Machine Learning for Thematic Equity Portfolios

Two Sigma is developing capabilities to construct thematic equity portfolios using machine learning. This involves analyzing historical equity sector and style factor behavior in different inflation environments, and then building supervised machine learning models (e.g., LightGBM) to predict stock returns based on various fundamental and factor loading data. The goal is to create portfolios that are designed to outperform in high inflation periods and underperform in low inflation periods, by focusing on features like relative employment size, industry, dividend payout ratio, and profit margin. The model is applied to construct a beta-neutral portfolio of individual stocks sensitive to inflation.