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Data driven investment strategies using Bayesian inference in regime switching models

Author

Listed:
  • Éléonore Blanchard

    (AMU - Aix Marseille Université)

  • Pierre-Olivier Goffard

    (UNISTRA - Université de Strasbourg)

Abstract

This article presents the benefits of using Bayesian algorithms to fit regime switching models to daily financial returns data in order to design trading strategies. Our study focuses on a Gaussian hidden Markov model. We show how the application of a simple smoothing technique preserves the hidden Markov structure and facilitates regime detection even in instances of highly volatile data. The effectiveness of a trading strategy, based on regime detection, may be hindered by a high rate of false signals, leading to numerous trades and, consequently, an escalation in transaction costs. By reducing variance through data smoothing, we enhance the persistence of regimes over time. We validate our statistical learning procedures using synthetic data prior to their application to real-world financial data.

Suggested Citation

  • Éléonore Blanchard & Pierre-Olivier Goffard, 2024. "Data driven investment strategies using Bayesian inference in regime switching models," Working Papers hal-04608937, HAL.
    • Handle: RePEc:hal:wpaper:hal-04608937
    Note: View the original document on HAL open archive server: https://hal.science/hal-04608937
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