Abstract
Investment is a behavior of coexistence of the benefits and risks. In the context of rich asset types, an effective investment portfolio can help investors obtain stable returns and diversify risks. In reality, portfolio problems often contain multiple constraints and objectives that cannot be effectively solved by traditional mathematical optimization methods. This paper proposes a hybrid beetle antennae search sine cosine algorithm based on non-linear inertia weight. Experiments are performed on five portfolio problem datasets in the real stock market. The results show that the proposed algorithm is effective and has some performance advantages in solving portfolio problems.
Highlights
How to make high-yield and low-risk investments in the stock market has always been a hot spot for investors
Huang Dongbin et al [2] used the entropy-TOPSIS method to calculate the comprehensive preference strength of effective factors based on the Shanghai and Shenzhen 300 constituent stocks, and proposed a comprehensive preference strength-mean-CVaR portfolio optimization model for effective factors
This paper proposes a hybrid beetle antennae search sine cosine algorithm (BASSCA) based on non-linear inertia weight
Summary
How to make high-yield and low-risk investments in the stock market has always been a hot spot for investors. The sine cosine algorithm [7] (SCA) is a new intelligent optimization algorithm based on mathematical features proposed in recent years. It has fast convergence speed, simple structure and easy implementation. The sine cosine algorithm has been used in many fields to solve practical problems, but there are few applied researches in the field of investment portfolio. At the same time, studying the improvement strategy of the algorithm to make it have better performance on the investment portfolio problem, which has important significance in both theoretical research and practical application. The experiment is based on the real market test data set in the OR-library, analyzes and compares the solution results of different algorithms, and verifies the effectiveness and superiority of the BASSCA to solve the portfolio problem with cardinality constraints
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