Gambling or cognitive monetization? Deconstructing the smart money path of prediction markets and eleven major arbitrage strategies

Author: Frank, PANews

As the narrative dividends of the cryptocurrency market gradually fade, funds are searching for the next certain outlet. Recently, prediction markets have emerged, not only because of their independent performance during turbulent times but also due to a series of high-return "smart money" strategies behind them, making them widely regarded as one of the most explosive potential tracks for 2026.

However, for most observers, prediction markets still resemble a black box wrapped in a blockchain exterior. Although they are built on smart contracts, oracles, and stablecoins, their core mechanisms differ significantly from traditional "coin speculation" logic. Here, K-lines are not considered; only probabilities matter; stories are not told; only facts are discussed.

For newcomers, questions arise one after another: How does this market operate efficiently? What are the essential differences between it and traditional cryptocurrency plays? What unknown arbitrage models does the legendary "smart money" possess? And does this seemingly fervent market truly have the capacity to accommodate trillions in funds?

With these questions in mind, PANews conducted a panoramic survey of the current prediction market. We will peel away the "gambling" facade, delve into the underlying mechanisms and on-chain data, deconstruct this mathematical war of cognitive monetization, and restore those risks and opportunities that may have been overlooked.

The Data Truth: The Eve of the Prediction Market's Explosion

From the actual development situation, the prediction market is indeed one of the few "bull market" tracks for 2025 (similar to stablecoins). In the past few months, while the entire cryptocurrency market has been sluggish, prediction markets led by Polymarket and Kalshi have continued to grow rapidly.

This trend is clearly visible in trading volumes. In September of this year, Polymarket's average daily trading volume remained in the range of $20-30 million, with Kalshi being similar. However, as the entire cryptocurrency market began to decline after mid-October, the daily trading volumes of these two leading prediction markets started to surge significantly. On October 11, Polymarket's daily trading volume reached $94 million, while Kalshi exceeded $200 million. The increase was around 3-7 times, and it has remained at a high level and continues to rise.

However, in terms of scale, the prediction market is still at an early level. The cumulative trading volume of Polymarket and Kalshi combined is only about $38.5 billion. This total trading volume is less than the daily trading volume of Binance, and the average daily trading volume of $200 million ranks only around 50th among all exchanges.

However, with the upcoming 2026 FIFA World Cup, the market generally expects the scale of prediction markets to further increase. Citizens Financial Group predicts that by 2030, the overall scale of prediction markets may reach the trillion-dollar level. Eilers & Krejcik (E&K) reports predict that by the end of this decade (around 2030), annual trading volume could reach $1 trillion. With this scale, the market still has dozens of times of growth potential, and several institutional reports have mentioned that the 2026 World Cup will also serve as a catalyst and stress test event for this market's growth.

Deconstructing Smart Money: Analysis of Eleven Arbitrage Strategies

Against this backdrop, the greatest attraction of prediction markets recently has been those timeless "wealth stories." After seeing these wealth stories, many people's first thought is to replicate or follow. However, exploring the core principles and conditions for implementing these strategies, as well as the risks behind them, may be a more reliable choice. PANews has summarized ten popular strategies currently discussed in the prediction market.

1. Pure Mathematical Arbitrage

Logic: Utilize the mathematical imbalance where Yes + No is less than 1. For example, when the YES probability of an event on Polymarket is 55%, and the NO probability on Kalshi is 40%, the total probability is 95%. At this point, placing orders for YES and NO on both sides results in a total cost of 0.95, and regardless of the final outcome, a profit of 5% is generated.

Conditions: This requires participants to have strong technical skills to quickly identify such arbitrage opportunities, as there are not many who can pick up the slack.

Risks: Many platforms have different criteria for determining the same event, and ignoring these criteria may lead to a double loss. As pointed out by @linwanwan823, during the 2024 U.S. government shutdown event, arbitrageurs found that Polymarket determined "shutdown occurs" (YES), while Kalshi determined "shutdown does not occur" (NO). The reason lies in Polymarket's settlement standard being "OPM issues a shutdown announcement," while Kalshi requires "actual shutdown for more than 24 hours."

