CGV Research | When News Gets Priced and Truth Gets Financialized - How Prediction Markets Could Revolutionize the News Industry

Published by: CGV Research

Authors: Shigeru & Cynic

In the age of information overload, we're flooded daily with countless pieces of "seemingly important" news. But the real question is - which ones are true? Which ones deserve trust?

Journalism once served as the central node of social trust, but it's now mired in the swamp of attention: algorithmic feeds let emotion trump reason, and clicks overshadow truth. Fact-checking sites race to keep up, while AI-generated fake news spreads by the second.

A 2024 MIT study found that false news spreads on social media six times faster than true news. When "truth" can't be verified in time, its value collapses.

Prediction markets - tools once confined to financial speculation - are being redefined. By aggregating collective intelligence through prices, they provide a real-time, dynamic, quantifiable answer to "what is true." In this logic, news is no longer just something to read - it's a signal to be priced.

Collective Intelligence: How Prediction Markets Approach Truth

The idea of collective intelligence stems from classic thought experiments and real-world trials showing that group judgment often surpasses individual insight.

As early as 1906, British statistician Francis Galton conducted a famous experiment at a country fair: nearly 800 people guessed the weight of an ox. Despite large individual errors, the average guess was off by just one pound - demonstrating the potential of crowd wisdom.

Launched in 1988, the Iowa Electronic Markets further validated this: by allowing small-stakes trading on US presidential outcomes, the platform frequently outperformed traditional polls, proving that market mechanisms can effectively aggregate dispersed information.

Collective intelligence works through diversity, independence, and information aggregation: diverse participants bring varied perspectives and avoid single-source bias; independent judgments prevent herding; market price mechanisms (bets and trades) quantify and integrate individual insights into consensus that nears the truth. This extends to today's markets like Polymarket and Augur, creating positive externalities - participants share accurate information for profit, prices become public signals, and overall decision quality improves. In news, this reduces misinformation, builds public trust, and incentivizes more accurate reporting, forming a virtuous cycle where social benefits exceed individual costs.

Case: Prediction Markets in the 2024 US Election

During the 2024 US election, Polymarket listed thousands of related contracts. The probability price for "Trump will win the election?" rose from 0.45 early in the year to 0.62 on the eve of the vote, aggregating the dispersed judgments of millions worldwide. While traditional polls fluctuated due to sampling bias, market prices steadily reflected collective intelligence: diverse traders - analysts, investors, ordinary people - integrated real-time news, social signals, and inside information via independent wagers, ultimately predicting the outcome accurately. This validated crowd wisdom and created positive externalities - mainstream media like the New York Times and CNBC cited these prices as reliable indicators, boosting trust in election coverage and reducing fake news, fostering a more transparent information ecosystem.

Where Information Economics Converge: Why News and Prediction Markets Are Natural Fits

News aims to disseminate facts; prediction markets aim to forecast factual outcomes.

They seem different but mirror each other. News tells us "what may happen"; prediction markets tell us "what people think will happen."

If news is "narrative," prediction markets are "the price of consensus." Information-as-Price is the key logic.

In prediction markets, each event has a probability price: for example, if "The Fed will cut rates in December" trades at 0.63, the market assigns a 63% chance.

Applied to news, this price mechanism forms a feedback loop:

News published → market trading → price feedback → newsroom adjustment → truth approached.

Information becomes dynamic - a truth-evolution process continuously "bet on" by many.

Case: Predictive Signals in the GameStop Frenzy

During the 2021 GameStop surge, media debated whether retail traders could keep pushing the stock up. Manifold Markets listed "Will GameStop break $500 next week?" Prices held around 0.25, signaling skepticism about sustainability. Days later, the price fell, aligning with the market's prior. Prediction markets can not only validate news but also offer forward-looking signals on complex events, complementing traditional coverage.

"Truth as a Tradable Asset": Marketizing Fact-Checking

Imagine CNN breaking: "Bitcoin ETF approval internally confirmed at the SEC." Social media erupts; official confirmation lags.

Traditionally, we'd wait for fact-checkers, reporter follow-ups, or official statements.

In prediction markets, a contract appears within minutes: "Will the SEC approve a Bitcoin ETF within 48 hours?" Thousands trade. The price jumps from 0.3 to 0.8 - collectively signaling "this is likely true."

Indeed, the SEC announced approval the next morning. This was a real Polymarket case in January 2024.

When information asymmetry is extreme, prediction markets can detect "truth signals" before the news cycle does.

Polymarket and Kalshi: Proportion of News Category Prediction Events

Another US politics example: when Biden's health drew scrutiny in 2024, Polymarket listed contracts like "Will Biden resign before 2025?" and "Will Harris run in 2024?" Price swings became a real-time thermometer for the credibility of rumors. Prediction markets are evolving into decentralized fact-checking engines: truth is validated not by a central authority but by dispersed market prices.

More importantly - debunking becomes an economic act. People don't just fact-check out of principle; they do it because "getting the truth right" can make money.

Case: Information Warfare in the Ukraine Conflict

Early in the 2022 Russia-Ukraine war, a report claimed "a Ukrainian city has fallen." Traditional media struggled to verify due to information blackouts, while a Polymarket contract - "Will the city be confirmed captured within 72 hours?" - traded at 0.12, signaling skepticism. Days later, independent reporters confirmed the city hadn't fallen, and the market went to zero. Prediction markets can provide fast, decentralized truth verification in information wars.

From Passive Reading to Active Forecasting: The Birth of Predictive News

Combining news with prediction markets creates a new profession: the predictive journalist - reporting facts and market signals.

For instance, at FT Alphaville, reporters have cited Kalshi price shifts to inform analysis: "Market expectations for a BoE rate cut in November rose from 42% to 59% over the past week, signaling mounting concern over economic slowdown."

