Prediction Markets Under Prejudice

Original Author: Jeff Park, Bitwise

Original Compilation: Saoirse, Foresight News

Last week, two media outlets, Axios and MorePerfectUS (MPU), took turns educating the public on what prediction markets are. Axios's Dan Primack attempted to create a neutral dialogue space for a multi-party discussion with the founders of the Kalshi platform, even though his own stance was not hard to discern; while Trevor Hayes from the other outlet took a clear, confrontational stance, deliberately sensationalizing conflict and portraying prediction markets as a type of social hazard.

Frankly, I agree with parts of both perspectives. Having worked at the intersection of Wall Street and the crypto industry for years, I deeply understand the growing public unease with excessive financialization, a trend that has already fostered a gambling culture seen as a public health crisis. However, these journalists commonly fall into a trap: they hastily draw conclusions, then work backwards to find a culprit, lumping issues like insider trading, online casinos, and gambling addiction into an overly simplistic, one-dimensional narrative.

Yet this is precisely the biggest public misconception about prediction markets: setting aside the various ills of excessive financialization brought by 0DTE options, swap-based ETFs, and meme stocks, prediction markets themselves deserve recognition. They empower individuals with high autonomy, uncover truth, and their decentralized nature holds intrinsic value.

Below, I will dissect this issue layer by layer.

The blurred line between investing and gambling depends solely on whether a participant's strategy has a positive expected value (+EV), not on whether the market mechanism itself is deterministic or random. In other words, the distinction lies with the person, not the game.

Let's break this down in detail. I noticed in MPU's report that Trevor Hayes often begins his arguments with a presupposition: "Since prediction markets are obviously gambling...", as if it's an unquestionable fact. This foundational assumption is precisely what needs re-examination.

The most significant trend in finance over the past two decades has been the eroding boundary between investing and gambling. The data proves it:

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

Meanwhile, the average daily trading volume in US stocks has more than tripled over the past decade, driven again by algorithmic trading. There's another irreversible trend: retail trading volume surpassed $5 trillion in 2025, an increase of about 50% compared to 2023.

Yet few financial commentators accuse stock trading itself of being gambling. Why? The public generally assumes stock picking isn't gambling because they subconsciously believe it requires skill. This is crucial: people unfairly lump skill-based games and pure probability games together as gambling. For instance, both slot machines and poker are called gambling, but they are worlds apart: slots are pure luck with negative expected value; poker relies on skill and strategy and can achieve positive expected value.

Put simply, the dividing line between investing and gambling is whether a strategy can achieve positive returns, not the game itself—whether that game involves deterministic arbitrage, fixed-outcome modes like slot machines, or random fluctuation modes like stock picking or poker.

Prediction markets, similar to poker, are random games with deterministic logic. Whether they count as investing or gambling is entirely up to the participant: it depends on whether you are a highly autonomous, highly skilled individual, a low-autonomy, low-cognition individual, or somewhere in between. This leads to the second question: if we understand gambling as human-driven speculation, how do such markets actually function, and where does liquidity come from?

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

All financial innovations are initially seen as gambling. Early stock markets were rife with rampant insider trading; in futures markets, Eurodollars even became tools for politicians' insider trading; today's commodity trading also defies traditional definitions of insider trading—all are examples. 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 is naturally born within private entities.

This touches on the most common criticism skeptics level at prediction markets: some markets are purely speculative, create no social value, and shouldn't exist. Their go-to example is sports betting. In the public's ingrained perception, sports are entertainment, and betting on entertainment holds no social value.

But this view is itself wrong. Entertainment is a form of social consumption for humans; one could even argue it's a core source of life satisfaction. More importantly, entertainment itself is an economic activity with a two-sided market. The global sports industry generates over $500 billion in annual revenue; adding peripheral industries like media, equipment, apparel, and sports nutrition, the total scale is estimated to exceed $1 trillion. Take Nike, for example: it invests huge sponsorship funds in teams and athletes, which itself requires capital allocation and risk hedging based on game outcomes and athlete performance. Simply because the US hasn't opened official, compliant markets, the public equates sports betting with casinos, completely ignoring its latent financial value.

The core value of derivatives is enabling risk transfer. This is the underlying logic of all insurance products and asset securitization. To achieve risk hedging, speculators must participate on the other side of the market; in an open, transparent, non-interventionist market, this structure is irreplaceable. In fact, problems in insurance systems often arise from government intervention distorting true market pricing. Insurance and securitization are also among the greatest financial innovations in human history for improving capital efficiency.

But we still can't avoid a core question: how do we define whether something is a social harm or a valuable financial service? How do we establish an event classification system? Next, I'll elaborate on the final core argument of this article.

Prediction markets differ from other derivatives in two key traits: precision and finite expiration.

Let's return to market-making fundamentals to understand. Ordinary financial markets rely on central limit order books to provide liquidity, with underlying assets having perpetual value. But prediction markets are completely different: once the corresponding event is settled, market liquidity drops directly to zero, with all buyers and sellers closing positions and exiting. The binary 0/1 payout outcome renders conventional dynamic hedging strategies completely ineffective, posing a huge challenge for professional market makers.

