How to Trade the Predictions Analysis

Prediction markets let you bet on the outcome of real-world events — elections, economic data, policy decisions. Convex analyses active Polymarket contracts to find ones where the crowd price doesn't match the AI's model probability. That mispricing is your opportunity.

How prediction markets work

A prediction market contract trades between $0 and $1. The current price IS the market's implied probability. A contract trading at $0.65 means the market thinks there's a 65% chance the event happens. If the event happens, you get $1. If it doesn't, you get $0.

YES direction (LONG) — You think the event is more likely to happen than the market believes. You buy the contract at, say, $0.55 and hope to sell at $1 (or higher before resolution).

NO direction (SHORT)— You think the event is less likely than the market believes. You effectively buy the "no" side at $0.45 (1 - $0.55) and profit if the event doesn't happen.

Prediction markets are conceptually similar to options — they have a fixed expiration (resolution date), a binary payoff, and a price that reflects implied probability. If you understand options, you already understand the basics.

How the AI finds edge

The system compares its model probability to the current market price. The difference is the edge — the same concept as finding mispriced assets in financial markets. For example, if the AI estimates a 75% probability but the market is pricing it at 62%, the edge is +13 percentage points on YES.

Only opportunities with an edge of 10% or more are flagged. This high threshold filters out marginal situations where the AI's model uncertainty might eat the edge.

Each opportunity includes a fair price (what the AI thinks the contract should trade at) and a confidence level. Higher confidence means the AI has more conviction in its probability estimate.

The biggest edges often appear on political or economic events where crowd sentiment diverges from data-driven analysis. Markets driven by narratives are more likely to be mispriced than markets driven by quantitative data — similar to how extreme sentiment creates opportunities in equities.

Timeframe and liquidity

Each recommendation includes a target timeframe — how long before the event resolves. This matters because prediction markets are illiquid compared to stock markets. If you need to exit early, you may face wide bid-ask spreads — similar to liquidity concerns in thinly traded financial instruments.

Short timeframes (days) mean faster resolution but less time for the market to re-price to your model. Longer timeframes (weeks/months) give more time for value to be realised but tie up capital.

Position sizing

The system suggests position sizes based on edge size and confidence. Larger edges with higher confidence get bigger allocations. But there's an important wrinkle: prediction markets are binary. The outcome is $1 or $0 — there's no stop-loss, no partial exit at invalidation.

This means your maximum loss is your entire position. Size accordingly. If the system suggests 3% of portfolio, that means you could lose 3% of your portfolio if the bet fails. This is a tail risk — make sure that loss is acceptable.

Diversify across multiple prediction market positions. Any single binary bet can go wrong, but a portfolio of +10% edge bets will likely profit over time. Diversification reduces drawdown risk.

What makes a good prediction bet

Edge of 10%+ (the bigger the better)
Clear reasoning you can evaluate — the AI should explain WHY the market is mispriced
Reasonable timeframe (not years away)
Sufficient liquidity to enter at the current price
Not a highly correlated bet with your other positions (e.g. betting on "no recession" while long equities doubles your exposure)

Key terms you'll see

Edge — The gap between the AI's fair probability and the market price. The core concept — no edge, no trade.
Fair Price — The AI's model probability expressed as a price ($0.00-$1.00). If fair price is $0.75 and market is $0.62, the contract is undervalued.
Tail Risk — The risk of unexpected outcomes. In prediction markets, a "sure thing" at 95% still fails 1 in 20 times. Don't bet your bankroll on any single contract.
Liquidity — How easily you can buy or sell at the quoted price. Low liquidity means you might not be able to exit before resolution.
Risk-On / Risk-Off — The broader market environment. Prediction markets on economic events (rate cuts, recession) are heavily influenced by the same macro forces that drive financial markets.

What NOT to do

Don't concentrate in a single prediction market. Binary bets are all-or-nothing. One large position that resolves against you can wipe out months of gains from other bets.

Don't bet against strong consensus when the consensus has information you don't. If a market is at 90% and the AI says 80%, that 10% edge might not be real — insiders and domain experts may know things the AI's model misses.

Don't hold through new information without reassessing. If a major development changes the probability landscape (e.g. a candidate drops out of a race), the AI's model probability was based on prior conditions. This is similar to holding through a black swan — wait for the next analysis update.

Don't treat prediction markets as free money. Even with a genuine 10% edge, you'll lose roughly 40-45% of your bets. The profit comes from the 55-60% that win paying more than the losses cost.

Don't forget about correlation with your other positions. A "YES on rate cut" bet + long gold + long bonds is three bets on the same thing. Check cross-asset correlation before stacking.

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