Bulgaria Presidential Election

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

Currently, Аndrey Gyurov is dominating the market with an overwhelming 21% chance of winning. Vassil Terziev follows in second place at 10%, while Nikolai Denkov sits in third with 9%. The betting volume for this specific market has already reached $428.6K, 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

• Аndrey Gyurov (21%): Currently commanding the highest probability, Аndrey Gyurov is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 21¢, signaling a high degree of market conviction. This contract alone has generated $32.2K in volume.

🥈 Tier 2: The Primary Challengers

• Vassil Terziev (10%): Positioned as the most viable alternative, Vassil Terziev maintains a 10% chance of resolving true. Its “Buy Yes” shares currently trade at 10¢.
• Nikolai Denkov (9%): Sitting in third place with a 9% probability, the market shows measured skepticism toward Nikolai Denkov, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~60.1%)

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 Boyko Borissov (2.1%), Natalia Kiselova (1.3%), and Yanaki Stoilov (1.2%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like Rosen Zhelyazkov 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 Аndrey Gyurov currently trades at 21%, but our AI places its Fair Value at just 20.3%. This creates a large negative EV Gap of -0.7%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies Atanas Atanasov as the premium value opportunity on the board. While the market only assigns it a 0.3% trading probability, our AI’s Fair Value assessment sits at 22.3% — yielding an impressive +22.1% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include Yanaki Stoilov (EV Gap: +18.5%) and Kostadin Kostadinov (EV Gap: +18.4%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.