Giovanni Angelini, Luca De Angelis
arXiv 5 Jun 2026 · Econometrics
arXiv:2606.07811 · PDF · DOI · OpenAlex · Extracted main text
How efficiently do markets update beliefs when public information arrives in rapid sequence? We use a real-time prediction market setting that combines binary payoffs, precisely observed public signals, and high-frequency market data, allowing us to compare market price changes with changes in a benchmark probability implied by publicly available information. We first show that prices are informative and become more accurate as resolution approaches. During the event, prices respond rapidly to public signals and move in the expected direction. However, directional responsiveness is not the same as efficient updating. Relative to an out-of-sample benchmark probability model, a one-minute change in the benchmark probability is associated with only about a 0.64-for-one contemporaneous change in market prices. The missing adjustment predicts future price drift over the following several minutes, including drift net of subsequent changes in the benchmark probability. We then study the mechanisms underlying this gradual adjustment. Salient public signals are incorporated relatively quickly in liquid markets, but the same signals generate substantially greater underreaction when liquidity is low. Underreaction gaps associated with salient states also predict stronger subsequent drift. The evidence therefore points to gradual price discovery shaped by the interaction between attention and trading frictions. The results contribute to the literatures on prediction markets, market efficiency, and behavioral finance. More broadly, they show that markets can aggregate public information quickly without necessarily incorporating it fully on impact. Market-implied probabilities are often directionally correct, yet adjustment remains incomplete and predictably depends on liquidity and salience.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Manski, Charles F (2006) Interpreting the Predictions of Prediction Markets | 0.511 | 2 | 1 | 100% |
| 2 | Wolfers, Justin and Zitzewitz, Eric (2006) Interpreting Prediction Market Prices as Probabilities | 0.511 | 2 | 1 | 100% |
| 3 | Amihud, Yakov and Mendelson, Haim (1986) Asset Pricing and the Bid–Ask Spread | 0.405 | 1 | 1 | 100% |
| 4 | Angelini, Giovanni and De Angelis, Luca and Singleton, Carl (2022) Informational Efficiency and Behaviour within In-Play Prediction Markets self | 0.405 | 1 | 1 | 100% |
| 5 | Barberis, Nicholas and Shleifer, Andrei and Vishny, Robert (1998) A Model of Investor Sentiment | 0.405 | 1 | 1 | 100% |
| 6 | Bordalo, Pedro and Gennaioli, Nicola and Shleifer, Andrei (2012) Salience Theory of Choice Under Risk | 0.405 | 1 | 1 | 100% |
| 7 | Bürgi, Constantin and Deng, Wanying and Whelan, Karl (2026) Makers and Takers: The Economics of the Kalshi Prediction Market | 0.405 | 1 | 1 | 100% |
| 8 | Croxson, Karen and Reade, J. James (2014) Information and Efficiency: Goal Arrival in Soccer Betting | 0.405 | 1 | 1 | 100% |
| 9 | Daniel, Kent and Hirshleifer, David and Subrahmanyam, Avanidhar (1998) Investor Psychology and Security Market Under- and Overreactions | 0.405 | 1 | 1 | 100% |
| 10 | DellaVigna, Stefano and Pollet, Joshua M (2009) Investor Inattention and Friday Earnings Announcements | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.