Next Prime Minister of Sweden

According to the latest prediction market data for the query “Next Prime Minister of Sweden”, traders have formed a strong consensus.

Currently, Magdalena Andersson is dominating the market with an overwhelming 73.5% chance of winning. Ulf Kristersson follows in second place at 24%, while Jimmie Åkesson sits in third with 2.1%. The betting volume for this specific market has already reached $2.1M, 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

• Magdalena Andersson (73.5%): Currently commanding the highest probability, Magdalena Andersson is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 74¢, signaling a high degree of market conviction. This contract alone has generated $93.4K in volume.

🥈 Tier 2: The Primary Challengers

• Ulf Kristersson (24%): Positioned as the most viable alternative, Ulf Kristersson maintains a 24% chance of resolving true. Its “Buy Yes” shares currently trade at 24¢.
• Jimmie Åkesson (2.1%): Sitting in third place with a 2.1% probability, the market shows measured skepticism toward Jimmie Åkesson, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~0.5%)

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 Ebba Busch (0.4%), Anna-Karin Hatt (0.3%), and Nooshi Dadgostar (0.2%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like Simona Mohamsson are still attracting notable interest.

Parliamentary elections are scheduled to be held in Sweden on September 13, 2026.

This market will resolve to the next individual who is officially appointed and assumes office as Prime Minister of Sweden following the next parliamentary election.

To count for resolution, the individual must officially assume office. Any interim or caretaker Prime Minister will not count toward the resolution of this market.

If no such Prime Minister assumes office by June 30, 2027, 11:59 PM ET, this market will resolve to “Other”.

The primary resolution source for this market will be official information from the Government of Sweden; 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 Magdalena Andersson currently trades at 73.5%, but our AI places its Fair Value at just 50%. This creates a large negative EV Gap of -23.5%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies Simona Mohamsson as the premium value opportunity on the board. While the market only assigns it a 0.2% trading probability, our AI’s Fair Value assessment sits at 19.5% — yielding an impressive +19.3% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include Elisabeth Thand Ringqvist (EV Gap: +15.8%) and Ebba Busch (EV Gap: +8.8%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.