Dynamic Investment Strategies Through Market Classification and Volatility: A Machine Learning Approach
基于市场分类与波动率的动态投资策略:机器学习方法
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…