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将COVID-19指标用于比特币收益预测

文章 arXiv papers · 作者: Imen Mahmoud et al.
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总结

本研究检验COVID-19健康指标是否能为比特币收益预测增加预测价值。研究比较了使用和未使用疫情相关特征（包括疫苗接种、住院和检测指标）训练的LightGBM回归模型。遗传算法通过31次独立运行优化模型；研究使用重叠分析和Mann–Whitney U检验比较R²、RMSE和MAE的分布。研究还使用置换特征重要性评估各输入变量的贡献。

报告结果显示，纳入COVID-19数据的模型表现更好：R²上升40%，RMSE下降2%，两项变化均被描述为具有高度统计显著性。疫苗接种指标，尤其是完全接种人数的第75百分位数，排名靠前。证据涉及该研究数据集和设定中的预测表现；它并不能证明这些特征会导致比特币收益变化，也不能证明它们会改善实盘交易。文档未详细说明样本期间、模型验证设计或交易成本，因此难以评估其实际价值和样本外价值。

核心观点

  • 研究比较纳入和未纳入COVID-19指标的LightGBM比特币收益模型。
  • 遗传算法通过31次独立运行优化模型。
  • 研究使用Mann–Whitney U检验和分布重叠度比较绩效指标。
  • 置换特征重要性分析将疫苗接种指标识别为重要预测变量。
  • 报告的提升表明该研究设定下的预测效果有所改善，并不能证明其具有实盘交易价值。

标签

加密资产机器学习统计学回测
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# 2508.00078


# Evaluating COVID 19 Feature Contributions to Bitcoin Return Forecasting: Methodology Based on LightGBM and Genetic Optimization









This study proposes a novel methodological framework integrating a LightGBM regression model and genetic algorithm (GA) optimization to systematically evaluate the contribution of COVID-19-related indicators to Bitcoin return prediction. The primary objective was not merely to forecast Bitcoin returns but rather to determine whether including pandemic-related health data significantly enhances prediction accuracy. A comprehensive dataset comprising daily Bitcoin returns and COVID-19 metrics (vaccination rates, hospitalizations, testing statistics) was constructed. Predictive models, trained with and without COVID-19 features, were optimized using GA over 31 independent runs, allowing robust statistical assessment. Performance metrics (R2, RMSE, MAE) were statistically compared through distribution overlaps and Mann-Whitney U tests. Permutation Feature Importance (PFI) analysis quantified individual feature contributions. Results indicate that COVID-19 indicators significantly improved model performance, particularly in capturing extreme market fluctuations (R2 increased by 40%, RMSE decreased by 2%, both highly significant statistically). Among COVID-19 features, vaccination metrics, especially the 75th percentile of fully vaccinated individuals, emerged as dominant predictors. The proposed methodology extends existing financial analytics tools by incorporating public health signals, providing investors and policymakers with refined indicators to navigate market uncertainty during systemic crises.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。