Prediction market platforms allow people to bet on
real-world events like politics, culture, economics, war,
and sports. Their defining feature is the buying and selling
of contracts. They operate on a peer-to-peer model in which
participants buy and sell contracts tied to the outcome of
future events rather than betting against a bookmaker. Most
contracts are binary: an event either happens or it does
not. Because contracts can be traded until they settle,
prices change continuously as new information becomes
available, with the market price reflecting the collective
judgment about the likelihood of an
outcome.
Supporters argue that this process aggregates
publicly available information into a real-time forecast,
allowing markets to reveal probabilities that sometimes
outperform polls or expert opinion. Critics counter that
these same incentives can reward speculation, manipulation,
or insider knowledge, raising questions about whether they
function primarily as forecasting tools, gambling platforms,
or something in between.
A contract’s price
fluctuates with the probability of an event occurring and reflects
that prediction. The price is intended to serve as the
implied probability of the event occurring. Every contract
includes a resolution date and rules that indicate how and
when it will be settled. When the resolution date arrives,
the market closes automatically, and the bettor is informed
about the outcome. One major advantage of prediction markets
is their simplicity. Will a given event occur by a certain
date? It’s either yes or no. On two of the most prominent
markets, Kalshi
and Polymarket,
a user buys one or more contracts that pay out $1 each if
they’re right and nothing if they’re wrong.
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Unlike
Kalshi, Polymarket relies
on UMA (Universal Market Access), a
decentralized oracle protocol, to resolve markets. Most
outcomes are finalized automatically through UMA’s
Optimistic Oracle, while disputed outcomes are referred to
UMA token holders for decentralized arbitration. When a
dispute arises, holders of UMA’s digital tokens debate the
situation in forums on the social media platform Discord before
voting on the outcome. UMA “governs this process to ensure
fairness and transparency,” Polymarket says
on its website. Because governance is decentralized and
voting power depends on token ownership, critics argue that
questions remain about transparency and influence over
disputed outcomes.
Several prediction
markets are relatively obscure. They include Good Judgment Open, a
forecasting services firm; the Iowa
Electronic Markets, a futures market operated for
research and teaching; Manifold,
a social prediction market; and PredictIt,
a prediction market for political and financial events;
among several others.
Prediction Markets Versus
Sports Betting
Prediction markets are similar to, yet
different from, sports
betting. Both are based on the outcomes of future
events. But while sports betting platforms set odds for a
given outcome before a game and adjust them throughout the
game based on real-time data feeds, prediction markets use a
central
limit order book that matches the highest available bid
with the lowest available ask to facilitate peer-to-peer
trading, much like traditional markets do. Unlike
traditional sportsbooks, prediction markets generally allow
participants to trade contracts with one another before an
event is resolved, meaning prices can rise or fall
continuously as new information becomes available.
Supporters argue that this dynamic allows prediction markets
to function not only as wagering platforms but also as
forecasting systems that reflect changing expectations about
future events.
Kalshi’s CEO and founder, Tarek
Mansour, distinguishes
between the “artificial risk” of a bet that requires a
bookie and the “natural risk” of trading on real-world
events. In his view, the bookie creates risk solely through
the odds he sets. The risk in prediction markets follows
from the actual possibilities they describe—the outcome of
an election or the risk of a wildfire—and markets let
users hedge against that natural risk.
The two leading
prediction-market platforms, Kalshi and Polymarket, take
different approaches to the same basic idea: letting people
buy and sell contracts tied to future events. Founded in
2018, Kalshi operates as a federally regulated exchange
under the oversight of the US Commodity Futures Trading
Commission (CFTC). Polymarket, launched in 2020, built its
global platform on blockchain technology and historically
operated outside the US regulatory framework. It has since
established a federally regulated US operation: QCX LLC,
doing business as Polymarket US, now operates as a CFTC-designated
contract market.
The distinction between
Polymarket’s global platform and Polymarket US illustrates
how rapidly the industry is evolving. Prediction markets
increasingly combine elements of traditional financial
exchanges, blockchain-based trading, and event wagering,
complicating efforts to determine which regulatory
frameworks should apply.
Why Prediction
Markets?
Are prediction markets “truth machines,”
as their advocates claim, or essentially casinos, as their
detractors argue? Some also argue that these markets serve
as vehicles for insider trading.
“Prediction markets
are not just forecasting tools; they are decision-support
tools,” says
Richard Warr, a professor of finance at the Poole School of
Management. In his view, they help people incorporate
changing information into decisions about uncertain future
events.
Prediction markets may legally fall outside
the definition of gambling. But they may be no different
from gambling.
After all, platforms like Kalshi and Polymarket resemble
poker, where a fee is collected on each hand even if it has
a strategic component.