Bloomberg Beta has tested similar workflows: AI monitors contract prices and feeds shifts into editorial systems. When a market's probability spikes, editors get an alert: "This might be news."

News and markets become mutual signals: news triggers trades; trades generate news.

This two-way mechanism gives journalism predictive capability - moving beyond post-hoc reporting toward data-driven views of the forming future.

Case: Bloomberg × Kalshi Experiment

In 2023, Bloomberg piloted a "predictive news" system with Kalshi. When the contract "Apple will release a new chip" jumped from 0.4 to 0.9, the system alerted editors. Reporters then investigated supplier order flows and broke the story early. This improved timeliness and let readers sense probabilities through prices - an early model of predictive journalism.

Rewiring Incentives: When Readers Become Participants

Traditional news monetizes attention for ads; prediction-market news monetizes judgment for returns.

Readers become market participants, not passive consumers. They can bet on events, express views, and gain or lose based on outcomes.

This reshapes incentives: clickbait loses appeal because it lacks "verifiable truth"; high-quality reporting gains economic value because it improves forecast accuracy.

Platforms like Manifold Markets and Insight Prediction already blend news and forecasting: users can predict directly under articles; AI scores journalists on accuracy based on market outcomes.

For the first time, "journalist reputation" becomes quantified and visible. News value depends not on clicks but on whether it moves prices.

Case: Manifold Markets' Community Experiment

In 2024, Manifold let users create prediction contracts directly under news posts. After an article on "EU to ban a certain cryptocurrency," users listed "Will the EU ban it by year-end?" Price swings drew more readers and incentivized more accurate follow-ups. Platform data showed high-quality articles attracted 3× the trading volume of low-quality ones - evidence the incentive model works.

Friction with Reality: Regulation, Manipulation, and Liquidity

Of course, behind the ideal, there are still many challenges to overcome.

• Regulatory ambiguity: In the US, the CFTC has long restricted political/news event contracts as "gambling-like." Kalshi's lawsuit with the CFTC has dragged on nearly two years without resolution.

• Manipulation risks: If news can move prices, actors may manufacture fake news to sway markets. "News-as-asset" requires AI verification and reputational penalties.

• Liquidity and scale: Current user bases are limited; prices can reflect "niche consensus." Scaling users and deepening liquidity are key to commercialization. New projects are experimenting - Zeitgeist with reputation-based models; the Foresight Institute with AI "fact arbiters" to deter price manipulation.

How to Resolve: The Damocles' Sword of Decentralized Prediction Market

Decentralized markets like Polymarket typically rely on oracles or community governance to resolve outcomes - for example, UMA's Optimistic Oracle gathers evidence and allows challenges during a dispute window. This aims for trustless truth verification but exposes flaws: subjectivity and controversy can cause delays or unfairness. Ambiguous events may lack sufficient evidence for consensus, delaying settlement for weeks. For edge cases (vague definitions or unforeseen contingencies), platforms fall back on community votes or external arbitration (e.g., DAO governance), introducing herding or minority capture risks.

Truth is essentially "verifiable consensus" - oracles pull from reliable sources (official announcements, on-chain data), but oracles aren't infallible: they can face Sybil attacks or tampered data sources, distorting "truth." To deter manipulation, platforms use economic incentives (staking and slashing for false reports), multi-layer verification (e.g., Chainlink's decentralized oracle networks), and AI anomaly monitoring. Still, none are bulletproof; a successful manipulation threatens the market's core credibility like the Sword of Damocles.

Case: Polymarket's 2024 Election Dispute

A Polymarket market on "Will election fraud claims be substantiated?" became contentious due to subjective evidence: the oracle referenced media reports and court filings, but community division delayed settlement by two weeks, ultimately resolving "not substantiated." This exposed reliability issues for subjective events. Although slashing deterred large-scale tampering, participants questioned the "truth standard," prompting a shift toward more robust hybrid oracle systems.

The Future Fusion of AI, News, and Prediction Markets

AI will amplify prediction markets by:

• Extracting forecastable events from news corpora

• Tracking outcomes in real time and adjusting prices

• Detecting influence operations and fake-news spread

This turns prediction markets from a "financial tool" into a "social cognition system." Prices reflect not only economic signals but also the dynamics of social trust.

Some call this "NewsFi" - news financialization: news becomes a "verifiable information asset." When information is put on-chain, traded, and priced, truth finally gets its own market.

Case: Perplexity Integrated with Prediction Markets

In 2025, Perplexity launched an AI tool that combines prediction market data with news to generate "event credibility scores." For example, on a breaking "Company X is bankrupt" story, the AI analyzes Polymarket prices and sources to output "75% credibility," aiding user judgment and accelerating NewsFi adoption.

Prediction markets push us to reconsider journalism's core question - the "price of truth."

In the future information ecosystem, journalists will, like traders, both produce content and interpret prices; readers will be both audience and participants; platforms will be both publishers and marketplaces. This is not just a technological shift in media - it's a revolution in the trust architecture.

Prediction markets embody a simple, powerful insight: the wisdom of crowds is not only in opinions, but in prices. When price becomes the language of truth, news no longer depends on authority to convince the world.

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About Cryptogram Venture (CGV):

CGV (Cryptogram Venture) is a crypto investment institution headquartered in Tokyo, Japan. Since 2017, its fund and predecessor funds have participated in investments in over 200 projects. Since 2022, CGV has invested in and incubated the licensed Japanese yen stablecoin JPYW, making an early move in the crypto stablecoin sector. Starting in 2024, CGV has expanded into the tokenized equity and RWA markets, participating in private placements of projects such as Nabate (NA) and Victory Securities (8540.HK). Currently, CGV also has branches in locations such as Hong Kong and Silicon Valley.

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