High-Frequency Trading: A Reading List

I first heard of high-frequency trading (HFT) via Charles Duhigg’s New York Times article in July 2009.

 

Over the past few years I have investigated facets of HFT. Below is an introductory reading list to HFT and the related area of algorithmic trading, which has recently ‘crossed the chasm’ from institutional to retail investors. It covers an historical overview; some relevant theory; and the use of computer algorithms and machine learning. Large-scale HFT firms spend millions on their computing and technological infrastructure.

 

The introductory reading list hopefully shows how you can use research skills to Understand a media debate or knowledge domain in greater detail.

 

This work connects with the Chaos Rules school of thought that I helped write for the Smart Internet 2010 Report (2005). One academic told my boss that Chaos Rules thinking “did not exist.” The two decades long research into HFT — and related areas like Bayesian econometrics and market microstructure — shows otherwise.

HFT Introductions

Dark Pools: The Rise of AI Trading Machines and the Looming Threat to Wall Street by Scott Patterson (Cornerstone Digital, 2012). (TS-3). A history of algorithmic and high-frequency trading on Wall Street, and the emergence of dark pools.

Inside The Black Box: A Simple Guide to Quantitative and High Frequency Trading (2nd ed.) by Rishi K. Narang (John Wiley & Sons, 2012). (TS-3). An introduction to quantitative trading models and coverage of the media debate about high-frequency trading. For a counter-view see Haim Bodek’s The Problem of HFT: Collected Writings on High Frequency Trading and Stock Market Structure Reform (CreateSpace, 2013) (TS-3), who was a source for Patterson’s Dark Pools.

HFT Theory: Bayesian Econometrics, High-Frequency Data, and Machine Learning

Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading by Joel Hasbrouck (New York: Oxford University Press, 2007). (TS-4). Hasbrouck explains the empirical approaches to market microstructure that underpin high-frequency trading.

Market Liquidity: Theory, Evidence and Policy by Thierry Foucault, Marco Pagano, and Ailsa Roell (New York: Oxford University Press, 2013). (TS-4). The current debates on how high-frequency trading has affected liquidity and price discovery in markets, and the growth of market microstructure frameworks.

Bayesian Reasoning and Machine Learning by David Barber (New York: Cambridge University Press, 2012). (TS-4). An introduction to Bayesian probability and data analysis using filters and machine learning. For an introduction to machine learning see Peter Flach’s Machine Learning: The Art and Science of Algorithms That Make Sense of Data (New York: Cambridge University Press, 2012) (TS-4).

Econometrics of High-Frequency Data by Nikolaus Hautsch (New York: Springer, 2011). (TS-4). An advanced overview of high-frequency data and relevant econometric models for liquidity, volatility, and market microstructure analysis.

Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, and Ionut Florescu (New York: John Wiley & Sons, 2011). (TS-4). An advanced reference on how to model high-frequency data.

HFT Algorithmic Trading

The Science of Algorithmic Trading and Portfolio Management by Robert Kissell (Academic Press, 2013). (TS-4). An advanced introduction to how algorithmic trading influences market microstructure, and is used for the transaction and execution systems of high-frequency trading. For an earlier introduction see Barry Johnson’s Algorithmic Trading & DMA: An Introduction to Direct Access Trading Strategies (4Myeloma Press, 2010) (TS-4).

Professional Automated Trading: Theory and Practice by Eugene A. Durenard (New York: John Wiley & Sons, 2013). (TS-4). Insights from mathematics and computer science about how to develop, test, and automate the algorithmic trading strategies, using agent-based learning.

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB by David Aronson and Timothy Masters (CreateSpace, 2013) (TS-4). The authors developed the TSSB software program that uses machine learning to implement algorithmic trading strategies.

15th June 2013: HFT, Disruptive Innovation & Theta Arbitrage

23rd July 2009 was perhaps the day that retail investors became aware of high-frequency trading (HFT).

 

That was the day that New York Times journalist Charles Duhigg published an article on HFT and market microstructure changes. Duhigg’s article sparked a public controversy about HFT and changes to United States financial markets.

