Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran.
PLoS One. 2022 Jul 28;17(7):e0271811. doi: 10.1371/journal.pone.0271811. eCollection 2022.
Portfolio optimization is one of the most important issues in financial markets. In this regard, the more realistic are assumptions and conditions of modelling to portfolio optimization into financial markets, the more reliable results will be obtained. This paper studies the knapsack-based portfolio optimization problem that involves discrete variables. This model has two very important features; achieving the optimal number of shares as an integer and with masterly efficiency in portfolio optimization for high priced stocks. These features have added some real aspects of financial markets to the model and distinguish them from other previous models. Our contribution is that we present an algorithm based on dynamic programming to solve the portfolio selection model based on the knapsack problem, which is in contrast to the existing literature. Then, to show the applicability and validity of the proposed dynamic programming algorithm, two case studies of the US stock exchange are analyzed.
投资组合优化是金融市场中最重要的问题之一。在这方面，将投资组合优化到金融市场中的假设和建模条件越现实，得到的结果就越可靠。本文研究了基于背包的投资组合优化问题，其中涉及离散变量。该模型具有两个非常重要的特点：最优股票数量为整数，以及对高价股票的投资组合优化具有高超的效率。这些特点为模型添加了一些金融市场的实际方面，并将其与其他先前的模型区分开来。我们的贡献在于，我们提出了一种基于动态规划的算法来解决基于背包问题的投资组合选择模型，这与现有文献不同。然后，为了展示所提出的动态规划算法的适用性和有效性，对美国股票交易所的两个案例进行了分析。
