International Evidence on Algorithmic Trading
EDHEC Business School
Kingsley Y. L. Fong
University of New South Wales - School of Banking and Finance; Financial Research Network (FIRN)
Juan (Julie) Wu
University of Georgia
March 27, 2014
AFA 2013 San Diego Meetings Paper
We study the effect of algorithmic trading (AT) intensity on equity market liquidity, short-term volatility, and informational efficiency between 2001 and 2011 in 42 equity markets around the world. On average, AT improves liquidity and informational efficiency, but it increases volatility. We cannot attribute the AT-related increase in volatility to more “good” volatility that would arise from faster price discovery. Moreover, this result does not reflect algo traders’ inclination to enter the market when volatility is high. On the contrary, these volatility-seeking traders are associated with declines in market quality. Our results are surprisingly consistent across markets and thus across a wide range of AT practices. But results vary in the cross-section of stocks. In contrast to the average effect, greater AT intensity reduces liquidity and worsens the volatility increase in the smallest tercile of stocks. Finally, AT becomes less beneficial when market making is difficult.
Number of Pages in PDF File: 50
Keywords: Algorithmic trading, high frequency trading, market structure
JEL Classification: G19, G15working papers series
Date posted: March 15, 2012 ; Last revised: April 10, 2014
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