7 Myths About Prediction Markets That Need Debunking
In Brief
Prediction markets, often misunderstood as gambling or niche tools, are increasingly recognized in 2025 as transparent, regulated, and accurate mechanisms for forecasting across finance, governance, and policy.
Prediction markets are often misunderstood—even derided—by critics who cling to outdated assumptions.
In 2025, as these markets gain traction in crypto, finance, and public policy, many myths persist.
Below are just seven myths about prediction markets that deserve a second look.
“Prediction markets are just gambling”
This is perhaps the most common of all the myths about prediction markets. Yes, prediction markets allow betting on outcomes, but the intent, incentives, and regulatory framing often set them apart.
Platforms like Kalshi in the U.S. operate under federal regulation (via the CFTC) and offer event contracts as financial instruments, not pure games of chance.
Statistically, prediction markets have outperformed public polls in multiple U.S. elections. Harry Crane’s studies comparing PredictIt and FiveThirtyEight found that markets were consistently closer to final outcomes. Crane emphasized that polls capture “sentiment,” while markets seek “accuracy and truth.”
So, the myth fails because these markets are about more than entertainment—they’re tools for forecasting risk, hedging uncertainty, and revealing aggregated belief backed by real stakes.
“They’re easy to manipulate”
Detractors warn that low-liquidity, insider trades, or coordinated manipulation can distort market prices. While no system is perfect, many platforms build in countermeasures: liquidity incentives, market-maker subsidies, stake weighting, and in regulated cases, oversight.
For example, only some U.S. states permit contracts on sports events. Kalshi has faced legal pushback in states like Massachusetts and New Jersey, but in several courts the platform has defended contracts under federal oversight. These legal rulings suggest confidence that the design and regulations can mitigate manipulation.
Expert observers argue that true manipulation is most likely only when markets are thin and localized. When liquidity, transparency, and regulatory compliance increase, these risks decline significantly.
“Only political or sports fans use them — not useful for finance or governance”
One of the most common myths about prediction markets is that it doesn’t move beyond entertainment. Real-world governance, corporate planning, DAO decisions, and risk management are increasingly woven into prediction markets’ use cases.
Kalshi offers markets on macroeconomic and regulatory outcomes, not just elections or sports. Crypto platforms like Polymarket also host varied event types, spanning finance, regulation, and global affairs.
These allow institutions and decentralized communities to gauge probability of regulatory changes or governance votes.
Researchers affirm that markets can produce forecasts that are “actionable for institutions,” especially when they want to anticipate taxes, regulation, or government policy changes. The data shows that such markets often respond faster and with more nuance than traditional expert-derived forecasts.
“They produce black-box signals — not explainable”
It’s often believed that because prediction markets aggregate so many inputs, they become opaque. On the contrary, many markets are built on transparent mechanisms: every trade is public, price movements are traceable, and rules of settlement are known in advance.
Polymarket, for instance, settles most markets based on verifiable data sources, with dispute resolution mechanisms clearly documented. Kalshi, being regulated, has to comply with rules about disclosures and contract laws.
Harry Crane noted that prediction markets show the “weight of belief” in public form, because stakeholders must risk real funds. That visibility creates accountability.
While markets don’t always reveal why someone made a trade, the collective outcome is “visible in trade volume, prices, and shifts,” helping users to interpret signal strength.
“Prediction markets are illegal or will always be banned”
Regulation matters, yes—but legality is far from uniform, and many prediction markets already operate under clear regulatory structures. For example, Kalshi has managed to get rulings in favor of its sports and election-event contracts in some U.S. states, and courts have blocked certain state-imposed attempts to treat such contracts as illegal gambling in other jurisdictions.
The CEO of Kalshi has also argued that its contracts for sporting events should not be classified as gambling because the business does not gain from its players’ losses, which makes a notable distinction under U.S. law.
So, legality is not a uniform prohibition—it depends on jurisdiction, contract design, and regulatory status. With the right licensing, compliance, and clarity, prediction markets can and do operate lawfully.
“Markets are worthless when liquidity is low”
Low liquidity does make markets less reliable—but again, that doesn’t mean they “fail” universally. Many platforms use tools to mitigate the problem: liquidity providers, incentive programs, AMMs (automated market makers), and cross-platform aggregation.
Evidence suggests that markets with higher liquidity and fewer fee restrictions tend to be more accurate. Crane observed that Polymarket’s smaller fee structure and higher liquidity placed it ahead of PredictIt in forecasting efficiency in recent election cycles.
Even when liquidity is low, markets can still provide directional or trend signals rather than precise probability values. For many users (protocols, funds), that is enough to move decisions.
“Prediction markets always out-perform experts and models”
One of the more dangerous myths about prediction markets. Yes, markets are powerful, but not infallible. They often outperform polls or simple models in long-horizon political or regulatory forecasting, but in near-term or highly complex technical domains, expert models can excel.
A study comparing PredictIt markets and published models for U.S. battleground states in the 2020 election showed that markets performed better several months out, but as the election drew nearer, model-based polling and statistical models sometimes became more accurate. The hybrid average often beats each component alone.
Thus, a more nuanced view: prediction markets are best used in combination with models and expert insight. They shine for long-horizon, macro, regulatory, or policy questions, while expert models might help with technical forecasting where data and domain knowledge dominate.
The Reality of Prediction Markets
Many myths about prediction markets are just not true: they’re not purely gambling, not inherently manipulable, not confined to politics or sports, nor eternally illegal.
When well-crafted and properly regulated, they exhibit higher accuracy, transparency, probabilistic signals, and real-time adaptability—all of which traditional forecast methods find hard to match.
As regulation, design, and adoption improve, prediction markets can go from being little curiosities in 2025 to being the key forecasting infrastructure, shaping policies and governance and finance, and much more.
Disclaimer
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
