Prediction Markets Cannot Exist Without Insider Trading, But Insider Trading Is Killing Them

marsbitPublished on 2026-04-27Last updated on 2026-04-27

Abstract

Prediction markets face a fundamental contradiction: they rely on insider trading to generate accurate prices, yet this same activity erodes public trust and threatens their survival. Recent scandals, such as a U.S. special forces member making $400,000 on Polymarket using classified information about a raid, highlight the severity of the issue. While platforms like Polymarket and Kalshi market themselves as venues where "everyone is an expert" and encourage informed trading, they also enforce policies against illegal insider trading. The core dilemma is that these markets depend on informed insiders for price efficiency but require uninformed retail participants to provide liquidity. If insider trading is too rampant, retail traders leave, perceiving the market as unfair. If restrictions are too strict, valuable information flow dries up, reducing the market’s predictive utility. The challenge is finding a balance that maintains both accuracy and perceived fairness—a task complicated by regulatory pressures and recurring scandals.

Author: Nic Carter

Compiled by: Deep Tide TechFlow

Deep Tide Introduction: A U.S. Special Forces soldier made $400,000 on Polymarket using classified information, and this is just the latest scandal. Nic Carter points out that prediction markets are stuck in a vicious cycle: they rely on insider trading to generate accurate prices, but this makes retail investors feel the market is manipulated and leave. This contradiction determines whether prediction markets can survive in the long term.

As I wrote in February of this year, prediction markets have a serious insider trading problem, and this is no accident. This leads to a major failure mode:

The social value of prediction markets comes from using monetary incentives to get insiders to leak confidential information, but this destroys retail investor confidence in the market over time.

Two days ago, the biggest scandal to date broke. The U.S. Department of Justice charged Special Forces Sergeant Major Gannon Ken Van Dyke with improper trading using classified information. Before the Maduro raid mission, he made $400,000 on Polymarket. He was not an ordinary soldier but a senior Green Beret member responsible for special operations planning and execution.

To be clear, although many people are calling for leniency for him because (legal) insider trading is widespread among members of Congress, he should still go to jail. His actions potentially leaked raid information to Venezuelans through trading activities, which is morally and legally problematic. Although the Venezuelans don't seem to have noticed, the government cannot set a precedent where elite operatives leak details of upcoming operations through market activities for personal gain. I sympathize with Van Dyke, but he did break the law and the confidentiality he swore to uphold.

This is just the latest in a series of real or suspected insider trading scandals on prediction markets. Previously, Israel arrested two reservists for using military intelligence to trade on Polymarket. Markets concerning the start time of the Iran war, ceasefire agreements, the killing of Khamenei, and Biden's pardon order have also been under suspicion, but no arrests have been made yet. Kalshi and Polymarket have also flagged and suspended accounts trading in markets where they had a vested interest, such as three congressional candidates betting on their own election markets.

You might think that as more people realize that trading with confidential information is illegal not only in securities markets but also in prediction markets, these problems would disappear. But I think the issue is deeper.

The premise of prediction markets is that they are informationally efficient because they reward informed insiders.

In other words, prediction markets are "good" because they aggregate a large number of uninformed retail investors, who create economic incentives for insiders to reveal private information. (This concept—that retail investors create incentives for informed insiders to participate—is well-documented in financial literature, and a recent paper further extends it to prediction markets.) Prediction markets can then reliably promote themselves as having social utility because they indeed provide better, more timely signals than other platforms (experts, polls, etc.). Kalshi and Polymarket both know this but are reluctant to explicitly admit it. But they do hint at it in their marketing!

Kalshi's CEO Tarek Mansour explicitly stated on the Sourcery podcast that "There is no such thing as insider trading in commodity markets. It's all insider trading actually," which is... an extremely creative interpretation of the law. He added:

I think there is a subset of non-public information that (traders) cannot trade on, but I think we are restricting it a bit too much right now.

Kalshi has used slogans like "Trade Anything" and "Everyone is an Expert at Something," both of which imply that ordinary people can monetize any privileged information they happen to have on the platform.

