Defining Historical Market Regimes for Machine Learning

Summary

The document frames the challenge of creating training labels for a machine learning model intended to predict market regime changes. Using the S&P 500 as an example, it describes visually identified bull and bear periods and asks how to classify historical data systematically. Hidden Markov models are raised as one candidate approach, alongside a request for alternatives.

The text does not provide a classification method, empirical comparison, or evidence that any approach works. It highlights a key research-design issue: a regime prediction model depends on how its training periods are defined, and visual judgments may not provide consistent labels. The examples are informal observations tied to a particular historical perspective, so they should not be treated as validated regime classifications. The document leaves open how to define regimes, choose inputs, or assess whether labels are useful for prediction.

Key ideas

• Regime prediction requires a clearly defined historical labeling scheme for training data.
• Visual inspection can suggest candidate bull and bear periods, but it does not establish objective labels.
• Hidden Markov models are proposed as one possible tool for identifying market regimes.
• The document offers no comparison, implementation details, or evidence favoring a particular method.

Tags

# 41012


# Given historical performance of a financial index, how to categorise different historical periods depending on the market regime at the time?












We are trying to work on a Machine Learning application to attempt to predict market regime changes (bull, bear, stale?). Generally a ML algorithm needs well defined training data for establishing its patterns. What we are looking for is how is the best way to define the training data set.

As an example, take SP500 historical chart:

A visual inspection suggests a bear regime around 2008, another around 2015 and that we may be currently experiencing one in 2018. Other years suggest bull regimes.

What are decent systematic ways to automatically identify such regimes? I know Hidden Markov Chains have been used for such purposes. Is it a good choice? Are there other alternatives?

Thanks

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.