Business School, Hanyang University, Seoul, Republic of Korea.
Qraft Technologies, Seoul, Republic of Korea.
PLoS One. 2023 Jul 31;18(7):e0281783. doi: 10.1371/journal.pone.0281783. eCollection 2023.
This study analyzes the explanatory power of the latent factor conditional asset pricing model for the Korean stock market using an autoencoder. The autoencoder is a type of neural network in machine learning that can extract latent factors. Specifically, we apply the conditional autoencoder (CA) model that estimates factor exposure as a flexible nonlinear function of covariates. Our main findings are as follows. The CA model showed excellent explanatory power not only in the entire sample but also in several subsamples in the Korean market. Also, because of this explanatory power, it can better explain market anomalies compared to the traditional asset pricing models. As a result of examining investment strategies using pricing error, the CA model measures the expected return of stocks better than the traditional asset pricing model. In addition, the CA model indicates that the firm characteristic variables are important in asset pricing conditional on macro-financial states, such as the global financial crisis and the coronavirus disease 2019 pandemic. The result shows that the major variables considered in the explanation of stock returns through the CA model may vary depending on the time. This is expected to provide a broader perspective on asset pricing through the CA model in the future.
本研究使用自动编码器分析潜在因子条件资产定价模型对韩国股票市场的解释能力。自动编码器是机器学习中的一种神经网络，可以提取潜在因素。具体来说，我们应用条件自动编码器 (CA) 模型，该模型将因子敞口估计为协变量的灵活非线性函数。我们的主要发现如下。CA 模型不仅在整个样本中，而且在韩国市场的几个子样本中都表现出了出色的解释能力。此外，由于这种解释能力，它可以比传统的资产定价模型更好地解释市场异常。通过使用定价误差检验投资策略，CA 模型比传统的资产定价模型更好地衡量股票的预期回报。此外，CA 模型表明，在全球金融危机和 2019 年冠状病毒病等宏观金融状况下，公司特征变量在资产定价中很重要。结果表明，通过 CA 模型解释股票回报的主要变量可能会随着时间的推移而变化。这有望为未来通过 CA 模型进行资产定价提供更广阔的视角。
