Author: Alexander Lin, Crypto KOL

Compiled by: Felix, PANews

The assessment of prediction markets has always been mixed; some view them as a revolutionary infrastructure that can disrupt traditional institutions, while others believe that prediction markets struggle to become a genuine part of mainstream finance. Recently, crypto KOL Alexander Lin published an article highlighting 23 flaws in prediction markets, detailed below.

1. Low capital efficiency

Prediction markets require full collateral without the option of using leverage. In comparison to the 5-10% nominal value margin requirement of perpetual contracts (Perps), the capital efficiency of prediction markets is inferior by a factor of 10 to 20 times. This does not even account for the zero returns on locked capital and the inability to cross-margin across positions.

2. Capital turnover rate is structurally compromised

Because capital is locked throughout the contract duration and ultimately generates a binary outcome, the capital turnover rate is structurally compromised. After contract settlement, positions become void (invalid), hence there is no balance sheet efficiency, and market maker assets cannot compound grow. The same funds, if used for perpetual account trading during the same period, would yield a significantly higher turnover rate (5-10 times): stocks are recycled, position extensions, and hedging operations continue.

3. LP inventory has fundamental flaws

At settlement, half of the assets in the liquidity pool inevitably go to zero. For instance, a spot liquidity pool would rebalance between the assets retaining value; however, in prediction markets, there is neither rebalancing nor residual value, leaving only the "binary collapse" of losers.

4. Lack of natural hedgers

Unlike commodities, interest rates, or foreign exchange, there are no "natural hedgers" providing opposing liquidity in prediction markets. No entity or trader has a natural economic need to oppose event risks. Market makers face pure adverse selection and lack structural trading counterparts. This is a fundamental barrier to scaling.

5. Adverse selection worsens as settlement approaches

As the market approaches settlement, adverse selection intensifies. Traders with superior or more accurate information can buy from losers pricing based on outdated prior information at better prices. This decay is structural and worsens over time.

6. Startup problems: structural liquidity traps

New markets lack liquidity, which leads informed traders to have no incentive to enter (to avoid losses from slippage); and as long as prices are inaccurate, no more traders will surface. Long-tail markets often fail before they begin, and no amount of subsidies can solve this problem.

7. No endogenous demand cycle

Every dollar of trading volume depends on external attention (such as 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, arbitrage brings in more funds.

8. Disconnection from institutional asset allocation

Prediction markets have no link to risk premium, position returns, or factor exposure. Institutional capital lacks a systematic framework to scale these positions or manage risks. These markets do not conform to any standard portfolio construction language or strategy, thus fail to achieve true scalability.

9. Liquidity resets to zero at each settlement

After each settlement, liquidity resets to zero, requiring a complete rebuild from scratch. The accumulated open interest (OI) and depth present in perpetual contracts over time is structurally impossible in prediction markets.

10. Subsidy-driven false prosperity

Subsidies are the only reason that the buy-sell spread does not permanently spiral out of control. Once incentives stop, market liquidity collapses. The "bribed" liquidity is fundamentally unstable and reflects short-termism in market structure.

11. Conflict between trading 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/function decisions.

12. Accuracy becomes an illusion

In high-attention markets, marginal participants without information advantage simply follow public consensus, leading prices to reflect what people "already believe" rather than pricing disparate signals. Accuracy turns into an illusion.

13. Unlimited market creation floods with noise

When markets can be listed without any cost, liquidity and attention become dispersed across thousands of markets. The dynamics of growth and selection are in direct opposition.

14. Problem design can become a tool for attack

The person writing the question controls the standards of the final result determination, with no neutral drafting process, nor incentive mechanisms to ensure question accuracy, and once someone exploits a loophole, there is no recourse.

15. Oracle risks

Decentralized oracles determine truth based on token weight. When an oracle's market cap is smaller than the value of the funds it secures (locks), initiating manipulation becomes a rational trade. Centralized settlement faces the risk of operators being captured or failing.

16. Nominal trading volume is inflated

Trading volume is reported without price adjustment. A $1 trading volume at a price of $0.9 is entirely different from a $1 trading volume at $0.5. The actual risk transfer volume is exaggerated by an order of magnitude, yet everyone cites that inflated number.

17. Reflexivity after scaling

When prediction markets reach sufficient scale, high-probability (e.g., >90%) predictions can change the behavior of relevant participants. This "truth discovery" logic has structural limitations.

18. Cross-platform credibility risks

If the settlement outcomes for the same event differ across platforms, the entire industry appears unreliable. Credibility is shared, and discrepancies between different platforms generally lead to negative expected values.

19. Meta market manipulation

Traders can ensure their positions in prediction markets (secondary markets) by manipulating real-world underlying events (primary markets). Currently, there has yet to be effective position limits or regulatory enforcement observed.

20. Manipulation risks

Due to the absence of position limits and limited regulatory enforcement against manipulation, this means a single wallet can leverage weak market depth and exploit such volatility for reverse trading without facing consequences (accountability is impossible), which is particularly severe on Polymarket compared to Kalshi.

21. Lack of complex financial instruments

There is no term structure, conditional orders, or combinability. Besides a single binary outcome, the entire derivatives toolkit is non-existent, which prevents professional institutions from entering.

22. Fragmented regulation

As regulations tighten, the differences between federal and state levels will force liquidity to fragment. When markets are divided among different participant pools, the price discovery function collapses.

23. Innovator's dilemma

Existing giants lack incentive to redesign architecture. If trading volumes continue to grow and regulatory barriers persist, any change in architecture will become increasingly costly. This illustrates the classic innovator's dilemma.

Related reading: Who is the king of prediction markets, Polymarket or Kalshi?

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