Towards a Theory of Confidence in Market-Based Predictions

Rupert Freeman, David Pennock, Daniel Reeves, David Rothschild, Bo Waggoner
Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:365-368, 2021.

Abstract

Prediction markets are a way to yield probabilistic predictions about future events, theoretically incorporating all available information. In this paper, we focus on the confidence that we should place in the prediction of a market. When should we believe that the market probability meaningfully reflects underlying uncertainty, and when should we not? We discuss two notions of confidence. The first is based on the expected profit that a trader could make from correcting the market if it were wrong, and the second is based on expected market volatility in the future. Our paper is a stepping stone to future work in this area, and we conclude by discussing some key challenges.

Cite this Paper

BibTeX
@InProceedings{pmlr-v147-freeman21a, title = {Towards a Theory of Confidence in Market-Based Predictions}, author = {Freeman, Rupert and Pennock, David and Reeves, Daniel and Rothschild, David and Waggoner, Bo}, booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications}, pages = {365--368}, year = {2021}, editor = {Cano, Andrés and De Bock, Jasper and Miranda, Enrique and Moral, Serafı́n}, volume = {147}, series = {Proceedings of Machine Learning Research}, month = {06--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v147/freeman21a/freeman21a.pdf}, url = {https://proceedings.mlr.press/v147/freeman21a.html}, abstract = {Prediction markets are a way to yield probabilistic predictions about future events, theoretically incorporating all available information. In this paper, we focus on the confidence that we should place in the prediction of a market. When should we believe that the market probability meaningfully reflects underlying uncertainty, and when should we not? We discuss two notions of confidence. The first is based on the expected profit that a trader could make from correcting the market if it were wrong, and the second is based on expected market volatility in the future. Our paper is a stepping stone to future work in this area, and we conclude by discussing some key challenges.} }
Endnote
%0 Conference Paper %T Towards a Theory of Confidence in Market-Based Predictions %A Rupert Freeman %A David Pennock %A Daniel Reeves %A David Rothschild %A Bo Waggoner %B Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2021 %E Andrés Cano %E Jasper De Bock %E Enrique Miranda %E Serafı́n Moral %F pmlr-v147-freeman21a %I PMLR %P 365--368 %U https://proceedings.mlr.press/v147/freeman21a.html %V 147 %X Prediction markets are a way to yield probabilistic predictions about future events, theoretically incorporating all available information. In this paper, we focus on the confidence that we should place in the prediction of a market. When should we believe that the market probability meaningfully reflects underlying uncertainty, and when should we not? We discuss two notions of confidence. The first is based on the expected profit that a trader could make from correcting the market if it were wrong, and the second is based on expected market volatility in the future. Our paper is a stepping stone to future work in this area, and we conclude by discussing some key challenges.
APA
Freeman, R., Pennock, D., Reeves, D., Rothschild, D. & Waggoner, B.. (2021). Towards a Theory of Confidence in Market-Based Predictions. Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 147:365-368 Available from https://proceedings.mlr.press/v147/freeman21a.html.

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