Machine learning methods in finance: Recent applications and prospects
梳理了机器学习在金融中的两类主要方法（监督与无监督学习），归纳出三种应用类型：构建新指标、降低预测误差、扩展计量工具，并展望未来方向，对金融研究者和从业者有参考价值。
Abstract We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.