Online auctions have become extremely popular in recent years. Ability to predict winning bid prices accurately can help bidders to maximize their profit. This paper proposes a number of strategies and algorithms for performing such predictions for the first price sealed bid reverse auctions (FPSBRA). The Neural Networks (NN) and Genetic Programming (GP) learning techniques are used in the models. The algorithms are tested in the Trading Agent Competition Supply Chain Management (TAC SCM) game, where manufacture agents compete for customers' orders following the rules of the FPSBRA. Although all the proposed algorithms demonstrate the potential for predicting winning bid prices in competitive and dynamic environments, some of them perform more accurately than the others.
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