![]() ![]() ![]() A specific example of this approach is a Gaussian Mixture Model (GMM), which is a type of unsupervised learning method. For example, one might categorize market regimes using “boom” and “bust” cycles, periods of high or low equity market volatility, changes in monetary policy, or “risk-on” versus “risk-off” sentiment, believing those to be good indicators of meaningfully changing market conditions.Īn alternative, more data-driven approach is letting historical data on assets and/or market risks delineate the regimes for you. One way is to specify regimes based on knowledge and experience in the markets. There are different approaches to establishing regimes. In order to understand how a portfolio might react to various regimes, one first needs to determine what the regimes are. 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. Modeling various market regimes can be an effective tool, as it can enable macroeconomically aware investment decision-making and better management of tail risks. 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. Financial markets have the tendency to change their behavior over time, which can create regimes, or periods of fairly persistent market conditions.
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