2. Cross-Platform/Cross-Chain Hedging Arbitrage

Logic: Utilize pricing discrepancies for the same event across different platforms (information silos). For instance, the odds for "Trump winning" on Polymarket and Kalshi may not be synchronized. For example, one side might be 40%, while the other is 55%, allowing for different directional purchases. This ultimately constructs a hedging result.

Conditions: Similar to the first type, requires extremely strong technical conditions to scan and discover.

Risks: Also needs to be cautious of different platforms' criteria for the same event.

3. High Probability "Bond" Strategy

Logic: Treat high-certainty events as "short-term bonds." When the outcome of an event is already clear (e.g., just before a Federal Reserve interest rate decision, market consensus has reached 99%), but the prediction market price remains at 0.95 or 0.96 due to capital occupation costs, this is akin to picking up "interest on time."

Conditions: Large capital volume is required, as the single yield is low and requires more significant funds to achieve meaningful profits.

Risks: Black swan events; if a small probability reversal occurs, it can lead to significant losses.

4. Initial Liquidity Sniping

Logic: Utilize the "central limit order book vacuum period" when a new market is just created. When there are no sell orders in the new market, the first person to place an order has absolute pricing power. Write scripts to monitor on-chain events. At the moment of opening, place a large number of extremely low-priced buy orders (0.01-0.05). Once liquidity normalizes, sell at prices usually around 0.5 or even higher.

Conditions: Due to numerous competitors, servers need to be hosted very close to nodes to reduce latency.

Risks: Similar to MEME's opening sniping; if the speed advantage is not present, it may turn into a losing position.

5. AI Probability Modeling Trading

Logic: Utilize AI large models after in-depth market research to discover conclusions that differ from the market. Then buy when there is arbitrage space. For example, after AI large model analysis, if the real probability of "Real Madrid winning the match" today is 70%, but the market price is only 0.5, then a purchase can be made.

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

Risks: AI prediction errors or unexpected events may lead to the loss of principal.

6. AI Information Gap Model

Logic: Utilize the time difference where "machine reading speed > human reading speed." Obtain information faster than ordinary users and buy in advance before market changes.

Conditions: Expensive information sources may require paid access to institutional-level APIs and precise AI recognition algorithms.

Risks: Fake news attacks or AI hallucinations.

7. Related Market Arbitrage

Logic: Utilize the lag in the causal chain transmission between events. The price change of the main event often occurs instantly, but the reaction of secondary related events may lag behind. For example, "Trump winning the election" and "Republicans winning the Senate."

Conditions: Must deeply understand the deep logical connections between political or economic events while being able to monitor price linkages across hundreds of markets.

Risks: Event association failure, such as the absence of a positive correlation between Messi's absence from a match and the team's loss.

8. Automated Market Making and Market Making Rewards

Logic: Be the one who "sells shovels." Do not bet on direction; just provide liquidity to earn the bid-ask spread and platform rewards.

Conditions: Professional market-making strategies and substantial capital.

Risks: Trading fees and black swan events.

9. On-Chain Copy Trading and Whale Tracking

Logic: Believe that "smart money" possesses insider information. Monitor high-win-rate addresses; once a whale makes a large position, the bot immediately follows.

Conditions: On-chain analysis tools are needed to clean data and exclude whales' "test orders" or "hedge orders." Quick response capability.

Risks: Whales' reverse harvesting and hedging intentions.

10. Exclusive Research-Based "Information Arbitrage"

Logic: Master "private information" unknown to the market. For example, during the 2024 U.S. election, French trader Théo discovered "invisible voter" tendencies through the "neighbor effect" and heavily invested against the odds when they looked unfavorable.

Conditions: Exclusive research plans and relatively high costs.

Risks: Research method errors may lead to obtaining incorrect "insider information," resulting in heavy investments in the wrong direction.

11. Manipulating Oracles

Logic: Regarding who the referee is. Due to the complexity of many events in prediction markets, the determination of these complex events cannot be simply decided by algorithms. Therefore, external oracles need to be introduced. Currently, Polymarket uses UMA's Optimistic Oracle. After each event concludes, a determination result must be submitted manually in the UMA protocol. If the voting rate exceeds 98% within two hours, this result is considered true. Disputed results require further community research and voting to resolve.

