BTC-Denominated Prediction Markets

• BTC-denominated prediction markets are platforms that allow participants to speculate on real-world outcomes using Bitcoin, emphasizing decentralization, liquidity, and game-theoretic incentives.
• They employ methodologies such as decentralized oracle mechanisms, automated market making, and DeFi-based collateral redirection to optimize liquidity and manage risk.
• Advanced predictive models and statistical methods, including PDE, Bayesian networks, and ensemble techniques, drive efficient price discovery and robust market signal integration.

BTC-denominated prediction markets are financial platforms enabling participants to speculate on real-world outcomes using Bitcoin as the settlement asset. By leveraging Bitcoin’s decentralization, liquidity, and deflationary nature, these markets aggregate distributed information through mechanisms designed to elicit honest reporting and efficient price discovery. Recent research has focused on designing decentralized oracles, improving liquidity provisioning, exploring game-theoretic incentive structures, modeling price dynamics for market prediction, and benchmarking the operational efficiency and risks associated with BTC-denominated versus stablecoin-denominated venues.

1. Decentralized Market Architecture and Oracle Mechanisms

Prediction markets historically rely on trusted arbiters or centralized event adjudicators, which introduce single points of failure and risk. Notable platforms such as Augur (Peterson et al., 2015) achieve decentralization via on-chain oracles operated by the holders of a native Reputation token (REP). In this architecture, market outcome resolution occurs through a sequence of dispute rounds:

• Any REP holder can report on the event outcome and stake REP as a “bond” in favor of that report.
• Disagreement triggers disputes, wherein other REP holders post progressively larger dispute bonds. The bond sizing formula is given by

$B(\omega, n) = 2A_n - 3S(\omega, n)$

where $A_n$ is the total stake at the beginning of the $n$-th round and $S(\omega, n)$ is the stake already placed on outcome $\omega$.

If the dispute escalates beyond a threshold (e.g., 2.5% of total REP), the system forks into universes corresponding to each possible outcome. REP tokens in incorrect universes lose value, providing strong economic incentives for accurate reporting. In the context of BTC-denominated markets, settlement and staking occur in Bitcoin, and a variant Bitcoin-native “reputation” token may substitute REP, provided adequate market capitalization relative to open interest (Peterson et al., 2015).

Alternatives such as “Decentralized Prediction Market without Arbiters” (Bentov et al., 2017) eliminate central adjudication entirely by employing game-theoretic consensus. Participants transact in BTC and are incentivized to report truthfully via reward structures tied to agreement with the emergent majority. The Nash equilibrium is preserved when each player reveals their true private signal, formalized as:

$U_i = \sum_j w_{ij} \cdot f(r_i, r_j) - C$

2. Liquidity Provisioning and Market Structure

Bootstrapping liquidity in BTC-denominated prediction markets requires reconciling trade-off between user safety, capital efficiency, and deployment convenience (Shabashev, 15 Sep 2025). Three principal methods have been analyzed:

a) Cross-market making

Professional market makers mirror bids/asks from liquid USD (or stablecoin) markets into BTC terms, dynamically hedging BTC/USD exposure by purchasing BTC put options. The transformation from source to downstream quotes is: | Source Market | BTC-Denominated Quote | Adjustment | |:--------------|:---------------------|:-----------| | bid_s | bid_d = bid_s / P_BTCUSD − ε_fee − (c_put/P_BTCUSD) | Subtract fees, hedge costs | | ask_s | ask_d = ask_s / P_BTCUSD + ε_fee + (c_put/P_BTCUSD) | Add fees, hedge costs |

b) Automated Market Making (AMM)

AMMs (e.g., constant product market makers) operate without order books. A standard CPMM maintains $x \cdot y = C$, where $x$ and $y$ are pool quantities of “YES” and “NO” contracts. Parametric formulations using $x^p \cdot y^{1-p} = C$ allow market initiators to seed liquidity per desired odds. AMMs expose LPs to “permanent loss” if the outcome becomes skewed, degrading capital efficiency.

c) DeFi-based Redirection

Users collateralize BTC (wrapped as wBTC), borrow USDC against it at a conservative LTV ratio (e.g., 30–40%), and place USDC-based bets. Profits/losses are reconverted to BTC at market settlement. Liquidation and exchange-rate risks require automated monitoring of health factors and guard thresholds (e.g. $HF_\text{guard} \approx 1.2$).

3. Game-Theoretic Incentive Structures and Token Dynamics

BTC-denominated prediction markets maintain trustless operation by leveraging incentive-compatible mechanisms:

• Participants stake reputation tokens (or the BTC equivalent) on outcomes. Honest reporting is incentivized by redistributing forfeited stake—post-resolution 80% goes to reporters on the truthful outcome, yielding a 40% ROI to those who successfully challenge false claims [(Peterson et al., 2015), Theorem 1].
• Early dispute rounds offer higher ROI due to smaller required bonds, discouraging frivolous disputes and promoting rapid convergence.
• The forking protocol enforces correct reporting by orphaning tokens associated with non-real outcomes. Maintaining the market cap at appropriate multiples of open interest is essential for security against economic attacks.

4. Price Discovery and Market Efficiency

BTC-denominated prediction markets interface with spot and derivatives venues for price feeds and validation. Price discovery and the choice of informative oracles are grounded in quantitative methodologies (Pascual et al., 10 Jun 2025):

• Hasbrouck’s Information Share: Measures each market’s contribution to the efficient price variance; centralized venues (CME, Binance) generally lead decentralized venues (Uniswap).
• Gonzalo–Granger’s Permanent–Transitory Decomposition: Dissects price into permanent (cointegrated trend) and transitory parts; the magnitude of adjustment coefficients indicates which venue drives error correction.
• Hayashi–Yoshida Lead–Lag Estimator: Captures microsecond-scale lead–lag relationships from high-frequency asynchronous data; futures markets frequently “lead” spot venues by fractions of a second during volatility events.

