The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of “big data” as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.

中文翻译：

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经验资产定价中的机器学习

近年来计算的巨大加速，当今的低数据存储成本，“大数据”的可用性以及广泛的免费开源软件，已经在机器学习技术在科学中的应用方面创造了复兴. 然而，这一新的研究浪潮不再局限于计算机科学或软件工程。其中，机器学习工具现在也用于金融问题设置。因此，本文在资产定价上下文中提到了机器学习的具体定义，并详细说明了机器学习在此上下文中的有用性。最重要的是，文献综述为读者提供了对采用机器学习技术的实证资产定价的最新学术研究的理论概述。全面的，该论文得出结论，机器学习可以为未来的研究带来好处。然而，研究人员应该对这些方法持批判态度，因为机器学习有其缺陷，并且对资产定价来说相对较新。