Original title: Everyone's Promising 20x Leverage on Prediction Markets. Here's Why It's Hard
Original author: @hyperreal_nick, crypto KOL
Original translation: Azuma, Odaily Planet Daily
Editor's note: This week, while organizing new projects that have emerged during the Solana Breakpoint cycle, I noticed that some prediction markets focusing on leveraged features are rising. However, looking around the market, the current situation is that leading platforms generally avoid leveraged features; new platforms claiming to support these features often face issues such as low multiples and small pools.
Compared to another hot track next door, 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 inconsistent. Therefore, I started collecting information to find answers, during which I came across two relatively high-quality analytical articles. One is a research report by Kaleb Rasmussen from Messari on this issue (Enabling Leverage on Prediction Markets), which is very well-reasoned but inconvenient to translate due to its length and mathematical calculations; the other is by Nick-RZA from Linera (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.
The following is the original content from Nick-RZA, translated by Odaily Planet Daily.
Currently, almost everyone wants to add leverage functionality 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 have been trying to solve this problem.
Polymarket has reached a valuation of $9 billion after investing $2 billion from its parent company on the New York Stock Exchange; its founder Shayne Coplan also appeared on (60 Minutes). Kalshi first raised $300 million at a $5 billion valuation and then completed a new round of financing at a $11 billion valuation.
The competition is heating up, and participants are competing for the next layer of demand—leverage. Currently, at least a dozen projects are trying to build 'leveraged prediction markets', with some claiming to achieve 10x, 20x, or even higher, but when you really study the analyses provided by the teams that are seriously addressing this issue (like HIP-4, Drift's BET, and Kalshi's framework)—you'll 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 contract trading
Let's start with the basics. Prediction markets allow you to bet on whether a certain event will happen: 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?
What you are purchasing is a type of 'share'; if you predict correctly, you will receive $1; if you are wrong, you get nothing, it's that simple.
If you think 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're right, you'll get back $100, netting $60; if you're wrong, the $40 is gone.
This mechanism brings three characteristics that are completely different from spot trading or perpetual contracts to prediction markets:
· First, there is a clear upper limit. The maximum value of 'YES shares' (and similarly for 'NO shares') is always $1. If you buy in at $0.90, the maximum upside is only 11%. This is different from buying a meme coin early.
· Second, the lower limit is true zero. Not a catastrophic drop close to zero, 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 outcome is usually instantaneous. There is no gradual price discovery process here; the election may be undecided one moment and the next moment the results are announced. Correspondingly, the price does not gradually rise from $0.80 to $1.00, but jumps directly past it.
The essence of leverage
The essence of leverage is borrowing money to amplify your bets.
If you have $100 and use 10x leverage, you are actually controlling a position of $1000—if the price rises by 10%, you do not earn $10, but $100; conversely, if the price falls by 10%, you do not lose $10, but your entire principal. This is also the meaning of liquidation—trading platforms will forcibly close positions before you lose beyond your principal to avoid losses for lenders (trading platforms or liquidity pools).
The reason leverage can exist in conventional assets is that there is a key premise: the price changes of assets are continuous.
If you go long on BTC at $100,000 with 10x leverage, you would likely be liquidated around $91,000 to $92,000, but BTC would not instantaneously drop from $100,000 to $80,000. It would only decrease bit by bit, even if at a very fast speed, it would still be linear—99500 → 99000 → 98400... During this process, the liquidation engine would intervene in a timely manner to close your position. You might lose money, but the system is safe.
Prediction markets have jumped out of this premise.
Core issue: price jumps
In the derivatives space, this is known as 'jump risk' or 'gap risk', while the cryptocurrency community might call it 'scam wicks'.
Using BTC as an example. Suppose the price does not gradually fall but jumps directly—one second $100,000, the next second $80,000, with no transaction prices in between, no $99,000, no $95,000, and certainly no $91,000 at which you could be liquidated.
In this situation, the liquidation engine still attempts to close at $91,000, but this price does not exist in the market at all, and the next executable price goes directly to $80,000. At this point, your position is not just liquidated but is in deep insolvency, and this loss must be borne by some role.
This is exactly the situation faced by prediction markets.
When election results are announced, game outcomes are decided, or major news breaks, prices will not move slowly and linearly but will jump directly. Additionally, leveraged positions within the system cannot be effectively unwound because there is simply no liquidity in between.
Kaleb Rasmussen of Messari wrote a detailed analysis on this issue (https://messari.io/report/enabling-leverage-on-prediction-markets). His final conclusion is that if lenders can correctly price jump risk, the fees they need to charge (similar to funding fees) should consume all the upside benefits of leveraged positions. This means that for traders, opening leveraged positions at fair rates does not provide an advantage over building positions without leverage, and they also need 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 risks (meaning someone is bearing uncompensated risks);
· Either the platform has used some undisclosed mechanism.
