概率金融预测的交易自适应聚合
Trade-Adaptive Aggregation of Probabilistic Financial Forecasts
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中文总结 AI 辅助
针对金融概率预测的顺序聚合，提出SpanPM预测市场机制，按交易收益跨度设定局部曲率费用，逐笔优于Bregman暴露，加速收敛并大幅降低共识误差与超额费用。
中文摘要 AI 辅助
金融自然语言处理系统从新闻、报告和文件中产生概率预测。预测市场可以按顺序聚合这些预测，但其费用必须在不过度收取低风险更新费用的情况下奖励信息。现有的二次费用机制使用状态无关的界限，而局部曲率包络则因为按最大允许跨度对每笔交易定价而显得保守。我们引入了SpanPM，一种预测市场机制，它根据每笔交易实现收益的跨度来设定局部曲率乘数。其费用在逐笔交易上优于精确的Bregman暴露，保持无套利、信息纳入、表达能力和有界的最坏情况损失，并随着交易跨度消失而产生更紧的过度收费因子趋近于1。重复的全局最佳响应收敛到共同信念，并在局部成为完整的牛顿步，从而获得二次而非阻尼线性收敛。我们实现了一个确定性的有界一维多盆地搜索，并针对密集网格进行了审计。在成对的合成实验中，SpanPM在相同的硬上限下，将20轮共识误差比固定包络的局部基线提高了几个数量级。随着信念的演变，它保留了使用精确Bregman费用实现的交易者剩余价值的96-97%，同时相对于全局二次机制将超额费用削减了94%。这些结果建立了一种用于概率金融预测顺序聚合的交易自适应预测市场机制。
英文摘要
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.
发表机构
- McGill University（麦吉尔大学）
- MBZUAI（穆罕默德·本·扎耶德人工智能大学）
- The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。
