Xi Jinping out before 2027?
Market 6.8% against model 18.5%. Resolves in 187d 1h, data updated 11d ago.
Decision layer
Actionable research signal
The model disagreement survives the current gates. This is still research context, not financial advice.
Expected value after costs, not raw probability spread.
How much support the model sees across available inputs.
Thin markets can erase apparent edge through spread and slippage.
Resolution ambiguity, timing, and data quality pressure the decision.
usable feature coverage.
Why / why not trade
One decision layer for the market read.
This public box mirrors the internal diagnostic style without exposing execution controls: decision, probability gap, cost-adjusted edge, blocker, and next thing to monitor.
side YES
18.5% model / 6.8% market
fees, spread, slippage, risk
Model edge survives the current public research gates.
Watch whether the market price moves toward or away from the model.
Read this market in three passes
Model 18.5% vs market 6.8%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
Model leans YES
Why this read matters
The model-market gap currently survives the decision gates, but it is still research context and must be judged against the public track record.
Xi Jinping out before 2027?
Volume $10,276,013
Market-implied vs model probability
Factor attribution
The model estimates a 12-point higher probability than the market, primarily driven by historical base rate and rate surprise.
| FACTOR | SIGNAL | WEIGHT | LOG-ODDS ΔLog-odds contribution measures how much each factor shifted the model's probability estimate in log-odds space — the mathematically correct way to stack independent evidence. Formula: Δlog-odds = weight × signal. Positive values push the probability up; negative values push it down. Log-odds are converted back to probability via the logistic function at the end. | DIRECTION | DESCRIPTION |
|---|---|---|---|---|---|
| Historical base rate | 24% | — | −1.140 | Bearish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
| Cross-market divergence | —This factor was not available for this market. No approved cross-venue link exists for this market. | 0.20 | — | — | Whether the same event is priced differently on another venue. A gap may signal an opportunity or a structural difference. |
| 7-day price momentum | —This factor was not available for this market. This factor was not available for this market. | 0.35 | — | — | 7-day drift in the market's own implied probability. Sustained directional moves carry information. |
| BTC/ETH 7-day momentum | —This factor was not available for this market. This factor applies to crypto markets only. | 0.20 | — | — | 7-day Bitcoin or Ethereum return, normalized. Applied to crypto-category markets only. |
| Rate surprise | −0.49 | 0.25 | −0.122 | Bearish | 2y-yield reaction in the 48h after the latest scheduled release — the observable proxy for surprise vs consensus. |
| Yield curve shift | −0.41 | 0.15 | −0.062 | Bearish | 30-day change in the 10y−2y slope. |
| News signal | —This factor was not available for this market. No news signal available for this market in the past 14 days. | 0.25 | — | — | Reliability-weighted direction of relevant news from the past 14 days. Official sources (filings, agency statements) carry more weight than commentary. |
| Crowd forecast | —This factor was not available for this market. Insufficient forecasters to compute crowd signal. Requires at least 5 calibration-weighted estimates. | 0.20 | — | — | Calibration-weighted average of user probability estimates. Only applied when 5 or more weighted forecasters have submitted estimates. |
| Model probability | 21.0% | Prior: 24% · Market: 6.7% | |||
| Confidence (λ)Confidence λ (lambda) controls how much weight to give the model vs. the market. Formula: p_final = λ·p_model + (1−λ)·p_market. λ is derived from data quality, factor agreement, and liquidity. When inputs are weak, the model shrinks toward the market — not toward 50%. | 0.83 | Final: 18.5% = λ·model + (1−λ)·market | |||
Comparable eventsseeded prior 24% - 0 matches (min 8 for historical)
| Event | Outcome | Relevance |
|---|---|---|
| Recession-within-a-year markets since 2008 | Persistently overpriced vs realized frequency | Macro doom trades carry a structural premium. |
Scenario treeEngine template
Node probabilities are conditional on the parent; hover for cumulative path probability. Leaf EV is per $1 YES contract at the current price, before fees (fee-adjusted EVs in the table on the left).
| Path | Path prob. | YES pays | EV (YES, after costs) |
|---|---|---|---|
| Threshold hit in first half of window | 8.3% | $1 | +90.5c |
| Threshold hit in second half | 10.2% | $1 | +90.5c |
| Never reaches threshold in window | 81.5% | $0 | -9.6c |
Root-implied probability 18.5% reconciles with the model's 18.5% (±1pt invariant).
