Research on investment portfolio strategy based on intelligent optimization algorithm

Abstract

The problem of portfolio optimization calculation is a hot and difficult issue of interest. The current research on it mainly focuses on traditional measurement methods, but these methods are still inadequate, and the data stochasticity and volatility that appear in their portfolios are not well addressed. In this paper, we address the optimization problem of current securities portfolio, and use Markowitz as the securities portfolio model, and use the genetic algorithm improved by acceleration search strategy to find the optimal analysis of the model, so as to obtain the optimal securities portfolio strategy. Through the empirical analysis, it can be learned that the portfolio solution method proposed in this paper obtains higher return and lower risk compared with the results obtained by other intelligent optimization optimization algorithms, which has good investment benefits and has important reference significance for investors.