However, this mechanism clearly has vulnerabilities and manipulation space. In July 2025, regarding "Did Ukrainian President Zelensky wear a suit before July?" although multiple media reported that Zelensky had worn a suit, in UMA's voting, four large holders used over 40% of the tokens to ultimately determine the result as "NO," causing users who invested against the trend to lose about $2 million. Additionally, events like "Did Ukraine sign a rare earth mineral agreement with the U.S.?" and "Will the Trump administration declassify UFO documents in 2025?" also showed varying degrees of manipulation. Many users believe that relying on a token with a market cap of less than $100 million like UMA to referee a market like Polymarket is not reliable.

Conditions: A large holding of UMA or controversial adjudication conditions.

Risks: After oracle upgrades, similar vulnerabilities will gradually be blocked. In August 2025, MOOV2 (Managed Optimistic Oracle V2) was introduced, limiting proposals to a whitelist to reduce spam/malicious proposals.

Overall, these strategies can be categorized into technical players, capital players, and professional players. Regardless of the type, they all establish profit models through exclusive asymmetric advantages. However, such strategies may only be effective during this market's short-term immature phase (similar to early arbitrage plays in the cryptocurrency market). As secrets are revealed and the market matures, most arbitrage spaces will become increasingly smaller.

Why Prediction Markets Can Become "The Antidote of the Information Age"

Behind the market growth and institutional optimism, what kind of magic does the prediction market possess? The mainstream view in the market believes that prediction markets solve a core pain point: in an era of information explosion and rampant fake news, the cost of truth is becoming increasingly high.

Three main reasons may lie behind this starting point.

1. "Real money" voting is more reliable than surveys. Traditional market research or expert predictions often lack actual costs regarding their accuracy, and the power to make these predictions is held by certain individuals and institutions with authority. This leads to many predictions lacking credibility, while the structure of prediction markets results from multiple investors' monetary games. This not only realizes the collective wisdom formed by multiple information sources but also adds weight to these predictions through monetary voting. From this perspective, prediction markets as a product solve the societal "truth problem," which inherently holds value.

2. The ability to convert individual expertise or information advantages into money. This is well reflected in the top smart money addresses in prediction markets. Although the strategies of these addresses are diverse, analyzing their success often boils down to their mastery of some professional or informational advantage in a particular area. For example, some individuals may have extensive knowledge of a specific sporting event, thus possessing a significant professional advantage in predicting various aspects of that event. Alternatively, certain users may use technical means to verify the outcome of an event faster than others, creating arbitrage space in the final stages of the prediction market. This represents a significant difference compared to traditional finance and the cryptocurrency market, where capital is no longer the greatest advantage (and may even be a disadvantage in prediction markets); technology and capability are. This has attracted many talented individuals to invest their vision into prediction markets, and these benchmark cases have garnered even more attention.

3. The simple logic of binary options has a lower threshold than cryptocurrency speculation. Essentially, the nature of prediction markets is binary options, where people bet on either "YES" or "NO." The trading threshold is lower, requiring less consideration of price direction, trends, technical indicators, and other complex trading systems. Additionally, the trading subjects are usually straightforward and easy to understand. Which of these two teams will win? Rather than what the technical principles of this zero-knowledge proof project are. This also determines that the user base of prediction markets may be much larger than that of the cryptocurrency market.

Of course, prediction markets also have their drawbacks, such as the typically short cycles of individual markets, insufficient liquidity in niche markets, risks of insider trading and manipulation, compliance issues, and so on. The most important reason, however, is that at the current juncture, prediction markets seem to be filling the boring "narrative vacuum" of the cryptocurrency market.

The essence of prediction markets is a pricing revolution about the "future." They piece together the fragmented cognition of countless individuals through monetary games to form the closest puzzle to the truth.

For observers, this is the "truth machine" of the information age. For participants, this is a smoke-free mathematical war. As 2026 approaches, the canvas of this trillion-dollar track has just begun to unfold. But regardless of how algorithms evolve and strategies iterate, the most fundamental truth of prediction markets has never changed: there is no free lunch here, only the ultimate reward for cognitive monetization.