After researching how to leverage in prediction markets, I found that this problem is almost unsolvable.

This article is from: Nick-RZA

Translation｜Odaily Planet Daily (@OdailyChina); Translator｜Azuma (@azumaeth)_

Editor's Note: This week, while organizing new projects that emerged during the Solana Breakpoint period, I noticed that several prediction markets focusing on leverage features are emerging. However, looking around the market, the current situation is that leading platforms generally shy away from leverage features; new platforms claiming to support these features often face issues such as low multiples and small pools.

Compared to the neighboring hot track of Perp DEX, it seems that the leverage space in the prediction market track has not been effectively explored. In the highly risk-tolerant cryptocurrency market, this situation is extremely incongruous. Therefore, I began to gather information to find answers, during which I came across two quite high-quality analytical articles. One is a research report by Messari's Kaleb Rasmussen titled "Enabling Leverage on Prediction Markets," which is well-argued but too lengthy and mathematically complex to translate conveniently; the other is Linera's Nick-RZA's "Everyone's Promising 20x Leverage on Prediction Markets. Here's Why It's Hard," which is more concise and accessible but sufficiently addresses the leverage dilemma in prediction markets.

Below is the original content by Nick-RZA, translated by Odaily Planet Daily.

Currently, almost everyone wants to add leverage features to prediction markets.

Earlier, I wrote an article titled "Expression Problem" — the conclusion is that prediction markets limit the intensity of belief that capital can express. It turns out that many teams are already trying to solve this problem.

Polymarket has reached a valuation of $9 billion after a $2 billion investment from the parent company of the New York Stock Exchange, and its founder Shayne Coplan even appeared on "60 Minutes." Kalshi initially raised $300 million at a $5 billion valuation and then completed a new round of financing at a $11 billion valuation.

The track is heating up, and competitors are vying for the next layer of demand — leverage. Currently, at least a dozen projects are attempting to build "leveraged prediction markets," with some claiming to achieve 10x, 20x, or even higher. However, when you really study the analyses provided by teams that are seriously addressing this issue (such as HIP-4, Drift's BET, Kalshi's framework) — you will find that their conclusions converge on a very conservative number: between 1x and 1.5x.

This is a huge gap, but where exactly is the problem?

Prediction Markets vs Spot and Futures Trading

Let's start with the basics. Prediction markets allow you to bet on whether a certain event will occur: Will Bitcoin rise to $150,000 by the end of the year? Will the 49ers win the Super Bowl? Will it rain in Tokyo tomorrow?

You are purchasing a "share," and if you predict correctly, you will receive $1; if you are wrong, you get nothing, it's that simple.

If you believe BTC will rise to $150,000 and the price of the "YES share" is $0.40, you can spend $40 to buy 100 shares. If you are correct, you will get back $100, netting a profit of $60; if you are wrong, the $40 is lost.

This mechanism gives prediction markets three characteristics that are completely different from spot trading or perpetual contracts:

First, there is a clear upper limit. The maximum value of a "YES share" (and similarly for "NO shares") is always $1. If you buy in at $0.90, the maximum upside is only 11%. This is not like buying a meme coin early.
Second, the lower limit is truly zero. It is not a near-zero drop but literally zero. Your position will not gradually lose value over time — you either predict correctly or it goes to zero.
Third, the outcome is binary, and the confirmation of the result is usually instantaneous. There is no gradual price discovery process here; an election may be undecided one moment and the next moment the result is announced. Correspondingly, the price will not gradually rise from $0.80 to $1 but will jump directly.

The Essence of Leverage

The essence of leverage is borrowing money to amplify your bet.

If you have $100 and use 10x leverage, you are effectively controlling a position of $1,000 — if the price rises by 10%, you earn not $10 but $100; conversely, if the price drops by 10%, you lose not $10 but your entire principal. This is also the meaning of liquidation — the exchange will forcibly close your position before your losses exceed your principal to avoid the lender (the exchange or liquidity pool) from bearing the loss.

Leverage can exist in conventional assets under one key premise: the price changes of the asset are continuous.

If you go long on BTC at a price of $100,000 with 10x leverage, you would likely face liquidation around $91,000 to $92,000, but BTC will not instantaneously drop from $100,000 to $80,000. It will only decline gradually, even if very quickly, it will be linear — 99,500 → 99,000 → 98,400… During this process, the liquidation engine will intervene in a timely manner and close your position. You may lose money, but the system is safe.

Prediction markets break out of this premise.

Core Issue: Price Jumps

In the derivatives field, this is known as "jump risk" or "gap risk," and the cryptocurrency community might call it "scam wicks."

Using BTC as an example again. Suppose the price does not gradually decline but jumps directly — one second it's $100,000, the next second it's $80,000, with no intermediate transaction prices, no $99,000, no $95,000, and certainly no $91,000 where you could be liquidated.

In this case, the liquidation engine still attempts to close at $91,000, but there is simply no price available in the market; the next executable price goes directly to $80,000. At this point, your position is not just liquidated but deeply insolvent, and this loss must be borne by some party.

This is precisely the situation faced by prediction markets.

When election results are announced, game outcomes are determined, or major news breaks, prices do not move slowly and linearly but jump directly. Moreover, leveraged positions within the system cannot be effectively unwound because there is simply no liquidity in between.

Kaleb Rasmussen from Messari wrote a detailed analysis on this issue (Odaily strongly recommends reading: https://messari.io/report/enabling-leverage-on-prediction-markets). His final conclusion is: If the capital lenders can correctly price the jump risk, the fees they need to charge (similar to funding fees) should consume all the upside gains of the leveraged position. This means that for traders, opening a leveraged position at a fair rate does not provide any advantage over opening a position without leverage, and they also have to bear greater downside risks.

