Forecast markets are not "truth machines"; a detailed explanation of seven major structural inefficiencies

Author: Pi Squared

Translation: Felix, PANews

Summary: The absence of “dumb money,” persistent arbitrage, rampant bots, feedback loops, fake news, insider trading, and low liquidity in niche markets.

Prediction markets are increasingly reshaping how the public thinks about the future. From predicting election outcomes, inflation rates, to product launches and major sporting events, they offer a simple yet powerful concept: invest funds based on beliefs, allowing the market to reveal what is most likely to happen.

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

Therefore, prediction markets are no longer just a curiosity for a few. Decision-makers, researchers, traders, and various institutions are increasingly leveraging them to better forecast outcomes in uncertain environments. With the rise of Web3, many such markets have migrated onto blockchains, utilizing smart contracts for open participation, transparent settlement, and automatic payouts.

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

Most discussions focus on obvious challenges such as regulation, lack of liquidity, or complex user interfaces. These issues are real but do not tell the whole story. Even if prediction markets appear active, liquid, and well-designed, they can still produce problems like price distortions, biased outcomes, and misleading signals.

This article will go beyond surface-level limitations to explore deeper, more covert inefficiencies in how prediction markets operate. Many of these hidden constraints—many structural rather than behavioral—quietly restrict accuracy, scalability, and trustworthiness. Understanding these issues is crucial not only for effectively utilizing prediction markets but also for building the next generation of predictive systems.

How Prediction Markets Actually Work

At their core, prediction markets are markets where people trade the outcomes of future events. Participants buy and sell 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 gross over $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 ranges from 0 to 1 dollar, and market prices are typically interpreted as the probability of the event.

For example, if a prediction contract for “Yes” on an election outcome trades at $0.7, the market effectively indicates a 70% chance of that outcome. As new information—public opinion 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 just expressing opinions; they are risking their own money. Correct predictions can yield financial rewards, while incorrect ones incur costs. This mechanism encourages people 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 results and policy issues. Kalshi, regulated by the US Commodity Futures Trading Commission, offers markets on economic indicators, geopolitical events, and real-world outcomes like interest rate changes or inflation levels. In the Web3 ecosystem, decentralized platforms like Polymarket and Augur run prediction markets on blockchain, using smart contracts to manage trades and automatically settle payouts once outcomes are determined.

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

Why Prediction Markets Are Effective (When They Are)

The popularity of prediction markets is no coincidence. Under the right conditions, they can be highly effective forecasting tools, sometimes even outperforming polls, surveys, and expert panels. Here are some key reasons:

Information Aggregation: No single participant has access to complete global information. Some traders may hold local insights, others focus on niche data sources, and some interpret public information differently. Prediction markets allow all this dispersed information to be aggregated into a single signal via prices. The market does not decide whose opinion matters most but measures the confidence and capital behind various viewpoints.

Incentive Mechanisms: Unlike polls where wrong answers carry no penalty, prediction markets require traders to risk their own funds. This “stake” mechanism suppresses casual guessing and rewards those who act based on more accurate information. Over time, participants with poor predictions lose capital and influence, while those with better forecasts gain.

Adaptability: Prices are not static predictions but are continuously updated as new information arrives. Breaking news, data releases, or credible rumors 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 incentives, adaptability, and information aggregation has yielded remarkable results. Political prediction markets often match or even outperform traditional polls, sometimes with greater accuracy. In finance and economics, market-based forecasts are frequently used as leading indicators because they reflect 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 market structures are sound, 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; for example, PredictIt and Kalshi are subject to strict jurisdictional rules that limit who can trade and how much they can invest. Liquidity tends to concentrate on high-profile events, leaving niche markets hollow and volatile.

In terms of usability, especially on Web3 platforms like Polymarket and Augur, registration processes can be cumbersome, trading fees high, and dispute resolution mechanisms underdeveloped. These issues are widely recognized and discussed in academic and industry circles.

Yet, focusing solely on these surface limitations misses a more critical point. Even in markets with high liquidity, legal compliance, and active trading, prediction markets can still produce price distortions, probability misrepresentations, and biased outcomes.

These problems are not always caused by low participation or poor incentives but stem from deeper, structural inefficiencies in how prediction markets process information, execute trades, and generate results. These hidden inefficiencies ultimately limit the reliability and scalability of prediction markets as forecasting tools. Some of the most significant 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. It’s like a poker table where everyone is a professional—no one wants to play.

Without enough retail traders adding volume, liquidity is insufficient to attract professional traders capable of pushing prices toward accurate estimates. This creates a chicken-and-egg problem, resulting in small market sizes and low efficiency.

2. Persistent Mispricing and Arbitrage Opportunities

When the combined “Yes” and “No” shares in a binary market deviate from a total of $1, there are riskless arbitrage opportunities. Since 2024, simple arbitrage strategies on Polymarket alone have generated over $39.5 million in profits.

These opportunities exist because market efficiency is imperfect, and errors in pricing are not immediately corrected. While this may seem like clever trading, it reveals that prices do not always accurately reflect true probabilities but instead reflect systemic inefficiencies.

3. Bots and Algorithmic Trading

Research indicates prediction markets are exploited and manipulated by automated bots. These systems execute trades faster than human traders, creating unfair competitive environments. Ordinary users often suffer losses due to these complex algorithms, undermining the market’s fairness and accuracy as a forecasting tool.

4. Self-Reinforcing Feedback Loops

Prediction markets can develop a problematic dynamic where odds become self-fulfilling, as traders treat market prices as true probabilities without adequately 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 observe what the market “says” and assume it is correct, creating a cycle that persists even when external evidence suggests otherwise.

5. Fake News and Information Quality Issues

During the 2020 US presidential election, prediction markets exhibited persistent, exploitable price anomalies, with some participants acting on false information, erroneously concluding Donald 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 quickly corrected, especially if many believe the false info.

6. Insider Trading and Asymmetric Information

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

Unlike the SEC’s ban on insider trading, 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 on internal plans can raise fairness issues.

7. Low Liquidity in Niche Markets

Markets with low liquidity are more susceptible to manipulation, and niche markets tend to be the least accurate. When few traders participate, large trades can cause significant price swings, and the lack of participants prevents correction of mispricings. This means prediction markets are mainly effective for popular, high-volume events, limiting their scope.

These covert inefficiencies are often invisible to casual users but can subtly influence outcomes even when markets appear to operate smoothly. For anyone interested in participating in prediction markets or building systems that surpass current limitations, understanding these issues is essential.

Addressing these problems requires rethinking the underlying architecture. Current prediction markets face a bottleneck: whether betting on elections or sports, all trades are queued in a single line. This delay extends arbitrage windows, preventing prices from reflecting the truth in real time.

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

Conclusion

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

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

Bridging this gap requires more than just increased participation or stronger 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.

Related reading: Prediction Markets and the Battle for Truth: When AI Learns to Fake Public Opinion