When investing in new stocks, it is difficult to predict returns and risks in a general way without the support of historical data. Therefore, a portfolio optimization model with an uncertain rate of return is proposed. On this basis, prospect theory is used for reference, and then the uncertain return portfolio optimization model is established from the perspective of expected utility maximization. An improved gray wolf optimization (GWO) algorithm is designed because of the complex nonsmooth and nonconcave characteristics of the model. The results show that the GWO algorithm is superior to the traditional particle swarm optimization algorithm and genetic algorithm.

中文翻译：

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基于前景理论的收益不确定的投资组合优化模型

在投资新股时，如果没有历史数据的支持，很难笼统地预测收益和风险。因此，提出了一种收益率不确定的投资组合优化模型。在此基础上，借鉴前景理论，从期望效用最大化的角度建立了不确定收益组合优化模型。针对该模型复杂的非光滑和非凹特性，设计了一种改进的灰狼优化（GWO）算法。结果表明，GWO算法优于传统的粒子群优化算法和遗传算法。