Billion-Dollar Valuation Deep Dive: Why is the Market Struggling to Support High Leverage After Reaching a Ten Billion Valuation?
Original Article Title: Everyone's Promising 20x Leverage on Prediction Markets. Here's Why It's Hard
Original Article Author: @hyperreal_nick, Crypto KOL
Original Article Translation: Azuma, Odaily Planet Daily
Editor's Note: This week, while compiling the new projects emerging during the Solana Breakpoint cycle, I noticed that some prediction markets emphasizing leverage functionality were popping up. However, upon surveying the market, the current situation is that while established platforms tend to steer clear of leverage functionality, the new platforms claiming to support the feature generally face issues such as lower multipliers and smaller pools.
Compared to the adjacent hot track of 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 is highly discordant. Therefore, I began gathering information to find answers, and during this process, I came across two quite high-quality analytical articles. One is Messari's Kaleb Rasmussen's research report "Enabling Leverage on Prediction Markets," which provides a very thorough argument, but is inconvenient to translate due to its lengthy nature and numerous mathematical calculations; the other is Nick-RZA's "Everyone's Promising 20x Leverage on Prediction Markets. Here's Why It's Hard" from Linera, which is more concise and straightforward, yet sufficient to address the leverage challenge in prediction markets.
Below is the original content by Nick-RZA, translated by Odaily Planet Daily.
Currently, almost everyone wants to add leverage functionality to prediction markets.
Earlier, I wrote an article titled "The Expression Problem," and the conclusion was that prediction markets restrict the intensity of beliefs that capital can express. It turns out that many teams are already trying to solve this problem.
After a $20 billion investment from the parent company of the New York Stock Exchange, Polymarket is now valued at $90 billion, and its founder Shayne Coplan even appeared on "60 Minutes." Kalshi was initially valued at $5 billion and raised $300 million, then completed a new $1 billion funding round at a valuation of $110 billion.
The race is heating up, with competitors vying for the next layer of demand—leverage. Currently, there are at least a dozen projects attempting to build a "leveraged prediction market," with some claiming to achieve 10x, 20x, or even higher leverage. However, when you delve into the analysis provided by teams genuinely tackling this issue (such as HIP-4, Drift's BET, Kalshi's framework)—you will find that their conclusions are converging on a very conservative number: between 1x and 1.5x.
This is a significant gap, so 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 an event will occur: Will Bitcoin reach $150,000 by the end of the year? Will Team 49 win the Super Bowl? Will it rain in Tokyo tomorrow?
You are purchasing a kind of "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, making a net profit of $60; if you are wrong, you lose your $40.
This mechanism brings three characteristics to prediction markets that are fundamentally different from spot trading or perpetual contracts:
· First, there is a clear upper limit. The highest value of a "YES share" (similarly for a "NO share") will always be $1. If you buy at $0.90, the maximum upside potential is only 11%. This is not like buying an early meme coin.
· Second, the lower limit is a true zero. It's not a nearly-zero crash; it's a literal zero. Your position will not gradually diminish over time—either you predict correctly, or it zeros out.
· Third, the outcome is binary, and the result is usually confirmed instantaneously. There is no gradual price discovery process here; elections may be undecided one moment and results are immediately announced the next. Consequently, the price does not slowly rise from $0.80 to $1; it "jumps" directly to $1.
The Essence of Leverage
The essence of leverage is to borrow money to amplify your bet.
If you have $100 and use 10x leverage, you are actually controlling a $1000 position— if the price goes up by 10%, you earn not $10, but $100; conversely, if the price drops by 10%, you don't lose $10, but your entire principal. This is also the meaning of liquidation— the trading platform will forcibly close your position before you lose more than your principal to prevent the lending party (the trading platform or liquidity pool) from incurring losses.
The key premise for leverage to work on traditional assets is that the price movement of the asset is continuous.
If you go long on BTC with 10x leverage at a $100,000 price point, you will likely be liquidated around $91,000–$92,000, but BTC will not instantaneously drop from $100,000 to $80,000. It will decline gradually, even if rapidly, in a linear manner— 99500 → 99000 → 98400 …… During this process, the liquidation engine will intervene timely and close your position. You may lose money, but the system is secure.
Predictive markets, however, break free from this premise.
Core Issue: Price Jumps
In the derivatives field, this is known as "jump risk" or "gap risk," while the cryptocurrency community might call it "scam wicks."
Let's stick with the BTC example. Imagine the price doesn't gradually drop but instead jumps directly— from $100,000 one second to $80,000 the next, with no trades in between at prices like $99,000, $95,000, or $91,000 where you could be liquidated.
In this scenario, the liquidation engine still attempts to close at $91,000, but that price simply doesn't exist in the market, and the next available price is $80,000. At this point, your position is not just liquidated but plunged into insolvency, and someone has to bear this loss.
This is precisely the situation predictive markets face.
When election results are announced, a match outcome is determined, or significant news breaks, the price doesn't move slowly in a linear fashion but experiences wild swings. Furthermore, leveraged positions within the system cannot be efficiently unwound because there is simply no liquidity in between.
Messari's Kaleb Rasmussen once wrote a detailed analysis on this issue (https://messari.io/report/enabling-leverage-on-prediction-markets). The ultimate conclusion he provided was: If the lender can correctly price jump risk, the fee they need to charge (similar to a funding rate) should consume all the upside gains of the leveraged position. This means that for a trader, opening a leveraged position at a fair rate does not offer any advantage in returns compared to directly taking a position without leverage, and also exposes them to greater downside risk.
