School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
PLoS One. 2020 Oct 12;15(10):e0239810. doi: 10.1371/journal.pone.0239810. eCollection 2020.
Portfolio construction is one of the most critical problems in financial markets. In this paper, a new two-phase robust portfolio selection and optimization approach is proposed to deal with the uncertainty of the data, increasing the robustness of investment process against uncertainty, decreasing computational complexity, and comprehensive assessments of stocks from different financial aspects and criteria are provided. In the first phase of this approach, all candidate stocks' efficiency is measured using a robust data envelopment analysis (RDEA) method. Then in the second phase, by applying robust mean-semi variance-liquidity (RMSVL) and robust mean-absolute deviation-liquidity (RMADL) models, the amount of investment in each qualified stock is determined. Finally, the proposed approach is implemented in a real case study of the Tehran stock exchange (TSE). Additionally, a sensitivity analysis of all robust models of this study is examined. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data.
投资组合构建是金融市场中最关键的问题之一。本文提出了一种新的两阶段稳健投资组合选择和优化方法，以应对数据的不确定性，提高投资过程对不确定性的稳健性，降低计算复杂性，并从不同的财务角度和标准对股票进行全面评估。在该方法的第一阶段，使用稳健数据包络分析（RDEA）方法衡量所有候选股票的效率。然后，在第二阶段，通过应用稳健均值-半方差-流动性（RMSVL）和稳健均值绝对偏差-流动性（RMADL）模型，确定每个合格股票的投资金额。最后，将提出的方法应用于德黑兰证券交易所（TSE）的实际案例研究。此外，还对本研究的所有稳健模型进行了敏感性分析。说明性结果表明，该方法在存在不确定数据的情况下，对投资组合的选择和优化是有效的。
