Prediction Markets Under Bias

Author: Jeff Park, Bitwise

Original compilation: Saoirse, Foresight News

Last week, two media outlets, Axios and MorePerfectUS (MPU), successively explained to the public what prediction markets are. Axios’s Dan Primack attempted to create a neutral dialogue space for a multi-party discussion with Kalshi platform founder, even though his own stance is not hard to detect; meanwhile, Trevor Hayes from the other media outlet took a clear stance, deliberately emphasizing conflicts and viewing prediction markets as a form of social hazard.

Honestly, I partly agree with both viewpoints. I have long been involved in the intersection of Wall Street and the crypto industry, and I deeply understand the public’s growing unease with increasing financialization. This trend has already fostered a gambling culture seen as a public health crisis. But these journalists generally fall into a common misconception: they hastily draw conclusions, retroactively trace the origins, and lump insider trading, online casinos, gambling addiction, and other issues into an overly simplistic and one-sided narrative.

But this is precisely the biggest misunderstanding the public has about prediction markets: setting aside the over-financialization issues brought by 0DTE options, swap ETFs, Meme stocks, etc., prediction markets themselves should be recognized. They can empower individuals with high autonomy, uncover truths, and their decentralized nature inherently holds legitimate value.

Below, I will analyze this issue layer by layer.

The blurry boundary between investing and gambling depends solely on whether participants’ strategies have a positive expected value (+EV), regardless of whether the market mechanism is deterministic or stochastic. In other words, the distinction is made by people, not by the game itself.

Let’s analyze in detail. I notice that in MPU’s report, Trevor Hayes often begins with a presupposed premise: “Since prediction markets obviously belong to gambling…”, as if this is an unquestioned fact. And this fundamental assumption needs to be re-examined.

The most significant trend in finance over the past twenty years is the continuous erosion of the boundary between investing and gambling. Data proves this:

• 60% of US stock trading volume comes from high-frequency trading, dominated by oligopolies like Jane Street and Citadel;
• Passive ETFs account for over 90% of total ETF assets under management (active strategies are only now slowly recovering);
• The average holding period for US stocks has shrunk from about 9 years in the mid-1970s to roughly 6 months in 2025.

Meanwhile, over the past decade, the daily trading volume of US stocks has tripled, driven still by algorithmic trading. There’s also an irreversible trend: retail trading volume will surpass $5 trillion in 2025, about a 50% increase over 2023.

Yet few financial commentators criticize stock trading itself as gambling. Why? Because the public generally assumes stock picking isn’t gambling, subconsciously believing it requires skill. This is crucial: people unfairly lump skill-based games and pure probabilistic games into the same category of gambling. For example, slot machines and poker are both called gambling, but they are vastly different: slot machines are purely luck-based with negative expected value; poker relies on skill strategies and can achieve positive expected value.

Simply put, the dividing line between investing and gambling depends solely on whether the strategy can achieve positive returns, and has nothing to do with the game itself—whether it’s a deterministic arbitrage, a slot machine with fixed outcomes, or stock picking and poker with random fluctuations.

Prediction markets are similar to poker, involving stochastic games with embedded deterministic logic. Whether they are considered investing or gambling depends entirely on the participant: whether you are highly autonomous and skilled, or low in autonomy and cognition, or somewhere in between. This leads to a second question: if gambling is understood as human-led speculation, how do these markets operate, and where does liquidity come from?

The other side of speculation is risk hedging (insurance).

All financial innovations are initially viewed as gambling. Early stock markets were rife with rampant insider trading; in futures markets, Eurodollars even became tools for politicians to conduct insider trading; today’s commodity trading also defies traditional insider trading definitions—all of these are true. The root cause is that speculation and hedging are two sides of the same coin. It’s a zero-sum game, centered on transferring risk; and not all information naturally originates from private entities.

This brings us to the most common criticism of prediction markets: some markets are purely speculative and cannot create social value, so they shouldn’t exist. The most cited example is sports betting. In the public’s perception, sports are entertainment, and betting on entertainment has no social value.

But this view is fundamentally wrong. Entertainment itself is a form of social consumption, and arguably one of the core sources of human happiness. More importantly, entertainment is an economic activity with bilateral market attributes. The global sports industry generates over $50 billion annually, and combined with media, equipment, apparel, sports nutrition, and related industries, the total scale is estimated to exceed $1 trillion. Take Nike, for example: it invests heavily in sponsorships for teams and athletes, which requires capital allocation based on game results and athlete performance to hedge risks. The fact that the US has not opened an official regulated market for sports betting does not mean it’s just gambling; it overlooks its potential financial value.

The core value of derivatives is risk transfer. This is the underlying logic of all insurance products and asset securitization. To achieve risk hedging, there must be speculators on the other side; in open, transparent markets without administrative interference, this structure is irreplaceable. In fact, most problems with insurance systems stem from government intervention distorting true market pricing. Insurance and securitization are among humanity’s greatest financial innovations for improving capital efficiency.

But the core issue remains: how to define whether an activity is a social hazard or a practical financial service? How to establish a classification system for events? Now, I will present the final core argument of this article.

