AI 中文总结
本研究利用Kalshi交易所五年数据，发现预测市场在七个美国城市中的六个上，其隐含预报在均方根误差上优于最佳公共预报NBM约10%，且市场信息领先于公共预报系统。
AI 中文摘要
我们越早收到信息，且信息越准确，就能做出更好的规划决策。每天，预测市场都允许任何人就全球各城市明日最高气温下注，从而形成一个基于分散信息的市场隐含预报。我们利用Kalshi交易所过去五年中七个美国城市的市场数据，逐小时提取市场隐含预报。我们将此预报作为测量工具，以了解市场在公共预报系统之前公开了多少关于气温的信息。我们将其与领先的美国和欧洲天气预报进行竞赛。在我们研究的七个城市中，有六个城市，市场击败了最准确的单一公共预报——国家模式融合（NBM）。汇总每个城市-日的数据，在市场交易的第一小时结束时，市场在均方根误差上比最佳单一公共产品好约10%，并在白天、夜间及至目标日保持领先。观察预报随时间的变化，我们发现国家模式融合在其发布之间向市场移动的距离是市场向NBM移动距离的四倍。市场对新天气预报更新没有反应；相反，预报缓慢地发布了市场早已公开共享的信息。
英文摘要
The sooner we receive information, and the more accurate it is, the better planning decisions we can make. Every day, prediction markets let anyone bet on tomorrow's high temperature in cities around the world, creating a market-implied forecast built on dispersed information. We use the past five years of market data from the Kalshi exchange for seven American cities to extract, hour by hour, the market-implied forecast. We use this forecast as a measuring instrument to see how much information about the temperature the market makes public before the public forecasting system does. We race it against the leading American and European weather forecasts. In six of the seven cities we study, the market beats the most accurate single public forecast, the National Blend of Models (NBM). Aggregating every city-day, at the end of the market's first hour of trading it beats the best single public product by about 10 percent in root-mean-square error, and holds its lead through the day, overnight, and into the target day. Looking at how the forecasts move over time, we find the National Blend travels four times further toward the market between its postings than the market travels toward the NBM. The market does not react to new weather forecast updates; instead, the forecast slowly publishes information that the market had already shared publicly.
