Most people's misconceptions about prediction markets: it's not excessive financialization, but subjectivity and truth discovery.

Source: Jeff Park, Bitwise advisor; Translation: Golden Finance Claw

Last week, both Axios and More Perfect Union (MPU) stepped forward to explain to the public what prediction markets are. Although Axios’s Dan Primack tried to provide a neutral platform for a debate with Kalshi’s founder (despite his bias being fairly transparent), MPU’s Trevor Hayes took a more direct stance—portraying prediction markets as a “social cancer.”

Honestly, I sympathize with parts of both viewpoints. As someone whose career sits at the intersection of Wall Street and crypto, I understand society’s growing concern about “over-financialization,” a trend that is fueling a culture of “a public health crisis caused by gambling.” But at the same time, a common mistake these reporters make is that they pre-assume the conclusion, then hunt for “accomplices,” often bundling multiple issues together in an overly simplified narrative. One moment we’re talking about “insider trading,” and the next it becomes “an online casino,” and in the end it reduces to “gambling addiction.”

But this is precisely the most common misunderstanding about prediction markets: no matter how you view the downsides of over-financialization (through 0DTE options, swap-based ETFs, Meme stocks, and more), the story of prediction markets should be celebrated for enabling high agency (High Agency), truth discovery (Truth Discovery), and decentralized moral rights.

The article below attempts to break down this view in greater depth.

The Blurred Line Between “Investment” and “Gambling”

Whether something is “investment” or “gambling” depends entirely on whether you believe the activity has “positive expected value” (+EV), not on whether the system itself is deterministic or random. In other words, it is defined by the player—not by the game.

Let’s unpack this. The first thing I noticed in MPU’s reporting is that Trevor Hayes often opens questions with “Since prediction markets are obviously gambling…” as if that were an established fact. That fundamental assumption needs to be examined first.

Over the past twenty years, the biggest trend in finance has been that the clear boundary between “investment” and “gambling” has grown increasingly blurry. Consider the following: 1) 60% of U.S. stock trading volume is high-frequency trading (HFT), monopolized in an oligarchic structure by firms such as Jane Street and Citadel; 2) passive ETFs account for more than 90% of ETF assets under management (even though active strategies are beginning a long-delayed rebound); 3) the average holding period for U.S. stocks has shortened from 9 years in the mid-1970s to only about 6 months in 2025! Meanwhile, driven by algorithmic trading, average daily trading volume has grown by more than two times over the past decade. On top of these data, there is another unstoppable trend: retail investors’ trading activity exceeded $5 trillion in 2025, up about 50% from 2023.

Yet you won’t find many experts coming out to accuse “stock trading” of being gambling. Why? Because most people agree that stock picking isn’t gambling—presumably because it requires skill. This is the key insight: the reason the word “gambling” has become unfair in its description is that it conflates “technical games” with “pure probability games.” For example, slot machines and poker are both called gambling, but many people can intuitively see the unfairness—slot machines are negative expected value (-EV) strategies based purely on luck, while poker can be a positive expected value (+EV) strategy based on real skill.

To put it plainly, whether something counts as “investment” or “gambling” mainly depends on whether a person believes the strategy allows for positive expected returns. It has nothing to do with the game itself—whether it is deterministic (risk-value arbitrage and slot machines are) or stochastic (stock picking and poker are).

Like poker, prediction markets are a form of stochastic game with deterministic components. Whether you see them as “gambling” or “investment” depends entirely on the player—that is, on you. It depends on whether you are a high-agency, high-skill person or a low-agency, low-skill person. That leads to the second question: if we think of gambling as player-driven “speculation,” how exactly do such markets operate? And who provides liquidity?

“Speculation’s Other Side Is Insurance”

All financial innovations initially look like gambling. Early stock markets were like that (full of frenzied insider trading), futures markets were like that (European dollars as the earliest political “insider trading” tool for government officials), and of course modern commodity markets are also like that (where classic insider trading is almost impossible to define). Strictly speaking, this is because the other side of speculation is insurance. They are two sides of the same coin—a zero-sum game defined by strict synthetic risk transfer. And not all “information” naturally originates from private enterprises.

This brings up the next question that prediction market critics often raise: “Some markets are, in function, pure speculation, and since they create no value for society, they shouldn’t exist.” The most commonly targeted example is sports betting. Because sports are entertainment, betting on entertainment is considered fundamentally unproductive.

But this view is wrong. Entertainment is social consumption. Some would even argue that entertainment is a fundamental reason humans discover life as fulfilling. More importantly, entertainment itself is economic consumption, which means it involves a two-sided market. The sports industry generates more than $50 billion in revenue, and if you add the surrounding ecosystem (media, equipment, apparel, nutritional products, and more), the estimated figure reaches over $1 trillion. Take Nike as an example: it pays players and teams millions of dollars in sponsorship fees, and those parties have real economic interests in how capital is allocated (and how risks are hedged)—all based on the outcomes of sports events and the performance of the players. Today, society has been widely brainwashed into thinking sports betting is purely “casino” behavior, simply because legal federal markets couldn’t exist before—completely missing the kind of unimaginable upside prediction markets can provide.

Derivatives are useful because they enable risk transfer. This is the basic principle behind all insurance models (and securitization). To have insurance, you need a counterparty: another speculator; in transparent, open markets without government intervention, there’s no other way. In fact, insurance most often fails when government intervention distorts real market prices. Insurance and securitization remain among the greatest financial innovations for capital efficiency.

