Financial Investment Optimization by Integrating Multifactors and GA Improved UCB Algorithm

Abstract

In complex financial markets, controlling risks while achieving high returns is a challenge for investors. Faced with market uncertainty and complexity, traditional investment strategies often struggle to meet the needs of modern investors. To address this issue, a new investment portfolio strategy was proposed by integrating the multifactor model with the upper confidence bound. Meanwhile, genetic algorithm was used to optimize and improve the weight allocation of the investment portfolio based on the upper confidence bound. These results confirmed that the cumulative return of GA-UCB was 187.4%, which was 68.3% higher than the cumulative return of 119.1% on the Shanghai and Shenzhen 300 indices, respectively. The maximum drawdown rate of GA-UCB was 13.5%, a decrease of 4.8% compared to the Shanghai and Shenzhen 300. In summary, the research on financial investment optimization by integrating multifactors and GA improved UCB effectively improves returns while controlling risks, providing a new perspective and tool for financial market investors.