Real case: dYdX's pitfalls
This is not just theory; 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 oracle data from Polymarket.
They are not unaware of the risks and have even designed multiple protective mechanisms for the system:
· Market makers can hedge dYdX's exposure in Polymarket's spot market;
· Set up an insurance fund to cover losses when smooth liquidation is not possible;
· If the insurance fund is exhausted, losses will be shared among all profitable traders (although no one likes it, it's better than the system going bankrupt; a more brutal version is ADL, which directly liquidates winning positions);
· Dynamic margin mechanisms will automatically reduce available leverage as open contracts increase.
Under the standards of perpetual contracts, this has already matured significantly. dYdX even openly released warnings about the risks of deleveraging. Then, election night came.
As the results gradually became clear, Trump's victory seemed almost certain; the price of 'YES shares' on Polymarket jumped directly from about $0.60 to $1.00—not incrementally but in a leap, and this jump 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 didn't have time to adjust their positions; the insurance fund was also breached... When positions cannot be smoothly liquidated, random deleveraging was initiated—the system forcibly closed some positions, regardless of whether the counterpart had sufficient collateral.
According to an analysis by Kalshi's crypto chief John Wang: 'Hedging delays, extreme slippage, and liquidity evaporation have caused traders who should have been able to execute to suffer losses.' Some traders who should have been safe—correct positions and sufficient collateral—still suffered losses.
This is not a risk-free garbage DEX, but once one of the largest decentralized derivatives trading platforms in the world, with multiple layers of protective mechanisms and clear warnings issued in advance.
Even so, its system still experienced some failures in a real market environment.
Industry solutions
Regarding the leverage issue in prediction markets, the entire industry has already differentiated into three camps, and this differentiation itself reveals each team's attitude towards risk.
Camp One: Limit leverage
Some teams, after seeing the mathematical reality, chose the most honest answer—almost no leverage.
· HyperliquidX's HIP-4 proposal sets the leverage cap at 1x—not because the technology can't do it, but because it's deemed the only safe level under binary outcomes.
· DriftProtocol's BET product requires 100% collateral, meaning full collateralization without borrowing.
· Kalshi's crypto head John Wang's framework similarly believes that without additional protective mechanisms, safe leverage is around 1–1.5 times.
Camp Two: Use engineering to combat risk
Another part of the team is trying to build a sufficiently complex system to manage risks.
· D8X will dynamically adjust leverage, fees, and slippage based on market conditions—the closer to settlement or extreme probability, the stricter the limits;
· dYdX has built the protective mechanisms we just saw fail on election night, and is still iterating;
· PredictEX's proposal is that when the risk of price jumps increases, fees will rise and maximum leverage will be reduced, and then relaxed when the market stabilizes—its founder Ben bluntly stated: 'If the perpetual contract model is applied directly, market makers will be completely blown up in the second when the probability jumps from 10% to 99%.'
These engineering teams do not claim to have solved the problem; they are just trying to manage risks in real time.
Camp Three: Get in first, fill in later
Some teams choose to go live quickly, directly claiming 10x, 20x, or even higher leverage, but have not disclosed how they handle jump risk. Perhaps they have an elegant solution that has not yet been made public, or they want to learn in a production environment.
The cryptocurrency industry has a tradition of 'running first and solidifying later'; the market will ultimately test which approach can stand the test.
What will happen in the future?
What we are facing is an extremely open design space problem, 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 risk for the entire position at once, but rather charge rolling fees based on changing conditions;
· Design auction mechanisms for price jumps that return value to liquidity providers;
· Build a system that allows market makers to continue to profit without being crushed by information advantages.
However, these solutions are essentially improvements on the existing architecture.
Deepanshu from EthosX proposed a more fundamental reflection; he has researched and built clearing infrastructures such as LCH, CME, and Eurex in JPMorgan's global clearing business. In his view, trying to leverage prediction markets using the perpetual contract model is itself solving the wrong problem.
Prediction markets are not perpetual contracts but extreme exotic options—more complex than traditional financial products usually handled. And exotic options are not traded on perpetual trading platforms; they are generally settled through clearing infrastructures specifically designed for their risks. Such infrastructures should be able to achieve:
· Give traders a time window for margin calls;
· A mechanism for transferring positions to allow other traders to take over before the position is out of control;
· Multi-layer insurance funds to socialise the acceptance of tail risks among participants.
These are not new—clearinghouses have been managing jump risks for decades. The real challenge is how to achieve all this on-chain, transparently, at the speed required by prediction markets.
Dynamic fees and leverage decay are just the starting point; ultimately, the teams that can truly solve the problem will likely not only create a better perpetual engine but will also build a 'clearinghouse-level' system. The infrastructure layer has yet to be resolved, while market demand has become very clear.
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