# Multi-period Trading Prediction Markets with Connections to Machine Learning

arXiv: https://arxiv.org/abs/1403.0648 · HTML: https://mlsanity.com/paper/1403.0648/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2014.

## Built on by (most cited first, 5 of 5)

- [Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Sets](https://mlsanity.com/paper/2101.01787/) (2021)
- [Bayesian Regression Markets](https://mlsanity.com/paper/2310.14992/) (2023)
- [Price Interpretability of Prediction Markets: A Convergence Analysis](https://mlsanity.com/paper/2205.08913/) (2022)
- [Human-AI Collaboration for Estimating Scientific Replicability](https://mlsanity.com/paper/2605.27394/) (2026)
- [Decentralized Aggregation of LLM Predictions via Wagering Mechanisms](https://mlsanity.com/paper/2607.04389/) (2026)

## Abstract (arXiv)

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.