Anthropic IPO by __?

According to the latest prediction market data for the query “Anthropic IPO by __?”, traders have formed a strong consensus.

Currently, December 31, 2026 is dominating the market with an overwhelming 70.5% chance of winning. October 31, 2026 follows in second place at 35.5%, while September 30, 2026 sits in third with 24.5%. The betting volume for this specific market has already reached $419.8K, 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

• December 31, 2026 (70.5%): Currently commanding the highest probability, December 31, 2026 is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 71¢, signaling a high degree of market conviction. This contract alone has generated $103.8K in volume.

🥈 Tier 2: The Primary Challengers

• October 31, 2026 (35.5%): Positioned as the most viable alternative, October 31, 2026 maintains a 35.5% chance of resolving true. Its “Buy Yes” shares currently trade at 36¢.
• September 30, 2026 (24.5%): Sitting in third place with a 24.5% probability, the market shows measured skepticism toward September 30, 2026, 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 September 15, 2026 (7.5%), July 31, 2026 (2%), and June 30, 2026 (0.4%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like September 15, 2026 are still attracting notable interest.

This market will resolve to "Yes" if Anthropic shares are listed on a public securities exchange and open for trading by 11:59 PM ET on the listed date. Otherwise, this market will resolve to "No."

If Anthropic is acquired by another company that is already public, this market will immediately resolve to "No."

The primary resolution source for this market is official filings and announcements from Anthropic and the relevant securities exchange on which the shares are listed, including SEC filings (e.g., Form S-1, Form 8-A), exchange listing confirmations, and official press releases from Anthropic; 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 July 31, 2026 currently trades at 2%, but our AI places its Fair Value at just 1%. This creates a large negative EV Gap of -1%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies October 31, 2026 as the premium value opportunity on the board. While the market only assigns it a 35.5% trading probability, our AI’s Fair Value assessment sits at 48.7% — yielding an impressive +13.2% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include September 30, 2026 (EV Gap: +6.7%) and September 15, 2026 (EV Gap: +1%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.