Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting

This paper presents a novel intelligent bidding system, called SOABER (Simultaneous Online Auction BiddER), which monitors simultaneous online auctions of high-value fine art items. It supports decision-making by maximizing bidders' surpluses and their chances of winning an auction. One key element of the system is a dynamic forecasting model, which incorporates information about the speed of an auction's price movement, as well as the level of competition both within and across auctions. Other elements include a wallet estimator, which gauges the bidders' willingness to pay, and a bid strategizer, which embeds the forecasting model into a fully automated decision system. We illustrate the performance of our intelligent bidding system on an authentic dataset of online art auctions for Indian contemporary art. We compare our system with several simpler ad-hoc approaches, and find it to be more effective in terms of both the extracted surplus and the resulting winning percentage.