Prediction market misconceptions: 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) came forward to explain to the public what prediction markets are. While Axios’s Dan Primack attempted to provide a neutral platform for a debate with Kalshi’s founder (despite his fairly transparent bias), MPU’s Trevor Hayes took a more direct stance, portraying prediction markets as a “social cancer.”

Honestly, I sympathize partly with both viewpoints. As someone whose career intersects Wall Street and crypto, I understand society’s growing concern over “over-financialization,” which fuels a culture of “public health crises caused by gambling.” But at the same time, a common mistake journalists make is: they preset a conclusion and then look for “accomplices,” often conflating multiple issues into an oversimplified narrative. One moment we’re discussing “insider trading,” and the next it’s “online casinos,” ultimately boiling down to “gambling addiction.”

But this is the core misunderstanding most people have about prediction markets: regardless of 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 promoter of high agency, truth discovery, and decentralized moral rights.

Below, I attempt to delve deeper into this perspective.

The Blurred Line Between “Investment” and “Gambling”

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

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

Over the past twenty years, the biggest trend in finance has been the increasingly blurred boundary between “investment” and “gambling.” Consider these facts: 1) 60% of US stock trading volume is high-frequency trading (HFT), monopolized by oligopolies like Jane Street and Citadel; 2) Passive ETFs account for over 90% of ETF assets under management (despite a late-starting rebound in active strategies); 3) The average holding period for US stocks shrank from 9 years in the mid-1970s to just about 6 months in 2025! Meanwhile, algorithmic trading has doubled daily trading volume over the past decade. On top of these data, there’s an unstoppable trend: retail investors’ trading activity in 2025 exceeded $5 trillion, up about 50% from 2023.

Yet, you won’t see many experts criticizing “stock trading” as gambling. Why? Because most agree that stock picking isn’t gambling—presumably because it requires skill. This is a key insight: the unfairness in calling it “gambling” stems from conflating “technological game” and “pure probability game.” For example, slot machines and poker are both called gambling, but many intuitively recognize the unfairness—slot machines are purely luck-based, with negative expected value (-EV), while poker can be based on real skill, with positive expected value (+EV).

Plainly put, whether something is “investment” or “gambling” mainly depends on whether the individual believes the strategy allows for positive expected value. It’s unrelated to the game itself, whether it’s deterministic (arbitrage of pure risk value and slot machines) or stochastic (stock picking and poker).

Prediction markets, like poker, are a form of stochastic game with deterministic components. Whether you see them as “gambling” or “investment” depends entirely on the player—that is, you. It depends on whether you are a high-agency, high-skill participant or a low-agency, low-skill one. This leads to the second question: if we consider gambling as player-driven “speculation,” then how do these markets operate? And who provides liquidity?

“Speculation’s Other Side Is Insurance”

All financial innovations initially look like gambling. The early stock markets did (filled with rampant insider trading), futures markets did (European dollars as early political “insider trading” tools for government officials), and modern commodity markets do (where classic insider trading is nearly impossible to define). Strictly speaking, this is because the other side of speculation is insurance. They are two sides of the same coin, defined as a zero-sum game of strictly synthetic risk transfer. And not all “information” naturally originates from private enterprises.

This brings us to the next common critique of prediction markets: “Some markets are purely speculative and don’t create any social value, so they shouldn’t exist.” The most common target is sports betting. Since sports are entertainment, betting on entertainment is considered fundamentally unproductive.

But this view is mistaken. Entertainment is a form of social consumption. Some even argue that entertainment is a fundamental reason humans find life fulfilling. More importantly, entertainment itself is economic consumption, which means it has a bilateral market. The sports industry generates over $50 billion in revenue, and when you include the surrounding ecosystem (media, equipment, apparel, nutrition, etc.), the figure exceeds $1 trillion. Take Nike, for example: it pays millions of dollars in sponsorships to players and teams, who have real economic interests in how they allocate capital (and hedge risks), based on the outcomes of sports events and their players. Today, society is widely brainwashed into thinking sports betting is purely “gambling,” simply because legal federal markets previously couldn’t exist, completely missing the enormous potential prediction markets can offer.

Derivatives are useful because they allow risk transfer. This is the fundamental principle behind all insurance models (and securitization). To have insurance, you need a speculator on the other side; in transparent, open markets without government interference, 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 capital efficiency.

