August 4, 2025

A Comparison of Machine Learning Models for Predicting the Direction of Bitcoin Using Technical and Macroeconomic Indicators

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Authors

HSHussain SabborChung Yuan Christian University DTDramane Thiombiano JCJo-Hui ChenChung Yuan Christian University

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Implication

Analysis reveals machine learning models enhance bitcoin return forecasts using technical indicators and macroeconomic variables, suggesting improved prediction capabilities.

Key Points

Machine learning models outperform traditional statistical methods in predicting bitcoin returns.
Gradient boosting and bagging exhibit the highest predictive performance across all evaluation metrics.
The study uses a dataset of daily bitcoin returns along with technical and macroeconomic indicators.
Feature selection effectively reduces multicollinearity, enhancing model accuracy and robustness.

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Sabbor et al. (2025) studied this question.

synapsesocial.com/papers/689a0f8de6551bb0af8d0c97 https://doi.org/10.21203/rs.3.rs-7090441/v1

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Implication

Analysis reveals machine learning models enhance bitcoin return forecasts using technical indicators and macroeconomic variables, suggesting improved prediction capabilities.

A Comparison of Machine Learning Models for Predicting the Direction of Bitcoin Using Technical and Macroeconomic Indicators

Discussion

Member takes

Implication

Key Points

Cite This Study

A Comparison of Machine Learning Models for Predicting the Direction of Bitcoin Using Technical and Macroeconomic Indicators

Discussion

Member takes

Implication

Key Points

Cite This Study

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