The effects of shilling on final bid prices in online auctions
Electronic Commerce Research and Applications
Stochastic Modelling and Optimisation of Internet Auction Processes
Electronic Notes in Theoretical Computer Science (ENTCS)
Predicting online auction final prices using time series splitting and clustering
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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Bids during an online auction arrive at unequally-spaced discrete time points. Our goal is to capture the entire continuous price-evolution function by representing it as a functional object. Various nonparametric smoothing methods exist to recover the functional object from the observed discrete bid data. Previous studies use penalized polynomial and monotone smoothing splines; however, these require the determination and storage of a large number of coefficients and often lengthy computational time. We present a family of parametric growth curves that describe the price-evolution during online auctions. This approach is parsimonious and has an appealing interpretation in the online auction context. We also provide an automated fitting algorithm that is computationally fast. Methods are illustrated using eBay data.