Machine Learning for Regime Modeling

Two Sigma is developing capabilities to model financial market regimes using machine learning. This involves applying Gaussian Mixture Models (GMMs) to historical factor return data to identify distinct market conditions (e.g., Crisis, Steady State, Inflation, Walking on Ice). The goal is to enable macroeconomically aware investment decision-making and better management of tail risks by understanding how portfolios react to different regimes. This post analyzes the recent decade of drawdown in value investing, attributing it to a regime change in the profitability and operating efficiency of stocks around the Global Financial Crisis (GFC). It highlights that traditional book-to-price based value strategies have underperformed because intangible assets, not captured by book value, have become significant drivers of profitability. The analysis suggests that a value strategy incorporating earnings yield, which accounts for intangibles, does not exhibit this regime change and has remained profitable.