Research of Improved Artificial Fish Swarm Portfolio Optimization Algorithm Based on Adaptive Levy Mutation

Liyi Zhang,
Xiufei Zhou,
Teng Fei,


With the rapid development of the economy, the stock market is growing like mushrooms, and a large number of enterprises and individuals invest in all kinds of securities. In the stock market, the investment itself with a certain risk, some assets have high risk, and some assets with low risk, investors must choose which securities products to makes higher income, it is particularly important for different investors. It has become very important to choose the investment plan reasonably so that investors can get the highest return on acceptable risk level, which has become the focus of many scholars. The research of portfolio theory has a very important influence on the stock market. In the process of investment market, we select appropriate proportion of investment so that reduce the risk of investment, and access to greater income. At present, most of the intelligent optimization algorithms could be used to optimize the investment portfolio model. In view of the basic artificial fish swarm algorithm is easy to fall into local extreme value and the search speed is slow, this paper combine basic artificial fish swarm algorithm with Levy mutation. Computer simulation shows the improved algorithm has better convergent performance, And this paper takes the stock exchange price data of five stocks in Shanghai stock exchange for 100 days as an example. The improved algorithm is used to solve the investment portfolio model. Experimental results show that the income is increased, and the risk is reduced.

Citation Format:
Liyi Zhang, Xiufei Zhou, Teng Fei, "Research of Improved Artificial Fish Swarm Portfolio Optimization Algorithm Based on Adaptive Levy Mutation," Journal of Internet Technology, vol. 20, no. 6 , pp. 1889-1898, Nov. 2019.

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