Multi-period Trading Prediction Markets with Connections to Machine Learning

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Jinli Hu, Amos Storkey ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1773-1781, 2014.

Abstract

We present a new model for prediction markets, in which we use risk measures to model agents and introduce a market maker to describe the trading process. This specific choice of modelling approach enables us to show that the whole market approaches a global objective, despite the fact that the market is designed such that each agent only cares about its own goal. In addition, the market dynamic 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 applying machine learning methods to the global objective; and 2) solve machine learning problems by setting up and running certain markets.

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