Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies

Abstract

This is the first in a series of articles dealing with machine learning in asset management. Asset management can be broken into the following tasks: (1) portfolio construction, (2) risk management, (3) capital management, (4) infrastructure and deployment, and (5) sales and marketing. This article focuses on portfolio construction using machine learning. Historically, algorithmic trading could be more narrowly defined as the automation of sell-side trade execution, but since the introduction of more advanced algorithms, the definition has grown to include idea generation, alpha factor design, asset allocation, position sizing, and the testing of strategies. Machine learning, from the vantage of a decision-making tool, can help in all these areas. TOPICS: Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurement Key Findings • Machine learning can help with most portfolio construction tasks like idea generation, alpha factor design, asset allocation, weight optimization, position sizing, and the testing of strategies. • This is the first in a series of articles dealing with machine learning in asset management and more narrowly on trading strategies equipped with machine-learning technologies. • Each trading strategy can end up using multiple machine learning frameworks. The author highlights nine different trading varieties each making use of a reinforcement-, supervised-, or unsupervised-learning framework or a combination of these learning frameworks.