Supporters argue that
prediction markets work because they create financial
incentives for participants to reveal what they genuinely
believe rather than what they merely hope or expect. This
idea is often described as the “wisdom of the crowd”:
market prices emerge from the combined judgments of many
participants, each with money at stake. Critics, however,
argue that market accuracy may depend less on the crowd as a
whole than on a relatively small number of well-informed
traders.
Where Prediction Markets Succeed
The
outcomes of real-life events that prediction markets allow
users to trade on range from election predictions to
financial markets to pop culture. They have drawn particular
attention for their election performance. During the 2024 US
presidential election, leading platforms consistently
assigned Donald Trump a higher probability of victory than
many traditional polls suggested. Supporters point to this
outcome as evidence that prediction markets can aggregate
dispersed information faster than conventional polling,
while critics caution that a successful prediction does not
necessarily validate the broader forecasting model. Polls
are scientific samples (with varying accuracy) that offer a
snapshot of voter sentiment at any given time. No one faces
consequences if those sampled—or talking heads—get it
wrong. But for those betting on a particular candidate, the
consequences are real: they either win or lose money.
Financial incentives may encourage participants to focus
less on personal preference and more on the outcome they
believe is most likely.
Prediction markets may also
provide useful signals for investors. Research
has found that they can incorporate new information more
quickly than traditional analyst forecasts. Because
participants have money at stake, they have an incentive to
update their expectations as new evidence emerges, allowing
market prices to adjust rapidly and sometimes respond faster
than conventional financial analysis.
Like stock
markets, prediction markets summarize expectations about
future events. But whereas stock prices reflect expectations
about the future performance of individual companies,
prediction markets estimate the probability that a specific
event will occur. Supporters point to this ability to
continuously aggregate information as one of the strongest
arguments for their value as forecasting
tools.
According to a 2026 Interactive Brokers analysis,
prediction markets may already be outperforming traditional
weather forecasts. By comparing weather forecasts from the
firm’s prediction markets with those of the US National
Weather Service, the analysis concluded that prediction
markets were more accurate because financial incentives
encouraged participants to incorporate new information more
effectively.
Financial incentives, however, can create
unintended consequences. Because prediction markets allow
participants to profit from real-world events, they may also
incentivize manipulation or exploitation of weaknesses in
the data used to settle contracts. Concerns about
weather-related prediction markets have ranged from attempts
to influence official weather measurements to ethical
questions about wagering on natural disasters. These
examples illustrate the broader challenge of designing
markets that reward accurate forecasting without encouraging
harmful behavior.
Where Prediction Markets Break
Down
Insider trading presents one of the greatest
challenges for prediction markets. Because participants may
have confidential government, corporate, or military
information before it becomes public, regulators must
distinguish legitimate expertise from the unlawful use of
material nonpublic information. As prediction markets expand
into politics, finance, and geopolitical events, that
distinction has become increasingly
important.
Supporters of prediction markets argue that
participants with specialized knowledge can improve forecast
accuracy. Critics counter that markets must prevent people
from profiting from material nonpublic information, making
the line between legitimate expertise and unlawful insider
trading one of the industry’s most persistent regulatory
challenges.
That concern is no longer merely
hypothetical. In August 2026, the CFTC
brought an insider-trading enforcement action against a
former White House teleprompter operator who used advance
access to presidential speeches to trade contracts based on
words or phrases the president would mention. According to
the CFTC, the trades generated more than $107,000 in profits
from material nonpublic information.
Contract wording
is essential because ambiguous language can lead to
disputes. Contracts based on whether public officials
mention particular words (so-called “mention markets”)
can attract increased trading before events such as Federal
Reserve press conferences. For example, a contract pays $1
if the Federal Reserve chair says a certain word and nothing
if the chair doesn’t. Karlos Arregi, a linguistics
professor at the University of Chicago, says in
a Bloomberg opinion piece that the rules appear
arbitrary and not based on any particular theory or
philosophy of language. “This looks like the kind of rules
you’d have in a game like Scrabble,” he says. “It’s
obvious to me these rules were not done by a
linguist.”
Rivka Levitan, a professor of computer
science and linguistics at Brooklyn College, CUNY, sees
prediction-market rules as “more legalistic than
linguistic.” A good set of rules needs to prioritize
logical consistency, she adds. That consistency applies not
only to how individual contracts are written but also to how
platforms determine which kinds of events may be traded.
Kalshi’s rules, for example, distinguish between event
types that might look similar at first
glance.
Contract disputes are not merely theoretical.
High-profile disagreements over how prediction-market
contracts should be interpreted show that seemingly small
wording differences can determine the outcome of wagers
involving millions of dollars. As prediction markets expand
into entertainment, sports, politics, and other domains, the
precision and transparency of contract language become
increasingly important to maintaining market
confidence.
