Juan (Julie) Wu University of Georgia 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 increases volatility. We can attribute the AT-related increase in volatility neither to more “good” volatility that would arise from faster price discovery nor to algorithmic 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.