Predictive markets have historically faced manipulation threats, dating back to 1916 and frequently reappearing through 2024. As the AI era unfolds, how can we maintain the informational value of markets while establishing effective governance mechanisms to reduce abuse? This article explores the risks of manipulation in predictive markets and strategies to address them. It is based on an article by Andy Hall, compiled, translated, and written by Felix for PANews.
(Background recap: Bloomberg: Coinbase to launch predictive markets and tokenized US stocks next week, moving towards the “Everything Exchange”)
(Additional context: a16z predicts four leading trends by 2026)

Table of Contents

• Learning from History: Beware of Market Manipulation Attempts
• How Significant Are These Attacks?
  • Bandwagon Effect
  • Overconfidence Effect
• Voters Aren’t Too Concerned About Election Intensity
• Manipulating Markets Is Difficult and Costly
• Recommendations for Response
  • For broadcasters:
  • For predictive market platforms:
  • For policymakers:
• Conclusion

Imagine a scenario: October 2028, Vance and Mark Cuban are neck and neck in the presidential race. Support for Vance on the predictive market suddenly surges. CNN, having partnered with Kalshi, provides continuous coverage of the market prices.

At the same time, no one knows why the prices initially spiked. Democrats claim the market was “manipulated.” They point out suspicious trades that moved the market towards supporting Vance without any new polls or obvious reasons.

Meanwhile, The New York Times reports that traders supported by Saudi Arabia’s sovereign wealth fund placed big bets in the election market to influence CNN’s coverage in favor of Vance. Republicans argue the prices are reasonable, noting no evidence that the surge affected the election outcome, and accuse Democrats of attempting to suppress free speech and censor election-related truths. The real truth remains unclear.

This article explains why such scenarios are highly likely in the coming years—despite few successful cases of market manipulation and little evidence they influence voter behavior.

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

However, abandoning predictive markets is a mistake. As traditional polls become more fragile in an AI-saturated environment—response rates are extremely low, and pollsters struggle to distinguish AI responses from genuine human respondents—predictive markets serve as a valuable supplementary signal, integrating dispersed information with real financial incentives.

The challenge lies in governance: building a system that preserves the informational value of predictive markets while reducing abuse. This may involve ensuring broadcasters focus on reporting more difficult-to-manipulate, more active markets, encouraging platforms to monitor for collusion, and adopting a more humble, rather than panic-driven, interpretation of market fluctuations. If achieved, predictive markets could evolve into a more robust and transparent component of the political information ecosystem—a tool that helps the public understand elections rather than breed distrust.

Learning from History: Beware of Market Manipulation Attempts

“Now everyone is watching betting markets. Their volatility is closely followed by millions of ordinary voters, who can’t gauge public sentiment directly and can only blindly rely on the opinions of those placing hundreds of thousands of dollars on each 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, US media frequently reported on betting markets. These reports kept the shadow of manipulation lingering.

In 1916, Democrats didn’t want to appear behind and claimed the betting market was “manipulated,” which was also reported by the media.

The threat of election manipulation has never disappeared. On the morning of October 23, 2012, during the Obama-Romney campaign, a trader placed a large order on InTrade, buying Romney shares, causing his price to surge about 8 points—from just below 41 cents to nearly 49 cents. If trusting the price, it indicated a near-tie. But the price soon retreated, and media paid little attention. The identity of the manipulator was never confirmed.

Sometimes, individuals openly explain their logic for attempting market manipulation. 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 biggest newspapers) publishes a political stock market (PSM) daily, with the current trade price of the FDP (FDP) at 4.23%. You can view PSM online. Many citizens don’t see PSM as a game but as a poll result. Therefore, it’s important for the FDP’s price to rise in the last days. Like any exchange, prices depend on demand. Please participate in PSM and buy FDP contracts. Ultimately, we all believe in our party’s success.”

Concerns like these resurfaced in 2024. On the eve of the election, The Wall Street Journal questioned whether Trump’s advantage on Polymarket—seemingly well beyond his poll support—was due to undue influence: “Large bets on Trump may not be malicious. Some observers think it’s just a big bettor convinced he will win, trying to make a profit. Others see these bets as influence operations aimed at creating buzz on social media.”

2024’s scrutiny is particularly intriguing because it raises fears of foreign influence. The results show that bets driving up Polymarket prices came from a French investor—though some speculate, there’s little reason to believe manipulation occurred. In fact, the investor commissioned private polls and seemed focused on profit, not market manipulation.

This history reveals two themes. First, cyberattacks are common and likely to recur. Second, even if attacks are ineffective, some can still stir fear.

How Much Do These Attacks Affect?

Whether these efforts sway voters depends on two factors: whether manipulation can genuinely influence market prices, and whether market price changes affect voter behavior.

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

Here are two ways predictive markets might influence election outcomes.

Bandwagon Effect

The bandwagon effect refers to voters tending to support seemingly winning candidates, whether due to herd behavior, the satisfaction of supporting a winner, or believing that market odds reflect candidate qualities.

If popularity helps candidates gain more support, then reporting market prices in the news creates an incentive to push these prices upward. Manipulators might attempt to inflate their preferred candidate’s chances, hoping to trigger a feedback loop: rising market prices → voters notice momentum → support shifts → prices rise again.

