Leverage Mechanisms and Market Efficiency in Blockchain-Based Prediction Market
Prediction markets aggregate dispersed information by letting participants trade tokenized claims on future outcomes; prices map to implied probabilities (e.g., 0.90 = 90%). In many markets a single outcome becomes the perceived frontrunner (p ≥ 0.90) well before resolution. We call these tail-end markets. They tend to attract few counterparties, exhibit thin liquidity, and often display persistent mispricing (over- or underestimation of probabilities).
Thesis will research whether leverage can counter those frictions. The premise is that carefully designed leverage can (i) draw in informed but capital-constrained traders, (ii) deepen books/pools, and (iii) improve calibration—without introducing excessive liquidation risk or bad debt. Our goal is to identify the leverage mechanism (and parameters) that maximizes market accuracy and capital efficiency while maintaining systemic stability.Research Objectives
We evaluate tail-end markets, diagnose their mispricing and participation patterns, and assess the introduction of leverage across multiple mechanism designs.
RQ1: An Empirical Analysis of Pricing Inefficiency in Polymarket's Tail-End EventsTo what extent do tail-end markets in prediction platforms like Polymarket exhibit pricing inefficiencies or constant volume declines?
- Quantify mispricing in tail-end markets by comparing pre-resolution implied probabilities to actual event outcomes, using calibration curves and Brier scores (Usual, Relative and Ordinal);
- Profitability and Liquidity Assessment Investigate how low liquidity and participant aversion contribute to mispricing and show correlation between volume of market and its probabilities;
RQ2: Mechanisms of Leverage in Blockchain-based Prediction Markets: Comparative Analysis of Perpetuals, Self Collateralized Lending, and USD-Backed Reverse PositionsHow do different leverage mechanisms - (A) perpetual/futures contracts with market probability as spot, (B) lending against the position token (self-collateralized looping), and (C) borrowing USD to purchase the reverse token — differ in capital efficiency, liquidation dynamics, risk of bad debt, and their practical impact on tail-end market pricing and liquidity?
RQ3: Market Stability, Liquidation Cascades & Bad DebtHow do prediction markets handle extreme price movements - do their mechanisms for liquidation and margining prevent cascading sell-offs and the accumulation of bad debt?
- Liquidity & Shock Absorption: Assess how well markets absorb large trades without triggering cascades.
- Liquidation Feasibility & Bad Debt Incidence: Assess whether protocols had sufficient time to liquidate positions given oracle latency, block time, and gas constraints.
Our empirical work combines live-market measurement with counterfactual simulation:
- Gamma Markets API : market metadata, prices, liquidity, timestamps
- dYdX (Leverage Mechanisms & Liquidations) documentation
- Flipr (Social/Leverage Layer on Polymarket) documentation
- Improved Liquidity for Prediction Markets research by Lukas Kapp-Schwoerer