Multi-period Trading Prediction Markets with Connections to Machine Learning
This work provides a theoretical framework linking prediction markets to machine learning, which is incremental in nature.
The paper tackles the problem of modeling multi-period prediction markets by using risk measures and a market maker to show that the market dynamics optimize a global objective, establishing a connection to machine learning for analysis and problem-solving.
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 on modelling tools brings us mathematical convenience. The analysis shows that the whole market effectively approaches a global objective, 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 applying machine learning methods to the global objective, and 2) solve machine learning problems by setting up and running certain markets.