Who will announce Presidential run before 2027?

According to the latest prediction market data for the query “Who will announce Presidential run before 2027?”, traders have formed a strong consensus.

Currently, Pete Buttigieg is dominating the market with an overwhelming 28.2% chance of winning. Rahm Emanuel follows in second place at 19%, while Phil Murphy sits in third with 16.5%. The betting volume for this specific market has already reached $1.2M, reflecting intense industry interest.

To better assess where each potential outcome stands, the market can be segmented into three distinct trading tiers based on implied probability and contract pricing:

🥇 Tier 1: The Dominant Leader

• Pete Buttigieg (28.2%): Currently commanding the highest probability, Pete Buttigieg is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 28¢, signaling a high degree of market conviction. This contract alone has generated $23.0K in volume.

🥈 Tier 2: The Primary Challengers

• Rahm Emanuel (19%): Positioned as the most viable alternative, Rahm Emanuel maintains a 19% chance of resolving true. Its “Buy Yes” shares currently trade at 19¢.
• Phil Murphy (16.5%): Sitting in third place with a 16.5% probability, the market shows measured skepticism toward Phil Murphy, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~36.4%)

Beyond the top three choices, a wide field of macro variables and long-shot outcomes are being tracked. While their individual probabilities hover low, they represent crucial hedges for speculative traders:

• Alternative Options: This includes Ron DeSantis (15.9%), Gavin Newsom (14%), and Alexandria Ocasio-Cortez (12.5%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like Mark Kelly are still attracting notable interest.

This market will resolve to “Yes” if the listed individual officially announces that they are running for U.S. President in the 2028 United States presidential election by December 31, 2026, 11:59 PM ET. Otherwise, this market will resolve to “No”.

An announcement by the listed individual will be sufficient to trigger a "Yes" resolution, regardless of whether they actually filed a nomination to run, or whether they actually file a nomination to run in the future.

The primary resolution source for this market will be official statements by the listed individual (ex: via speech, social media, etc.), however a consensus of credible reporting may also be used.

While human consensus and speculative volume shape the broader prediction market, our quantitative algorithms offer a data-driven counter-perspective. By analyzing fundamental signals, underlying trends and historical distributions, our AI Valuation model calculates an independent “Fair Value” probability for each outcome.

Comparing this Fair Value against the current Trade Value uncovers major disparities — known as the Expected Value (EV) Gap. Contracts with a positive EV Gap represent statistically underpriced outcomes, whereas a negative EV Gap flags a potential market overreaction.

Top AI Alpha & Mispriced Arbitrage Opportunities

Based on the latest data model run, several key contracts stand out with significant deviations:

• The Most Overvalued Outcome Pete Buttigieg currently trades at 28.2%, but our AI places its Fair Value at just 13.3%. This creates a large negative EV Gap of -14.8%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies Marjorie Taylor Greene as the premium value opportunity on the board. While the market only assigns it a 3.6% trading probability, our AI’s Fair Value assessment sits at 24.3% — yielding an impressive +20.7% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include Byron Donalds (EV Gap: +19.8%) and Chelsea Clinton (EV Gap: +19.1%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.