Using Hidden Markov Models to Classify Stock-Market Regimes

Summary

The article explains hidden Markov models as a way to infer unobserved market regimes from observable data. It illustrates the idea with Chinese equities, treating market conditions such as bull, bear, and sideways phases as hidden states and returns and volume changes as observations. A six-state model is trained on CSI 300 data using one-day and five-day log returns and a five-day log-volume change. The strategy labels states by their observed returns, buys in the two strongest states, exits in the two weakest, and avoids trading in intermediate states.

The article describes one state-fit exercise using historical data from 2005 to 2013 and a strategy setup using 2012–2015 observations, followed by a daily backtest over a later period. However, the supplied text gives no backtest performance figures, so it provides no basis for judging profitability. It notes that state rankings can vary between samples and selects states by relative performance. The method also assumes Gaussian emissions and relies on historical state behavior continuing; the text does not discuss transaction costs, risk controls, or out-of-sample validation in detail.

Key ideas

• An HMM can represent market regimes as hidden states inferred from observable returns and volume features.
• The described CSI 300 model uses one-day and five-day log returns plus a five-day log-volume change.
• States are ranked by their historical returns to assign buy, sell, and no-trade behavior.
• The article recognizes that the best and worst states can vary across samples.
• The strategy description includes a Gaussian assumption but supplies no backtest outcome figures.

Tags

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