Two-Stage Portfolio Optimization Integrating Optimal Sharp Ratio Measure and Ensemble Learning

The traditional portfolio theory has relied heavily on historical asset returns while ignoring future information. Based on ensemble learning and maximum Sharpe ratio portfolio theory, this paper proposes a two-stage portfolio optimization method by considering asset forecast information, aiming to improve the performance and robustness of a portfolio. In the first stage, concerning the underlying asset selection, we integrate six individual prediction models using the ensemble learning method to forecast the future return of assets where the assets with higher potential returns are selected for portfolio optimization. In the second stage, we propose a novel investment strategy by combining the forecasted returns of selected assets with the maximum Sharpe ratio portfolio model. In the empirical analysis, we employ the constituent stocks of the China Securities Index 300 (CSI 300) to test the out-of-sample performance of the proposed strategy with several traditional portfolio strategies, including minimum variance portfolio strategy, traditional maximum Sharpe ratio portfolio strategy, 1/N portfolio strategy, and CSI 300 index. Our analytical results show that (1) compared to individual forecasting models, the ensemble learning method is more accurate in forecasting stock returns, and (2) the proposed portfolio strategy largely outperforms most of its competitors in terms of the Sharpe ratio, Sortino ratio, Omega ratio, and Calmar ratio. This indicates that the proposed two-stage portfolio optimization method is of potential to construct a promising investment strategy due to its trade-off between historical and future information of assets.