Original | Odaily Planet Daily (@OdailyChina)

Author | Azuma (@azuma_eth)

In the past two days, there has been a lot of discussion on X about the formula for prediction markets Yes + No = 1. The origin can be traced back to a piece written by the big player DFarm (@DFarm_club), which dissects the shared order book mechanism of Polymarket, triggering a collective emotional resonance regarding the power of mathematics — the original article link is “Understanding Polymarket: Why YES + NO Must Equal 1?”, highly recommended for reading.

In the ensuing discussions, several prominent figures, including Blue Fox (@lanhubiji), mentioned that Yes + No = 1 is another minimalist yet powerful formula innovation following x * y = k, which is expected to unlock a trillion-dollar information flow trading market. I completely agree with this, but I also feel that some discussions are overly optimistic.

The key point is this — “Anyone can add liquidity, become a market maker, earn trading fees, lower the participation threshold, and amplify liquidity.” Many people might think that Yes + No = 1 has solved the threshold limitation for ordinary people to participate in market making, so the liquidity in prediction markets will rise like that of AMMs with x * y = k, but the reality is far from it.

The difficulty of market making in prediction markets is inherently higher

In practical environments, whether one can enter the market to provide liquidity and build market making is not just a matter of participation thresholds, but also an economic issue of profitability. Compared to AMM markets based on the x * y = k formula, the difficulty of market making in prediction markets is actually much higher.

For example, in a classic AMM market (like Uniswap V2) that fully adheres to the x * y = k formula, if I want to provide liquidity for the ETH/USDC trading pair, I need to invest ETH and USDC into the pool in a specific ratio based on the real-time price relationship of the two assets in the liquidity pool. When that price relationship fluctuates, the amount of ETH and USDC I can retrieve will vary with the fluctuations (i.e., the familiar impermanent loss), but I can also earn trading fees. Of course, the industry has since innovated around the x * y = k formula, such as Uniswap V3 allowing market makers to concentrate liquidity within specified price ranges to pursue greater risk-reward ratios, but the essence of the model remains unchanged.

In this market making model, if the trading fees can cover impermanent losses within a certain time frame (and it often takes longer to accumulate fees), then it is profitable — as long as the price range is not too aggressive, I can be relatively lazy in market making and check back occasionally. However, in prediction markets, if you want to market make with a similar attitude, the outcome is likely to be a total loss.

Taking Polymarket as another example, let’s assume a basic binary market where I want to market make in a market with a “YES real-time price of $0.58.” I can place a buy order for YES at $0.56 and a sell order for YES at $0.60 — DFarm explained in the article that this is essentially placing a buy order for NO at $0.40 and a sell order for NO at $0.44 — that is, providing order support at specific points slightly above and below the market price.

Now that the orders are placed, can I just lie back and ignore them? When I check back next time, I might see one of the following four situations:

1. Both buy and sell orders remain unfilled;
2. Both buy and sell orders have been filled;
3. Orders in one direction have been filled, and the market price is still within the original order range;
4. Orders in one direction have been filled, but the market price has deviated further away from the remaining orders — for example, I bought YES at $0.56, the sell order at $0.60 is still there, but the market price has dropped to $0.50.

So what situation would lead to a profit? I can tell you that, in low-frequency attempts, different situations may yield different profit and loss results, but if you operate in this lazy manner in a real environment for a long time, the final outcome will basically be a loss. Why is that?

The reason is that prediction markets do not operate on the same liquidity pool market making logic as AMMs; rather, they are closer to the order book market making model of centralized exchanges (CEX), and the operational mechanisms, operational requirements, and risk-reward structures of the two are completely different.