However, the “event” problem remains: under what circumstances does an event truly become a social cancer rather than a naturally useful financial service? How do we develop an “event taxonomy”? This is my final point.

The Difference Between Prediction Markets and Other Derivatives

“The difference between prediction markets and other derivatives comes down to two features: 1) they are precise (Precise), and 2) they have limited expiration dates (Expiry).”

To understand what this means, let’s go back to the “Market Maker 101” course. In most financial markets, the role of the central limit order book (CLOB) is to measure and provide liquidity, because assets often have perpetual value. But prediction markets are different: once an event catalyst is realized, liquidity collapses to zero, and the other side no longer has any buyers or sellers. This creates a huge challenge for liquidity providers, because a binary outcome of 0 or 1 destroys the assumption behind continuous dynamic hedging.

More importantly, prediction markets are markets based on “odds,” not “prices.” This means that liquidity around the midline (50) is far higher than liquidity around 98%, because the odds payout in the latter is exponentially heavier per point. In other words, liquidity cannot be sustained solely through bid-ask spreads. Fixed-income derivatives traders understand this concept very well (i.e., a 10 basis point move when rates are at 4% means something entirely different from a 10 basis point move when rates are at 0.5%).

All of this implies that in markets where information is extremely asymmetric and outcomes can be precisely predicted, professional market makers are unlikely to provide large amounts of liquidity. It also means that many assumptions about insiders “cash[ing] out” using inside information ultimately involve very small amounts of money. The market ultimately decides what people care about. Yes, I have ultra-secret information about whether “Jeff Park’s next recorded session will include him wearing a Bitwise sweater,” but the opportunity for liquidity in that market is minuscule. Most arguments against insider trading assume insiders make big money, but that isn’t true in most markets. In short, irrelevant markets won’t generate natural liquidity. In fact, I’d bet that liquidity itself is precisely priced according to the value of that information. That is how an “event taxonomy” develops organically.

So: why are prediction markets useful enough that their benefits exceed potential costs?

I mentioned earlier that they are precise. This is one of the most virtuous aspects prediction markets must emphasize. In a world where over-financialization causes asset prices to be driven more by technicals and capital flows than by fundamentals analysis, prediction markets stand out by uniquely restoring the cleanest form of basis risk of “truth” (Basis Risk). In the future, if you believe you have fundamental alpha on whether Tesla’s revenue will beat expectations, you should consider betting in prediction markets rather than buying the stock—because stock prices can be distorted by other external factors. If you think you have an edge on non-farm employment data, you should bet on that data rather than trade Eurodollars or E-mini futures. In other words, greater precision more strongly rewards genuine excess returns, real research, and real skill.

Many people believe the narrative that prediction markets prey on financial illiterates assumes that “gamblers” lose money, making it a social vice. But in fact, prediction markets have the fairest mechanism to reward the real skills of high-agency investors. Even more powerfully, prediction markets have no “house.” Unlike casinos in Las Vegas that kick out positive-EV players, prediction markets welcome your arrival.

Citadel Securities and Charles Schwab have announced that they are exploring entering prediction markets. Are they “preying on vulnerable economic groups”? I’m highly skeptical. They simply understand better than most that “the other side of speculation is insurance”: that your convexity (passively bearing risk) is my convexity (actively hedging risk).

Why “Gray Lady” Fears Truth Markets

This leads to my final closing note. If you’ve read the above, you may at least be starting to appreciate the power of correctly regulated prediction markets. If we believe the benefits outweigh the costs, we can address “gambling problems” and “social ills” in a variety of ways. However, there’s a question you may have realized we’ve glossed over: “What about insider trading in markets that matter to the public interest—doesn’t that just privatize profit?”

This remains a complex issue, and I plan to answer it in another article. But I want to leave you with an idea, and a book I recently read—Ashley Rindsberg’s The Gray Lady Winked. It documents decades of media failures that were not accidental: Durandty’s suppression of Stalin’s Great Famine, Castro’s bizarre rise in Cuba, the push for Iraq’s weapons of mass destruction claims, and the systematic whitewashing of Hitler’s rise. In these events, The New York Times (the “Gray Lady”) was always involved—leveraging access, ideology, and institutional self-preservation to muddy the public’s demand for truth.

If you read this book, you’ll understand how it reframes “media bias” from a left/right debate into a more interesting structural question: how do prestige institutions manufacture consensus and then retroactively whitewash their own mistakes. In fact, that brings us back to where we started: Axios and More Perfect Union are not unbiased actors in this space. For those reasons, you will continue to see many media criticisms of prediction markets. But don’t misunderstand this: the reason they dislike it is precisely why you should support it.

Information has a price. There’s no dispute about that. I often say that the opposite of misleading information isn’t necessarily the truth; the opposite of misleading information is actually “state-controlled information.”

The focus of this debate is: who has the right to set prices, who has the right to profit from them, and whether all of this has already happened before you even see it. When insiders hoard asymmetric information, financial incentives take a back seat to the exchange of power. By taxing everyone else’s ignorance, this information can be weaponized to sway sentiment or spread falsehoods, and even the truth markets themselves can be captured.

Therefore, the real reason to oppose insider trading is less about economic efficiency and more about access rights. The fact is, some people trade based on what they know, and the rest of us trade based on what we’re allowed to know.

Once you see this clearly, you won’t feel cynical about prediction markets. You’ll only become more precise about the world. That’s why I strongly believe that staying optimistic about prediction markets is one of the most democratic values a person can hold.