So, when you see a platform claiming to offer 10x, 20x leverage in the prediction market, there are only two possibilities:
· Either their fees do not accurately reflect the risk (meaning someone is bearing uncompensated risk);
· Or the platform is using some undisclosed mechanism.
Real-Life Example: Lesson Learned from dYdX
This is not just theoretical talk, we have already had real-life examples.
In October 2024, dYdX launched TRUMPWIN—an leveraged perpetual market on whether Trump would win the election, supporting up to 20x leverage, with the price feed coming 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;
· There was an insurance fund to cover losses in case of unsuccessful liquidation;
· If the insurance fund was depleted, losses would be shared among all profitable traders (though no one likes it, it's better than bankruptcy; a harsher version is ADL, liquidating winning positions directly);
· A dynamic margin mechanism would automatically reduce available leverage as open interest increased.
By the standards of perpetual contracts, this was already quite mature. dYdX even publicly issued a warning about deleveraging risks. Then, election night arrived.
As the results gradually became clear, a Trump victory became almost certain. The price of the "YES shares" on Polymarket jumped from around $0.60 to $1—not gradually, but in a jump, piercing through the system.
The system attempted to settle underwater positions, but there was simply not enough liquidity, and the order book was thin; the market maker that was supposed to hedge on Polymarket also couldn't adjust their position in time; the insurance fund was also depleted... When positions couldn't be smoothly settled, a random deleveraging event was triggered—forcing the system to close part of the positions, regardless of whether the counterparty had sufficient collateral.
According to the analysis by Kalshi's crypto lead John Wang: "Hedging delays, extreme slippage, and liquidity evaporation have caused originally executable traders to suffer losses." Some traders who should have been safe—with correct positions and sufficient collateral—still suffered losses.
This is not a risk-management-lacking junk DEX; it used to be one of the world's largest decentralized derivatives trading platforms, with multiple layers of protection and issued clear warnings in advance.
Nevertheless, its system still experienced partial failure in a real market environment.
Industry-proposed Solutions
Regarding 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: Limiting Leverage
Some teams, after realizing the mathematical reality, have chosen the most honest answer—hardly providing any leverage.
· HyperliquidX's HIP-4 proposal sets the leverage cap at 1x—this is not because it's technically unfeasible, but because they believe it's the only safe level in a binary outcome.
· DriftProtocol's BET product requires 100% collateral, meaning full collateralization with no borrowing.
· Kalshi's crypto lead John Wang's framework similarly suggests that, without additional protective mechanisms, safe leverage is around 1–1.5x.
Camp Two: Leveraging Engineering to Mitigate Risk
Another set of teams are attempting 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 probability, the stricter the limits;
· dYdX has developed the safeguard mechanism that we just witnessed fail on election night and is still continuously iterating;
· The solution proposed by PredictEX is to increase fees and reduce maximum leverage when price jump risk rises, and then relax these measures when the market stabilizes — its founder Ben put it bluntly: "If the perpetual contract model is directly applied, market makers will be completely wiped out in one second when the probability jumps from 10% to 99%."
These engineering-oriented teams do not claim to have solved the problem; they are simply attempting real-time risk management.
Camp Three: Launch First, Fill in the Gaps Later
There are also teams that choose to launch quickly, directly claiming 10x, 20x, or even higher leverage without publicly disclosing how they handle jump risk. Perhaps they have elegant solutions that have not been disclosed, or perhaps they intend to learn in a production environment.
The crypto industry has always had a tradition of "move fast and break things," and the market will ultimately determine which approach holds up.
What Will Happen in the Future?
What we are facing is a problem with an extremely open design space, which is precisely its most interesting aspect.
Kaleb Rasmussen's report from Messari not only diagnoses the problem but also proposes some possible directions:
· Do not price risk for the entire position at once but charge rolling fees based on changing conditions;
· Design an auction mechanism for price jumps to give value back to liquidity providers;
· Build a system that allows market makers to sustainably profit without being crushed by information asymmetry.
However, these solutions are essentially improvements within the existing framework.
Deepanshu from EthosX has proposed a more fundamental rethink. Having previously researched and built clearing infrastructures at JPMorgan's Global Clearing Business for LCH, CME, Eurex, and others, he believes that trying to add leverage to the prediction market using the perpetual contract model is fundamentally addressing the wrong problem.
The prediction market is not a perpetual contract; it is an extreme form of exotic options — more complex than products usually dealt with in traditional finance. Exotic options do not trade on perpetual trading platforms; they are generally settled through payment systems designed specifically for their risks. Such infrastructure should be able to:
· Provide traders with a time window for margin call response;
· Allow other traders to take over positions before they get liquidated;
· Implement a multi-layered insurance fund to make participants explicitly accept the socialization of tail risk.
These are not new—clearinghouses have been managing tail risk for decades. The real challenge is—how to achieve all this on-chain, transparently, at the speed dictated by the market's needs.
Dynamic fees and leverage decay are just the beginning; the teams that will truly solve the problem are likely not only to build a better perpetual engine but also to construct a "clearinghouse-level" system. The infrastructure layer remains unresolved, while the market demand has become very clear.
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