Sparse and Multiple Risk Measures Approach for Data Driven Mean-CVaR Portfolio Optimization Model

This paper studies the out-of-sample performance of the data driven Mean-CVaR portfolio optimization(DDMC) model, in which the historical data of the stock returns are regarded as the realized returns and used directly in the mean-CVaR portfolio optimization formulation. However, in practical portfolio management, due to a limited number of monthly or weekly based historical data, the out-of-sample performance of the DDMC model is quite unstable. To overcome such a difficulty, we propose to add the penalty on the sparsity of the portfolio weight and combine the variance term in the DDMC formulation. Our experiments demonstrate that the proposed method mitigates the fragility of out-of-sample performance of the DDMC model significantly.