We use a large sample from 2001 – 2009 that incorporates 39 exchanges and an average of 12,800 different common stocks to assess the effect of algorithmic trading (AT) intensity on liquidity in the equity market, short-term volatility, and the informational efficiency of stock prices. We exploit the first availability of co-location facilities to identify the direction of causality. We find that, on average, greater AT intensity improves liquidity and informational efficiency, but increases volatility. The volatility increase is robust to a range of different volatility measures and it is not due to more “good” volatility that would arise from faster price discovery. These patterns are widespread and are not limited to a few markets, but they vary in the cross-section of stocks. In contrast to the average effect, more AT reduces liquidity in small stocks; has little effect on the liquidity of low-priced or high-volatility stocks; and leads to greater increases in volatility in these stocks. Finally, during days when market making is difficult, AT provide less liquidity, improve efficiency more, and increase volatility more than on other days.