Periods of macroeconomic stress or network congestion (e.g., ETF announcements, record gas fees) can temporarily disrupt lead–lag relationships, but rapid arbitrage restores equilibrium. Prediction markets may need real-time oracle updates and noise filtering to optimize accuracy in odds-setting.

5. Predictive Modeling and Market Signal Integration

The predictability of Bitcoin price—and by extension, the reliability of BTC-denominated prediction markets—has been extensively studied via statistical and machine learning models:

• Partial Differential Equation (PDE) Models: The Bitcoin transaction network, segmented into “chainlets,” is analyzed in continuous space. The PDE for price influence is:

$$\frac{\partial u(x, t)}{\partial t} = \frac{\partial}{\partial x} [d(x) \frac{\partial u(x, t)}{\partial x}] + r(t) u(x, t) h(x)$$

where $u(x, t)$ models the local influence of chainlet cluster $x$ at time $t$, $d(x)$ is the diffusion coefficient, $r(t)$ the temporal factor, and $h(x)$ spatial heterogeneity. Integration over $x$ yields price predictions with relative accuracy $\approx 0.82$ (Wang et al., 2020).

• Dynamic Bayesian Networks (DBN): Causal interdependencies among OHLCV data, technical indicators (RSI, MACD, Bollinger Bands, OBV), and external features (social media sentiment, macro indices) are modeled in a time-sliced probabilistic framework. For Bitcoin, DBNs outperform SVM, ARIMA, LSTM, random forest, and support vector regression, achieving directional precision of 72.19% versus 44–53% for baselines (Amirzadeh et al., 2023).
• Decision Ensemble Frameworks: Customized attention-BiLSTM and XGBoost models are error-weighted to refine BTC-USD forecasts, achieving MAPE of 0.0037 and RMSE of 106.14. The reciprocal error method dynamically reassesses model weights as:

$$W_{bl}(t) = E_{xg}/(E_{bl}+E_{xg}),\,\,\,\, W_{xg}(t) = E_{bl}/(E_{bl}+E_{xg})$$

facilitating approximately 27–40% accuracy improvement over single model approaches (Din et al., 5 Jan 2024).

• Technical Indicator-based ML Classification: XGBoost models integrating MACD, RSI, and Bollinger Bands achieve buy/sell signal accuracy above 92% in BTC trend prediction (Hafid et al., 9 Oct 2024).

Despite these advances, empirical studies document that, in general, Bitcoin’s market tends towards weak-form efficiency. Random walk and martingale properties prevail except for episodic anomalies driven by behavioral or informational asymmetries, challenging the systematic identification and exploitation of arbitrage (Bournassenko, 26 Apr 2025).

6. Comparative Analysis: BTC vs. Stablecoin Denomination

BTC-denominated markets differ considerably from stablecoin alternatives such as USDC, DAI, or USD settlement:

• Asset Exposure: BTC markets enable users to retain upside from Bitcoin appreciation, analogous to gold-based settlement systems. Stablecoin venues entail opportunity cost vis-à-vis fiat risk-free rates and Bitcoin appreciation.
• Risk Profiles: Liquidation and FX risks are inherent in BTC venues (especially with DeFi-based redirection) whereas stablecoin venues avoid these but lose native BTC exposure.
• Liquidity and Adoption: Deep liquidity pools currently favor stablecoin venues; bootstrapping in BTC venues may require cross-market making with active makers or platform subsidies (Shabashev, 15 Sep 2025).
• User Experience: Professional cross-market making in BTC minimizes collateral management and directional risks compared to DeFi redirection, which necessitates margin monitoring and exposes users to volatility-induced liquidations.

The overall success of BTC-denominated prediction markets depends on judicious selection of liquidity provisioning mechanisms, dynamic risk management, and alignment of market cap for staking tokens (if used) to open interest.

7. Challenges, Limitations, and Regulatory Considerations

BTC-denominated prediction markets confront several implementation and operational challenges:

• Scalability: Bitcoin’s transaction throughput and latency may limit real-time market updates, especially relative to stablecoin-based smart contract venues (Bentov et al., 2017).
• Capital Efficiency: Automated market making can be capital-inefficient, and DeFi-based redirection underutilizes collateral due to conservative loan-to-value constraints (Shabashev, 15 Sep 2025).
• Security Risks: The decentralized design reduces arbiter risk but demands robust cryptographic and economic safeguards against manipulation and consensus vulnerabilities.
• Regulatory Uncertainty: Anonymity, cross-border participation, and asset volatility present regulatory hurdles; policymakers may consider tighter oversight of brokers and derivatives to improve transparency and reduce manipulation (Bournassenko, 26 Apr 2025).

Table: Comparative Features of BTC Liquidity Provisioning Methods

Method	User Risk	Capital Efficiency
Cross-market making	Low (hedged)	High (for users)
Automated Market Making	LP permanent loss	Low
DeFi-based Redirection	Liquidation risk	Moderate

Conclusion

BTC-denominated prediction markets represent an application of decentralized finance mechanisms in which Bitcoin serves as both a wager and settlement asset. Protocols such as Augur (Peterson et al., 2015) and colored coins-based approaches (Bentov et al., 2017) illustrate paths to trustless event resolution, while contemporary market studies (Shabashev, 15 Sep 2025, Pascual et al., 10 Jun 2025) detail the complexity of liquidity provisioning and price discovery. Accurate price signaling and robust risk management depend on statistical modeling, incentive design, and market-microstructure selection. Continued research is needed to optimize efficiency, security, and user safety—particularly as these markets evolve alongside regulatory developments and further integration with DeFi primitives.