Prediction markets differ from other derivatives in two key features: precision and limited expiration.

Let’s revisit the fundamental market-making principle. Ordinary financial markets rely on a central limit order book to provide liquidity, with underlying assets having perpetual value. Prediction markets are entirely different: once the event settles, market liquidity drops to zero, and all traders close their positions. The binary 0/1 payout results in the failure of conventional dynamic hedging strategies, posing a significant challenge for professional market makers.

More importantly, prediction markets are odds-based rather than price-based. This means that small fluctuations within a 50% probability range have much higher liquidity than fluctuations in the 98% extreme probability range—where each percentage point change in odds exponentially increases the payout cost. Therefore, liquidity cannot be sustained solely through bid-ask spreads; derivatives traders understand this well (for example, a 10 basis point move at a 4% benchmark rate versus at 0.5% differs vastly in significance).

In summary, in event markets with significant information asymmetry and participants with absolute informational advantage, professional market makers are almost never willing to provide liquidity. This also means that the idea of “insiders profiting massively from informational advantage,” as critics claim, is extremely limited in most scenarios. The market naturally filters out events that the public truly cares about.

For example, I am very sure whether I will wear a Bitwise hoodie in my next podcast, but the prediction market for that will likely have no liquidity. A major concern about insider trading is that insiders will make huge profits, but in reality, this is not the case: obscure, low-value events inherently lack liquidity, and market liquidity itself has already priced in the value of information. A reasonable event classification system will naturally form from this.

So, what is the actual value of prediction markets, enough to cover their potential risks?

The most valuable trait is their precision. Currently, global finance is overly financialized, with asset prices more influenced by capital flows and technical trends than fundamentals and facts; prediction markets are among the few tools that can directly anchor prices to facts and eliminate extraneous interference.

In the future, if you have a fundamental judgment that Tesla’s revenue will beat expectations, instead of directly buying or selling Tesla stock (which can still be affected by macro, market, and capital factors), you can bet on the prediction market; if you want to forecast non-farm payroll data, you don’t need to trade Eurodollar futures or stock index futures—just participate in the corresponding prediction market. This precision will truly reward in-depth research, professional judgment, and genuine informational advantages.

Many external critics argue that prediction markets exploit financially naive individuals, with participants generally losing money, posing a social hazard. But the reality is quite the opposite: prediction markets have the fairest mechanism, rewarding professional investors with informational advantages. Unlike Las Vegas casinos, which exclude consistently profitable players, prediction markets welcome all participants with informational advantages.

Citadel Securities and Charles Schwab have announced plans to enter the prediction market business. Are these giants exploiting vulnerable groups? Clearly not. They understand better than the public: speculation and hedging are two sides of the same coin, and one party’s risk exposure is the other’s profit opportunity.

Why do authoritative media fear this true market

(Note: Gray Lady refers to The New York Times. Historically, NYT’s pages were mostly grayish paper with black-and-white layouts and few color images, giving a solemn, conservative, and authoritative appearance; thus, it’s nicknamed Gray Lady by readers and industry insiders. Here, it refers to the longstanding, authoritative, mainstream American media, the voice of elite opinion and the traditional major media.)

By now, you should understand that under proper regulation, prediction markets have enormous potential. As long as the benefits outweigh the risks, issues like gambling addiction and social negative effects can be addressed. But there remains a key concern: could insider trading on major public events lead to unfair private monopolies and profiteering?

This is a very complex issue, and I will write a separate article to explore it in detail. For now, I want to share a thought and a recent book I read—Ashley Rindsberg’s “The Gray Lady Winked.”

The book details decades of systemic negligence by this authoritative media, not accidental mistakes: concealing Stalin’s Great Famine, glorifying Castro’s rise, spreading rumors about Iraq’s WMDs, downplaying Nazi resurgence risks. The NYT has always relied on information channels, ideology, and institutional self-preservation to distort truth.

Reading this book makes it clear that media bias isn’t just a matter of left-right stance, but a deeper structural problem: top-tier institutions actively manufacture social consensus and whitewash their own reporting errors afterward.

Returning to the initial topic: Axios and MPU are not neutral industry players. This is also why more and more media outlets will criticize prediction markets in the future. But you must understand: their reasons for opposing prediction markets are precisely the reasons you should support them.

Information has a price, and that’s beyond dispute. I’ve always believed: the opposite of false information is not absolute truth, but information under official control.

The real debate is never about the pricing of information itself, but about who has the authority to define information, who can profit from it, and whether it has been monopolized and exploited before the public can access it.

When insiders hoard asymmetric information, profiteering is secondary; the core issue is a power struggle. Using the public’s informational disadvantage to harvest benefits, information can be used to manipulate public opinion, create false narratives, and monopolize the entire truth dissemination system.

Therefore, the core of opposing insider trading has never been about economic efficiency, but about equal access to information: some people trade with exclusive information, while ordinary people only access filtered and permitted information.

Once you understand this layer, you won’t be pessimistic about prediction markets; instead, you’ll see the world through a more precise and rational lens. That’s why I firmly believe: supporting prediction markets is itself a highly democratic idea.