Cold Reflection After a Valuation of 10 Billion: Why Is It Difficult to Predict the Market for High Leverage?

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 sorting through new projects that emerged during the Solana Breakpoint cycle, I noticed that several prediction markets emphasizing leverage features are surfacing. However, looking around the market, the reality is that leading platforms generally shy away from leverage features; new platforms claiming to support these features often face issues like 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 yet to be effectively explored. In the highly risk-tolerant cryptocurrency market, this situation feels extremely discordant. Therefore, I began gathering information to seek answers, during which I came across two quite high-quality analytical articles. One is Messari's Kaleb Rasmussen's research report titled "Enabling Leverage on Prediction Markets," which presents a well-supported argument but is 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," concluding that prediction markets limit the intensity of beliefs 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 its parent company on the New York Stock Exchange, and its founder Shayne Coplan even 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 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 truly 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 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 purchase is 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 brings three characteristics to prediction markets 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 at $0.90, the maximum upside is only 11%. This is unlike buying a meme coin early.

· Second, the lower limit is truly zero. It is not a near-zero drop; it is 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; an election may be undecided one moment, and the next moment the result is announced. Correspondingly, the price does not gradually rise from $0.80 to $1.00 but jumps 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 $1,000 position—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—trading platforms will forcibly close your position before your losses exceed your principal to prevent the lender (the trading platform or liquidity pool) from incurring losses.

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

If you go long on BTC at a price of $100,000 with 10x leverage, you will 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 at a fast pace, 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 refer to it as "scam wicks."

Using BTC as an example. Suppose the price does not gradually decline but jumps directly—$100,000 one second, $80,000 the next, with no intermediate 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 such price in the market; the next executable price goes straight 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; they jump directly. Furthermore, 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 (https://messari.io/report/enabling-leverage-on-prediction-markets). His final conclusion is: If lenders can correctly price jump risk, the fees they need to charge (similar to funding fees) should consume all the upside profits of leveraged positions. This means that for traders, opening leveraged positions at fair rates offers no advantage over opening positions without leverage, while also bearing greater downside risk.

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 feeds 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 established 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 the system going bankrupt; a more brutal version is ADL, which directly liquidates winning positions);

· A dynamic margin mechanism would automatically reduce available leverage as open contracts increased.

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

As results gradually became clear and Trump's victory was almost certain, the price of the "YES share" on Polymarket jumped directly from about $0.60 to $1.00—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 counterparties had sufficient collateral.

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

This is not a garbage DEX without risk control; it was 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 partial failure in a real market environment.

Industry Solutions Offered

In response to the leverage issue in prediction markets, the entire industry has split into three camps, and this division itself reveals each team's attitude toward risk.

Camp One: Limit Leverage

Some teams, after recognizing the mathematical reality, chose the most honest answer—offering almost no leverage.

· HyperliquidX's HIP-4 proposal sets the leverage cap at 1x—not because the technology cannot achieve more, but because they believe this is 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 also believes that without additional protective mechanisms, safe leverage is about 1x to 1.5x.

Camp Two: Use Engineering to Counter Risks

Another group of teams attempts to build sufficiently complex systems to manage risks.

· D8X dynamically adjusts leverage, fees, and slippage based on market conditions—the closer to settlement or extreme probabilities, the stricter the limits;

· dYdX built the protective mechanisms we just saw fail on election night and continues to iterate;

· PredictEX's solution is to raise fees and lower maximum leverage when jump risk increases, then relax them when the market stabilizes—its founder Ben stated bluntly: "If we directly apply the perpetual contract model, market makers will be completely wiped out in a second when probabilities jump from 10% to 99%."

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

Camp Three: Launch First, Fill Gaps Later

Some teams choose to launch quickly, directly claiming 10x, 20x, or even higher leverage, without disclosing how they handle jump risks. Perhaps they have yet-to-be-disclosed elegant solutions, 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?

What we are facing is a problem with an extremely open design space, which is 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; instead, 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.

But these solutions are essentially improvements within the existing framework.

Deepanshu from EthosX proposed a more fundamental reflection; he has researched and built clearing infrastructures like LCH, CME, and Eurex during his time at JPMorgan's global clearing business. In his view, attempting to leverage prediction markets using the perpetual contract model is fundamentally solving 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 trading platforms; 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 before positions become uncontrollable;

· Multi-layered 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 is—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 also build a "clearinghouse-level" system. The infrastructure layer remains unresolved, while market demand is already very clear.

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