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Using Hidden Markov Regimes for Asset Allocation

Article Quant Q&A · Author: Nourhaine Nefzi
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Summary

The document outlines a basic way to use a hidden Markov model (HMM) in portfolio allocation. The model estimates latent market states, asset drifts, and state-specific covariance matrices. For each state, the estimated return and risk inputs can then be passed to a portfolio optimizer to determine an allocation appropriate to that regime.

The discussion also notes practical implementation routes: seek supplementary code from a paper's authors or implement the model using a specified transition structure and expectation-maximization fitting. It does not provide code, specify how to choose the number or interpretation of states, or address estimation uncertainty, regime changes, transaction costs, and out-of-sample validation. The method is a high-level workflow rather than evidence that regime-conditioned allocation improves results.

Key ideas

  • An HMM can estimate latent states and the probability of being in each state.
  • Asset drift and covariance estimates can be calculated separately for each state.
  • State-specific return and risk estimates can serve as inputs to a portfolio optimizer.
  • The suggested model fitting uses a transition model and the expectation-maximization algorithm.
  • The document gives no implementation example or evidence of allocation performance.

Tags

Machine learningPortfolio constructionStatistics
Full text
# Asset allocation problem using Hidden Markov Model


# Asset allocation problem using Hidden Markov Model












I am recently getting more interested in Hidden Markov Models (HMM) and its application on financial assets to understand their behavior. But what captured my attention the most is the use of asset regimes as information to portfolio optimization problem. I am refering to this article I searched in many sites for the code to apply an asset allocation problem based on HMM estimations but I can't find .. I am extremely interesed ..I would be very grateful if you could provide me any code example that uses HMM to asset allocation problem.

## Answer by vonjd (score 3)

https://quant.stackexchange.com/a/28349

The basic approach is as follows:

When you estimate the HMM you estimate three things:

- When you are in which state

- The drifts of your assets

- The covariance matrices of your assets

You would then take 2. and 3. for each state (1.) and feed it into your favourite allocation optimizer to estimate your optimal portfolio for each state.

Voila!

## Answer by SmallChess (score 1)

https://quant.stackexchange.com/a/28346

I don't think you'll find anything. Why don't you contact the authors? They must have some code to generate the HMM simulations in the paper, maybe they can share the code with you?

Have you checked the Supplementary Materials? Some papers have it.

If you're really determined, you can implement a HMM model yourself. You'll need to supply the Markov transition model (included in the paper), then use the EM algorithm to fit a model. Python scikit-learn has a module for that.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

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