Forecast markets have emerged as a unique force during the crypto market downturn, becoming one of the few “bullish” tracks in 2025. This article delves into eleven smart money arbitrage strategies, revealing the opportunities and risks behind this mathematical war of cognition monetization.
(Background summary: Why are prediction markets not gambling? Clarifying the data value of on-chain events and policy development suggestions)
(Additional context: Why is the prediction market still in the exploration stage? Uncovering the five major challenges of Prediction Market)

Table of Contents

• Truth in Data: The Night Before the Explosion of Prediction Markets
• Deconstructing Smart Money: Analysis of Eleven Major Arbitrage Strategies
• Why Prediction Markets Can Become the “Cure for the Information Age”

As the narrative benefits of the crypto market gradually fade, capital is searching for the next certain exit. Recently, prediction markets have risen rapidly, not only because they demonstrate independent trends amid turbulent markets but also due to a series of high-return “smart money” strategies behind them, making them one of the most promising tracks for explosive growth in 2026.

However, for most onlookers, prediction markets still appear as a black box wrapped in blockchain exterior. Although built on smart contracts, oracles, and stablecoins, their core mechanisms differ greatly from traditional “altcoin trading” logic. Here, we look beyond candlestick charts, focusing on probabilities; beyond storytelling, on facts.

For newcomers, questions abound: How does this market operate efficiently? What is the fundamental difference from traditional crypto gameplay? What unknown arbitrage models does the legendary “smart money” hold? And, does this seemingly feverish market truly have the capacity to carry trillions of dollars?

With these questions in mind, PANews conducted a panoramic investigation into the current prediction markets. We will peel back the “gambling” facade, explore the underlying mechanisms and on-chain data, deconstruct this mathematical war of cognition monetization, and reveal overlooked risks and opportunities.

Truth in Data: The Night Before the Explosion of Prediction Markets

From actual development, prediction markets are indeed one of the few “bullish” tracks in 2025 (similar to stablecoins). Amid the recent crypto market slump, leading prediction markets like Polymarket and Kalshi continue to grow rapidly.

This trend is evident in trading volume. In September this year, Polymarket’s average daily trading volume remained between 20-30 million USD, with Kalshi similar. After the entire crypto market started declining in mid-October, the daily trading volumes of these two giants surged significantly. On October 11, Polymarket reached a daily volume of 94 million USD, and Kalshi exceeded 200 million USD. The increase was roughly 3 to 7 times, and they remain high and soaring to this day.

However, in terms of scale, prediction markets are still in an early stage. The total trading volume of Polymarket and Kalshi combined is about 38.5 billion USD. This total is still less than Binance’s daily trading volume, and an average daily volume of 200 million USD ranks only around 50th among all exchanges.

Nevertheless, with the 2026 FIFA World Cup approaching, the market generally expects the prediction market’s scale to further expand. Citizens Financial Group predicts that by 2030, the overall size of prediction markets could reach trillions of dollars. The Eilers & Krejcik (E&K) report forecasts that by the end of this decade (around 2030), annual trading volume could reach 1 trillion USD. Based on this scale, there is still decades of growth potential, and several institutional reports mention that the 2026 World Cup will serve as both a catalyst and a stress test for this market’s growth.

Deconstructing Smart Money: Analysis of Eleven Major Arbitrage Strategies

Against this backdrop, the greatest recent attraction of prediction markets remains the timeless “wealth stories.” After seeing these stories, many people’s first instinct is to copy or follow. However, exploring the core principles, implementation conditions, and underlying risks of these strategies may be a more reliable approach. PANews has summarized ten popular prediction market strategies currently discussed in the market.

1. Pure Mathematical Arbitrage

Logic: Exploit the mathematical imbalance where Yes + No < 1. For example, if the probability of “YES” for an event on Polymarket is 55%, and the probability of “NO” on Kalshi is 40%, the total probability sums to 95%. Placing bets on YES and NO on both sides respectively, with a total cost of 0.95, guarantees a guaranteed payout of 1 regardless of the outcome, creating a 5% arbitrage opportunity.

