Paper: arXiv 2609.23969

Authors: Alexander W. Crosier

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a clear and empirically strong analysis of prediction markets versus traditional weather forecasts. While the mathematical complexity is moderate, the rigorous data analysis and novel application of prediction markets to a real-world forecasting challenge are commendable. The findings are well-supported and clearly communicated.

Research Flowchart

  flowchart TD
    A[Research Goal: Do Prediction Markets Beat Weather Forecasts on Tomorrow's High Temperature?] --> B{Methodology: Compare Prediction Market Forecasts vs. Public Weather Forecasts};
    B --> C[Data/Inputs: 5 Years of Kalshi Market Data (7 US Cities), Leading American & European Weather Forecasts];
    C --> D{Computational Process: Extract Hourly Market-Implied Forecasts, Calculate Root-Mean-Square Error (RMSE)};
    D --> E[Key Finding 1: Market Beats NBM in 6/7 Cities by ~10% RMSE at 1st Hour, Maintains Lead];
    E --> F[Key Finding 2: NBM Moves 4x Further Toward Market Between Postings; Market Doesn't React to NBM Updates];
    F --> G[Key Outcome: Prediction Markets Share Information About Temperature Publicly Before Public Forecasting Systems];