The authors offer a data-driven approach to modeling market regimes by applying a Gaussian Mixture Model (a machine learning method) to the factors in the Two Sigma Factor Lens.
Financial markets have the tendency to change their behavior over time, which can create regimes, or periods of fairly persistent market conditions. Investors often look to discern the current market regime, looking out for any changes to it and how those might affect the individual components of their portfolio’s asset allocation. Modeling various market regimes can be an effective tool, as it can enable macroeconomically aware investment decision-making and better management of tail risks.
In this Street View, we present a machine learning-based approach to regime modeling, display the historical results of that model, discuss its output for today’s environment, and conclude with practical use cases of this analysis for allocators.
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