Abstract: A limited time offer is a special deal that lasts a limited period of time. Observing that firms usually actively advertise limited time offers in the real world, we present a price-directed search model to underline that firms use this sales tactic for gaining search prominence. We find that limited time offers maximize total welfare through inducing the socially optimal search order with and without competition. In a monopoly market, the monopolist finds it optimal to use a limited time offer if and only if searching the firm before the outside option is socially optimal. In a competitive market, all firms employ limited time offers and are visited in the first-best search order in equilibrium. By contrast, if limited time offers are not allowed, competing firms may all make higher profits despite strictly lower total welfare.
What the Past Tells about the Future: Historical Price in the Durable Good Market, with Zheng Gong and Yuxin Chen, Revise and resubmit, Management Science
Abstract: We investigate the dynamic pricing strategy of a durable good monopolist in a new setting that assumes away perfect consumer information on historical prices. We first show that when all consumers with heterogeneous tastes are not informed of historical prices, the monopolist charges a high regular price for most of the time and periodically holds low-price sales. Then we consider the case in which a small proportion of consumers (such as price-tracker users) become informed of historical prices. At the new equilibrium, the monopolist lowers the regular price and advances sales, implying shorter price cycles, more frequent sales, and a positive spillover effect of price-tracker users' informational advantage on the rest of uninformed consumers. By analyzing how the presence of price trackers affects market outcomes, this paper also provides managerial implications for sellers and platforms on price history disclosure policies.
A Model of Two Learning Processes, Revise and resubmit, Marketing Science
Abstract: Standard observational learning models assume that each individual has a costless private signal. I extend the framework to incorporate individuals' learning decisions when acquiring private information is costly. Under the assumption of costly signal, a cascade occurs earlier given a history of actions, an incorrect herd is more likely to arise, and the economy fails to obtain complete learning even if signals are perfect. I also consider a dynamic learning environment where each individual can acquire costly information continuously. Complete learning is obtained only if information increases monotonically over time. Otherwise, if the information source is constant, successors fully free-ride on their predecessor's learning efforts. Further, I provide some discussions on how pricing, advertising, and word of mouth affect learning dynamics.
Should Google Profit like a Taxi Driver?
Abstract: In recent years, numerous European countries have taken or have considered taking regulatory actions against Google News with the aim of improving news quality. This paper explains how news aggregators affect newspapers' incentives in quality investment from two novel perspectives: (1) a positive market-expansion effect of news aggregators by eliminating information asymmetry between newspapers and news readers, and (2) a negative business-stealing effect by displaying excerpts of newspaper articles (snippets) on news aggregators' own sites, which are substitutes of original news. The model illustrates both effects and can be used to evaluate taxation policies on snippets. A tax proportional to how much information extracted from the original news, or a click-through subsidy paid by newspapers to aggregators can discourage news aggregators from showing free previews to appropriate traffic. Moreover, I extend the benchmark setting from one single newspaper to multiple newspapers, capturing an additional competition-in-traffic effect among newspapers. Finally, I also show that the model is robust to many other generalizations.
Abstract: This paper studies learning in the stock market. Our contribution is to propose a model to illustrate the endogenous timing decision on trading, taking into account the incentive of learning from others about the fundamental value. The model is similar to Easley and O'Hara (1992), except that we introduce less-informed traders whose private information is inferior to fully-informed traders, but superior to that of random noise traders and a zero-profit market maker. We also allow both types of informed traders to optimize timing of trading. We show that fully-informed traders act as early birds because it is optimal for them to buy or sell at the earliest possible time; meanwhile, less-informed traders could be better off as second mice by delaying transactions to learn from previous trades. The greater information asymmetry between the less-informed traders and the market maker, the larger profits the former could make even though the latter is learning from all trades.
Work in Progress
Over-investment Due to Demand Uncertainty and Relocation Cost, with Yuxin Chen