2025/03/19 by Jinhui Li, Li, Jinhui, Wenjia Xie +3
Decision Sciences · Economics, Econometrics and Finance · #Stock Market Forecasting Methods #Financial Markets and Investment Strategies #Complex Systems and Time Series Analysis
paper · pdf · doi:10.48550/arxiv.2504.02841
This study introduces a dynamic investment framework to enhance portfolio management in volatile markets, offering clear advantages over traditional static strategies. Evaluates four conventional approaches : equal weighted, minimum variance, maximum diversification, and equal risk contribution under dynamic conditions. Using K means clustering, the market is segmented into ten volatility-based states, with transitions forecasted by a Bayesian Markov switching model employing Dirichlet priors and Gibbs sampling. This enables real-time asset allocation adjustments. Tested across two asset sets, the dynamic portfolio consistently achieves significantly higher risk-adjusted returns and substantially higher total returns, outperforming most static methods. By integrating classical optimization with machine learning and Bayesian techniques, this research provides a robust strategy for optimizing investment outcomes in unpredictable market environments.