Nobel Peace Prize Winner 2027

According to the latest prediction market data for the query “Nobel Peace Prize Winner 2027”, traders have formed a strong consensus.

Currently, Committee to Protect Journalists is dominating the market with an overwhelming 10% chance of winning. Donald Trump follows in second place at 8.5%, while Volodymyr Zelenskyy sits in third with 6.5%. The betting volume for this specific market has already reached $174.6K, 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

• Committee to Protect Journalists (10%): Currently commanding the highest probability, Committee to Protect Journalists is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 10¢, signaling a high degree of market conviction. This contract alone has generated $9.0K in volume.

🥈 Tier 2: The Primary Challengers

• Donald Trump (8.5%): Positioned as the most viable alternative, Donald Trump maintains a 8.5% chance of resolving true. Its “Buy Yes” shares currently trade at 9¢.
• Volodymyr Zelenskyy (6.5%): Sitting in third place with a 6.5% probability, the market shows measured skepticism toward Volodymyr Zelenskyy, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~75%)

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 Yulia Navalnaya (5.5%), Jimmy Lai (5.5%), and Sviatlana Tsikhanouskaya (5.5%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like Sudan's Emergency Response Rooms are still attracting notable interest.

This market will resolve to "Yes" if the listed individual or organization is awarded the 2027 Nobel Peace Prize, as announced by the Norwegian Nobel Committee. Otherwise, this market will resolve to "No".

If the prize is awarded jointly, each listed individual or organization named among the recipients will resolve to "Yes". This market is not limited to a single winner.

If no official announcement regarding the 2027 Nobel Peace Prize has been made by March 31, 2028, 11:59 PM ET, this market will resolve to "No".

The resolution source for this market will be the first official announcement from the Norwegian Nobel Committee.

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 Jimmy Lai currently trades at 5.5%, but our AI places its Fair Value at just 1%. This creates a large negative EV Gap of -4.5%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies Julian Assange as the premium value opportunity on the board. While the market only assigns it a 2.5% trading probability, our AI’s Fair Value assessment sits at 25.4% — yielding an impressive +22.9% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include Elon Musk (EV Gap: +18.7%) and Pope Leo XIV (EV Gap: +13.6%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.