The Holy Grail of sector rotation quantification: How to capture excess returns using economic cycles and machine learning
In the quantitative temple of Wall Street, sector rotation strategy is regarded as the "jewel in the crown," as it attempts to answer an ultimate question: how to allocate capital to industries about to take off at different stages of the economic cycle? This article will deeply analyze this complex art combining macroeconomics and statistics. I will share my experience deploying rotation strategies at top hedge funds, starting from the classical Merrill Lynch Investment Clock theory, progressively dissecting how to use quantitative models to identify economic states, construct dynamic factors, and leverage machine learning to predict rotation inflection points. The article will include two in-depth historical case backtests (the 2008 financial crisis and the 2020 pandemic shock), and provide implementable Python frameworks and risk management key points, helping you systematically capture structural market opportunities.
