Author: Alexander Lin, Crypto KOL
Compiled by: Felix, PANews
Original title: 23 Major Defects of Prediction Markets
Opinions on prediction markets have always been mixed. Some see them as new infrastructure that can disrupt traditional institutions, while others believe prediction markets will struggle to become a mainstream part of finance. Recently, crypto KOL Alexander Lin published an article outlining 23 flaws of prediction markets. The details are as follows.
1. Low Capital Efficiency
Prediction markets require full collateral and do not allow leverage. Compared to the 5-10% notional value margin requirement for perpetual contracts (Perps), the capital efficiency of prediction markets is 10 to 20 times worse. This doesn't even account for the zero yield on locked capital and the inability to cross-margin across positions.
2. Structurally Broken Capital Turnover
Since capital is locked for the entire duration of the contract and ultimately produces a binary outcome, capital turnover is structurally broken. After contract settlement, positions become worthless (expire), so there is no balance sheet efficiency, and market makers' assets cannot grow through compounding. The same capital used for perpetual trading over the same period would yield a higher turnover rate (5-10 times): inventory is recycled, positions are rolled over, and hedging operations continue.
3. Fundamentally Flawed LP Inventory
At settlement, half of the assets in the liquidity pool are destined to go to zero. For example, spot pools rebalance between assets that retain value; but for prediction markets, there is no rebalancing, no residual value—only the "binary collapse" of the losers.
4. Lack of Natural Hedgers
Unlike commodities, interest rates, or foreign exchange, there are no "natural hedgers" in prediction markets providing counter liquidity. No entity or trader has a natural economic need to be on the opposite side of event risk. Market makers face pure adverse selection, lacking structural counterparties. This is a fundamental barrier to scaling.
5. Adverse Selection Intensifies Near Settlement
As the market approaches settlement, adverse selection intensifies. Traders with an advantage or more accurate information can buy the winning side from losers, who are still pricing based on outdated prior information, at a better price. This attrition is structural and worsens over time.
6. The Bootstrapping Problem: Structural Liquidity Trap
New markets have no liquidity, so informed traders have no incentive to enter (to avoid losses from slippage); and as long as the price is inaccurate, no more traders will appear. Long-tail markets often die before they even start, and no subsidy can solve this problem.
7. No Endogenous Demand Loop
Every dollar of trading volume relies on external attention (e.g., elections, news, sports events), with no support between events. In contrast, perpetual contracts create an internal flywheel: trading generates funding rates, funding rates create arbitrage opportunities, and arbitrage brings in more capital.
8. Disconnected from Institutional Asset Allocation
Prediction markets have no connection to risk premia, carry, or factor exposure. Institutional capital has no systematic framework for scaling the allocation or risk management of these positions. These markets do not fit any standard portfolio construction language or strategy, so they cannot truly achieve scale.
9. Liquidity Resets to Zero at Each Settlement
Liquidity resets to zero after each settlement and must be rebuilt from scratch. The open interest (OI) and depth accumulated over time in perpetual contracts are structurally impossible in prediction markets.
10. Subsidy-Driven False Prosperity
Subsidies are the only reason bid-ask spreads haven't permanently spiraled out of control. Once incentives stop, order book liquidity collapses. Liquidity "bribed" out in this way is essentially a broken and short-termist market structure.
11. The Trade-off Between Volume and Information Quality
Platforms profit from trading volume (e.g., "We need gambling volume!") rather than accuracy, while regulators require predictive utility to justify the existence of these platforms. This trade-off leads to suboptimal product/feature decisions.
12. Accuracy as an Illusion
In high-attention markets, marginal participants with no information advantage simply follow the public consensus, causing prices to reflect what people "already believe" rather than pricing dispersed signals. Accuracy becomes an illusion.
13. Unlimited Market Creation Creates Noise
When listing costs nothing, liquidity and attention are fragmented across thousands of markets. The incentive for growth is directly opposed to the incentive for curation.
14. Question Design as an Attack Vector
The person writing the question controls the criteria for determining the final outcome. There is no neutral drafting process, no incentive to ensure question precision, and no recourse if someone exploits a loophole.
15. Oracle Risk
Decentralized oracles determine truth by token weight. When the oracle's market cap is less than the value of the funds it secures (locks), manipulation becomes a rational trade. Centralized settlement faces the risk of operator capture or failure.
16. Inflated Nominal Trading Volume
Reported trading volume is not price-adjusted. $1 of volume at a price of $0.9 is completely different from $1 of volume at $0.5. The actual amount of risk transfer is exaggerated by an order of magnitude, but everyone quotes the inflated number.
17. Reflexivity at Scale
When prediction markets become large enough, high-probability predictions (e.g., >90%) themselves change the behavior of the relevant participants. This "truth discovery" logic has structural limits.
18. Cross-Platform Credibility Risk
If the same event settles differently on different platforms, the entire industry appears unreliable. Credibility is shared, and discrepancies between platforms create negative expected value overall.
19. Meta-Market Manipulation
Traders can manipulate the actual underlying event (primary market) to secure their prediction market (secondary market) positions. Effective position limits or regulatory enforcement have not yet been seen.
20. Manipulation Risk
With no position limits and limited regulatory enforcement for manipulation, this means a single wallet can move thin markets and trade against that volatility with no consequences (no accountability). This problem is particularly severe on Polymarket compared to Kalshi.
21. Lack of Sophisticated Financial Instruments
No term structure, conditional orders, or composability. The entire derivatives toolkit is completely absent beyond a single binary outcome, preventing professional institutions from entering.
22. Regulatory Fragmentation
As regulation tightens, federal vs. state differences will force liquidity fragmentation. When markets are split into different participant pools, price discovery breaks down.
23. The Innovator's Dilemma
Existing giants have no incentive to redesign the architecture. If volume continues to grow and regulatory moats form, any architectural change becomes more expensive. This is the classic innovator's dilemma.
Twitter:https://twitter.com/BitpushNewsCN
Bitpush TG Discussion Group:https://t.me/BitPushCommunity
Bitpush TG Subscription: https://t.me/bitpush
