Prediction Markets: Misunderstood or Misrepresented?

Original author: Jeff Park, Bitwise

Saoirse, Foresight News

Last week, two media outlets, Axios and MorePerfectUS (MPU), sequentially introduced the general public to the concept of prediction markets. Axios’s Dan Primack attempted to create a neutral forum for multi-party discussions involving the founders of Kalshi, despite his own stance being readily apparent; meanwhile, Trevor Hayes of the other outlet took a clear position, deliberately amplifying conflict by portraying prediction markets as a societal concern.

To be honest, I agree with aspects of both perspectives. Having spent years working at the intersection of Wall Street and the crypto industry, I deeply understand the growing public unease about escalating over-financialization, which has fostered a gambling culture now regarded as a public health crisis. But many journalists fall into a common trap: they hastily draw conclusions and retroactively assign blame, conflating issues like insider trading, online casinos, and gambling addiction into an overly simplistic and one-sided narrative.

But this is precisely the biggest misconception about prediction markets: setting aside the various harms of over-financialization brought by 0DTE options, swap-based ETFs, and meme stocks, prediction markets themselves deserve recognition—they empower individuals with high levels of autonomy, uncover truth, and possess inherent value through their decentralized nature.

Below, I will break down this issue layer by layer.

The blurred line between investing and gambling depends solely on whether the participant’s strategy has a positive expected value (+EV), regardless of whether the market itself is deterministic or random. In other words, it is people—not the game—that define the difference.

Let’s break this down in detail. I’ve noticed that in MPU’s reporting, Trevor Hayes often begins his arguments with a presupposed premise: “Since prediction markets are clearly gambling...” as if this were an unquestionable, established fact. Yet this foundational assumption is precisely what needs to be reexamined.

Over the past two decades, one of the most prominent trends in finance has been the growing blurring of the line between investing and gambling. Evidence includes:

• 60% of U.S. stock trading volume comes from high-frequency trading, and this sector is 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 beginning to recover);
• The average holding period for U.S. stocks has shortened from nine years in the mid-1970s to approximately six months by 2025.

Meanwhile, over the past decade, the average daily trading volume in U.S. equities has more than tripled, driven primarily by algorithmic trading. In addition, there is an irreversible trend: retail trading volume is projected to exceed $5 trillion in 2025, representing a roughly 50% increase from 2023.

But very few financial commentators accuse stock trading itself of being gambling. Why? The public generally assumes that selecting stocks for investment is not gambling, because people subconsciously believe it requires professional expertise. This is crucial: people unfairly lump together skill-based games and pure chance games under the broad label of gambling. For example, both slot machines and poker are called gambling, yet they are vastly different: slot machines rely purely on luck and have negative expected value, while poker depends on skill and strategy and can absolutely generate positive expected value.

Simply put, the distinction between investing and gambling depends solely on whether the strategy can generate positive returns, not on the nature of the game itself—whether it’s a deterministic arbitrage, a slot machine with fixed outcomes, or stock picking, poker, and other randomly volatile activities.

Prediction markets are similar to poker: they are stochastic games with inherent deterministic logic. Whether they constitute investment or gambling is entirely up to the participant: it depends on whether you are highly autonomous and highly skilled, low in autonomy and low in cognitive ability, or somewhere in between. This leads to a second question: if gambling is understood as human-driven speculation, how do such markets function, and where does their liquidity come from?

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

All financial innovations were initially regarded as gambling at their inception. Early stock markets were rife with rampant insider trading, and in futures markets, Eurodollars even became tools for politicians to engage in insider trading; today, insider trading in commodities markets remains difficult to define by traditional standards—this has always been the case. The root lies in the fact that speculation and hedging are two sides of the same coin. It is a zero-sum game whose core is the transfer of risk; moreover, not all information is inherently generated by private entities.

This raises the most common criticism of prediction markets: that some markets have only speculative value and create no societal benefit, and therefore shouldn’t exist at all. The most frequently cited example is sports betting. In the public’s conventional view, sports are entertainment, and betting on entertainment has no social value.

But this viewpoint is itself incorrect. Entertainment is inherently a form of social consumption, and one could even argue that it is one of the core sources of human happiness. More importantly, entertainment is itself an economic activity with characteristics of a two-sided market. The global sports industry generates annual revenues exceeding $50 billion; when combined with related industries such as media, equipment, apparel, and sports nutrition, the overall market size is estimated to surpass $1 trillion. Take Nike as an example: its substantial sponsorships of teams and athletes require capital allocation and risk hedging based on game outcomes and athlete performance. Simply because the U.S. has not opened an official, regulated market, the public equates sports betting with casinos, completely overlooking its potential financial value.

The core value of derivatives lies in risk transfer—the fundamental logic behind all insurance products and asset securitization. To achieve risk hedging, there must be speculators on the other side of the market; in an open, transparent, and non-interventionist market, this structure is irreplaceable. In fact, problems in insurance systems often arise when government intervention distorts true market pricing. Insurance and securitization are among the greatest financial innovations in human history for enhancing capital efficiency.

Yet, a core issue remains: how do we determine whether an event constitutes a social harm or a practical financial service? How can we establish a classification system for such events? The following section presents the paper’s final central argument.

Prediction markets differ from other derivatives in two key characteristics: precision and a defined expiration date.

