When AI learns to forge public opinion, how will the prediction market respond to the major test of manipulation?

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Author: Andy Hall, Professor at Stanford Graduate School of Business and Hoover Institution

Translation: Felix, PANews (Content has been edited)

Imagine a scenario: it’s October 2028, and Wans and Mark Cuban are neck and neck in the presidential election. Wans’s support rate in the prediction markets suddenly begins to soar. CNN, having partnered with Kalshi, provides round-the-clock, continuous coverage of the prediction market prices.

Meanwhile, no one knows the initial reason for the price surge. Democrats insist the market has been “manipulated.” They point out that a large volume of suspicious trades, without any new polls or other obvious reasons, has driven the market toward supporting Wans.

At the same time, The New York Times reports that traders supported by the Saudi Arabian sovereign wealth fund have placed big bets in the election market to influence CNN’s favorable coverage of Wans. Republicans argue that the prices are reasonable, noting there’s no evidence that the price surge could affect the election outcome, and accuse Democrats of trying to suppress free speech and censor truthful information about the election. The true story remains uncertain.

This article will explain why such scenarios are highly likely to occur in the coming years—despite the rarity of successful prediction market manipulations and the almost complete lack of evidence that they influence voter behavior.

Attempting to manipulate these markets is inevitable, and when manipulation occurs, the political impact could far exceed the direct influence on election results. In an environment where any abnormal phenomenon is easily viewed as a conspiracy, even brief distortions can trigger accusations of foreign interference, corruption, or elite collusion. Panic, accusations, and loss of trust may overshadow the actual effects of the initial actions.

However, abandoning prediction markets would be a mistake. As traditional polls become more fragile in an AI-saturated environment—characterized by extremely low response rates and pollsters struggling to distinguish AI responses from real human respondents—prediction markets offer a useful complementary signal. They aggregate dispersed information and are driven by real financial incentives.

The challenge lies in governance: building a system that preserves the informational value of prediction markets while reducing abuse. This may involve ensuring broadcasters focus on reporting markets that are harder to manipulate and more active, encouraging platforms to monitor for signs of collusion, and shifting how market fluctuations are interpreted—approaching them with humility rather than panic. If achieved, prediction markets can evolve into a more robust and transparent component of the political information ecosystem: helping the public understand elections rather than fueling distrust.

Learning from history: beware of attempts to manipulate markets

“Now everyone is watching the betting markets. Their fluctuations are of intense interest to ordinary voters, who cannot directly gauge public sentiment and can only blindly rely on the opinions of those who bet hundreds of thousands of dollars in every election.” — The Washington Post, November 5, 1905.

In the 1916 presidential election, Charles Evans Hughes led Woodrow Wilson in New York betting markets. Notably, during that era in American politics, media frequently reported on betting markets. These reports cast a long shadow of potential manipulation. In 1916, Democrats, eager not to appear behind, claimed the market was “being manipulated,” and the media covered these claims.

The threat of election manipulation has never disappeared. On the morning of October 23, 2012, during Barack Obama and Mitt Romney’s campaign, a trader placed a large order on InTrade to buy Romney shares, causing his price to surge about 8 points—from just below 41 cents to nearly 49 cents—implying the race was nearly tied if one believed the prices. But the price quickly retreated, and the media paid little attention. The identity of the manipulator was never confirmed.

Sometimes, you even see people openly explaining their logic for trying to manipulate the market. A 2004 study documented a deliberate market manipulation case during the 1999 Berlin state election. The authors cited a real email from local party officials urging members to bet on the prediction market:

“‘Daily Mirror’ (one of Germany’s largest newspapers) publishes a political stock market (PSM) daily, with the current FDP (Free Democratic Party) trading price at 4.23%. You can view the PSM online. Many citizens do not see PSM as a game but as a poll result. Therefore, it’s important that the FDP’s price rises in the final days. Like any exchange, the price depends on demand. Please participate in the PSM and buy FDP contracts. Ultimately, we all believe in our party’s success.”

