RT Journal Article SR Electronic T1 Machine Learning in Asset Management: Part 2: Portfolio Construction—Weight Optimization JF The Journal of Financial Data Science FD Institutional Investor Journals SP jfds.2020.1.029 DO 10.3905/jfds.2020.1.029 A1 Derek Snow YR 2020 UL https://pm-research.com/content/early/2020/03/12/jfds.2020.1.029.abstract AB This is the second in a series of articles dealing with machine learning in asset management. This article focuses on portfolio weighting using machine learning. Following from the previous article (Snow 2020), which looked at trading strategies, this article identifies different weight optimization methods for supervised, unsupervised, and reinforcement learning frameworks. In total, seven submethods are summarized with the code made available for further exploration.TOPICS: Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurementKey Findings• Machine learning can help with most portfolio construction tasks, such as idea generation, alpha factor design, asset allocation, weight optimization, position sizing, and the testing of strategies.• This is the second of a series of articles dealing with machine learning in asset management and more narrowly weight optimization strategies equipped with machine learning.• Following from the previous article, different weight optimization methods are considered for supervised, unsupervised, and reinforcement learning frameworks.