Numbers aren't events

"YES” and “NO” are holding prediction markets back from their full potential

Kalshi recently announced a partnership with StockX that enabled people to trade on the future price of Labubus. Bloomberg’s inimitable Matt Levine had a minor and hilarious meltdown.

But beyond the absurdity of Labubu derivatives, Mr. Levine touched on something technical and in my opinion, under-discussed in the rise of prediction markets as an asset class:

Stop it. Why … why is this an event contract? Surely this should just be a regular futures contract? Like the natural way to bet on the price of a sneaker is to buy a futures contract on the sneaker. You buy a contract for future delivery of the sneaker today, and at maturity you get the sneaker or you settle up in cash. You pay $200 for the sneaker futures today, and if the sneaker ultimately trades at $175 or $250 you lose $25 or make $50.

He’s right to be exasperated. Prediction markets are forcing numeric outcomes—prices, percentages, temperatures—into binary YES/NO frameworks. It’s like trying to measure your speed with a series of “Are you going faster than X mph?” checkpoints instead of just looking at the speedometer. The tool fundamentally doesn’t match the task.

The same structural absurdity that makes Levine throw up his hands is crippling prediction markets’ ability to serve their most important functions: helping people hedge real economic risks and producing prescient information via the prices.

The problem is that this structure, while simple to understand, creates all sorts of weird incentives and terrible UX for market participants.

We’ll never see “hedging” behavior with this microstructure

Say you run a small business that gets hurt when inflation goes up.

The market offers you a contract: “Will inflation be above 3.0%?” It’s currently trading at 40¢, meaning the market thinks there’s a 40% chance inflation exceeds 3%. You buy $1,000 worth, so if inflation comes in above 3%, you get $2,500 back—a $1,500 profit to offset your compressing margins.

Great! Except... what if inflation comes in at 2.95%? Your costs still went up significantly—2.95% inflation still hurts!—but your hedge pays out exactly zero. The market said “above 3%” and 2.95% is not above 3%, so tough luck. You’re out your $1,000 hedge premium AND suffering from higher costs.

This is insane from a risk management perspective, and it’s not useful enough to hedgers for them to decide to accept the negative-EV that is the lifeblood of the derivatives industry.

The binary trap

The problems compound at every level. Want to express a view that inflation will be “highish”? You need to buy multiple contracts at different strikes, each with its own bid-ask spread and fees. A 3¢ spread on four contracts means 6¢ of edge lost just to put on a single view.

At low probability levels, this gets even worse. A contract trading at a 10¢ midpoint with a 4¢ spread means you’re paying 12¢ to get 10¢ of expected value—a 20% transaction cost.¹

For market makers, it’s worse. In normal markets, they can hedge by trading the underlying asset. But what’s the “underlying” for a binary inflation contract? There isn’t one! Market makers are stuck warehousing risk with no way to hedge.

The binary structure unnecessarily amplifies small differences in beliefs. A maker can think the mean is at 4.0 and the taker can think 4.1, but binaries makes risk management extremely difficult. You can be wrong by a tiny amount but lose the maximum amount.² This means market makers have to be super conservative with their spread and liquidity.

This totally freaking sucks if you’re the market maker in a new asset class that is trying to establish the liquidity and order flow flywheel and solve the cold-start problem that plagues any new tradable asset.

How did we end up here?

It’s bizarre - why are we doing these markets like this in the first place? To be honest, I’m not really sure. Kalshi was incepted as the “stock market for events” and thus born into this binary world. That might explain why they don’t have scalar event markets (yet).

That then begs the question: why haven’t CME, ICE, or CBOE done this then? Their bread and butter is scalar futures contracts. They could have been listing Beanie Baby futures back in the day, and Labubu futures today… right?

I think this is just the result of the strategies of the exchanges and regulatory path-dependence. Historically, the aforementioned derivatives exchanges have been largely focused on large, institutional market participants. Getting the right market microstructure, regulatory all-clear, and liquidity providers and demanders all together was paramount.

Kalshi came along and turned that on its head by pursuing a retail customer base that preferred straightforward ergonomics (it doesn’t get much simpler than YES/NO) and a large breadth of markets over depth of liquidity³.

Better microstructure. Better liquidity. Papa John’s.

What prediction markets really need for numeric events are... numeric payoffs. Revolutionary, I know. Instead of “Will inflation be above 3%?” you’d have a contract that pays out based on what inflation actually is. If inflation comes in at 3.1%, you get $31. If it’s 2.9%, you get $29. Simple, intuitive, and it actually works for hedging.⁴

The benefits cascade throughout the system:

• Hedgers can actually hedge (your payout scales with your risk)

• Expressing views requires one trade, not six

• Market makers can manage risk better and provide more liquidity

• Transaction costs drop dramatically

• The forecasting information created is higher fidelity

This is how futures markets have worked for the last century. But, what does the next century look like?

“Patrick Mahomes passing over 110 yards” is a binary market on both Kalshi and sportsbooks. What if we extended scalar logic to sports betting? How accretive and revolutionary would a more elegant microstructure be for the ages-old industry?

Or, culture? Imagine a scalar futures version of the Taylor Swift Spotify streams market, which is a binary ladder as of now. If it had the right microstructure, might it actually have a chance at becoming liquid enough that Ms. Swift herself could use the market to hedge the economic risk of her album flopping?

What if there was an index that produced a single scalar number by rolling up the market prices of a bunch of underlying events that actually are events, making something like the S&P 500 of Democrats? What if there was an ETF that accomplished the same thing, thus siphoning order flow and liquidity from the securities market into prediction markets?

The derivatives industry figured the microstructure for numbers a long, long time ago. What they didn’t have was the remit to tap into an entire world of measurable outcomes. That is perhaps the more important innovation of Kalshi’s strategy - not the YES/NO microstructure that defines prediction markets… for the moment.

1. Even if spreads were zero, this structure is terrible for market makers - which is exactly why spreads aren’t zero. The harder it is to make markets, the wider the spreads get.

2. And you can be wrong in different dimensions. For example, we could both agree that the mean is 4.0, but you think the volatility is higher than I do so you hammer me on the tails, putting tons of extra risk on my position because I’m buying 95¢ NOs and you’re buying 5¢ YES on a market that only settles 12 times a year. That kind of PnL volatility isn’t cool and keeps liquidity low.

3. As an aside, I believe this is the underrated anti-pattern that explains a lot of how and why this industry is the way it is now. Smaller average trade sizes lower the strength of the liquidity network effect that has defined the derivatives industry for decades. This is why sportsbooks won’t hedge and also I think why Robinhood decided to directly compete another exchange for the first time in its history.

4. The irony is that sometimes numbers actually are events—like Fed rate decisions. CME FedWatch tries to extract binary probabilities from futures curves using convoluted math that assumes the Fed only moves in 25bps increments. Prediction markets handle these discrete decisions cleanly, without the mathematical gymnastics.

It is plainly obvious how prediction markets solve this particular problem that has long plagued the derivatives industry.