Such concerns also appeared in 2024. On the eve of the election, The Wall Street Journal published an article questioning whether Trump’s advantage on Polymarket—seemingly far exceeding his polling support—was the result of improper influence: “Large bets on Trump may not be malicious. Some observers think it’s just a big gambler who believes Trump will win and wants to make a profit. Others see these bets as an influence operation aimed at generating social media buzz for the former president.”

The scrutiny in 2024 is especially intriguing because it raises fears of foreign influence. The results showed that a French investor placed bets that pushed up Polymarket prices—though, despite speculation, there’s little reason to believe this was manipulation. In fact, the investor commissioned private polls and seemed focused on making money rather than manipulating the market.

This history reveals two themes. First, cyberattacks are common and likely to occur again in the future. Second, even when attacks are ineffective, some can still incite fear and panic.

How significant are these attacks?

Whether these efforts influence voters depends on two factors: whether manipulation can truly impact market prices, and whether changes in market prices influence voter behavior.

Let’s explore why market manipulation (if possible) could help achieve political goals: because it’s not as obvious as people think.

Here are two ways prediction markets might influence election outcomes.

Herd Effect

The herd effect refers to voters tending to support candidates who appear to be winning, whether due to herd mentality, the satisfaction of supporting the winner, or believing that market odds reflect candidate quality.

If popularity helps a candidate gain more support, then reporting prediction market prices in the news creates an incentive to push those prices higher. Manipulators might try to inflate their favored candidate’s chances of winning, hoping to trigger a feedback loop: rising market prices → voters perceive momentum → voters shift support → prices rise again.

In the Wans-Cuban example, the manipulator’s goal was to make Wans look stronger, which would help him win.

Satisfaction Effect

On the other hand, if voters support a candidate who is far ahead, they might choose not to vote. But if the race is close or their preferred candidate seems to be losing, they may be more motivated to vote. In this case, widespread prediction market signals can create market pressure to keep the odds close to 50-50. Once the market leans toward a candidate, traders will see that supporters of that candidate are losing enthusiasm, pulling the price down.

This also facilitates market manipulation. Leading candidates worried about overconfidence among supporters might quietly buy opposing stocks to tighten the market and suggest a more competitive race. Conversely, supporters of trailing candidates might further depress their stocks, encouraging the opposing camp to believe victory is assured and give up voting. In this way, the market becomes a self-fulfilling prophecy: the signals meant to reflect expectations instead undermine them.

Despite controversy, some argue Brexit is an example of this phenomenon. As a report from the London School of Economics notes: “It is well known that polls influence turnout and voting behavior, especially when one side appears to have a commanding lead. It seems that more supporters of Remain chose not to vote, perhaps because they believed Remain would win.”

Voter engagement isn’t strongly affected by election closeness

But the issue is that, even if herd or satisfaction effects exist, evidence suggests their influence is usually small. U.S. elections tend to be quite stable—driven mainly by party loyalty and fundamental factors like the economy—so if voters reacted strongly to who is ahead, election results would be more chaotic. Moreover, when researchers try to directly alter perceptions of election closeness or importance, behavioral effects are always limited.

For example, studies by Enos and Fowler on a Massachusetts state legislative race that ended in a tie found that even extreme measures—such as telling some voters their last election was decided by one vote—had minimal impact on turnout.

Similarly, Gebser et al. showed in large field experiments that presenting voters with different poll results changed their perceptions of competitiveness but had little effect on turnout. A study on Swiss national referendums found a slightly larger effect: close polls seemed to marginally increase turnout, but only by a few percentage points.

It’s possible that, at times, signals of a close race do influence some voters to change their minds, but such effects are likely very small. This doesn’t mean election fraud isn’t a concern; it means that the subtle influences in close races should be the focus, rather than factors that turn evenly matched elections into landslides.

