Last Minute Bidding on Tradera

Abstract: In online auctions bidding frequency tends to increase towards the last minutes of the auction. This thesis investigates why bidders might choose to engage in last minute bidding, sniping, in Tradera auctions (Tradera is a Swedish subsidiary of eBay). Tradera uses a hard-close rule and a second-price rule. In hard-close auctions the dead-line occurs at a specific time, after which new bids are not considered. Due to e.g. erratic network traffic, bids submitted close to the dead-line (snipe bids) might not be successfully transmitted. The second-price rule prescribes that the good is to be rewarded to the highest bidder, for a price equal to the second highest bid (see Vickrey, 1961). Utilizing the hard-close and second-price rule we consider two different models to examine why bidders might snipe. In both models there is a positive probability, a, that a snipe bid is successfully transmitted. First we construct a static Bayesian game to evaluate how the presence of a shill affects bid timing. A shill is a seller that bid on his own item in order to boost up the final price thereby reducing the winner’s surplus (see e.g. Bhargava et. al., 2005). In this model there is a probability p that a shill is present. We show that if a and p are sufficiently high; an equilibrium in which all players snipe may exist. Secondly, we review the discontinuous eBay-model proposed by Ockenfels and Roth (2005). eBay and Tradera auctions are by large identical, allowing us to directly apply their model for our purpose. In this model, sniping can be a best response against incremental bidding. An incremental bidder is interpreted as an inexperienced bidder that mistakes the second-price rule for a first-price rule. Using data from Tradera consisting of 200 Iphone auctions and 200 art auctions we empirically test the theoretical predictions. The effects of a shill upon bid timing cannot be confirmed. Relevant coefficients exhibit the expected signs, but cannot be accepted on any relevant level of significance. The weak results are probably due to the restrictive environment of the game and inaccurate estimates of p. When testing for the effects of incremental bidding we observe a statistically significant and positive relationship between bidder rank and sniping. A bidder’s rank is assumed to approximate his experience. This implies that rational (high rank) bidders snipe in order to avoid an early price war with incremental (low rank) bidders.