• In terms of operational mechanisms, AMM market making involves pooling funds into a liquidity pool to collectively provide liquidity, which will disperse liquidity across different price ranges based on the x * y = k formula and its variants; order book market making requires placing buy and sell orders at specific points, and liquidity support only exists if there are orders, with transactions achieved through order matching.
• In terms of operational requirements, AMM market making only requires depositing both tokens within a specific price range into the pool, and as long as the price does not move out of the range, it can continue to be effective; order book market making requires proactive and continuous order management, constantly adjusting quotes to respond to market changes.
• In terms of risk-reward composition, AMM market making primarily faces impermanent loss risk, earning fees from the liquidity pool; order book market making must contend with inventory risk in one-sided markets, with profits coming from the bid-ask spread and platform subsidies.

Continuing with the previous example, knowing that the main risk I face when market making on Polymarket is inventory risk, and that profits mainly come from the bid-ask spread and platform subsidies (Polymarket provides liquidity subsidies for certain orders close to the market price, see official homepage), the potential profit and loss situations for the four scenarios are as follows:

1. In the first situation, I miss out on the bid-ask spread but can receive liquidity subsidies;
2. In the second situation, I have profited from the bid-ask spread but will no longer receive liquidity subsidies;
3. In the third situation, I have taken a position in YES or NO, becoming directional (i.e., inventory risk), but in some cases, I can still receive some liquidity subsidies;
4. In the fourth situation, I have also become a directional position, and the position has incurred unrealized losses, while I can no longer receive liquidity subsidies.

Two points need to be noted here. First, the second situation is actually always derived from the third or fourth situation, as often only one side of the order will be filled first, so it may have temporarily become a directional position, but the risk ultimately did not materialize, and the market price fluctuated in the opposite direction, triggering the other side of the order; second, compared to the relatively limited market making profits (the spread and subsidy amounts are often fixed), the risk of directional positions is often unlimited (the upper limit is that all of my YES or NO could go to zero).

In summary, if I want to continuously make money as a market maker, I need to capture profit opportunities while avoiding inventory risk — so I must actively optimize my strategy to maintain the first situation as much as possible, or quickly adjust the order range after one side of the order is triggered to turn it into the second situation, avoiding being stuck in the third or fourth situations for long.

Doing this well over the long term is not easy. Market makers need to first understand the structural differences between different markets, comparing subsidy strength, volatility, settlement times, determination rules, etc.; then they need to track or even predict market price changes more accurately and quickly based on external events and internal capital flows; subsequently, they must actively adjust orders in response to changes while designing and managing inventory risk in advance… This clearly exceeds the capabilities of ordinary users.

A wilder, more erratic, and less principled market

If it were just this, it might not be so bad, as the order book mechanism is not a new concept. In CEX and Perp DEX, order books remain the mainstream market making mechanism, and market makers active in these markets can easily transfer their strategies to prediction markets to continue profiting while injecting liquidity into the latter. However, the reality is not that simple.

Let’s think about this issue together: what is the situation that market makers fear the most? The answer is simple — one-sided markets, because one-sided markets often exacerbate inventory risk, leading to a breakdown of balanced allocations and causing massive losses.

However, compared to traditional cryptocurrency trading markets, prediction markets are inherently wilder, more erratic, and less principled, with one-sided markets appearing more exaggerated, abrupt, and frequent.

Being wilder means that, if we extend the timeline in conventional cryptocurrency trading markets, mainstream assets will still exhibit a certain oscillation pattern, with price trends often rotating in cycles; whereas in prediction markets, the trading targets are essentially event contracts, each with a clear settlement time, and the Yes + No = 1 formula dictates that ultimately only one contract's value will become $1, while all other options will go to zero — this means that bets in prediction markets will ultimately conclude in a one-sided market format starting from a certain point in time, thus market makers need to design and execute inventory risk management more rigorously.

Being more erratic means that the volatility in conventional trading markets is determined by the ongoing battle of emotions and capital, and even with extreme fluctuations, price changes are continuous, allowing market makers some room to adjust inventory, control spreads, and dynamically hedge; but the volatility in prediction markets is often driven by discrete real-world events, with price changes being jumpy — one second the price might be at $0.5, and the next second a real-world dynamic could drive it directly to $0.1 or $0.9, and often you find it very difficult to predict when and why the market will change dramatically, leaving market makers with very little reaction time.

