Factor Models, Machine Learning, and Asset Pricing
综述了利用因子模型和机器学习进行资产定价的最新方法，涵盖预期收益、因子、风险暴露、风险溢价和随机贴现因子的估计，以及模型比较和alpha检验，为金融经济学家提供严谨、稳健的研究工具。
We survey recent methodological contributions in asset pricing using factor models and machine learning. We organize these results based on their primary objectives: estimating expected returns, factors, risk exposures, risk premia, and the stochastic discount factor as well as model comparison and alpha testing. We also discuss a variety of asymptotic schemes for inference. Our survey is a guide for financial economists interested in harnessing modern tools with rigor, robustness, and power to make new asset pricing discoveries, and it highlights directions for future research and methodological advances.