Predictive markets are increasingly reshaping the way the public thinks about the future, with applications ranging from election outcomes to inflation rate forecasts. However, even when markets appear active and well-designed, deep-seated issues such as price distortions and unfair outcomes persist. This article delves into seven hidden structural inefficiencies. It is based on an article by Pi Squared, compiled, translated, and written by PANews.
(Background note: Are prediction markets about “truth,” or just “money laundering” for insider trading?)
(Additional context: The first shot fired in the prediction market ban! A Massachusetts judge in the U.S. has ordered Kalshi to cease participation in sports betting.)

Table of Contents

• How prediction markets actually operate
• Why prediction markets are effective (when they are)
• Limitations of prediction markets
  • 1. The “dumb money” problem
  • 2. Persistent mispricing and arbitrage opportunities
  • 3. Robot-driven and algorithmic trading
  • 4. Self-reinforcing feedback loops
  • 5. False information and information quality issues
  • 6. Insider trading and information asymmetry
  • 7. Low liquidity in niche markets
• Conclusion

Prediction markets are increasingly transforming how the public envisions the future. From forecasting election results, inflation rates, to product launches and major sporting events, they offer a simple yet powerful idea: invest capital based on beliefs, and let the market reveal what is most likely to happen.

This approach has proven surprisingly effective. In many cases, the performance of prediction markets rivals, or even surpasses, traditional polls and expert forecasts. By allowing individuals with different information, motivations, and perspectives to trade on the same question, these markets aggregate dispersed knowledge into a single signal: the price. Typically, a contract trading at $0.7 implies a 70% probability of the event occurring, reflecting the collective judgment of all participants.

Thus, prediction markets are no longer just tools for curiosity among a few. Decision-makers, researchers, traders, and institutions increasingly leverage them to better forecast outcomes in uncertain environments. With the rise of Web3, many such markets have migrated onto blockchains, enabling open participation, transparent settlement, and automated payments via smart contracts.

However, despite their growing popularity and theoretical appeal, prediction markets are far from perfect.

Most discussions focus on obvious challenges such as regulation, low liquidity, or user complexity. While these issues are real, they do not tell the whole story. Even when markets seem active, liquid, and well-designed, they can still produce price distortions, unfair outcomes, and misleading signals.

This article goes beyond surface-level limitations to explore deeper, more covert inefficiencies in how prediction markets operate. Many of these hidden constraints—most of which are structural rather than behavioral—quietly restrict accuracy, scalability, and trustworthiness. Understanding these issues is crucial not only for effective use of prediction markets but also for building next-generation forecasting systems.

How Prediction Markets Actually Operate

At their core, prediction markets are platforms where people trade on the outcomes of future events. Participants are not buying shares in a company but contracts linked to specific questions, such as:

• Will Candidate X win the next election?
• Will the inflation rate exceed 5% this year?
• Will Company Z release a new product before June?
• Will a movie’s opening weekend box office surpass $5 million?

Each possible outcome is represented by a contract. In the simplest case, if the event occurs, the contract pays $1; if not, it pays $0. The trading price of these contracts, ranging from 0 to 1, is generally interpreted as the probability of the event happening.

For example, if a contract predicting a “Yes” outcome for an election trades at $0.7, the market effectively assigns a 70% chance to that outcome. As new information—polls, news reports, economic data, or rumors—emerges, traders update their positions, causing prices to fluctuate.

The appeal of prediction markets lies not only in their mechanism but also in their incentive structure. Participants are not merely expressing opinions; they are risking capital. Correct predictions yield financial rewards, while incorrect ones incur costs. This “stake-based” system encourages participants to seek more accurate information, challenge mainstream views, and act swiftly when new evidence appears.

Over time, prices evolve into continuously updated, crowdsourced forecasts.

In practice, prediction markets take various forms. Platforms like PredictIt focus on political predictions, allowing users to trade on election outcomes and policy issues. Kalshi, regulated by the U.S. Commodity Futures Trading Commission, offers markets on economic indicators, geopolitical events, interest rate changes, or inflation levels. In the Web3 ecosystem, decentralized platforms such as Polymarket and Augur run prediction markets on blockchain, utilizing smart contracts for trade management and automatic settlement once outcomes are determined.

Despite differences in regulation, architecture, and user experience, all these platforms share the same premise: market prices serve as powerful signals of collective belief about the future.

Why Prediction Markets Are Effective (When They Are)

The popularity of prediction markets is no accident. Under suitable conditions, they can be highly accurate forecasting tools, sometimes even outperforming polls, surveys, and expert panels. Key reasons include:

Information Aggregation: No single participant has access to complete global information. Some traders may have local insights, others focus on niche data sources, and some interpret public information differently. Prediction markets allow all this dispersed knowledge to be aggregated into a single signal via prices. The market does not decide whose opinion matters most; it weights various views based on beliefs and capital.

Incentive Mechanisms: Unlike polls where wrong answers carry no penalty, prediction markets require traders to risk their money. This “stake-based” system discourages random guessing and rewards those who act based on more accurate information. Over time, participants with poor predictions lose capital and influence, while more accurate traders gain.

Adaptability: Prices are not static forecasts but continuously update as new information arrives. A sudden news event, data release, or credible rumor can quickly shift market sentiment. This makes prediction markets especially useful in fast-changing or uncertain environments, where static forecasts quickly become outdated.

Historically, this combination of incentive alignment, adaptability, and information aggregation has yielded remarkable results. Political prediction markets often match or surpass traditional polls in accuracy, sometimes even outperforming them. In finance and economics, market-based forecasts are frequently used as leading indicators, reflecting real-time expectations rather than lagging reports.

