Paper: arXiv 2401.10931

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.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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

Cited References (21)

  1. Blockchain without waste: Proof-of-stake (2021)
  2. Evaluation of performance and security of proof of work and proof of stake using blockchain (2021)
  3. Proof-of-stake consensus mechanisms for future blockchain networks: fundamentals, applications and opportunities (2019)
  4. What proof of stake is and why it matters (2013)
  5. Ethereum white paper (2013)
  6. Cosmos whitepaper (2019)
  7. Equilibrium staking levels in a proof-of-stake blockchain (2021)
  8. Long short-term memory (1997)
and 13 more
  1. Optimal Staking and Liquid Token Holding Decisions in Cryptocurrency Markets (2023)
  2. Reap the harvest on blockchain: A survey of yield farming protocols (2022)
  3. Staking pool centralization in proof-of-stake blockchain network (2020)
  4. Staking pools on blockchains (2022) — link
  5. A survey on machine learning models for financial time series forecasting (2022)
  6. Machine Learning approaches in stock price prediction: A systematic review (2022)
  7. Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices (2021)
  8. Deep learning in predicting cryptocurrency volatility (2022)
  9. A systematic review of the bubble dynamics of cryptocurrency prices (2020)
  10. Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey (2021)
  11. Applying Artificial Intelligence in Cryptocurrency Markets: A Survey (2022)
  12. Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach (2020)
  13. A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms (2021)

0 of 21 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.