This article evaluates the accuracy of Bitcoin price forecasts generated by Synth Subnet miners. It compares their dynamic, market-responsive models with a standard Geometric Brownian Motion (GBM) benchmark, using simulated price paths and statistical measures like log-likelihoods and Mean Squared Error (MSE).

Key Points:

Objective

Assess how well the Synth Subnet forecasts Bitcoin price movements and liquidation probabilities, with a focus on helping traders manage risk and set leverage levels.

Methodology

The study analyzed data from February 14th to March 10th, 2025. It involved running 1,000 simulations per miner (top 10 miners were selected) to estimate the probability of Bitcoin price changes over different time horizons (6, 12, and 24 hours) at various thresholds (±1%, ±5%, ±10%). The forecasts were then compared against a GBM model using log-likelihood differences and relative improvements in MSE.

Results:

• Forecast Responsiveness: Synth’s dynamic approach adapts more quickly to market shifts than the static GBM model.

• Statistical Superiority: The Synth models significantly outperformed GBM in log-likelihood scores, particularly from March 1st onward for longer time horizons.

• MSE Improvements: For a 6-hour horizon, Synth models achieved an approximate 19.64% reduction in MSE compared to GBM.

Implications for Traders

Enhanced liquidation probability forecasts can lead to better risk management and more informed decisions regarding leverage and position sizing. The analysis distinguishes between long and short trading scenarios, offering tailored insights for each strategy.

For a more indepth analysis please see the full article here

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