HODL Strategy or Fantasy? 480 Million Crypto Market Simulations and the Macro-Sentiment Effect

ArXiv ID: 2512.02029 “View on arXiv”

Authors: Weikang Zhang, Alison Watts

Abstract

Crypto enthusiasts claim that buying and holding crypto assets yields high returns, often citing Bitcoin’s past performance to promote other tokens and fuel fear of missing out. However, understanding the real risk-return trade-off and what factors affect future crypto returns is crucial as crypto becomes increasingly accessible to retail investors through major brokerages. We examine the HODL strategy through two independent analyses. First, we implement 480 million Monte Carlo simulations across 378 non-stablecoin crypto assets, net of trading fees and the opportunity cost of 1-month Treasury bills, and find strong evidence of survivorship bias and extreme downside concentration. At the 2-3 year horizon, the median excess return is -28.4 percent, the 1 percent conditional value at risk indicates that tail scenarios wipe out principal after all costs, and only the top quartile achieves very large gains, with a mean excess return of 1,326.7 percent. These results challenge the HODL narrative: across a broad set of assets, simple buy-and-hold loads extreme downside risk onto most investors, and the miracles mostly belong to the luckiest quarter. Second, using a Bayesian multi-horizon local projection framework, we find that endogenous predictors based on realized risk-return metrics have economically negligible and unstable effects, while macro-finance factors, especially the 24-week exponential moving average of the Fear and Greed Index, display persistent long-horizon impacts and high cross-basket stability. Where significant, a one-standard-deviation sentiment shock reduces forward top-quartile mean excess returns by 15-22 percentage points and median returns by 6-10 percentage points over 1-3 year horizons, suggesting that macro-sentiment conditions, rather than realized return histories, are the dominant indicators for future outcomes.

Keywords: HODL strategy, Monte Carlo simulations, Bayesian multi-horizon local projection, survivorship bias, conditional Value at Risk (CVaR), Crypto Assets

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical methods such as 480 million Monte Carlo simulations, Bayesian multi-horizon local projection, and Bayesian hierarchical random-effects meta-analysis, demonstrating high mathematical density. It also shows strong empirical rigor through massive-scale backtesting across 378 assets, incorporation of real-world costs (fees, opportunity costs), and use of high-frequency macro-sentiment data.

  flowchart TD
    A["Research Goal:<br/>Evaluate the HODL Strategy &<br/>Identify Drivers of Crypto Returns"] --> B["Methodology Part 1:<br/>480M Monte Carlo Simulations"]
    A --> C["Methodology Part 2:<br/>Bayesian Multi-Horizon Local Projection"]
    
    B --> D{"Data: 378 Non-Stablecoin Assets<br/>Inputs: Net of Fees & T-Bill Cost"}
    D --> E["Computational Process:<br/>Simulate Buy & Hold Across Horizons"]
    E --> F["Outcome Part 1:<br/>Survivorship Bias & Extreme Downside<br/>Median Return: -28.4%<br/>Top Quartile: +1,326.7%"]
    
    C --> G{"Data: Macro-Finance Factors<br/>(e.g., Fear & Greed Index)"}
    G --> H["Computational Process:<br/>Analyze Sentiment Impact on Future Returns"]
    H --> I["Outcome Part 2:<br/>Macro-Sentiment Dominates History<br/>1 Std. Dev. Shock ↓ Returns by 15-22%"]