Zheng Gong, Jin Huang, and Yuxin Chen. What the past tells about the future: Historical prices in the durable goods market. Management Science, 68(12):8857–8871, 2022 [PDF] [Online Appendix]
Summary: This paper addresses the role of "price trackers," particularly in the context of periodic sales. We show that when some consumers can obtain historical price information through price trackers (which implies that they become informed about how many low-valuation consumers have accumulated since the last sale and can thus predict how soon the firm will hold the next sale), the firm adjusts pricing by lowering the regular price and increasing the frequency of sales. This pricing adjustment can result in a positive information spillover to non-price tracker users.
A Model of Two Learning Processes
Revise and resubmit, Marketing Science
Abstract: Many studies have shown that before making a purchase decision, consumers make quality inferences from the purchase decisions of others and also seek information from sources such as third-party product reviews and various forms of word-of-mouth communication (e.g., user reviews). This paper proposes a novel model that integrates two consumer learning processes: observational learning and active learning. Within the standard observational learning framework, this model allows for dynamic choice of information acquisition by each consumer after observing the purchase decisions of predecessors. We consider the case where the information source is exogenous and the case where the information increases with sales, and we find that the long-term market outcomes are immediate herd and complete learning, respectively. These two outcomes represent the two limit cases of a generalized version of the model, where firms can control the information flow directly or indirectly through marketing tools. Among other things, this model predicts that the endogenous increase in quality information (e.g., through word of mouth) does not help the market increase the probability of identifying high-quality products, but helps the market identify and eliminate low-quality products. In addition, we discuss the optimal marketing strategies of low-quality and high-quality products under different information settings.
Limited Time Offer and Consumer Search, with Zheng Gong
Revise and resubmit, Management Science
Summary: This paper examines the role of "limited-time offers" in search markets. We demonstrate that these offers are the most effective pricing tool for firms to manipulate the endogenous search order: to induce "search prominence" or "search discrimination." In the existing literature, limited-time offers have been associated with hold-up and anticompetitive effects (Armstrong and Zhou, 2015, "Search Deterrence"). Our findings reveal that in markets where prices are observable, using limited-time offers may increase total welfare by inducing a more socially efficient search order.
The Strategic Failure of Emission Targets, with Yuxin Chen and Zheng Gong
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.