Man vs. Machine: Quantitative and Discretionary Equity Management∗

I use a machine learning technique to classify the universe of active US equity mutual funds as quantitative , who mostly rely of computer-driven models and xed rules, or discretionary , who mostly rely on human judgement. I rst document several new facts about quantitative investing. I then propose an equilibrium model in which quantitative funds have a greater information processing capacity but follow less exible strategies than discretionary funds. The model predicts that quantitative funds specialize in stock picking, hold more stocks, display pro-cyclical performance, and that their trades are vulnerable to overcrowding . In contrast, discretionary funds alternate between stock picking and market timing, display counter-cyclical performance and focus on stocks for which less overall information is available. My empirical evidence supports these