Being less principled means that there are many players in prediction markets who are close to or are sources of insider information, who do not engage in battles with trading counterparts based on predictions of future markets, but rather come in with clear outcomes to harvest — in front of these players, market makers are naturally at an information disadvantage, and the liquidity they provide becomes a channel for these players to cash out. You might ask, don’t market makers have insider information? This is also a typical paradox; if I already know the insider information, why would I bother to market make? I could just place bets directly on the direction and earn more.

It is precisely because of these characteristics that I have long agreed with the statement that “the design of prediction markets is structurally unfriendly to market makers” and I do not recommend ordinary users to easily engage in market making.

So is there no profit to be made in prediction market making? Not necessarily. Buzzing founder Luke ([@DeFiGuyLuke]) has disclosed that, based on market experience, a relatively robust expectation is that market makers on Polymarket can earn about 0.2% of trading volume in profits.

In short, this is not an easy way to make money; only professional players who can accurately track market changes, timely adjust order statuses, and effectively execute risk management can sustain operations over a longer time period and earn money through real skills.

The prediction market track may find it hard to flourish

The market-making challenges in prediction markets raise higher demands on the capabilities of market makers and pose a challenge for platforms in building liquidity.

The difficulty of market making means that liquidity construction is limited, which directly impacts the trading experience of users. To address this issue, leading platforms like Polymarket and Kalshi have chosen to invest real money to subsidize liquidity in order to attract more market makers.

Analyst Nick Ruzicka, focusing on the prediction market track, cited a report from Delphi Digital in November 2025, stating that Polymarket has invested approximately $10 million in liquidity subsidies, once paying over $50,000 daily to attract liquidity. As its leading position and brand effect have solidified, Polymarket has significantly reduced the subsidy intensity, but on average, it still needs to subsidize $0.025 for every $100 in trading volume.

Kalshi also has a similar liquidity subsidy plan and has spent at least $9 million for this purpose. In addition, Kalshi utilized its compliance advantages in 2024 (Odaily Note: Kalshi is the first prediction market platform to receive regulatory approval from the CFTC; Polymarket also obtained approval in November 2025) to sign a market-making agreement with Wall Street's top market-making service provider, Susquehanna International Group (SIG), greatly improving the platform's liquidity situation.

Whether in terms of capital reserves or compliance thresholds, these are solid moats for leading platforms like Polymarket and Kalshi — a few months ago, Polymarket accepted a $2 billion investment from the parent company of the New York Stock Exchange, ICE, at a valuation of $8 billion, and there are reports that it is planning the next round of financing at a valuation exceeding $10 billion. On the other hand, Kalshi has also completed a $300 million financing at a valuation of $5 billion, giving both leaders a substantial reserve of capital.

Currently, prediction markets have become a hotbed for entrepreneurship in the entire market, with new projects continuously emerging, but I am not very optimistic. The reason is that the leading effect of prediction markets is actually stronger than many people imagine. In the face of the continuous subsidies from leading platforms like Polymarket and Kalshi, as well as the dimensional reduction partnerships from the compliance world, what do new projects have to compete directly? How much capital do they have to exhaust against them? While some new projects may have strong backing to generate profits, it is clear that not every one of them does.

Haseeb Qureshi from Dragonfly recently posted his predictions for 2026, stating, “The development of prediction markets is rapid, but 90% of prediction market products will be completely ignored and gradually disappear by the end of the year.” I do not know what his reasoning is, but I agree that this is not alarmist.

Many people are looking forward to a flourishing prediction market track and fantasizing about profiting from past experiences, but this situation may be hard to come by. If one really wants to place bets, it is better to focus directly on the leaders.

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