Multi-period trading prediction markets with connections to machine learning

We present a new model for prediction markets, in which we use risk measures to model agents and in-troduce a market maker to describe the trading process. This specific choice on modelling tools brings us mathematical convenience. The analysis shows that the whole market effectively approaches a global objec-tive, despite that the market is designed such that each agent only cares about its own goal. Additionally, the market dynamics provides a sensible algorithm for optimising the global objective. An intimate connection between machine learning and our markets is thus established, such that we could 1) analyse a market by ap-plying machine learning methods to the global objective, and 2) solve machine learning problems by setting up and running certain markets.