arXiv:2607.06166v1 Announce Type: new Abstract: Prediction markets aggregate dispersed beliefs into prices that act as probabilistic forecasts of uncertain events. Classical theory establishes a clean equivalence between forecasting accuracy and trading profit, but only for the specific automated market maker (AMM) design. However, the largest exchanges today are based on central limit order books in which informed forecasters routinely lose money while uninformed strategies can profit on simple heuristics. We resolve this discrepancy by establishing a formal equivalence between predictive acc
This paper addresses a long-standing discrepancy in prediction market theory, providing a formal equivalence that resolves why informed traders often lose money in central limit order books (CLOBs) despite classical theory suggesting they should profit.
Understanding the dynamics of profit and loss in prediction markets, especially the discrepancy between theoretical models and real-world CLOBs, is crucial for designing more efficient markets and for participants to devise effective trading strategies.
The clarified understanding of when informed forecasters profit (or lose) in different market mechanisms provides a more accurate basis for market design and trading algorithm development beyond simple automated market makers.
- · Sophisticated market designers
- · Quantitative traders with nuanced models
- · Platforms accurately modeling CLOBs
- · Uninformed 'prophet' traders
- · Prediction market platforms relying solely on AMM theory
- · Simple heuristic trading strategies
This research will lead to improved design and regulation of prediction markets to better incentivize accurate forecasting.
More robust prediction markets could emerge, attracting greater participation and capital by addressing current inefficiencies and paradoxical outcomes.
Enhanced prediction market accuracy and reliability might make them a more trusted source for aggregating public wisdom on critical future events.
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Read at arXiv cs.AI