 

Then on 6th May 2010 came the Flash Crash. HFT was again the villain.

 

For the past few years HFT has inspired both pro and con books from publishers. HFT has changed how some retail investors and portfolio managers at mutual and pension funds view financial markets. Now, Matthew Philips of Bloomberg Businessweek reports that 2009-10 may have been HFT’s high-point in terms of being a profitable strategy.

 

Philips’ findings illustrate several often overlooked aspects of Clayton Christensen‘s Disruptive Innovation Theory. Scott Patterson notes in his book Dark Pools (New York: Crown Business, 2012) that HFT arose due to a combination of entrepreneurial innovation; technological advances in computer processing power; and changes to US Securities and Exchanges Commission regulations. Combined, these advances enabled HFT firms to trade differently to other dotcom era and post-dotcom firms that still used human traders or mechanical trading systems. This trading arbitrage fits Christensen’s Disruptive Innovation Theory as a deductive, explanatory framework.

 

The usually overlooked aspect of Disruptive Innovation Theory is that this entrepreneurial investment and experimentation gave HFT firms a time advantage: theta arbitrage. HFT firms were able to engage for about a decade in predatory trading against mutual and pension funds. HFT also disrupted momentum traders, trend-followers, scalping day traders, statistical arbitrage, and some volatility trading strategies. This disruption of trading strategies led Brian R. Brown to focus on algorithmic and quantitative black boxes in his book Chasing The Same Signals (Hoboken, NJ: John Wiley & Sons, 2010).

 

Paradoxically, by the time Duhigg wrote his New York Times article, HFT had begun to lose its profitability as a trading strategy. Sociologist of finance Donald MacKenzie noted that HFT both required significant capex and opex investment for low-latency, and this entry barrier increased competition fueled ‘winner-takes-all’ and ‘race to the bottom’ competitive dynamics. HFT’s ‘early adopters’ got the theta arbitrage that the late-comers did not have, in a more visible and now hypercompetitive market.  Duhigg’s New York Times article wording and the May 2010 Flash crash also sparked an SEC regulatory debate:

 

  • On the pro side were The Wall Street Journal’s Scott Patterson; author Rishi K. Narang (Inside The Black Box); and industry exponent Edgar Perez (The Speed Traders).
  • On the con side were Haim Bodek of Decimus Capital Markets (The Problem With HFT), and Sal L. Arnuk and Joseph C. Saluzzi of Themis Trading (Broken Markets) which specialises in equities investment for mutual and pension fund clients.
  • The winner from the 2009-12 debate about HFT regulation appears to be Tradeworx‘s Manoj Narang who was both pro HFT yet who also licensed his firm’s systems to the SEC for market surveillance, as a regulatory arbitrage move. The SEC now uses Tradworx’ systems as part of the Market Information Data Analytics System (MIDAS, Philips reports.

 

Philips announced that HFT firms now have new targets: CTAs, momentum traders, swing traders, and news sentiment analytics. That might explain some recent changes I have seen whilst trading the Australian equities market. Christensen’s Disruptive Innovation Theory and theta arbitrage both mean that a trading strategy will be profitable for a time before changes in market microstructure, technology platforms, and transaction and execution costs mean that it is no longer profitable.

29th August 2012: FT/Goldman Sachs Business Book of the Year Longlist

The longlist for the FT/Goldman Sachs Business Book of the Year is out. I’ve eyed off Steve Coll’s Private Empire for several months as the exemplar investigative reportage that I like (see this 2008 conference paper which mentions Coll’s previous work). I’m about halfway through Guy Lawson’s Octopus book on the Bayou hedge fund scam and things are starting to get surreal. I bought John Coates’ The Hour Between Dog and Wolf  to understand trading psychology. I saw Charles Duhigg’s SXSW presentation on The Power of Habit but he will still be known for a now-infamous New York Times article on high-frequency trading. I’m hoping Coll will win with Coates and Lawson as ‘dark horse’ bets.