Minxia Chen /MIN-shyah/

A watchlist records a form of attention prior work could not see: a self-authored, dated commitment to follow a stock, made before any purchase. Using 28,990 Chinese retail investors whose every purchase is linked to a timestamped watchlist entry, I show that the stocks investors watch but do not buy outperform the stocks they buy — yet among purchases, those routed through the watchlist (warm) underperform purchases made on sight (cold) by 234 basis points of characteristic-adjusted return over the next sixty trading days. The list’s signal is good; the trap is in the conversion from watching to buying. Roughly 45% of the gap reflects composition — watched purchases carry more costly speculative tendencies. The surviving mechanism for the rest is reference-dependent conversion: the daily first-purchase hazard changes slope sharply as the price crosses the level at which the stock was added and the extremes witnessed while it waited.

Why does the disposition effect survive in information-rich trading environments? Using clickstream and trading records for 10,000 investors on a large Chinese online trading platform, we find that active information collection sharpens gain-side exits but worsens loss-side outcomes: clicked-on retained losers underperform by an additional 1.5–2.0 percent, offsetting the net trading benefit. Two mechanisms drive the asymmetry: an ostrich effect in acquisition, whereby focal-stock losses reduce abnormal clicking by 39 percent, and cognitive inertia in processing, whereby investors who examine losses fail to act. Because the preferences that generate the bias also allocate attention, self-directed information collection cannot correct it.

On February 8, 2025, a large Chinese retail brokerage swapped the large language model behind its in-app chatbot for DeepSeek, leaving the chatbot’s sibling services untouched. Measuring exposure to the upgrade with strictly predetermined pre-launch usage, I find that more-exposed investors consulted the news less often, while the composition of what they still read — concentration, slant, staleness, pre-trade diligence — is bounded near zero: the upgrade changed how often investors read, not what. Around the same upgrade, the risk-adjusted performance of these investors’ subsequent first purchases deteriorated relative to observably identical engaged users — a within-stock, mistimed-entry effect that survives reversion controls, permutation inference, and nine placebo launch dates. As AI assistants become a front door to financial information, a quality upgrade can change when investors act without changing what they know.

Can ownership change not only what consumers hold, but also where their attention goes? In fractional music-royalty auctions on a major Chinese streaming platform, winners acquire royalty shares of individual songs while near-miss bidders in the same auction do not. Comparing winners with the closest unsuccessful bidders in a difference-in-differences design, we find that ownership increases listening to the owned song — and, beyond it, to other, non-owned songs in the same genre. The spillover is concentrated in active, user-initiated listening, with no increase in recommendation-driven passive listening, and realized royalty income explains little of the response. The evidence points to ownership-induced attention reallocation: a financial stake redirects consumers’ attention toward related content and what they choose to experience.

Generative AI increasingly assists the creation of cultural goods, and regulators are mandating that AI involvement be labeled — on the premise that a label taxes the artists who use it. Does disclosing AI assistance actually cost artists, or does the apparent penalty reflect the strategic choice of which work they reveal? We study a leading music-streaming platform whose integrated AI tool let artists choose whether to credit AI use, before a platform-wide mandate removed that choice. Under voluntary disclosure, concealed AI songs outperform the same artist’s credited AI songs roughly threefold in plays — but artists credit AI on songs that enter new genres or carry a more visible human hand. Exploiting the mandatory-label rollout in a difference-in-differences design, we estimate the causal listener penalty at about five percent of plays: observational data overstate the market cost of mandated AI disclosure by more than an order of magnitude.