Why this mattersTemplate (no LLM key)
A 11.9% probability gap at a 6.7% price translates to 9.1% expected value per dollar of payout exposure after costs on the YES side. EV — not the raw probability gap — is the comparable number: the same gap is worth very different amounts at 50¢ and at 92¢.
What could make this wrongTemplate (no LLM key)
The model's edge depends on its inputs being right. Concretely: the base rate of 24.2% may not apply if this event differs structurally from its reference class; the macro.rate_surprise factor could be noise rather than information at this horizon; and with confidence at 0.83, the model itself concedes meaningful estimation error. Resolution risk remains: the contract pays on the precise criteria — "This market will resolve to "Yes" if China's General Secretary of the Communist Party, Xi Jinping, is removed from power for any length of t…" — not on the thesis.
- - Risk score 16/100 — composite of liquidity, volatility, time-to-resolution, data quality and category risk.
- - Factor agreement 0.95: factors broadly agree, but shared blind spots are possible.
- - Data quality 0.64 (simulated input in MVP).
- - Simulated model values — this brief demonstrates structure, not live research.
Description
This market will resolve to "Yes" if China's General Secretary of the Communist Party, Xi Jinping, is removed from power for any length of time between July 3, 2025, and December 31, 2026, 11:59 PM ET. Otherwise, this market will resolve to "No". CCP General Secretary Xi Jinping will be considered removed from power if he announces his resignation from his role as General Secretary, or is otherwise dismissed, detained, disqualified, or otherwise loses his position or is prevented from fulfilling his duties as General Secretary within this market's timeframe. The primary resolution source for this market will be a consensus of credible reporting.
Resolution criteria (verbatim, with analyzer flags)
analyzed by heuristicThis market will resolve to "Yes" if China's General Secretary of the Communist Party, Xi Jinping, is removed from power for any length of time between July 3, 2025, and December 31, 2026, 11:59 PM ET. Otherwise, this market will resolve to "No". CCP General Secretary Xi Jinping will be considered removed from power if he announces his resignation from his role as General Secretary, or is otherwise dismissed, detained, disqualified, or otherwise loses his position or is prevented from fulfilling his duties as General Secretary within this market's timeframe. The primary resolution source for this market will be a consensus of credible reporting.
Resolves Thu, 31 Dec 2026 00:00:00 GMT. The contract pays on these exact criteria, not on the thesis.
Suggested paper position
Paper position only. No real-money execution
Live open-market tracking
Since the first stored model read on 2026-06-15, the market has moved from 6.7% to 6.8%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Data quality64/100 - usable
Missing: Cross-market divergence, 7-day price momentum, News signal, Crowd forecast
When features are unavailable, the model increases uncertainty and weights the final estimate closer to the market price. Lower data quality does not mean the market is wrong. It means the model is being appropriately humble.
Risk factor breakdownsim
| Inverse liquidity | 12 | |
| Price volatility | 1 | |
| Resolution proximity | 0 | |
| Data quality | 64 | |
| Category base risk | 50 | |
| Resolution ambiguity | 8 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 16/100, higher = riskier.
Related markets
| Market | Mkt | Delta |
|---|---|---|
| Category context | ||
| [DEMO] Crypto regulatory bill passes Senate vote this week? category context: wording overlap | 55.0% | +8pt |
Divergences > 5pt flagged in amber. For cross-venue pricing, see the Scanner.