[I propose and test a capital-flow-based explanation for some well-known empirical regularities concerning return predictability—the persistence of mutual fund performance, the "smart money" effect, and stock price momentum. First, I construct a measure of demand shocks to individual stocks by aggregating flow-induced trading across all mutual funds, and document a significant, temporary price impact of such uninformed trading. Next, given that mutual fund flows are highly predictable, I show that the expected part of flow-induced trading positively forecasts stock and mutual fund returns in the following year, which are then reversed in subsequent years. The main findings of the paper are that the flow-driven return effect can fully account for mutual fund performance persistence and the smart money effect, and can partially explain stock price momentum.]

This paper proposes and tests an investment-flow based explanation for three empirical findings on return predictability -the persistence of mutual fund performance, the "smart money" effect, and stock price momentum. Since mutual fund managers generally scale up or down their existing positions in response to investment flows, and the portfolios of funds receiving capital generally differ from those that lose capital, investment flows to mutual funds can cause significant demand shocks in individual stocks. Moreover, given that mutual fund flows are largely predictable from past fund performance and past flows, this paper further establishes that flow-induced price pressure is predictable. Finally, this paper shows that such flow-based return predictability can fully account for mutual fund performance persistence and the "smart money" effect, and can partially explain stock price momentum.

We exploit a novel setting in which the same piece of information affects two sets of firms: one set of firms requires straightforward processing to update prices, while the other set requires more complicated analyses to incorporate the same piece of information into prices. We document substantial return predictability from the set of easy-to-analyze firms to their more complicated peers. Specifically, a simple portfolio strategy that takes advantage of this straightforward vs. complicated information processing classification yields returns of 118 basis points per month before transaction costs. Consistent with processing complexity driving the return relation, we further show that the more complicated the firm, the more pronounced the return predictability. In addition, we find that sell-side analysts are subject to these same information processing constraints, as their forecast revisions of easy-to-analyze firms predict their future revisions of more complicated firms.