In summary, these features explain why prediction markets are increasingly regarded as serious forecasting tools, not just gambling platforms. When participation is broad, information quality is high, and markets are well-structured, prices can provide meaningful estimates of future outcomes.

However, these advantages rely on assumptions that do not always hold in reality. When these assumptions break down, prediction markets can mislead.

Limitations of Prediction Markets

Like any market-based system, prediction markets have well-known limitations. Participation is often constrained by regulation; platforms like PredictIt and Kalshi face strict jurisdictional rules that limit trader identities and capital. Liquidity tends to concentrate on high-profile events, leaving niche markets sparse and volatile.

In terms of usability, especially on Web3 platforms like Polymarket and Augur, registration hurdles, high transaction costs, and imperfect dispute resolution mechanisms remain ongoing challenges. These issues are widely recognized in academic and industry discussions.

Yet, focusing solely on these surface issues overlooks a more fundamental problem. Even in markets with high liquidity, legality, and active trading, prices can still be distorted, probabilities misrepresented, and outcomes unfair.

These problems are not always due to low participation or poor incentives but stem from deeper, structural inefficiencies in how prediction markets process information, execute trades, and generate results. It is these hidden inefficiencies that ultimately limit their reliability and scalability as forecasting tools. Some of the most critical covert inefficiencies include:

1. The “Dumb Money” Problem

Prediction markets require participation from both professional traders and ordinary users, but they struggle to attract enough retail traders to generate sufficient volume. Think of it this way: if everyone at the poker table is a professional, no one wants to play.

Without enough retail participation to boost liquidity, the market cannot attract professional traders capable of moving prices toward true probabilities. This creates a chicken-and-egg problem, resulting in small, inefficient markets.

2. Persistent Mispricing and Arbitrage Opportunities

When the total price of “Yes” and “No” contracts in a binary market deviates from $1, arbitrage opportunities arise. Since 2024, simple arbitrage strategies on Polymarket alone have generated over $39.5 million in profit.

These opportunities exist because market efficiency is insufficient to correct mispricings immediately. While seemingly clever trading, they reveal that prices do not always accurately reflect real probabilities, but rather systemic inefficiencies.

3. Robot-Driven and Algorithmic Trading

Research indicates that prediction markets are exploited by algorithmic traders manipulating market inefficiencies. Automated trading systems execute trades faster than humans, creating unfair competitive advantages. Ordinary users often suffer losses due to these complex algorithms, undermining the fairness and accuracy of the market as a forecasting tool.

4. Self-Reinforcing Feedback Loops

Prediction markets can develop a problematic feedback loop: odds in betting markets become self-fulfilling, as traders treat market prices as true probabilities without sufficiently updating based on external information.

This is especially dangerous because it can cause the market to diverge from reality. Traders do not aggregate new information but merely follow the market consensus, believing it to be correct—creating a cycle that persists even when external evidence suggests otherwise.

5. False Information and Data Quality Issues

During the 2020 US presidential election, prediction markets exhibited persistent anomalies, with some participants acting on false information, incorrectly concluding Trump would win.

In markets with low trading volume, a few participants amplifying false information can significantly distort prices. This exposes a fundamental problem: when misinformation enters the market, it is not always corrected swiftly, especially if many believe the false data.

6. Insider Trading and Information Asymmetry

One of the biggest concerns in prediction markets is the prevalence of information asymmetry—some participants possess private information unavailable to others, gaining unfair advantages.

Unlike the SEC’s ban on insider trading in securities markets, the CFTC’s framework for prediction markets often permits trading based on non-public information. For example, athletes betting on their own injury status, or politicians trading based on confidential plans, pose fairness issues.

7. Low Liquidity in Niche Markets

Markets with low liquidity are more susceptible to manipulation. Niche markets tend to be less accurate because fewer traders mean that large trades can cause significant price swings, and the lack of participants prevents correction of mispricings. This limits prediction markets primarily to popular, high-volume events, restricting their broader applicability.

These inefficiencies are often subtle to casual users but can quietly distort outcomes even when markets seem to operate smoothly. For anyone involved in prediction markets or building systems beyond their current limitations, understanding these issues is vital.

Addressing these problems requires rethinking the underlying architecture. Currently, most prediction markets face a bottleneck: whether betting on elections or sports, all trades are queued in a single order book. This delay prolongs arbitrage opportunities and prevents prices from reflecting the truth in real time.

Innovations like FastSet aim to solve this by enabling parallel settlement. It can process conflicting trades simultaneously, achieving final consistency in under 100 milliseconds. When settlement speeds are fast enough, arbitrage windows close before they can be exploited at scale, allowing prices to more accurately reflect true probabilities. Ordinary traders are also protected from systemic delays. This is not just about performance; it represents a fundamental shift toward fairer, more efficient prediction markets.

Conclusion

Prediction markets convert opinions into prices, beliefs into bets. When functioning well, their ability to forecast the future is remarkable—sometimes even surpassing polls, experts, and analysts.

But their effectiveness is not guaranteed. Beyond well-known challenges like regulation and adoption, deeper inefficiencies quietly distort prices and weaken market signals. Liquidity traps, persistent mispricing, algorithmic dominance, feedback loops, misinformation, and fragile resolution mechanisms all contribute to a gap between actual performance and their promise.

Bridging this gap requires more than increased participation or incentives; it demands a fundamental re-examination of the assumptions and structures shaping today’s prediction markets. Only by addressing these core constraints can prediction markets evolve into truly reliable decision-making tools.