Prediction markets are used in real life to predict outcomes of interest such as presidential elections. In this work we introduce a mathematical theory for Artificial Prediction Markets for supervised classifier aggregation and probability estimation. We introduce the artificial prediction market as a novel way to aggregate classifiers. We derive the market equations to enforce total budget conservation, show the market price uniqueness and give efficient algorithms for computing it. We show how to train the market participants by updating their budgets using training examples. We introduce classifier specialization as a new differentiating characteristic between classifiers. Finally, we present experiments using random decision rules as specialized classifiers and show that the prediction market consistently outperforms Random Forest on real and synthetic data of varying degrees of difficulty.