In Part I of the Prediction Markets: Intelligence Series, we discussed How Prediction Markets Work — And Why They're Smarter Than Any Single Expert, a piece that digs deep into what prediction markets are all about and how they help shape decisions, and gives the best impression of what the market looks like.

In Part II of this series, we talk about leverage in Prediction Markets. We discuss the case for and against leverage on PMs. Is it possible to have leveraged betting markets? Why is it difficult to price leverage fees? What are the solutions that could help grow levered prediction markets?

The long and short of it is that leveraging prediction markets is not a regulatory or structural issue but a mathematical one.

This piece is inspired by the nice work of the Messari researcher, Kaleb Rasmussen, in his research paper, Enabling Leverage on Prediction Markets.

Introduction: The Missing Piece in an Otherwise Perfect Market

Prediction markets have spent the past five years quietly proving something profound: when people are allowed to bet on outcomes, prices become forecasts. Not opinions, not narratives — probabilities.

From election cycles to monetary policy, sports, and crypto, platforms like Polymarket demonstrated that markets could synthesize dispersed information faster and more accurately than most institutional models. During the 2024 U.S. election cycle, the platform processed over $2 billion in monthly volume at its peak, becoming, in effect, a real-time probabilistic news layer.

And yet, for all their apparent sophistication, prediction markets remain structurally incomplete.

They lack leverage.

In traditional finance, leverage is not an optional feature — it is the mechanism that allows informed participants to scale conviction. It is what transforms a correct insight into meaningful profit, and more importantly, into price impact. Without leverage, even the most accurate trader is capital-constrained.

The problem arrives the moment you try to engineer a financing structure around a binary instrument, and it arrives with unusual mathematical brutality.

In equity markets, the margin engine works because prices move continuously. A position that moves against a levered trader triggers a margin call. The margin call triggers liquidation. Liquidation happens at a price close enough to the margin threshold that the financier — the entity providing the leverage — recovers most or all of the exposure.

So why hasn't leverage emerged naturally in prediction markets?

The answer is not regulatory. It is not cultural. It is not even technological.

It is mathematical.

So what exactly are we talking about?

Imagine you spot a market that is currently trading 'YES' shares at 92 ¢ — implying a 92% probability the event does occur — this appears safe by any conventional risk metric. However, deep within the architecture of the trade, obscured from conventional risk models, lies a structural fact that fundamentally distinguishes prediction markets from every other leveraged instrument in finance: the reality that this outcome can collapse from near-certainty to zero in less time than any margin engine on earth can execute a liquidation.

Therein lies the fundamental problem with leveraging prediction markets: a single concept known as jump risk. Not only that, but for binary prediction markets, fairly priced leverage may be fundamentally useless.

I. For Argument: Why Leverage Matters in Prediction Markets

To understand why leverage matters, consider the basic payoff structure of a prediction market.

A contract priced at 70¢ implies a 70% probability of occurring. If the event resolves "YES," the payoff is $1. If it resolves "NO," it is $0.

This creates an asymmetry that is easy to overlook but critical in practice:

• You risk 70¢
• To win 30¢

Even if you have a strong informational edge, your upside is capped relative to your capital deployment.

For a retail trader, this may be acceptable. For an institutional participant, it is prohibitive.

a. The Capital Constraint Problem

In equity or derivatives markets, leverage allows a trader to express a view without tying up the full notional value of the position. A hedge fund that believes a stock is undervalued does not deploy 100% cash — it borrows against collateral, scales the position, and amplifies the signal.

Prediction markets do not allow this.

A trader who believes a 70¢ contract should be 85¢ cannot efficiently size that conviction. The capital required to express the view increases linearly, while the payoff remains bounded.

The result is structural under-participation by the very actors that prediction markets are designed to attract: informed, well-capitalized traders.

b. The Hedging Argument

This limitation extends beyond speculation.

Prediction markets are, in theory, the most natural hedging instruments for event risk:

• A company is exposed to regulatory approval
• A fund exposed to election outcomes
• A macro portfolio sensitive to central bank decisions

These are not price risks. They are binary or quasi-binary outcomes.

Traditional derivatives markets struggle to price these events directly. Prediction markets solve that problem elegantly — but without leverage, they cannot be used at a meaningful scale.

c. The Liquidity Feedback Loop: Price Accuracy

There is also a second-order effect: liquidity.

Leverage attracts market makers. Market makers tighten spreads. Tighter spreads improve price accuracy. More accurate prices attract more participants.

This is the same feedback loop that transformed crypto perpetual futures into trillion-dollar markets.

Prediction markets have proven they can aggregate information. What they have not yet proven is that they can do so at an institutional scale.