In the Vance-Cuban example, manipulators’ bets aim to make Vance appear stronger, helping him actually win.

Overconfidence Effect

Conversely, if voters support a leading candidate, 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 dissemination of prediction market trends can exert market pressure, keeping the perceived chances near 50-50. Once the market favors a candidate, traders realize their supporters are losing enthusiasm, and so the prices fall.

This also facilitates market manipulation. Leading candidates worried about overly optimistic supporters might quietly buy opponents’ shares to tighten the market and suggest fiercer competition. Conversely, trailing candidates’ supporters may push their stock prices lower to induce the other side to believe victory is assured, discouraging turnout. In this scenario, the market becomes a self-fulfilling prophecy: signals intended to reflect expectations instead undermine them.

Despite controversy, some argue Brexit exemplifies this phenomenon. As the LSE report states: “It’s well known that polls influence voting turnout and behavior, especially when one side appears to have a clear lead. More supporters of Remain seem to have opted not to vote, perhaps because they believed Remain would win.”

Voters Aren’t Too Concerned About Election Intensity

But even if bandwagon or overconfidence effects exist, existing evidence suggests their influence is generally small. US elections are quite stable—primarily driven by party loyalty and economic fundamentals—so if voters react strongly to who’s ahead, results might be more chaotic. And when researchers try directly to alter perceptions of race closeness or importance, behavioral effects remain limited.

For example, studies by Ansolabehere and Fowler on a Massachusetts state legislative race, which ended in a tie, showed that telling some voters that the last election was decided by just one vote had minimal impact on turnout. Similarly, Guber et al. found in large field experiments that showing different poll results altered perceptions of competitiveness but hardly affected voting rates. A Swiss referendum study found that close polls slightly increased turnout, but only by a few percentage points.

It’s possible that signals of a close race sometimes prompt some voters to change their minds, but such effects are probably minor. This doesn’t mean election fraud isn’t a concern but suggests that subtle influences matter more in closely contested races than in artificially turning competitive races into landslides.

Manipulating Markets Is Difficult and Costly

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

Rhode and Strumpf’s study of the Iowa electronic market during the 2000 election found manipulation attempts to be costly and difficult to sustain. In a typical case, a trader repeatedly placed large buy orders to push the odds toward their preferred candidate. Each push temporarily affected the odds, but other traders exploited arbitrage opportunities, restoring the market to normal. Manipulators invested heavily but suffered losses, and the market demonstrated strong mean reversion and resilience.

In the hypothetical Vance-Cuban case, this is crucial. Manipulating the presidential market in October would require significant capital, with many traders ready to sell after a price spike. Such small fluctuations might persist until CNN broadcasts them, but once CNN’s Anderson Cooper discusses the surge, prices may have already returned to baseline.

However, when market liquidity is low, the situation differs. Research shows that in low-liquidity environments, long-term prices can be more easily manipulated; no one can prevent such manipulation.

Recommendations for Response

Perhaps evidence suggests that attempting major election market manipulation is unlikely to produce significant effects, but that doesn’t mean we can be complacent. In a world where prediction markets merge with social media and cable news, price manipulation could have a bigger impact than ever. Even if direct effects are limited, such concerns can influence the public perception of political fairness. How should we address this?

For Broadcasters:

Implement liquidity thresholds. CNN and other news outlets reporting on election prediction market prices should focus on highly active markets, as prices there are more likely to reflect accurate expectations and are harder to manipulate; avoid reporting prices from illiquid markets, which are less reliable and cheaper to manipulate.

Include 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. If large discrepancies appear between market prices and other signals, look for signs of manipulation.

For Prediction Platforms:

Build monitoring capabilities. Develop systems and teams capable of detecting deceptive trades, wash trading, sudden spikes in single-sided volume, and coordinated account activity. Platforms like Kalshi and Polymarket may already possess some such capabilities but could invest more to be responsible.

Intervene during abnormal price swings. This includes implementing simple circuit breakers in illiquid markets to handle sudden changes, and temporarily suspending trading with subsequent call auctions to re-establish fair prices.

Report price indicators with robustness. Use volume-weighted or time-weighted prices for public displays to reduce susceptibility to manipulation.

Enhance transparency. Transparency is key: publish indicators of liquidity, order book concentration, and unusual trading patterns (without revealing identities) so journalists and the public can assess whether price movements reflect genuine information or order book noise. Large 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 interference. Because election markets are highly vulnerable to foreign influence and campaign finance issues, policymakers should consider two safeguards:

(1) Track trader nationality to monitor foreign manipulation, leveraging existing U.S. KYC laws critical for prediction market operations.

(2) Establish disclosure rules or bans for expenditures related to campaigns, PACs, and senior political operatives. If manipulative spending qualifies as undisclosed political expenditure, regulators should treat it as such.

( Conclusion

Prediction markets can clarify elections rather than make them more chaotic—if established responsibly. The partnership between CNN and Kalshi suggests future market signals will coexist with polls, models, and media coverage as part of the political information ecosystem. It presents an opportunity: in an AI-saturated world, tools that extract dispersed information without distortion are needed. But success depends on good governance—standards for liquidity, regulation, transparency, and more cautious interpretation of market signals. If managed properly, prediction markets can improve public understanding of elections and support a healthier democratic ecosystem in the age of algorithms.

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