A blow to the head from a surfing incident left me couch-ridden over the holidays. For someone who struggles with taking time off, the forced sedentary lifestyle was a challenge. Naturally, my brother and I became prediction market fanatics. With news and sports now one of my primary information flows, the order book became our competitive arena.

We traded everything: from geopolitical tail risks like the probability of a U.S. invasion of Venezuela (a short position that paid out at the end of the year – just barely), to the NBA. We eventually landed on a strategy that 12x’d our account over 10 days, primarily by identifying mispriced underdogs where the order book skewed too heavily toward favorites. You could calculate the deviation between the Vegas lines and the prediction market books pretty easily. For instance we took the Timberwolves against OKC at nearly 10:1 odds. If you’ve seen Anthony Edwards play recently, you know the man is a monster; it was a fundamental mispricing. Last, we looked at arbs across platforms with some opportunities for almost 10% “risk free” profits, ignoring settlement risks.

But as we watched the liquidity move and risk adjust in real-time, the cracks in the current infrastructure became obvious. While the wisdom of the crowds is a poetic concept, the current reality of prediction markets faces a structural ceiling.

The State of Markets Today

The primary hurdle for prediction markets today is a lack of deep liquidity. Liquidity is the bridge between a theoretical price and a tradable one; it ensures that a high-conviction trade reveals new information to the market rather than simply breaking the order book. If a prediction market is thin (low liquidity), it's just a place where a few people are guessing. For it to become a real financial tool, it needs to be deep (high liquidity) so that big players can move millions of dollars without accidentally breaking the price. Liquidity and execution reliability are the crux of trading infrastructure. The inability for Market Makers (MMs) to update spreads quickly in deep orderbooks causes one to be picked off. When MMs can't hedge, or price accurately, liquidity vanishes.

Today, most of the liquidity is concentrated in sports. This is because the Designated Contract Market (DCM) license (only 20 issued total) allows people to trade on sports in states where sports betting is illegal.

To move beyond sports betting, these markets must solve the Toxic Flow problem.

Prediction markets inherently encourage participants with inside information to place asymmetric bets. In a traditional equity market, insider trading is a crime; in a prediction market, it is a mechanism for price discovery. This creates a hostile environment for MMs. If an MM knows they are constantly being run over by insiders, they widen their spreads or leave the book entirely. This is known as the adverse selection problem. 

In addition, one of the most significant barriers to professionalizing prediction markets is the lack of capital efficiency. Currently, most prediction markets are fully collateralized (1:1 margin). If you want to bet \$100, you must put up \$100.

In the world of professional trading, this is an anomaly. In traditional derivatives, like Perpetual Swaps (Perps) or Futures, traders utilize leverage without tying up their entire balance sheet. Without leverage, you cap the potential Return on Equity (ROE) necessary to prioritize these markets. Because there is no margin, unwinding a position can be more difficult. In a fully collateralized market, you can't simply 'net' your way out of a losing position; you are forced to hold your high-cost, binary bet to the bitter end unless you find a new buyer willing to pay the full face value upfront. This capital lock-up is a non-starter for high-frequency market makers who need to recycle capital every millisecond.

The Future of Prediction Markets?

If prediction markets are to become a multi-trillion dollar asset class, they must evolve into something resembling Lloyd’s of London – a marketplace where specialized groups compete to underwrite unique, high-stakes risks.

The value proposition isn't just knowing who will win an election; it’s the ability to represent and trade risk that was previously unquantifiable specific to your financial situation. Imagine a world where:

• Apple can uniquely hedge supply chain risk tied to TSMC’s output.
• Price discovery for these niche risks becomes public, allowing the market to price corporate stability more accurately.

The price signal provided to the rest of the world might be a prediction market's greatest utility. By allowing corporations to hedge specific existential risks, prediction markets move from a retail speculators tool to a fundamental piece of financial infrastructure.

The Counterparty Paradox

There is, however, a final boss in this evolution: Counterparty Risk.

An old colleague of mine, who built the derivatives desk at a major bank, reminded me that in high-stakes finance, people sometimes prefer counterparty risk; provided they know who to sue. There is a psychological and legal comfort in facing a regulated bank that has a history of government backstops (the Too Big to Fail insurance).

This leads to a fundamental question for the next generation of traders and corporate hedgers:

Would you rather face the settlement risk of a decentralized protocol like Uma or Polymarket, or would you rather face a Tier-1 bank?

While many people view code as law, the institutional world still prefers a throat to choke. For prediction markets to reach the scale of the global derivatives market, they must bridge this gap between the trustless efficiency of the blockchain and the legal recourse of traditional finance.

Standardizing the Rules of Engagement

To fix this, prediction markets need to look more like the ISDA (International Swaps and Derivatives Association).

The ISDA Master Agreement is the Holy Grail of finance. It’s a standardized contract that dictates exactly what happens when things go wrong: defaults, bankruptcies, or settlement disputes. It removes the need for a middleman to decide the winner because the rules are pre-agreed upon globally.

We are starting to see a move away from:

1. The Centralized Processor: Where a platform like Kalshi, or a single committee decides the outcome.
2. The Social Oracle: Where a decentralized protocol like UMA relies on a vote to determine truth.

The future is Standardized Event Contracts. These are environments where the settlement source (e.g., a specific BLS data point or a federal court filing) is hard-coded into the contract structure. By removing the centralized processor, or the human oracle, we move toward a world where a prediction market contract is as legally and financially robust as a Japanese yen swap. One could argue that event contracts are overexposed to definitional edge cases, which make them inherently difficult to compare to ISDA.

However, standardization enables the institutionalization of finite outcomes: elections, referenda, regulatory decisions as tradeable financial instruments. Events like Brexit, or the 2016 U.S. election created enormous economic consequences, yet there was no direct way for institutions to express, hedge, or transfer that risk. Standardized event contracts turn discrete outcomes into portfolio components, allowing investors to size exposure, hedge downstream impacts, and construct event-driven strategies with the same rigor applied to rates, FX, or credit. That capability unlocks a new layer of demand from asset managers, corporates, and risk desks who have historically had views on these events, but no clean way to trade them. This is how prediction markets become a trillion dollar asset class.

The potential is there. The monster in the room isn't just Anthony Edwards; it's the untapped liquidity of corporate risk waiting for a robust enough venue to call home.

‍