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

Source: Jeff Park, Bitwise advisor; Compiled by: Jinse 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 (even if his bias was fairly transparent), MPU’s Trevor Hayes took a more direct stance, painting prediction markets as a “social cancer.”

To be honest, I have some sympathy for 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 “public health crises caused by gambling.” But at the same time, the common mistake these reporters make is that they presume their conclusion first, then search backward for “culprits,” often blending multiple issues into an overly simplified narrative. One moment we’re discussing “insider trading,” and the next it turns into “online casinos,” and finally everything boils down to “gambling addiction.”

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

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

The Blurry Line Between “Investment” and “Gambling”

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

Let’s unpack this. The first thing I noticed in Trevor Hayes’s reporting for MPU is that he often opens questions with “Since prediction markets are obviously gambling…,” as if that were an established fact. This foundational 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 become increasingly blurred. Consider the following facts: 1) 60% of U.S. stock trading volume is high-frequency trading (HFT), monopolized by institutions such as Jane Street and Citadel; 2) passive ETFs account for more than 90% of assets under management for ETFs (even though active strategies are just starting a belated rebound); 3) the average holding period for U.S. stocks shrank from 9 years in the mid-1970s to only about 6 months in 2025! At the same time, driven by algorithmic trading, average daily trading volume has more than doubled over the past decade. On top of all this data, there is an unstoppable trend: in 2025, retail investors’ trading activity exceeded $5 trillion, up by roughly 50% from 2023.

Yet you won’t find many experts rushing to condemn “stock trading” as gambling. Why? Because most people agree that stock picking isn’t gambling, because (presumably) it requires skill. This is a key insight: what makes the term “gambling” unfair in 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 pure luck-based strategies with negative expected value (-EV), while poker can be a strategy based on real skill with positive expected value (+EV).

Put plainly, whether something is “investment” or “gambling” mainly depends on whether a person believes the strategy enables them to obtain positive expected returns. It has nothing to do with the game itself, whether it is deterministic (pure risk-value arbitrage and slot machines are) or stochastic (stock picking and poker are).

Prediction markets, like poker, are stochastic games with deterministic components. Whether you view 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. This leads to the second question: if we believe gambling is player-driven “speculation,” then how exactly do such markets operate? And who provides the liquidity?

The Other Side of Speculation Is Insurance

All financial innovations initially look like gambling. Early stock markets were like this (filled with rampant insider trading); futures markets were like this (European dollars as the earliest political “insider trading” tools used by government officials); and of course modern commodity markets are like this too (where it’s almost impossible to define classic insider trading). Strictly speaking, this is because the other side of speculation is insurance. They are two sides of the same coin, because it’s a zero-sum game defined by strict synthetic risk transfer. And not all “information” naturally originates inside private enterprises.

This then leads to the next question prediction market critics commonly raise: “Some markets are, in function, purely speculative—since they create no value for society, they shouldn’t exist.” The most common target is sports betting. Because sports are entertainment, betting on entertainment is considered fundamentally unproductive.

But that view is wrong. Entertainment is social consumption. Some would even argue that entertainment is a fundamental reason humans find life 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; when you add the surrounding ecosystem (media, equipment, apparel, nutrition products, etc.), the estimated figure rises to $1 trillion or more. Take Nike as an example: it pays players and teams millions of dollars in sponsorship fees, and they have real economic interests in how capital is allocated (and how risks are hedged), based on the outcomes of sports events and the performance of the players. Today, society has been widely brainwashed into believing that sports betting is purely “casino behavior” simply because legal federal markets couldn’t exist before—completely missing the kind of unrealized potential that prediction markets can offer beyond mere gambling.

Derivatives are useful because they allow risk to be transferred. 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 true market prices. Insurance and securitization remain among the greatest financial innovations for releasing capital efficiency.

However, the “events” 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”? That brings me to my final point.

The Difference Between Prediction Markets and Other Derivatives

“The difference between prediction markets and other derivatives lies in two features: 1) they are precise (Precise), 2) they have limited expiry (Expiry).”

To understand what that means, let’s return 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 tend to have perpetual value. But prediction markets are different: once the event catalyst is realized, liquidity collapses to zero, and the other side no longer has buyers or sellers. This creates a huge challenge for liquidity providers, because a binary outcome of 0 or 1 breaks the assumption behind continuous dynamic hedging.

More importantly, prediction markets are markets based on “odds,” not “prices.” This means that liquidity at the midpoint (50) is far higher than liquidity around 98%, because the odds payout at 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 well—namely, that a 10 basis point move when interest rates are at 4% is completely 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 predicted precisely, professional market makers are unlikely to provide large amounts of liquidity. It also implies that most assumptions about insiders “cashing out” with inside information involve amounts that are ultimately very small. In the end, the market decides what people care about. Yes, I have access to “secret information” about whether Jeff Park’s next recorded video will feature a Bitwise sweater—but the opportunity for liquidity in that market is effectively negligible. Most “anti-insider trading” arguments assume insiders will make big money, but that isn’t true in most markets. In short, irrelevant markets don’t generate natural liquidity. In fact, I’d wager 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 outweigh the potential costs?

I mentioned earlier that they are precise. This is one of the most virtuous aspects prediction markets must emphasize. In a world shaped by over-financialization where asset prices are driven more by technicals and capital flows than by fundamental analysis, prediction markets uniquely restore the cleanest kind of basis risk—the gap between what is expected by the market and what is actually true. In the future, if you believe you have fundamental alpha on whether Tesla’s revenue will beat expectations, you should consider betting on the prediction market rather than buying the stock—because stock prices may be influenced by other external factors and can behave abnormally. If you think you have an edge on non-farm payroll data, you should bet on that rather than trade Eurodollars or E-mini futures. In other words, precision better rewards genuine excess returns, real research, and true skill.

Many people claim that prediction markets “prey on the financially illiterate,” assuming that “gamblers” lose money and therefore that this is a social vice. But in fact, prediction markets have the fairest mechanism for rewarding the real skills of high-agency investors. Even more powerfully, prediction markets have no “house.” Unlike Las Vegas casinos that kick out positive-EV players, prediction markets welcome your participation.

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

Why “Gray Ladies” Fear Truth Markets

That brings me to my final note. If you’ve read the content above, you may have at least begun to appreciate the power of properly regulated prediction markets. If we believe benefits outweigh costs, we can address “gambling problems” and “social harms” in various ways. But you may also have noticed we glossed over a key question: “What about insider trading in markets that matter to the public interest? Isn’t that a privatization of profit?”

This is still 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, not by coincidence: Dulaney’s suppression of Stalin’s Great Famine, Castro’s strange rise in Cuba, the push for Iraq’s weapons of mass destruction, and the systematic whitewashing of Hitler’s rise. In all these cases, The New York Times (the so-called “Gray Lady”) was always involved—leveraging access, ideology, and institutional self-preservation to muddy the public’s demand for truth.

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

Information has a price. That’s not in dispute. I also often say that the opposite of misleading information isn’t necessarily 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 profits from them, and whether all of this happens before you even see it. When insiders hoard asymmetric information, money incentives take a backseat to exchanges of power. By taxing others’ ignorance, this information can be weaponized to sway sentiment or spread falsehoods—and even the truth market itself can be captured.

Therefore, the real reason to oppose insider trading isn’t really about economic efficiency; it’s 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 be cynical about prediction markets. You’ll only become more precise about the world. That is why I firmly believe that maintaining optimism about prediction markets is one of the most democratic values a person can hold.