More importantly, prediction markets are odds-based, not price-based. This means that small fluctuations within the 50% probability range have far higher liquidity than fluctuations in extreme 98% probability ranges—where each point change in odds corresponds to an exponentially increasing payout cost. Therefore, liquidity cannot be sustained solely by bid-ask spreads, something fixed-income derivatives traders understand well (e.g., a 10-basis-point move when the base rate is 4% versus 0.5% is worlds apart).

In summary, in event markets with huge information asymmetry, where participants possess absolute information advantages, professional market makers are almost unwilling to enter and provide liquidity. This means the scenario critics envision—"insiders profiting massively by harvesting with information advantages"—has extremely limited profit potential in most situations. The market itself spontaneously filters for events the public genuinely cares about.

For example, I know perfectly well whether I'll wear a Bitwise hoodie in my next podcast, but a corresponding prediction market would basically generate zero liquidity. A major public concern opposing insider trading is that insiders will reap huge profits, but reality isn't like that: obscure, valueless events inherently lack liquidity; market liquidity itself already prices in information value. A reasonable event grading system would naturally form from this.

So, where does the value of prediction markets lie, enough to outweigh their potential risks?

The precision mentioned earlier is its most precious trait. Today's global finance is swept up in excessive financialization, where asset prices are more influenced by capital flows and technical trends, detached from fundamentals and facts themselves. Prediction markets are one of the few tools that allow prices to directly anchor to facts, stripping away extraneous noise.

In the future, if you have a fundamental view that Tesla's earnings will beat expectations, instead of directly buying or selling Tesla stock (whose price is also affected by macro, market, and capital flow factors), you could place a bet in a prediction market. If you want to anticipate non-farm payroll data, you don't need to trade Eurodollar futures or stock index futures; you can directly participate in the corresponding prediction market. This precision truly rewards deep research, professional judgment, and genuine information advantages.

Many external critics argue that prediction markets prey on financially illiterate ordinary people, with participants generally losing money, constituting a social harm. The opposite is true: prediction markets have the fairest mechanisms, rewarding professional investors with information advantages. Moreover, they have no house edge or platform rake, completely unlike Las Vegas casinos—casinos eject consistently profitable +EV players, while prediction markets welcome all participants with information advantages.

Citadel Securities and Charles Schwab have both announced plans to enter the prediction market business. Are these giants preying on vulnerable groups? Obviously not. They understand more profoundly than the public: speculation and hedging are inseparable; one side's risk exposure is the other side's profit opportunity.

Why the Establishment Media Fears This Truth Market

(Note: Gray Lady refers to The New York Times. In its early years, The Times used plain gray newsprint, black-and-white layout, and very few color images, giving its pages a solemn, austere appearance. Combined with its rigorous, conservative writing style, dignified tone, and the steady, authoritative demeanor of an old-guard media institution, it earned the respectful nickname "Gray Lady" from readers and the industry. Here, it broadly refers to established, authoritative, mainstream American media outlets that set the tone for public discourse and hold sway over the information narrative—the traditional media giants.)

By now, you should understand that, under reasonable regulation, prediction markets hold immense potential. As long as the benefits outweigh the risks, issues like gambling addiction and negative social effects can find solutions. But we're left with one key question: Could insider trading on major public events lead to unfair private monopoly profits?

This question is complex, and I will address it in a separate, detailed article. Here, I want to share a thought and a book I recently read—Ashley Rindsberg's "The Gray Lady Winks."

The book chronicles decades of systemic failures by this authoritative newspaper, which were not accidental missteps: concealing Stalin's famine, whitewashing Castro's rise, promoting the Iraq WMD rumor, downplaying the Nazi rise. The New York Times has consistently distorted truth dissemination based on information channels, ideology, and institutional self-preservation needs.

Understanding this book reveals that media bias isn't a simple left-right political battle but a deeper structural issue: top-tier authoritative institutions actively manufacture social consensus and later whitewash their own reporting errors.

Returning to the initial topic: Neither Axios nor MorePerfectUS are neutral industry players. This is why more and more media outlets will attack prediction markets in the future. But you must understand: their reasons for rejecting prediction markets are precisely the reasons you should support them.

Information has a price; there's no need to debate that. I've always believed: The opposite of misinformation is not absolute truth; the opposite of misinformation is officially controlled information.

The real debate is never about pricing information itself, but about who has the right to define information, who profits from it, and whether information is monopolized and exploited before the public knows.

When insiders hoard asymmetric information, profiting is secondary; the core issue is a power struggle. Harvesting profits by leveraging the public's information disadvantage means information is used to manipulate public opinion, create false narratives, and the entire truth dissemination system becomes monopolized and held hostage.

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

Once you understand this layer, you won't view prediction markets pessimistically, but will see the world with a more precise and rational perspective. This is also why I remain convinced: Being bullish on prediction markets is, in itself, an idea of immense democratic value.