So, when you see a platform claiming to offer 10x or 20x leverage in prediction markets, there are only two possibilities:

Either their fees do not correctly reflect the risk (meaning someone is bearing uncompensated risk);
Or the platform is using some undisclosed mechanism.

Real Case: dYdX's Pitfall Lesson

This is not just theoretical; we have real cases.

In October 2024, dYdX launched TRUMPWIN — a leveraged perpetual market on whether Trump would win the election, supporting up to 20x leverage, with price oracles sourced from Polymarket.

They were not unaware of the risks and even designed multiple protective mechanisms for the system:

Market makers could hedge dYdX's exposure in Polymarket's spot market;
An insurance fund was set up to cover losses when liquidation could not proceed smoothly;
If the insurance fund was exhausted, losses would be shared among all profitable traders (though no one likes it, it's better than system bankruptcy; a more brutal version is ADL, which directly liquidates winning positions);
A dynamic margin mechanism would automatically reduce available leverage as the number of open contracts increased.

Under the standards of perpetual contracts, this was already quite mature. dYdX even publicly issued warnings about de-leveraging risks. Then, election night arrived.

As the results became clearer and Trump's victory was almost certain, the price of the "YES share" on Polymarket jumped directly from about $0.60 to $1 — not gradually, but in a leap that broke the system.

The system attempted to liquidate underwater positions, but there was simply not enough liquidity; the order book was thin; market makers who should have hedged in Polymarket could not adjust their positions in time; the insurance fund was also depleted… When positions could not be liquidated smoothly, random de-leveraging was triggered — the system forcibly closed some positions, regardless of whether the counterpart had sufficient collateral.

According to the analysis results from Kalshi's crypto head John Wang: "Hedging delays, extreme slippage, and evaporating liquidity caused otherwise executable traders to suffer losses." Some traders who should have been safe — with correct positions and sufficient collateral — still incurred losses.

This is not a garbage DEX without risk control, but rather one of the largest decentralized derivatives exchanges in the world, equipped with multiple protective mechanisms and having issued clear warnings in advance.

Even so, its system still experienced partial failures in a real market environment.

Industry Solutions

Regarding the leverage issue in prediction markets, the entire industry has divided into three camps, and this division itself reveals each team's attitude towards risk.

Camp One: Limit Leverage

Some teams, after recognizing the mathematical reality, have chosen the most honest answer — to provide almost no leverage.

HyperliquidX's HIP-4 proposal sets the leverage cap at 1x — not because it is technically impossible, but because they believe this is the only safe level under binary outcomes.
DriftProtocol's BET product requires 100% margin, meaning full collateralization without borrowing.
Kalshi's crypto head John Wang's framework also believes that, without additional protective mechanisms, safe leverage is around 1–1.5x.

Camp Two: Engineering Against Risk

Another group of teams is trying to build sufficiently complex systems to manage risk.

D8X dynamically adjusts leverage, fees, and slippage based on market conditions — the closer to settlement or extreme probabilities, the stricter the limits;
dYdX has built the protective mechanisms we just saw fail on election night and is still iterating;
PredictEX's solution is to increase fees and reduce maximum leverage when the risk of price jumps rises, and then relax them when the market stabilizes — its founder Ben stated bluntly: "If we directly apply the perpetual contract model, market makers would be completely wiped out in the second when the probability jumps from 10% to 99%."

These engineering teams do not claim to have solved the problem; they are merely trying to manage risk in real-time.

Camp Three: Launch First, Fill in Later

Some teams choose to launch quickly, directly claiming 10x, 20x, or even higher leverage, without publicly disclosing how they handle jump risk. Perhaps they have an elegant solution that has not yet been disclosed, or perhaps they want to learn in a production environment.

The crypto industry has a tradition of "running first and reinforcing later," and the market will ultimately test which approach can stand firm.

What Will Happen in the Future?

We are facing a problem with an extremely open design space, which is precisely what makes it most interesting.

Kaleb Rasmussen's Messari report not only diagnosed the problem but also proposed some possible directions:

Do not price the risk for the entire position at once, but charge rolling fees based on changing conditions;
Design an auction mechanism for price jumps to return value to liquidity providers;
Build a system that allows market makers to continue profiting without being crushed by information advantages.

However, these solutions are essentially improvements within the existing framework.

Deepanshu from EthosX offered a more fundamental reflection, having studied and built clearing infrastructures like LCH, CME, and Eurex at JPMorgan's global clearing business. In his view, attempting to leverage prediction markets using the perpetual contract model is itself addressing the wrong problem.

Prediction markets are not perpetual contracts; they are extreme exotic options — more complex than the products typically handled in traditional finance. And exotic options are not traded on perpetual exchanges; they are generally settled through clearing infrastructures specifically designed for their risks. Such infrastructures should be able to:

Provide traders with a time window for margin calls;
Allow a position transfer mechanism for other traders to take over before a position spirals out of control;
Implement multi-tiered insurance funds, enabling participants to clearly accept socialized tail risks.

These are not new — clearinghouses have been managing jump risks for decades. The real challenge lies in — how to achieve all of this on-chain, transparently, and at the speed required by prediction markets.

Dynamic fees and leverage decay are just the starting point; the teams that can truly solve the problem will likely not only create better perpetual engines but will also build a "clearinghouse-level" system. The infrastructure layer remains unresolved, while market demand is already very clear.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。