I use a machine learning technique to classify the universe of active equity mutual funds into "quantitative funds" and "discretionary funds." I propose an equilibrium model in which quantitative funds have greater information processing capacity but less adaptive strategies. The model predicts that quantitative funds hold more stocks and display pro-cyclical performance, but their trades are vulnerable to "overcrowding." Discretionary funds alternate between stock picking in expansions and market timing in recessions, display counter-cyclical performance and focus on stocks for which less overall information is available. My empirical evidence supports these predictions.
Abis, Simona. "Man vs. Machine: Quantitative and Discretionary Equity Management." Columbia Business School, July 31, 2017.
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