Polymarket's CEO Shayne Coplan had this exchange with CBS last year:

Anderson Cooper: But prediction markets do rely on some people having inside information.

Shayne Coplan: Mhmm. Yes. I think it's a good thing that people have an edge in the market. Obviously, you need to manage them, need to be very clear and strict about drawing the lines, like on the ethics side, we spend a lot of time on that. But it's somewhat inevitable, and there's a lot of good that comes from it. You know, people will adapt.

Shayne has also said that prediction markets are "the most accurate thing that we have as humans right now, until someone creates some sort of super crystal ball." Some of that accuracy comes from insiders.

Robinhood's CEO Vlad Tenev (partnering with Kalshi) said:

Prediction markets actually allow you to get news faster, in some cases even before it happens. I think it does have tremendous economic value.

Economist Robin Hanson, considered by many to be the godfather of prediction markets, directly accepts this view and has written extensive defenses of insider trading in prediction markets. In 2024 he said:

If the purpose of the (prediction) market is to get accurate information in the price, then you definitely want to allow insiders to trade, even if that makes other people less willing to bet because they think it's unfair, because it makes the price more accurate. That's the priority.

I must point out that both Kalshi and Polymarket have anti-insider trading policies. Kalshi is regulated by the CFTC and has been explicit about prohibiting trading based on Material Non-Public Information (MNPI) and conducts market surveillance. When I wrote my last blog in February, I noted that Polymarket did not explicitly sanction insider trading, but in March they updated their rulebook, adding detailed prohibitions against trading of the following types:

  • Trading based on stolen confidential information (if you are a soldier, the battle plan does not belong to you, it belongs to the government)
  • Trading based on information illegally passed to you by an insider
  • Trading on any contract where you can influence the outcome

The point of this section is not to blame Kalshi or Polymarket or their leadership for implying that traders have an informational advantage. I think their policies (updated in March 2026) are clear enough. Instead, I want to point out the fundamental contradiction plaguing these markets:

Prediction markets rely on informed traders to generate accurate prices, but they also rely on uninformed traders to create the economic incentive to attract informed order flow. This creates a tension:

  • If they are too permissive towards insider trading, uninformed traders may exit due to a perceived lack of fairness
  • If they are too restrictive towards insider trading, the markets may exclude their most valuable source of information

Thus, there is a trade-off between informational efficiency and perceived fairness. Here is a visual version of the same idea:

Chart: The Trade-off Curve Between Informational Efficiency and Perceived Fairness

So we end up with a few different failure modes:

Too Many Sharks, They Eat All the Fish

Insider trading standards are too loose, the market becomes very informationally efficient, but retail investors clearly feel the market is "rigged," that they are always betting against insiders. Therefore, retail leaves, and market liquidity decreases. This is the failure mode I talked about before. This is where we are now, but I think we will rebound in the other direction.

No Sharks, No Edge

This is the other end of the spectrum. Insider trading is strictly policed on the platform, with real-time market surveillance and strong regulatory reporting, so informed order flow stays away. The markets thus produce less socially valuable information, becoming mere sentiment aggregators rather than generating "news before the news." Therefore, the platform cannot market itself effectively.

The existential question is whether there is a golden mean: where liquidity is maximized, retail feels the market is "fair enough," and informed order flow is still compensated for its information gathering. The chart suggests it might exist, but reality is messier.

My prediction from February still holds. As I said then:

A serious risk remains that insider trading scandals will make retail traders feel the market is manipulated, causing them to abandon the platform. I predict a string of insider trading events this year that will convince platforms to significantly strengthen market surveillance and lead Polymarket in particular to move away from anonymous modes.

I expect Polymarket will remove the ability to trade without KYC entirely (this is currently the case for the non-US platform) and strengthen the flagging of suspicious trading on the platform. There will be a slew of criminal cases regarding stolen insider information, but the temptation will remain. While platforms won't admit it, there is a "socially optimal" amount of insider trading. But can they calibrate it optimally? Will regulators allow them to?

