Evaluating COVID 19 Feature Contributions to Bitcoin Return Forecasting: Methodology Based on LightGBM and Genetic Optimization — 2025-08-04
Summary
This study explores the influence of COVID-19-related data on Bitcoin return predictions using a methodology that combines the LightGBM regression model with genetic optimization. The research reveals that incorporating pandemic-related indicators, particularly vaccination rates, significantly enhances the accuracy of Bitcoin return forecasts. The study highlights that COVID-19 metrics improve prediction accuracy by capturing extreme market fluctuations, with the 75th percentile of fully vaccinated individuals emerging as a dominant predictor.
Why This Matters
Understanding the impact of health-related data on financial predictions can provide investors and policymakers with refined tools to navigate market uncertainties during crises. By demonstrating that COVID-19 indicators can improve Bitcoin return forecasts, the study underscores the importance of integrating non-traditional data sources into financial models. This approach extends existing financial analytics capabilities and offers a new perspective on market behavior during systemic disruptions.
How You Can Use This Info
Professionals in finance and investment can leverage pandemic-related data to better anticipate market shifts during crises, potentially improving hedging strategies. Policymakers might use these insights to develop targeted financial stability measures informed by the link between public health and market dynamics. Furthermore, this study suggests that incorporating diverse data sources, such as health statistics, into predictive models can enhance decision-making processes in volatile environments.