Trade-Adaptive Aggregation of Probabilistic Financial Forecasts

2026-09-12

AI Summary

本文提出SpanPM机制，通过调整每笔交易的实际收益差来设定局部曲率乘数，以更有效地聚合金融预测，减少过度收费并提高收敛速度。

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.

Problem

• Trade-Adaptive
• Probabilistic Financial Forecasts
• Prediction Markets
• Fees
• Information Aggregation

Innovation

• SpanPM
• local-curvature multiplier
• realized payoff spread
• trade-adaptive
• prediction-market mechanism