对于韩国股票市场的有条件自动编码资产定价模型
Eunchong Kim1, Taehee Cho2, Bonha Koo3
1Business School, Hanyang University, Seoul, Republic of Korea.
条件自编码器 (CA) 模型对韩国股票市场具有很强的解释能力,其表现优于传统的资产定价模型. 这种机器学习方法更好地预测股票回报率,并解释市场异常.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 计量经济学 计量经济学
背景情况:
- 传统的资产定价模型难以捕捉复杂的市场动态.
- 对于分析金融市场潜在因素的先进方法的需求正在增长.
- 机器学习为金融建模提供了新的方法.
研究的目的:
- 评估韩国股票市场条件自编码器 (CA) 模型的解释能力.
- 将CA模型的表现与传统的资产定价模型进行比较.
- 调查不同宏观金融条件下的公司特征的作用.
主要方法:
- 使用条件自编码器 (CA),一种机器学习技术,来提取隐藏的因素.
- 估计因子暴露作为协变量的灵活非线性函数.
- 分析定价错误,以评估投资策略的表现.
主要成果:
- 在韩国市场的整个样本和子样本中,CA模型表现出极好的解释能力.
- 与传统模型相比,CA模型显著改善了对市场异常的解释.
- 基于CA模型的定价错误的投资策略产生了更好的预期股票回报.
- 公司的特点被认为对资产定价至关重要,因为它取决于诸如危机之类的宏观金融状况.
结论:
- 该CA模型为韩国股票市场的资产定价提供了一个卓越的框架.
- 该模型适应不断变化的宏观金融条件的能力提高了其实际适用性.
- 未来的研究可以利用CA模型来更全面地了解资产定价动态.
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