Bootstrapping Liquidity in BTC-Denominated Prediction Markets

ArXiv ID: 2509.11990 “View on arXiv”

Authors: Fedor Shabashev

Abstract

Prediction markets have gained adoption as on-chain mechanisms for aggregating information, with platforms such as Polymarket demonstrating demand for stablecoin-denominated markets. However, denominating in non-interest-bearing stablecoins introduces inefficiencies: participants face opportunity costs relative to the fiat risk-free rate, and Bitcoin holders in particular lose exposure to BTC appreciation when converting into stablecoins. This paper explores the case for prediction markets denominated in Bitcoin, treating BTC as a deflationary settlement asset analogous to gold under the classical gold standard. We analyse three methods of supplying liquidity to a newly created BTC-denominated prediction market: cross-market making against existing stablecoin venues, automated market making, and DeFi-based redirection of user trades. For each approach we evaluate execution mechanics, risks (slippage, exchange-rate risk, and liquidation risk), and capital efficiency. Our analysis shows that cross-market making provides the most user-friendly risk profile, though it requires active professional makers or platform-subsidised liquidity. DeFi redirection offers rapid bootstrapping and reuse of existing USDC liquidity, but exposes users to liquidation thresholds and exchange-rate volatility, reducing capital efficiency. Automated market making is simple to deploy but capital-inefficient and exposes liquidity providers to permanent loss. The results suggest that BTC-denominated prediction markets are feasible, but their success depends critically on the choice of liquidity provisioning mechanism and the trade-off between user safety and deployment convenience.

Keywords: prediction markets, Bitcoin denomination, liquidity provisioning, automated market making, cross-market making, Cryptocurrency / Prediction Markets

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Philosophers
  • Why: The paper’s mathematical complexity is moderate, relying on basic arithmetic and pricing formulas without advanced derivations, while empirical rigor is low as it lacks backtests, datasets, or implementation details, focusing instead on conceptual trade-offs.
  flowchart TD
    A["Research Goal: Feasibility of<br>Bootstrapping Liquidity in BTC-Denominated Prediction Markets"] --> B{"Methodology: Comparative Analysis"}
    B --> C["Liquidity Provisioning Methods<br>Cross-Market Making<br>Automated Market Making<br>DeFi Trade Redirection"]
    C --> D{"Evaluation Criteria<br>Execution Mechanics, Capital Efficiency, Risks"}
    D --> E["Risk Assessment<br>Slippage, Exchange-Rate Volatility, Liquidation"]
    E --> F{"Computational Analysis<br>Comparative Risk & Efficiency Modelling"}
    F --> G["Key Findings & Outcomes<br>1. Cross-Market Making: Best user safety, requires capital<br>2. DeFi Redirection: Fast bootstrapping, high volatility risk<br>3. AMM: Simple deployment, capital-inefficient"]
    G --> H["Conclusion: BTC Markets Feasible<br>Success depends on balancing user safety vs deployment convenience"]