使用机器学习预测比特币价格
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
概括
这项研究使用机器学习预测比特币价格变动. 一个后勤回归模型实现了66%的准确性,这表明比特币市场不是弱形式的高效.
科学领域:
- 量化金融 量化金融
- 计算经济学计算经济学
- 机器学习应用 机器学习应用
背景情况:
- 有效市场假说 (EMH) 认为资产价格反映了所有可用的信息.
- 比特币的价格波动和独特特征对传统金融市场效率构成挑战.
- 预测加密货币的移动需要强大的分析框架.
研究的目的:
- 开发和评估用于比特币价格预测的机器学习模型.
- 评估各种金融和宏观经济变量对比特币的预测能力.
- 为了测试比特币市场的弱形式效率.
主要方法:
- 从2014年12月到2019年7月编制了24个解释变量的数据集.
- 开发了使用历史比特币价格,加密货币,汇率和宏观经济数据的预测模型.
- 比较了后勤回归,线性支向量机和随机森林算法的性能.
主要成果:
- 后勤回归模型表现出卓越的性能,达到66%的预测准确度.
- 该模型结合了过去的比特币价值,其他加密货币,汇率和宏观经济变量是有效的.
- 在预测比特币移动方面表现优于线性支向量机和随机森林模型.
结论:
- 比特币市场表现出偏离弱形式效率的特征.
- 机器学习,特别是后勤回归,为比特币价格预测提供了一种可行的方法.
- 进一步的研究可以探索更复杂的模型和额外的变量,以提高预测准确度.
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