◽

Effective Response ◽

Flash Crash ◽

Price Impacts ◽

Volume Limit ◽

The Stability

Abstract This paper assesses the recently enacted securities regulation, called the volume limit, by the Securities and Exchange Board of India. It reviews the literature on the negative consequences of large sale volumes on the stability of the stock market. The paper also examines the recent development of high-frequency trading in India. The two investigations unveil areas in which the regulation is effective and those in which it is inadequate. That is, the effectiveness of the regulation of the volume limit lies in reducing large price impacts due to genuine transactions. However, the inadequacy of this regulation is exposed when manipulation tactics arise regarding order display, such as spoofing by certain high-frequency traders.

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High-Frequency Financial Econometrics

10.23943/princeton/9780691161433.001.0001 ◽

2014 ◽

Cited By ~ 20

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.

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