A summary of recent research related to automated or high-frequency trading and its impact on various markets, including U.S. equities, European equities, foreign exchange and futures. The summary was provided on behalf of the following automated professional trading firms: RGM Advisors, Quantlab Financial, and Hudson River Trading.  

By Peter Gomber, University of Frankfurt
Björn Arndt
Marco Lutat, University of Frankfurt
Tim Uhle, Goethe University Frankfurt
June 2011  

This paper was commissioned by Deutsche Börse and aims to provide up-to-date background information on HFT from a European perspective. Some of the main findings are that 1) HFT is not a trading strategy but a technical means to implement established trading strategies; 2) HFT is a natural evolution of the securities markets instead of a completely new phenomenon, and 3) any assessment of HFT based strategies has to take a functional rather than an institutional approach because HFT is applied by different groups of market players. 

By Sven S. Groth
Goethe University  
May 2011

This paper is based on a unique set of data on high-frequency trading in stocks listed on Xetra, the electronic trading platform operated by Deutsche Boerse, during two months of trading during 2010. The paper finds strong evidence that algorithmic trading does not exceedingly increase volatility, at least not more than human traders do. The paper’s empirical analyses cover several potential reasons why algorithmic trading could increase volatility, such as less diverse trading strategies than humans. The paper also investigates whether or not algorithmic traders withdraw liquidity from the market during periods of high volatility.

Does Algorithmic Trading Improve Liquidity?

Terrence Hendershott, Haas School of Business, University of California Berkeley
Charles M. Jones, Columbia Business School
Albert J. Menkveld, VU University Amsterdam
February 2011

This paper examines the question of whether algorithmic trading improves market quality by analyzing electronic message traffic at the New York Stock Exchange as a proxy for changes in the supply of liquidity. The paper finds that for large stocks in particular, automated trading narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that automated trading improves liquidity and enhances the informativeness of quotes.

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