Bulgaria Presidential Election

According to the latest prediction market data for the query “Bulgaria Presidential Election”, traders have formed a strong consensus.

Currently, Iliana Iotova is dominating the market with an overwhelming 86% chance of winning. Аndrey Gyurov follows in second place at 14.5%, while Kostadin Kostadinov sits in third with 0.1%. The betting volume for this specific market has already reached $1.5M, 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

• Iliana Iotova (86%): Currently commanding the highest probability, Iliana Iotova is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 86¢, signaling a high degree of market conviction. This contract alone has generated $544.6K in volume.

🥈 Tier 2: The Primary Challengers

• Аndrey Gyurov (14.5%): Positioned as the most viable alternative, Аndrey Gyurov maintains a 14.5% chance of resolving true. Its “Buy Yes” shares currently trade at 14¢.
• Kostadin Kostadinov (0.1%): Sitting in third place with a 0.1% probability, the market shows measured skepticism toward Kostadin Kostadinov, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~0%)

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 Nikolai Denkov (0.1%), Blagomir Kotsev (0.1%), and Rosen Zhelyazkov (0.1%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like Natalia Kiselova are still attracting notable interest.

Presidential elections in Bulgaria are expected to be held in the Fall of 2026.

This market will resolve according to the listed candidate who wins the next presidential election in Bulgaria.

This market includes any potential second round. If the result of this election isn't known by December 31, 2027, 11:59 PM ET, the market will resolve to "Other".

This market will resolve based on the result of the election, as indicated by a consensus of credible reporting. If there is ambiguity, this market will resolve based solely on the official results as reported by the Bulgarian government, specifically the Central Election Commission (Centralna izbiratelna komisia, CEC) (www.cik.bg/).

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 Iliana Iotova currently trades at 86%, but our AI places its Fair Value at just 73.7%. This creates a large negative EV Gap of -12.3%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies Rosen Plevneliev as the premium value opportunity on the board. While the market only assigns it a 0.1% trading probability, our AI’s Fair Value assessment sits at 21.6% — yielding an impressive +21.6% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include Atanas Atanasov (EV Gap: +15.8%) and Yanaki Stoilov (EV Gap: +11.6%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.