As stock market concentration has risen, regulatory limits on fund portfolio concentration have become increasingly binding, especially for large-cap growth funds. When funds approach these limits, they trim their largest holdings and reduce equity exposure. Funds perform worse when constrained. A constraint-based ownership measure predicts stock returns, particularly among the largest firms. These findings suggest that high market concentration can distort stock prices by limiting the ability of optimistic investors to scale their positions. Just like short-sale constraints can produce overpricing by limiting pessimistic investors' views, constraints on long positions can generate underpricing by suppressing optimists' views.

Financial markets are fragmented, with different asset classes trading in separate markets. Yet most empirical asset pricing research pays little attention to this type of fragmentation. As a result, we know relatively little about how financial intermediaries operate across markets or how to design policies that account for cross-market dynamics. We construct a unique dataset that traces trading by registered broker-dealers and exchange members---dealers---across all major Canadian markets for bonds, stocks, and exchange-traded derivatives. We show that dealers concentrate their activity in specific markets and asset types, with stronger specialization across markets. We provide suggestive evidence that this specialization reflects a mix of firm organization, market frictions, and client demand, and show that more specialized dealers consistently obtain better prices, indicating a trading advantage.

Prediction markets are widely promoted as efficient mechanisms for aggregating dispersed information and generating signals for the broader economy. We document information spillover from Polymarket to traditional financial markets during the 2024 U.S. presidential election. Changes in Polymarket-implied probabilities predict next-day returns of ``Trump trades'' across asset classes, followed by partial reversals, indicating that both information and noise are transmitted. Using the blockchain-based transaction data, we classify Polymarket traders as informed or uninformed at the wallet level and show that the price impact of uninformed trading contributes to the next-day predictability, but, unlike the price impact of informed trading, does not persist and fades over the following days. As the transmitted noise cannot reflect the sequential arrival of fundamental information, it identifies an active cross-market learning channel through which both information and noise propagate. We further document an ``information hangover'' effect: recent informed trading amplifies the traditional market's subsequent reliance on Polymarket signals, which in turn facilitates noise spillovers.