Data driven investment strategies using Bayesian inference in regime switching models

Éléonore Blanchard () and Pierre-Olivier Goffard ()
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Éléonore Blanchard: AMU - Aix Marseille Université
Pierre-Olivier Goffard: UNISTRA - Université de Strasbourg

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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.

Keywords: Hidden Markov Models Bayesian Inference Market Regimes Financial Time Series Smoothing Transaction Costs Mitigation; Hidden Markov Models; Bayesian Inference; Market Regimes; Financial Time Series; Smoothing; Transaction Costs Mitigation (search for similar items in EconPapers)
Date: 2024-06-03
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