Xiaoquan Zhang

Cyclical Bid Adjustments in Search-Engine Advertising

Keyword advertising, or sponsored search, is one of the most successful advertising models on the Internet. One distinctive feature of keyword auctions is that they enable advertisers to adjust their bids and rankings dynamically, and the payoffs are realized in real time. We capture this unique feature with a dynamic model and identify an equilibrium bidding strategy. We find that under certain conditions, advertisers may engage in cyclical bid adjustments, and equilibrium bidding prices may follow a cyclical pattern: price-escalating phases interrupted by price-collapsing phases, similar to an “Edgeworth cycle” in the context of dynamic price competitions. Such cyclical bidding patterns can take place in both first-and second-price auctions. We obtain two data sets containing detailed bidding records of all advertisers for a sample of keywords in two leading search engines. Our empirical framework, based on a Markov switching regression model, suggests the existence of such cyclical bidding strategies. The cyclical bid-updating behavior we find cannot be easily explained with static models. This paper emphasizes the importance of adopting a dynamic perspective in studying equilibrium outcomes of keyword auctions. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.

Dynamic price competition on the internet: advertising auctions

The development of Internet advertising is phenomenal. In merely five years, its market size has grown from the 1999’s

All online friends are not created equal: discovering influence structure in online social networks

3.5 billion to 2005’s

Internet-Facilitated Feedback Trading

12.9 billion. Among the different types of online advertising, keyword auctions, where positions in a search engine’s result pages are sold to advertisers through auctions, has grown most rapidly and is widely credited for the revitalization of the search engine business. These keyword auctions offer one way for us to study dynamic price competition. In reality, firms react to each other continually. Some firms check the prices of their competitors and make adjustments. Some others even offer price-match guarantees that whenever a competitor offers a lower price, they will match the difference. With the advent of the Internet, these types of price reactions become even more prevalent. In this paper, we propose to use a Markovian framework to study dynamic price competition in the keyword auctions market.

Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics

Online social networking services generate an online map of socially connected individuals. All online friends are not created equal. Despite similarity in information exposure, two different online contacts of an individual may exert significantly different influence. First, online contacts of a user could be grouped into different categories, between which the strength of influence differs. Second, ties within each category also differ. A Bayesian model is proposed to describe and estimate the influence structure on top of the network structure taking both user-reported categories and behavior observation into consideration. Our results show that there is significant heterogeneity in the social influence structure. We further identify how individual-level influence structure relates to other user characteristics.

Exploring the value of online product reviews in forecasting sales: The case of motion pictures

The ease of Internet trading has lured relatively inexperienced investors into the financial markets. We study the impact of the influx of these uninformed traders in a dynamic setting. Our results show that these strategic uninformed online traders who adopt feedback strategies do not outperform noise traders and feedback trading does not affect market volatility. An insiders equilibrium strategy and expected profit remain the same as if the feedback traders were noise traders. On the individual level, feedback traders earn higher risk-adjusted expected profits than noise traders. The presence of feedback trading in the market changes market depth, but the speed at which information gets incorporated into prices is constant.