Predicting Bitcoin Prices Using Machine Learning
Athanasia Dimitriadou1, Andros Gregoriou2
1College of Business, Law and Social Sciences, University of Derby, Lonsdale House, Quaker Way, Derby DE1 3HD, UK.
Entropy (Basel, Switzerland)
|May 27, 2023
Summary
This study predicts Bitcoin price movements using machine learning. A logistic regression model achieved 66% accuracy, suggesting the Bitcoin market is not weak-form efficient.
Area of Science:
- Quantitative Finance
- Computational Economics
- Machine Learning Applications
Background:
- The efficient market hypothesis (EMH) suggests asset prices reflect all available information.
- Bitcoin's price volatility and unique characteristics present a challenge to traditional financial market efficiency.
- Predicting cryptocurrency movements requires robust analytical frameworks.
Purpose of the Study:
- To develop and evaluate machine learning models for Bitcoin price prediction.
- To assess the predictive power of various financial and macroeconomic variables on Bitcoin.
- To test the weak-form efficiency of the Bitcoin market.
Main Methods:
- Compiled a dataset of 24 explanatory variables from December 2014 to July 2019.
- Developed forecasting models using historical Bitcoin prices, cryptocurrencies, exchange rates, and macroeconomic data.
- Compared the performance of logistic regression, linear support vector machine, and random forest algorithms.
Main Results:
- The logistic regression model demonstrated superior performance, achieving 66% prediction accuracy.
- The model incorporating past Bitcoin values, other cryptocurrencies, exchange rates, and macroeconomic variables was effective.
- Outperformed linear support vector machine and random forest models in predicting Bitcoin movements.
Conclusions:
- The Bitcoin market exhibits characteristics that deviate from weak-form efficiency.
- Machine learning, particularly logistic regression, offers a viable approach for Bitcoin price forecasting.
- Further research can explore more complex models and additional variables for enhanced prediction accuracy.
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