SUN Huixia, ZHAO Huimin, ZHANG Chao, ZHENG Tiantian
By introducing the framework of ambiguity decomposition in machine learning domain into the optimization problem and combining multiple portfolio weights, this study proposes a new portfolio strategy called ensemble global minimum variance (EGMV) strategy, which outperforms the classical global minimum variance (GMV) strategy. Specifically, based on the common method of decomposing quadratic error function in machine learning domain, the ambiguity decomposition, we change the optimization problem of GMV, and introduce two extra parameters, the number of sub-strategies and diversity parameter to build the new strategy, EGMV. When the diversity parameter is larger than 1, EGMV can generate more diversified component weights to hedge against the estimation error in different assets, improving the out-of-sample performance of the weighted strategy. In order to verify the effectiveness of the EGMV approach, we test the EGMV strategy in the Chinese and US stock markets, and compare it with other common portfolio strategies. The empirical results show that in Chinese A-share market, EGMV can outperform GMV on average, in terms of the out-of-sample Sharpe ratio and turnover, and remains robust under 160 parameter combination sets, indicating good stability of EGMV.