Multi-period Trading Prediction Markets with Connections to Machine Learning

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-hu14, title = {Multi-period Trading Prediction Markets with Connections to Machine Learning}, author = {Hu, Jinli and Storkey, Amos}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1773--1781}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/hu14.pdf}, url = {https://proceedings.mlr.press/v32/hu14.html}, 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.} }
Endnote
%0 Conference Paper %T Multi-period Trading Prediction Markets with Connections to Machine Learning %A Jinli Hu %A Amos Storkey %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-hu14 %I PMLR %P 1773--1781 %U https://proceedings.mlr.press/v32/hu14.html %V 32 %N 2 %X 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.
RIS
TY - CPAPER TI - Multi-period Trading Prediction Markets with Connections to Machine Learning AU - Jinli Hu AU - Amos Storkey BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-hu14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 1773 EP - 1781 L1 - http://proceedings.mlr.press/v32/hu14.pdf UR - https://proceedings.mlr.press/v32/hu14.html AB - 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. ER -
APA
Hu, J. & Storkey, A.. (2014). Multi-period Trading Prediction Markets with Connections to Machine Learning. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):1773-1781 Available from https://proceedings.mlr.press/v32/hu14.html.

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