Bitcoin Price Movement Prediction: A Machine Learning Comparison

DOI

10.2991/978-2-38476-585-0_29How to use a DOI?

Keywords

Bitcoin Price Prediction; Machine Learning Benchmark; Financial Time-Series Validation

Abstract

In this work, a machine learning benchmark is set for the prediction of the evolution of the price of Bitcoins from OHLCV data available for 2,713 trading days from 2014 to 2023. The three most basic algorithms (logistic regression, random forest and XGBoost) are tested under time-based validation aimed at giving some financial plausibility, resulting in the corresponding accuracies of 54%, 48% and 49%.Overall these outcomes identify a difficult yet exciting landscape for applying machine learning to cryptocurrency markets, especially when the class imbalance influences prediction accuracy in a negative way for times that are beyond rising bull markets. There seems to be a “hidden”, regular signal in the price data of BTC as even these linear models perform well better than the nonlinear on the raw price data. The overall insight these outcomes raises is an enhanced focus on time validation of financial ML, in order to avoid any data leakage or overfitting. Above findings present important baselines to the studies in the future and motivate us to look into better methods in volatile asset predicting. There are more to study for the feature engineering and imbalance correction in future research as well.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ruilin Zheng
PY  - 2026
DA  - 2026/06/18
TI  - Bitcoin Price Movement Prediction: A Machine Learning Comparison
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 247
EP  - 253
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_29
DO  - 10.2991/978-2-38476-585-0_29
ID  - Zheng2026
ER  -