But the “event” problem persists: under what circumstances does an event truly become a social cancer rather than a natural useful financial service? How do we develop an “event taxonomy”? This leads to my final point.

The Difference Between Prediction Markets and Other Derivatives

“Prediction markets differ from other derivatives in two features: 1) They are precise, and 2) They have limited expiry.”

To understand what this means, let’s revisit the “Market Maker 101” course. In most financial markets, the central limit order book (CLOB) functions to measure and provide liquidity because assets tend to have perpetual value. But prediction markets are different: once an event catalyst occurs, liquidity collapses to zero, with no more buyers or sellers. This presents a huge challenge for liquidity providers because the binary outcome of 0 or 1 invalidates the assumption of continuous dynamic hedging.

More importantly, prediction markets are markets based on “odds,” not “prices.” This means that a market with 2 points around the midpoint (50%) has much higher liquidity than a market with 2 points near 98%, because the latter’s odds payout grows exponentially with each point. In other words, liquidity cannot be sustained solely through bid-ask spreads; fixed-income derivatives traders understand this well (e.g., a 10 basis point move at a 4% rate is very different from a 10 basis point move at 0.5%).

All this implies that in markets where information is extremely asymmetric and outcomes can be predicted with high precision, professional market makers are unlikely to provide large liquidity. This also means that most assumptions about insiders “cashing out” with inside information are likely to involve very small amounts. Ultimately, the market will decide what people care about. Yes, I have secret information about whether Jeff Park will wear a Bitwise sweater in his next recording, but the opportunity for liquidity in that market is minimal. Most “anti-insider trading” arguments assume insiders will make big money, but that’s not the case in most markets. In short, irrelevant markets won’t generate natural liquidity. In fact, I bet liquidity itself will be precisely priced according to the value of that information. That’s how an “event taxonomy” organically develops.

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

I’ve mentioned 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 fundamentals, prediction markets uniquely restore the cleanest basis risk of “truth”. In the future, if you believe you have fundamental alpha on whether Tesla’s revenue will beat expectations, you should bet on the prediction market rather than buy the stock—because the stock price can be affected by external factors and behave abnormally. If you think you have an edge on non-farm payroll data, you should bet on that data rather than trade Eurodollars or E-mini futures. In other words, better precision rewards genuine excess returns, research, and skill.

Many critics who claim prediction markets exploit financial illiterates assume “gamblers” lose money, making it a social vice. But in fact, prediction markets have the fairest mechanism to reward truly high-agency investors’ skills. Even more powerful, 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 announced they are exploring entering prediction markets. Are they “exploiting vulnerable economic groups”? I am highly skeptical. They simply understand better than most that “speculation’s other side is insurance”: that your convexity (passively bearing risk) is my convexity (actively hedging risk).

Why “Gray Ladies” Fear Truth Markets

This brings me to my final note. If you’ve read the above, you might at least begin to appreciate the power of well-regulated prediction markets. If we believe the benefits outweigh the costs, we can address “gambling issues” and “social harms” in various ways. But there’s one problem you might notice we skipped: “What about insider trading in markets of significant public interest? Isn’t that privatized profit?”

That remains a complex issue I plan to address 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 coincidentally: the suppression of Stalin’s Great Famine, Castro’s strange rise in Cuba, the propaganda around Iraq’s WMDs, and the systematic whitewashing of Hitler’s rise. In these events, The New York Times (the “Gray Lady”) consistently played a role, leveraging access, ideology, and institutional self-preservation to muddy the public’s demand for truth.

If you read that book, you’ll understand it reframes “media bias” from left/right debates into a more interesting structural problem: how reputation institutions manufacture consensus and then retrospectively whitewash their mistakes. In fact, this returns us to where we started: Axios and MPU are not unbiased actors in this space. For these reasons, you will continue to see many media criticisms of prediction markets. But don’t be mistaken: their dislike of it is precisely why you should support it.

Information has a price. That’s undisputed. I often say that the opposite of misleading information isn’t necessarily truth; it’s “state-controlled information.”

The core of this debate is: who has the right to set prices, who profits from them, and whether all this happens before you see it? When insiders hoard asymmetric information, monetary incentives take a backseat to power exchanges. Taxing others’ ignorance, this information can be weaponized to sway sentiment or spread falsehoods, and prediction markets themselves can be captured.

So, the real reason to oppose insider trading isn’t 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’s why I believe maintaining optimism about prediction markets is one of the most democratic values a person can hold.