Forecasting Cryptocurrency Staking Rewards

ArXiv ID: 2401.10931 “View on arXiv”

Authors: Unknown

Abstract

This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors. We investigate two predictive methodologies: a) a straightforward sliding-window average, and b) linear regression models predicated on historical data. The findings reveal that ETH staking rewards can be forecasted with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively, using a 7-day sliding-window average approach. Additionally, we discern diverse prediction accuracies across various cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is identified as superior to the moving-window average for perdicting in the short term for XTZ and ATOM. The results underscore the generally stable and predictable nature of staking rewards for most assets, with MATIC presenting a noteworthy exception.

Keywords: Staking Rewards, Time Series Forecasting, Cryptocurrency, Linear Regression, Sliding Window Average, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders
• Why: The paper employs straightforward predictive models like sliding-window averages and linear regression, with minimal advanced mathematical formalism. However, it demonstrates high empirical rigor through specific error metrics (RMSE) across multiple assets, backtesting protocols with 1- and 7-day forecasts, and detailed discussion of data sources and implementation challenges.

  flowchart TD
    A["Research Goal:<br>Predict Cryptocurrency Staking Rewards"] --> B["Data Input:<br>Historical Staking Reward Data"]
    B --> C{"Methodology Selection"}
    C --> D["Sliding Window<br>Average Model"]
    C --> E["Linear Regression<br>Model"]
    D --> F["Computation:<br>RMSE Evaluation"]
    E --> F
    F --> G["Key Findings & Outcomes"]
    
    subgraph G [" "]
        direction LR
        G1["ETH: High Accuracy<br>RMSE 0.7% - 1.1%"]
        G2["XTZ/ATOM: Linear Regression<br>Better for Short Term"]
        G3["MATIC: Exception<br>Less Predictable"]
    end