Portfolio Optimization Based on Novel Risk Assessment Strategy with Genetic Algorithm

Stock selection is an important issue when it comes to investing in the stock market. However, it is worth investigating the problem of selecting portfolios while considering not only low risk but also high return on investment. The calculation process of the traditional method is highly complex and is not comprehensive in terms of what it takes into consideration. Hence, this paper proposes a new method to calculate portfolio risk. We utilize funds standardization in order to consider the risk of a portfolio and drastically reduce computation complexity. Funds standardization is able to represent fluctuations of investor mood. Moreover, using a Genetic algorithm (GA) combined with the Sharpe Ratio is able to identify the low risk and stable returns of a portfolio. Moreover, over-fitting is a common problem in the stock market, and so this paper uses sliding windows to avoid the over-fitting problem, and tests all kinds of training periods and testing periods that impact on the portfolio. The experimental results show that the proposed method, compared with the traditional method of calculating risk, is able to identify the optimal portfolio and performs efficiently and outstandingly when it comes to this problem.