Conditions: Requires participants to have strong technical skills to quickly identify such arbitrage opportunities, as only a few can catch leaks.

Risks: Different platforms may have varying criteria for event resolution. Ignoring these can lead to double losses. For example, as pointed out by @linwanwan823, during the US government shutdown in 2024, arbitrageurs found that Polymarket settled on “Shutdown occurred” (YES), while Kalshi settled on “Shutdown did not occur” (NO). The reason was Polymarket’s settlement standard was “OPM issued shutdown notice,” whereas Kalshi required “actual shutdown exceeding 24 hours.”

2. Cross-Platform / Cross-Chain Hedging Arbitrage

Logic: Exploit pricing discrepancies for the same event across different platforms (information islands). For example, Polymarket and Kalshi may have different odds for “Trump winning.” If one side is 40% and the other 55%, buy different positions on both sides to hedge.

Conditions: Similar to the first, requiring strong technical skills to scan and discover.

Risks: Also need to be cautious of different platforms’ criteria for event resolution.

3. High-Probability “Bond” Strategy

Logic: Treat high-certainty events as “short-term bonds.” When an event’s outcome is already clear (e.g., before the Fed rate decision, market consensus is 99%), but the prediction market price remains at 0.95 or 0.96 due to capital costs, this is akin to “collecting time value.”

Conditions: Large capital volume is necessary, as low yields per trade require bigger funds for meaningful profits.

Risks: Black swan events. If a low-probability reversal occurs, losses can be substantial.

4. Initial Liquidity Sniping

Logic: Exploit the “vacuum period” when a new market is created and no sell orders exist. The first person to place an order has absolute pricing power. Use scripts to monitor on-chain events. At market open, place大量 low-price buy orders (e.g., 0.01-0.05). After liquidity normalizes, sell at higher prices (e.g., 0.5 or more).

Conditions: Due to competition, servers should be hosted close to the node to reduce latency.

Risks: Similar to meme coin sniping, if speed advantage is lacking, you may end up as the bagholder.

5. AI Probability Modeling Trading

Logic: Use AI large models to analyze market depth and derive conclusions different from the market. When arbitrage exists, buy in. For example, AI analysis shows “Real Madrid will win” with a 70% probability, but the market price is only 0.5, so buy.

Conditions: Complex data analysis tools and machine learning models; AI computing costs are high.

Risks: AI prediction errors or unexpected events can lead to principal loss.

6. AI Information Disparity Model

Logic: Leverage “faster machine reading > human reading” time advantage. Obtain information faster than ordinary users and buy before market moves.

Conditions: Expensive information sources, possibly requiring paid institutional APIs and precise AI recognition algorithms.

Risks: Fake news attacks or AI hallucinations.

7. Correlated Market Arbitrage

Logic: Use causal chains between events with lag. Price changes in primary events are often instant, but secondary related events respond slowly. For example, “Trump wins the election” and “Republicans win the Senate.”

Conditions: Deep understanding of political/economic logical links, and ability to monitor hundreds of markets’ price correlations.

Risks: Failure of event correlation, e.g., Messi missing a match and the team losing may not be positively correlated.

8. Automated Market Making and Incentives

Logic: Play the “selling shovels” role. Do not bet on directions, only provide liquidity, earning spreads and platform rewards.

Conditions: Professional market-making strategies and substantial capital.

Risks: Trading fees, black swan events.

9. On-Chain Follow Trading and Whale Tracking

Logic: Trust “smart money” with insider info. Monitor high-success addresses; when whales build large positions, bots follow immediately.

Conditions: On-chain analysis tools, data cleaning to exclude test or hedge orders, fast response.

Risks: Whale counter-trades and hedging intentions.

