An Overview of Machine Learning for Asset Management
综述了机器学习在资产管理中的多种模型应用，包括分类、回归、时间序列预测、自然语言处理等，并讨论了数据质量、可解释性等实施挑战，适合金融从业者了解机器学习在资产管理中的现状。
Machine learning has been widely used in the asset management industry to improve operations and make data-driven decisions. This article provides an overview of machine learning for asset management by presenting various machine learning models in the context of their applications, including general classification and regression, time series forecasting, natural language processing, dimension reduction, reinforcement learning, data generation, recommendation, and clustering. Additionally, it highlights the challenges of implementing machine learning in asset management, such as data quality and quantity, interpretability, and fairness.