An Experimental Analysis of Ending Rules in Internet Auctions

A great deal of late bidding has been observed on internet auctions such as eBay, which employ a second price auction with a fixed deadline. Much less late bidding has been observed on internet auctions such as those run by Amazon, which employ similar auction rules, but use an ending rule that automatically extends the auction if necessary after the scheduled close until ten minutes have passed without a bid. This paper reports an experiment that allows us to examine the effect of the different ending rules under controlled conditions, without the other differences between internet auction houses that prevent unambiguous interpretation of the field data. We find that the difference in auction ending rules is sufficient by itself to produce the differences in late bidding observed in the field data. The experimental data also allow us to examine how individuals bid in relation to their private values, and how this behavior is shaped by the different opportunities for learning provided in the auction conditions.

Buy-It-Now prices in eBay Auctions - The Field in the Lab

Electronic commerce has grown extraordinarily over the years, with online auctions being extremely successful forms of trade. Those auctions come in a variety of different formats, such as the Buy-It-Now auction format on eBay, that allows sellers to post prices at which buyers can purchase a good prior to the auction. Even though, buyer behavior is well studied in Buy-It-Now auctions, as to this point little is known about how sellers set Buy-It-Now prices. We investigate into this question by analyzing seller behavior in Buy-It-Now auctions. More precisely, we combine the use of a real online auction market (the eBay platform and eBay traders) with the techniques of lab experiments. We find a striking link between the information about agents provided by the eBay market institution and their behavior. Information about buyers is correlated with their deviation from true value bidding. Sellers respond strategically to this information when deciding on their Buy-It-Now prices. Thus, our results highlight potential economic consequences of information publicly available in (online) market institutions

Late and Multiple Bidding in Second Price Internet Auctions: Theory and Evidence Concerning Different Rules for Ending an Auction

In second price internet auctions with a fixed end time, such as those on eBay, many bidders ‘snipe’, i.e., they submit their bids in the closing minutes or seconds of an auction. Late bids of this sort are much less frequent in auctions that are automatically extended if a bid is submitted very late, as in auctions conducted on Amazon. We propose a model of second price internet auctions, in which very late bids have a positive probability of not being successfully submitted, and show that sniping in a fixed deadline auction can occur even at equilibrium in auctions with private values, as well as in auctions with uncertain, dependent values. Sniping in fixed-deadline auctions also arises out of equilibrium, as a best reply to incremental bidding. However, the strategic advantages of sniping are eliminated or severely attenuated in auctions that apply the automatic extension rule. The strategic differences in the auction rules are reflected in the field data. There is more sniping on eBay than on Amazon, and this difference grows with experience. We also study the incidence of multiple bidding, and its relation to late bidding. It appears that one substantial cause of late bidding is as a strategic response to incremental bidding.

Bid early and get it cheap - Timing effects in Internet auctions

Most internet auction sites, like eBay, use a proxy bidding system where bidders can put in their maximum bid and let a proxy bidder (a computer) bid for them. Yet many bidders speculate about how to bid and employ bidding strategies. This paper examines how the timing of bids can affect the final price. In a unique data set of 17,000 Scandinavian furniture auctions it turns out that early price increases, i.e. much early bidding, scare off bidders and therefore result in lower prices, whereas much late bidding results in higher prices. Sniping is therefore not a successful strategy to avoid bidding wars.Internet auctions, Auction fever, Pseudo-endowment, Bidding behavior, eBay, Strategies, WTP

Optimal Design Of English Auctions With Discrete Bid Levels

This paper considers a form of ascending price English auction widely used in both live and online auctions. This discrete bid auction requires that the bidders submit bids at predetermined discrete bid levels, and thus, there exists a minimal increment by which the bid price may be raised. In contrast, the academic literature of optimal auction design deals almost solely with continuous bid auctions. As a result, there is little practical guidance as to how an auctioneer, seeking to maximize its revenue, should determine the number and value of these discrete bid levels, and it is this omission that is addressed here. To this end, a model of a discrete bid auction from the literature is considered, and an expression for the expected revenue of this auction is derived. This expression is used to determine both numerical and analytical solutions for the optimal bid levels, and uniform and exponential bidder’s valuation distributions are compared. Finally, the limiting case where the number of discrete bid levels is large is considered. An analytical expression for the distribution of the optimal discrete bid levels is derived, and an intuitive understanding of how this distribution maximizes the revenue of the auction is developed

