Bayesian estimation of bid sequences in Internet auctions using a generalized record-breaking model

A sequence of bids in Internet auctions can be viewed as record-breaking events in which only those data points that break the current record are observed. We investigate stochastic versions of the classical record-breaking problem for which we apply Bayesian estimation to predict observed bids and bid times in Internet auctions. Our approach to addressing this type of data is through data augmentation in which we assume that participants (bidders) have dynamically changing valuations for the auctioned item, but the latent number of bidders ‘competing’ in those events is unseen. We use data from notebook auctions provided by one of the largest Internet auction sites in Korea. We find significant variation in the number of latent bidders across auctions. Our other primary findings are as follows: (1) the latent bidders are significant in number relative to observed bidders, (2) the latent number of remaining bidders is considerably smaller than that of new entrants to the auction after a given bid, and (3) larger bid and time increments significantly influence the bidding participation behavior of the remaining bidders. As part of our substantive contribution, we highlight the model’s ability to understand brand equity in an Internet auction context through a brand’s ability to simultaneously bring in bidders, higher bid amounts, and faster bidding.