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arXiv · 2609.14042

Trade-Adaptive Aggregation of Probabilistic Financial Forecasts

Yankai Chen·Rassul Magauin·Bowei He·Anuar Aimoldin·Sirui Song·Bin Xiao·Zangir Iklassov·Xue Liu
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Abstract

Financial NLP systems produce probabilistic forecasts from news, reports, and filings. Prediction markets can aggregate these forecasts sequentially, but their fees must reward information without overcharging low-risk updates. Existing quadratic-fee mechanisms use a state-blind bound, while a local-curvature envelope remains conservative because it prices every trade at the largest permitted span. We introduce SpanPM, a prediction-market mechanism that sets the local-curvature multiplier from each trade's realized payoff spread. Its fee dominates exact Bregman exposure trade by trade, preserves no arbitrage, information incorporation, expressiveness, and bounded worst-case loss, and yields a tighter overcharge factor approaching one as trade span vanishes. Repeated global best responses converge to a common belief and become full Newton steps locally, giving quadratic rather than damped-linear convergence. We implement a deterministic bounded one-dimensional multi-basin search, audited against a dense grid. Across paired synthetic experiments, SpanPM improves 20-round consensus error by several orders of magnitude over a fixed-envelope local baseline under the same hard cap. With evolving beliefs, it preserves 96--97\% of the trader surplus achieved with exact Bregman fees while cutting excess fees by 94\% relative to the global quadratic mechanism. These results establish a trade-adaptive prediction-market mechanism for sequential aggregation of probabilistic financial forecasts.

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Yankai Chen, Rassul Magauin, Bowei He, Anuar Aimoldin, Sirui Song, Bin Xiao, Zangir Iklassov, Xue Liu. 2026-09-12. Trade-Adaptive Aggregation of Probabilistic Financial Forecasts. https://arxiv.org/abs/2609.14042

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