Profiling Price Dynamics in Online Auctions Using Curve Clustering

Electronic commerce, and in particular online auctions, have received an extreme surge of popularity in recent years. While auction theory has been studied for a long time from a game-theory perspective, the electronic implementation of the auction mechanism poses new and challenging research questions. In this work, we focus on the price formation process and its dynamics. We present a new source of rich auction data and introduce an innovative way of modelling and analyzing price dynamics. We represent auctions as functional objects by accommodating the special structure of bidding data. We then use functional data analysis to characterize different types of auctions. Our findings suggest that there are several types of dynamics even for auctions of comparable items. By profiling these differences with respect to features associated with the auction format, the seller and the winner we find new relationships between dynamics and auction settings, and we tie these findings to the existing literature on online auctions