In a recent Street View, our colleagues on the Two Sigma Portfolio Management team 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.

Market participants are often interested in understanding market regimes, how they change over time, and how each regime might affect their portfolio. In this Street View article, 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.

At Venn, we believe there are multiple use cases for allocators looking to apply this type of analysis. For example, investors can enhance their risk management scenario analysis by sampling from the distributions of these market conditions to stress test their portfolios. Specifically, Venn uses this methodology in our Scenario Analysis tool, when entering "extreme" shocks (greater than 2 standard deviation events) to the scenario indices.

Read the full article entitled “A Machine Learning Approach to Regime Modeling” here.

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