Regulatory Challenges
Those
different regulatory models create different legal
challenges. Prediction markets can operate across national
borders and under different financial, gambling, and
technology regimes, meaning that regulation often depends
not only on what is being traded but also on where and how a
platform operates.
Regulation operates at two levels.
Government agencies establish the legal framework under
which prediction markets may operate, while the platforms
themselves are responsible for monitoring trading activity,
verifying users, investigating suspicious transactions, and
cooperating with regulators and law enforcement. Supporters
argue that these internal safeguards help preserve market
integrity. Critics counter that voluntary self-policing
cannot replace clear legal standards or effective
enforcement.
Insider trading remains one of the most
difficult regulatory questions. Existing securities and
commodities laws do not always map neatly onto prediction
markets, making it hard to determine when trading on
nonpublic information becomes unlawful. As former CFTC
enforcement director Aitan Goelman observed,
many of these legal questions remain largely
untested.
Regulatory priorities can also shift as
administrations change, influencing how aggressively
regulators supervise prediction markets and how quickly they
approve new products.
States have also responded
differently. Some have challenged whether prediction-market
platforms operate as lawful financial exchanges or
unlicensed gambling businesses. In contrast, federally
regulated exchanges have argued that the Commodity Exchange
Act gives the CFTC authority over their event contracts.
That disagreement has produced conflicting federal court
decisions over whether state gambling laws can apply to
sports-related prediction markets. In September 2026, New
Jersey asked
the US Supreme Court to decide whether federal
commodities law preempts state regulation of certain
prediction-market contracts after federal appeals courts
reached different conclusions. A separate
federal appeals court ruling that month held that
Kalshi’s sports-event contracts offered on tribal lands
were likely subject to federal Indian gaming law. The
disputes underscore how unsettled the legal boundary remains
between federally regulated derivatives and
gambling.
Regulatory debates extend beyond the United
States. Different countries have adopted different
approaches to prediction markets, with some allowing them
under financial-market regulations, others treating them as
gambling, and still others restricting or prohibiting them
altogether. These differing approaches reflect the lack of
an international consensus on how to regulate prediction
markets.
Prediction Markets Enter the
Mainstream
Prediction markets have grown rapidly in
popularity. Combined monthly global trading volume on Kalshi
and Polymarket rose from less than $5 billion in September
2025 to about $24 billion in April 2026, according to a Pew
Research Center analysis. Sports, cryptocurrency, and
politics were among the most popular
categories.
Despite the controversies, Kalshi and
Polymarket have attracted several prestigious affiliations.
News organizations, financial firms, and professional sports
leagues increasingly view them not simply as places to wager
on uncertain events, but as sources of real-time information
about public expectations and emerging
trends.
Supporters argue that prediction markets can
distinguish meaningful signals from short-term noise by
aggregating the judgments of thousands of participants with
financial incentives to be accurate. As a result, some news
organizations, financial institutions, and other
organizations have begun treating prediction markets not
simply as wagering platforms but as another source of
real-time information about uncertain events.
The
growing interest from established institutions reflects a
broader shift in how people perceive prediction markets.
What began as a niche forecasting experiment is increasingly
being treated as part of the modern information ecosystem,
with proponents arguing that market prices can complement
polling, expert analysis, and other traditional forecasting
methods.
The Future of Trading on
Uncertainty
Prediction markets offer both promise and
risk. They can aggregate information, improve forecasting,
and provide useful signals about uncertain events. At the
same time, they raise important questions about regulation,
insider trading, market manipulation, gambling addiction,
and public trust.
Whether prediction markets become a
widely accepted forecasting tool will depend not only on
their predictive accuracy but also on the confidence
participants, regulators, and the public place in the
markets’ integrity. Like stock exchanges and other
financial institutions, they ultimately succeed only if
people trust that the rules are fair, transparent, and
consistently enforced.
By Leslie Alan
Horvitz
Author Bio: Leslie Alan
Horvitz is an author and journalist specializing in science;
he is also a contributor to the Observatory.
His nonfiction books include Eureka:
Scientific Breakthroughs That Changed the World, Understanding
Depression (with Dr. Raymond DePaulo of Johns
Hopkins University), and The
Essential Book of Weather Lore. His articles have
been published in Travel + Leisure, Scholastic, The
Washington Times, and Insight on the News, among
others. Leslie has served on the board of Art Omi and is a member of PEN America. He is based in New
York City. Find him online at lesliehorvitz.com.
This
article was produced for the Observatory
by the Independent Media Institute. It is licensed under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0
International License (CC
BY-NC-SA
4.0).
