在经济理论的背景下预测比特币 (BTC) 价格:一种机器学习方法
Sahar Erfanian1, Yewang Zhou1, Amar Razzaq1
1Business School, Huanggang Normal University, No. 146 Xinggang 2nd Road, City Development Zone, Huanggang 438000, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究使用机器学习和经济理论预测比特币 (BTC) 价格. 支持向量回归 (SVR) 证明在准确的BTC价格预测方面优越,优于传统模型.
科学领域:
- 金融经济学 金融经济学
- 计算金融是指计算金融.
- 计量经济学 计量经济学
背景情况:
- 比特币 (BTC) 是第一个加密货币,由于其分散和任意的性质,它面临着定价挑战,导致市场怀疑.
- 目前关于BTC价格预测的研究往往缺乏强大的分析和理论基础.
- 需要探索机器学习的潜力与准确的BTC价格预测的统计方法相比.
研究的目的:
- 通过将宏观经济和微观经济理论与先进的机器学习技术相结合,解决BTC价格预测问题.
- 根据既定的经济理论,研究宏观经济,微观经济,技术和区块链指标的预测能力.
- 为了比较各种机器学习模型 (Ensemble,SVR,MLP) 和传统方法 (OLS) 在BTC价格预测中的有效性.
主要方法:
- 使用普通最小平方 (OLS),合体学习,支持向量回归 (SVR) 和多层感知器 (MLP) 的比较分析.
- 利用基于经济理论的宏观经济,微观经济,技术和区块链指标.
- 对短期和长期BTC价格预测的评估模型性能.
主要成果:
- 技术指标被确定为BTC短期价格波动的重要预测指标,验证了技术分析.
- 宏观经济和区块链指标对长期BTC价格预测具有重要意义,支持供需和基于成本的定价理论.
- 与其他机器学习和传统模型相比,支持向量回归 (SVR) 显示出更高的性能.
结论:
- 该研究证实了经济理论在BTC价格预测中的预测能力,特别是通过SVR.
- 建议SVR作为比特币价格预测的优越模型,而不是传统的统计和其他机器学习方法.
- 调查结果为国际金融,资产定价,投资决策提供了宝贵的见解,并作为金融预测中机器学习的基准.
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