EXPLORING AND MODELING OF BIDDING BEHAVIOR AND STRATEGIES OF ONLINE AUCTIONS

Internet auctions, as an exemplar of the recent boom in e-commerce, are grow- ing faster than ever in the last decade. Understanding the reasons why bidders be- have a certain way allows invaluable insight into the auction process. This research focuses on methods for modeling, testing and estimation of bidders' behavior and strategies. I start my discussion with bid shading, which is a common strategy bidders believe obtains the lowest possible price. While almost all bidders shade their bids, at least to some degree, it is impossible to infer the degree and volume of shaded bids directly from observed bidding data. In fact, most bidding data only allows researchers to observe the resulting price process, i.e. whether prices increase fast (due to little shading) or whether they slow down (when all bidders shade their bids). In this work, I propose an agent-based model that simulates bidders with different bidding strategies and their interaction with one another. The model is calibrated (and hence properties about the propensity and degree of shaded bids are estimated) by matching the emerging simulated price process with that of the observed auction data using genetic algorithms. From a statistical point of view, this is challenging because it requires matching functional draws from simulated and real price processes. I propose several competing fitness functions and explore how the choice alters the resulting ABM calibration. The method is applied to the context of eBay auctions for digital cameras and show that a balanced fitness function yields the best results. Furthermore, in light of the discrepancy find from the model in bidders' be- havior and optimal strategies proposed from online auction literature. I extract empirical bidding strategies from auction winners and utilize the agent based model to simulate and test the performance of twenty-four different empirical and theo- retical strategies. The experiment results suggest that some empirical strategies perform robustly when compared to theoretical strategies and taking into account other bidders' ability to learn. In addition, I expended the online auction framework from single auction to multiple auction simulation, which acts as a platform for investigating and test- ing more complicated situations that involves the competition among concurrent auctions. This framework facilitates my investigation of bidders' switching behavior and enables me to answer a series questions. For example, is it beneficial for auction website to promote bidders' switching behavior? Will bidders and even sellers get any advantage from bidders' switching? What is the best auction recommendation strategy for online auction website to obtain higher profit and/or a better customer experience? Through careful experiment design, it has been showed that higher switching frequency leads to higher profit for auction website and reduces the price dispersion, which leads to reduced risk for both bidders and sellers. In addition, the best auction recommendation strategy is providing the five earliest closing auctions so that bidders can choose the lowest price auction

Economic Insights from Internet Auctions: A Survey

This paper surveys recent studies of Internet auctions. Four main areas of research are summarized. First, economists have documented strategic bidding in these markets and attempted to understand why sniping, or bidding at the last second, occurs. Second, some researchers have measured distortions from asymmetric information due, for instance, to the winner's curse. Third, we explore research about the role of reputation in online auctions. Finally, we discuss what Internet auctions have to teach us about auction design.

Online Auctions

The economic literature on online auctions is rapidly growing because of the enormous amount of freely available field data. Moreover, numerous innovations in auction-design features on platforms such as eBay have created excellent research opportunities. In this article, we survey the theoretical, empirical, and experimental research on bidder strategies (including the timing of bids and winner's-curse effects) and seller strategies (including reserve-price policies and the use of buy-now options) in online auctions, as well as some of the literature dealing with online-auction design (including stopping rules and multi-object pricing rules).

Public procurement auctions in Brazil

This thesis provides an empirical analysis of data generated by ComprasNet, the online procurement bidding platform developed and used by the Brazilian federal government. ComprasNet is a large bidding platform used since 2001 by more than 2200 public purchasing units who list around one million lots each year. Over 70,000 unique bidders have participated in these auctions. In 2010, 46 percent of all procurement for the federal government was conducted through ComprasNet, totaling R$ 27 billion, or 0.7 percent of Brazil’s GDP. In short, these auctions represent a large share of federal tenders and a substantial amount is contracted through them each year. Chapter 1 provides an overview of ComprasNet. After reviewing the literature on various topics which this dissertation contributes to, I describe the institutional background surrounding ComprasNet. I then present the baseline data used throughout the remainder of this dissertation. Chapter 2 addresses one important aspect of designing an online ascending auction, namely how to end the auction. ComprasNet varied its ending rules over time, providing an unique opportunity to test theories of bidder behaviour, as well as assessing the impact of ending rules on auction outcomes. Chapter 3 analyses a two-stage auction format which ComprasNet uses. Two-stage designs have long been proposed by the theoretical literature, but there are virtually no empirical works apart from experimental studies. Finally, chapter 4 analyses a bid preference programme targeted at small and micro enterprises (SMEs). The programme consists of setting aside eligible lots for SMEs. We first use eligibility rules as a source of exogenous variation in the treatment assignment to estimate the effects of the programme on auction outcomes. We then set up an open auction model with endogenous entry and asymmetric bidders and estimate the model’s primitives. In particular, we estimate entry costs, which we interpret as red tape costs