It's worth noting that not all informed traders are insiders. You can become informed by collecting public information and trading on it. But a subset of informed traders are indeed insiders misappropriating information.

Related Questions

QWhat is the core contradiction that prediction markets face according to the article?

APrediction markets rely on informed traders (insiders) to generate accurate prices, but they also depend on uninformed traders to create the economic incentive that attracts that informed flow. This creates a tension: if insider trading is too tolerated, uninformed traders may leave feeling the market is unfair; if it is too restricted, the market loses its most valuable source of information.

QWhat are the two main failure modes for prediction markets described in the text?

AThe two failure modes are: 1. Too many sharks (insiders), which scares away the fish (retail traders) because the market feels manipulated. 2. No sharks, no edge, where strict regulation of insider trading drives away informed traders, reducing the market's social value and making it merely an aggregator of sentiment.

QHow did the recent case involving Gannon Ken Van Dyke illustrate the problem with prediction markets?

AThe case illustrated the problem as Van Dyke, a senior Green Beret, used classified information about an impending raid on Maduro to trade on Polymarket, making $400,000. This is a clear example of how the markets incentivize the leakage of confidential information, which can damage operational security and public trust.

QWhat is the proposed 'social value' of prediction markets, and what is the mechanism that enables it?

AThe proposed social value is that prediction markets provide a better, more accurate, and timely signal than other platforms like experts or polls. The mechanism that enables this is the aggregation of uninformed retail traders, which creates a financial incentive for informed insiders to reveal their private information, thus making the market informationally efficient.

QWhat specific policy change did Polymarket make in March 2026 in response to insider trading concerns?

AIn March 2026, Polymarket updated its rulebook to include a detailed ban on trading based on: 1. stolen confidential information, 2. information illegally passed to you from an insider, and 3. any contract where you can influence the outcome.

Related Reads

Anthropic's IPO Launch: Commercial Miracle or Valuation Bubble?

Anthropic has confidentially filed for an IPO, led by Morgan Stanley and Goldman Sachs, potentially going public by October. Following its latest $650 billion funding round, its pre-IPO valuation stands at $965 billion, with projections reaching up to $2 trillion at listing, which would make it the highest-valued private company ever. The article, written by Fu Sheng, addresses skepticism that this represents an AI bubble akin to the 2000 dot-com crash. It argues the current situation differs fundamentally. Unlike the internet bubble era, which relied on speculative narratives with little revenue, Anthropic's valuation is backed by unprecedented, measurable financial performance. Key data points include: * **Revenue Growth:** ARR skyrocketed from $10 billion in early 2025 to $470 billion by May 2026, targeting $100 billion by year-end—a growth curve unmatched in business history. * **Profitability:** It achieved operating profitability in Q2 2026 with an estimated $5.6 billion profit. * **Efficiency:** With ~3,000 employees and ~$470 billion ARR, its revenue per employee exceeds $10 million. Products like Claude Code, launched less than a year ago, already generate $25 billion in annualized revenue. * **Enterprise Adoption:** It boasts a strong enterprise client base, with 8 of the Fortune 10 and over 1,000 large firms spending over $1 million annually on Claude. The valuation is framed using a traditional SaaS model (e.g., a 10x Price-to-Sales multiple on $100 billion revenue). The author contends the core question for analysts has shifted from "How big could this be?" to "How much is it earning and will earn next quarter?" The discussion extends beyond Anthropic to a broader paradigm shift: the transition from a "carbon-based" to a "silicon-based" economy. Companies are increasingly prioritizing investment in compute and AI capabilities over human resources, as these directly scale productivity and competitive advantage. Anthropic's IPO is thus positioned not just as a corporate milestone, but as a price anchor for this new economic era.

链捕手2h ago

Anthropic's IPO Launch: Commercial Miracle or Valuation Bubble?

链捕手2h ago

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbit4h ago

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbit4h ago

Trading

Spot
Futures
活动图片