10. Exclusive Research “Information Arbitrage”

Logic: Possess “private information” unknown to the market. For example, during the 2024 US election, French trader Théo used “neighborhood effect” to identify “invisible voters” and took contrarian positions when odds were bearish.

Conditions: Exclusive research plans and higher costs.

Risks: Research errors leading to wrong “insider info” and wrong heavy positions.

11. Manipulating Oracle

Logic: About who is the referee. Due to complex events in prediction markets, simple algorithms cannot decide these. External oracles are needed. Currently, Polymarket uses UMA’s Optimistic Oracle. After each event, a human submits a ruling to UMA. If within 2 hours, voting exceeds 98%, the result is accepted; otherwise, further community research and voting are needed.

However, this mechanism has vulnerabilities and manipulation potential. In July 2025, “Did Ukrainian President Zelensky wear a suit before July?” media reports indicated yes, but in UMA voting, four large holders with over 40% tokens declared “NO,” causing about 2 million USD in losses for the opposing users. Similar manipulation traces appeared in events like “Did Ukraine sign a rare earth mineral agreement with the US” or “Did Trump declassify UFO files in 2025.” Many users believe that relying on a token with a market cap under 100 million USD (UMA) to arbitrate such markets is unreliable.

Conditions: Large UMA holdings or controversial rulings.

Risks: Upgrades to oracles (e.g., MOOV2 in August 2025) aim to patch vulnerabilities, such as whitelist proposals to reduce spam/malicious proposals.

Overall, these strategies can be categorized into technical players, capital players, and professional players. Regardless of type, they leverage exclusive, asymmetric advantages to build profit models. However, such strategies may only be effective during this immature, short-term phase of the market (similar to early crypto arbitrage). As secrets are exposed and the market matures, most arbitrage opportunities will diminish.

Why Prediction Markets Can Become the “Cure for the Information Age”

Behind market growth and institutional optimism, what is the true magic of prediction markets? Mainstream views suggest that prediction markets solve a core pain point: in an era of information explosion and fake news, the cost of truth is rising.

There are three main reasons behind this.

1. “Real money” voting is more reliable than surveys. Traditional market research or expert predictions often lack actual costs, and the right to make predictions is held by a few influential individuals or institutions. This results in many predictions lacking confidence. Prediction markets, on the other hand, aggregate the wisdom of multiple investors through monetary betting, increasing the weight of forecasts. From this perspective, prediction markets address the societal “truth dilemma,” which itself has value.

2. Convert individual expertise or information advantage into money. This is well exemplified by top “smart money” addresses in prediction markets. Although their strategies vary, their success generally hinges on mastering some professional or informational advantage. For example, some may have deep knowledge of a sports event, giving them an edge in predicting multiple factors. Others use technical means to verify event outcomes faster, creating arbitrage space at the final stage. Compared to traditional finance and crypto markets, capital is no longer the biggest advantage; technology and capability are. This attracts many talented individuals to the prediction market, and successful cases garner more attention.

3. The simple binary option logic, with lower entry barriers than trading coins. Essentially, prediction markets are binary options—betting on “YES” or “NO.” The trading threshold is lower, with no need to consider complex price directions, trends, or technical indicators. The underlying assets are usually straightforward and easy to understand. Who will win? Not the technical principles of zero-knowledge proofs. This likely broadens the user base beyond crypto.

Of course, prediction markets also have drawbacks, such as short cycle durations, low liquidity in niche markets, insider and manipulation risks, and regulatory issues. The most important reason is that, at present, prediction markets seem to fill the “narrative vacuum” in the boring crypto space.

The essence of prediction markets is a pricing revolution about “the future.” It pieces together countless individual cognition fragments through monetary game theory, forming the closest approximation to reality.

For outsiders, it is the “truth machine” of the information age. For participants, it is an ongoing, smoke-free mathematical war. As 2026 approaches, this trillion-dollar track is just beginning. Regardless of algorithm evolution or strategy iteration, the simplest truth remains: there is no free lunch here—only the ultimate reward for cognition monetization.