Let’s return to the fundamental principles of market making to understand this. Traditional financial markets rely on a central limit order book to provide liquidity, with underlying assets possessing perpetual value. Prediction markets, however, are fundamentally different: once the corresponding event is resolved, market liquidity drops to zero, as all buyers and sellers close their positions. The binary 0/1 payout structure renders conventional dynamic hedging strategies ineffective, presenting significant challenges for professional market makers.

More importantly, prediction markets are odds-based markets, not price-based markets. This means that small fluctuations within the 50% probability range have far greater liquidity than fluctuations in the extreme probability zones, such as 98%—where the payout cost per unit of odds movement rises exponentially. Therefore, liquidity cannot be sustained solely by bid-ask spreads, a reality well understood by fixed-income derivatives traders (for example, a 10-basis-point move at a benchmark rate of 4% is vastly different from a 10-basis-point move at 0.5%).

In summary, in event markets characterized by extreme information asymmetry and where participants hold absolute informational advantages, professional market makers rarely enter to provide liquidity. This means that the notion—often criticized—of insiders exploiting information advantages for massive profits is, in most scenarios, severely limited in its potential returns. The market itself naturally filters out events that truly matter to the public.

For example, I am very clear whether I’ll wear a Bitwise hoodie in my next podcast, but the corresponding prediction market will generate virtually no liquidity. A major concern the public has about insider trading is that insiders will make huge profits—but in reality, this isn’t the case: obscure, low-value events naturally lack liquidity, and the market’s liquidity itself already prices in the value of information. A sensible event grading system will therefore emerge naturally.

So, what is the value of prediction markets that justifies their potential risks?

The precision mentioned earlier is its most valuable trait. Today, global finance is overwhelmed by over-financialization, where asset prices are more influenced by capital flows and technical trends than by fundamentals or actual facts; prediction markets are among the few tools that can directly anchor prices to reality and eliminate unnecessary noise.

If you have a fundamental judgment that Tesla’s revenue will exceed expectations, instead of directly buying or selling Tesla stock—whose price may be influenced by unrelated factors such as macroeconomic conditions, market trends, or capital flows—consider placing a bet on a prediction market. Similarly, if you want to forecast non-farm payroll data, you don’t need to trade Eurodollar futures or stock index futures; simply participate in the corresponding prediction market. This precision rewards in-depth research, expert judgment, and genuine informational advantages.

Many external criticisms claim that prediction markets exploit ordinary people with weak financial literacy, resulting in widespread losses among participants and posing a social harm. The truth is the opposite: prediction markets feature the most equitable mechanism, rewarding professional investors with informational advantages. Moreover, they have no house or platform taking a cut—unlike Las Vegas casinos, which expel consistently profitable players, while prediction markets welcome all participants with informational advantages.

Citadel Securities and Charles Schwab have both announced their entry into the prediction markets. Are these giants exploiting vulnerable populations? Clearly not. They understand better than the general public that speculation and hedging are two sides of the same coin—the risk exposure of one party is precisely the profit opportunity of the other.

(Note: Gray Lady refers to The New York Times. In its early years, the newspaper featured a gray, uncolored paper stock, black-and-white layout, and minimal use of color images, giving it a solemn, somber appearance. Combined with its rigorous, conservative writing style, formal language, and steady reputation as a venerable media institution, it earned the respectful title of Gray Lady from readers and industry insiders. Here, it broadly refers to established, authoritative, mainstream American media outlets that set public opinion standards and serve as the voice of the American elite.)

By now, you should understand that prediction markets hold tremendous potential under reasonable regulation. As long as returns outweigh risks, issues such as gambling addiction and negative social impacts can be addressed. However, one key question remains: Could insider trading related to major public events lead to unfair private monopolies for profit?

This issue is highly complex, and I will address it in detail in a separate article. For now, I’d like to share a line of thought, along with a book I recently read—Ashley Rindsberg’s The New York Times’ Complicit Accommodation.

The book documents the authoritative media’s decades-long systemic failures—not accidental errors—but deliberate actions: concealing Stalin’s famine, glorifying Castro’s rise, fueling rumors about Iraq’s weapons of mass destruction, and downplaying the threat of Nazi ascension. The New York Times has consistently distorted the truth in its reporting, relying on information channels, ideology, and institutional self-preservation.

Reading this book will reveal that media bias is not merely a simple left-right ideological conflict, but a deeper structural issue: top authoritative institutions actively shape social consensus and later justify their reporting errors.

Back to the original topic: Axios and MorePerfectUS are not neutral parties in the industry. This is why more and more media outlets will increasingly criticize prediction markets in the future. But you must understand: the very reasons they reject prediction markets are precisely the reasons you should support them.

Information always has a price; this is not debatable. I have always believed: the opposite of false information is never absolute truth—the opposite of false information is information controlled by authorities.

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

When insiders hoard asymmetric information, profit-seeking is secondary; the core issue is a power struggle. Exploiting the public’s information disadvantage, this information is used to manipulate public opinion and fabricate false narratives, leading to the monopolization and hijacking of the entire truth dissemination system.

Therefore, the core opposition to insider trading has never been about economic efficiency, but about equal access to information: some individuals trade based on exclusive information, while ordinary people can only access information that has been filtered and approved for dissemination.

Once you understand this layer, you won’t view prediction markets with pessimism; instead, you’ll see the world through a more precise and rational lens. This is precisely why I’ve always believed: supporting prediction markets is itself a理念 with profound democratic value.