Journal
QUANTITATIVE FINANCE
Volume 19, Issue 9, Pages 1461-1471Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/14697688.2019.1622285
Keywords
Prediction markets; Wisdom of crowds; Collective intelligence; Market selection; Machine learning; Model aggregation
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Funding
- European Union's Horizon 2020 research and innovation program [640772-DOLFINS]
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We investigate market selection and bet pricing in a repeated prediction market model. We derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information content of prevailing prices in the case of fractional Kelly traders with heterogeneous beliefs. It turns out that, apart some non-generic situations, prices do not converge, neither almost surely nor on average, to true probabilities, nor are they always nearer to the truth than the beliefs of all surviving agents. This implies that, in general, prediction market prices are not maximum likelihood estimators of the true probabilities. However, when more than one agent survives, the average price emerging from a prediction market approximates the true probability with lower information loss than any individual belief.
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