Leverage is the missing mechanism.

II. Against Argument: Why Binary Markets Break Leverage

The argument for leverage is intuitive, almost obvious.

The argument against it is not.

It comes from a deeper examination of how binary markets behave under stress — an analysis most clearly articulated in the Messari research by Kaleb Rasmussen.

At the center of that analysis is a single claim:

Binary prediction markets are not continuous systems. They are discontinuous. And discontinuity breaks leverage.

a. The Nature of Jump Risk

Previously, we briefly explained what jump risk is. In equity markets, prices move (mostly) continuously. Even during volatility, there are intermediate price points. This allows liquidation engines to function:

• Positions can be partially unwound
• Collateral can be adjusted
• Losses can be contained

Binary markets do not behave this way. They "jump." A contract trading at 92¢ can go to 0¢ in seconds if new information resolves the outcome. There is no glide path. No gradual decline. No opportunity to liquidate.

A financier must correctly price jump risk and charge fees that reflect the full expected value of that risk. And because binary prediction markets are, by definition, zero-sum — the probability of a jump to one and the probability of a jump to zero sum to one — the expected loss from jump risk, properly priced, exactly offsets the expected return of the leveraged position (Read more in Kaleb Rasmussen's paper for equations). The implication is stark: fair leverage on binary prediction markets has zero net benefit over unleveraged positions. The fees necessary to price the risk consume the expected return from having leverage in the first place.

b. The Zero-Upside Proof

It is important to state this idea clearly, because it is frequently misunderstood. The claim is not that leverage is risky — all leverage is risky. The claim is that binary markets, unlike scalar markets or equity markets, have a structural property that makes the actuarially fair price of leverage equal to the leverage's benefit.

You cannot build a product that gives traders more efficient capital deployment, charges what the risk is actually worth, and leaves anything on the table. Something has to give. Either the fees are too low (someone is absorbing uncompensated risk), or the fees consume the benefit (the product has no real utility), or the market is not actually binary (in which case you are trading a scalar instrument).

Simply, if a financier prices jump risk correctly, the cost of leverage must fully offset the expected profit from leverage.

In other words:

Fairly priced leverage on binary prediction markets has zero expected upside.

Why?

Because the financier must charge enough to compensate for the possibility of total loss in a single jump event. That cost scales with leverage.

The trader's amplified gains are exactly offset by the financing cost required to make the system solvent.

This is not a regulatory constraint. It is a mathematical equilibrium.

And if it holds, it implies something radical:

Leverage does not improve outcomes in binary prediction markets. It simply redistributes risk.

Conclusion: Where Leverage Breaks — and Where the Opportunity Begins

This article has explored a deceptively simple but deeply structural question: why leverage — so fundamental to every major financial market — struggles to exist in prediction markets. What initially appears to be a missing feature reveals itself as something far more profound. The challenge is not regulatory hesitation or product immaturity, but a core mathematical reality: binary prediction markets are discontinuous systems, and that discontinuity introduces jump risk that traditional leverage cannot survive.

On one hand, the case for leverage remains compelling. It is the mechanism that allows informed traders to scale conviction, improve capital efficiency, deepen liquidity, and ultimately enhance price discovery. Without it, prediction markets remain constrained — powerful as forecasting tools, but limited in their ability to attract institutional participation or serve as meaningful hedging instruments for real-world event risk.

On the other hand, the case against leverage is even more fundamental. The same binary structure that makes prediction markets clean and intuitive also makes them fragile under leverage. When outcomes can move from near-certainty to zero instantly, liquidation becomes impossible, and the financier must price in the risk of total loss. The result is the uncomfortable but critical conclusion: if leverage is priced fairly, its cost cancels out its benefit. In this sense, leverage in binary prediction markets is not just risky — it may be economically neutral, offering no true advantage beyond redistributing risk between participants.

This leaves the industry at an inflection point. If binary markets cannot support meaningful leverage without breaking, then the question is no longer whether leverage can be added — but what form prediction markets must evolve into to support it. In the next article, we will go deeper into this frontier. We will explore emerging approaches such as epoch-based pricing models, which attempt to segment and manage jump risk over time, as well as scalar markets, where continuous outcomes may restore the conditions necessary for liquidation, margining, and sustainable leverage. We will also examine whether entirely new structures — such as information perpetuals — offer a more viable path forward.

If you're interested in where prediction markets are actually going — not just as products, but as a new layer of financial and informational infrastructure — this is a conversation worth following closely. These structural questions will determine whether prediction markets remain niche tools or evolve into a core component of global markets.

Follow along as we continue breaking down the mechanics, the misconceptions, and the opportunities shaping the future of prediction markets.