Manipulating markets is difficult and costly

This leads to the second question: how hard is it to manipulate prediction market prices?

Research by Rhode and Strumpf on the Iowa electronic markets during the 2000 election found that attempts at manipulation are costly and difficult to sustain. In a typical case, a trader repeatedly placed large buy orders to push the price toward their preferred candidate. Each attempt temporarily changed the odds, but was quickly exploited by arbitrageurs, who eliminated the distortion and brought prices back to normal. Manipulators invested heavily but suffered losses, while the market showed strong mean reversion and resilience.

This is crucial in the hypothetical Wans-Cuban scenario. Manipulating the presidential market in October would require substantial funds, and many traders would be ready to sell once prices surged. Such small fluctuations might last until CNN broadcasts the story, but by the time CNN anchor Anderson Cooper discusses it, prices could have already reverted.

However, in low-liquidity environments, the situation differs. Researchers have shown that in such conditions, long-term prices can be manipulated: no one can prevent this.

Recommendations for dealing with manipulation

While evidence suggests that attempts to manipulate major election markets are unlikely to have a significant impact, this does not mean inaction is justified. In a world where prediction markets are integrated with social media and cable news, the influence of price manipulation could be greater than ever. Even if the direct impact is small, such concerns can undermine public trust in the fairness of the political system. How should this be addressed?

For broadcasters:

• Implement liquidity thresholds. CNN and other news outlets should focus on reporting prices from active markets, as these are more likely to reflect accurate expectations and are harder to manipulate; avoid reporting prices from illiquid markets, which are less accurate and easier to influence.

• Incorporate other election expectation signals. News organizations should also monitor polls and other indicators. Although these have their own flaws, they are less susceptible to strategic manipulation. Significant discrepancies between market prices and other signals should prompt investigations for potential manipulation.

For prediction platforms:

• Develop monitoring capabilities. Establish systems and personnel capable of detecting deceptive trades, false trades, sudden surges in unilateral trading volume, and coordinated account activity. Companies like Kalshi and Polymarket may already have some of these capabilities, but if they aim to be responsible platforms, they should invest more.

• Intervene during abnormal price swings. This includes implementing simple circuit breakers in illiquid markets to handle sudden price changes and temporarily halting trading for reassessment when prices move abnormally.

• Improve price reporting to resist manipulation. Use time-weighted or volume-weighted prices for public display.

• Increase transparency. Transparency is vital: publish metrics on liquidity, order book concentration, and unusual trading patterns (without revealing personal data), so journalists and the public can discern whether price movements reflect genuine information or order book noise. Larger markets like Kalshi and Polymarket already show order books, but more detailed metrics and user-friendly dashboards would be very helpful.

For policymakers:

• Combat market manipulation. The first step is to clarify that any attempt to manipulate election prediction prices (to influence public opinion or media coverage) falls under existing anti-manipulation laws. When unexplained large price swings occur before elections, regulators should act swiftly.

• Regulate foreign and domestic political influence on markets. Given the susceptibility of election markets to foreign interference and campaign finance issues, policymakers should consider two safeguards:

  (1) Track traders’ nationality to monitor foreign manipulation, leveraging existing U.S. KYC laws that are crucial for prediction market operations.

  (2) Establish disclosure rules or bans for expenditures related to campaigns, PACs, and senior political officials. If manipulation involves unreported political spending, regulators should treat it as political expenditure.

Conclusion

Prediction markets can make elections clearer rather than more chaotic—provided they are established responsibly. The partnership between CNN and Kalshi signals that future market signals will become part of the political information environment alongside polls, models, and reporting. This is a real opportunity: in an AI-saturated world, tools are needed to extract dispersed information without distortion. But this future depends on good governance, including liquidity standards, regulation, transparency, and more cautious interpretation of market dynamics. If these aspects are managed well, prediction markets can improve public understanding of elections and support a healthier democratic ecosystem in the age of algorithms.

Related reading: A decade of prediction markets—who will be the next?