Abstract
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.
Highlights
The portfolio selection and optimization problems are two of the main branches of studies in investment management
The main advantages of the proposed approach in this study can be summarized as follows: (1) the presented approach can be applied in the presence of uncertain data, (2) computational complexity of portfolio optimization is decreased by the first phase in order to satisfy cardinality constraint, (3) conservatism levels of the investment process is increased using of twophases method and considering uncertainty, (4) all candidate stocks for investment are comprehensively assessed from different financial aspects and criteria by employing the multi-criteria decision making (MCDM) approaches
For conservative perspective to selection of the best stocks in first phase, top k stocks will be selected based on the average rank of per stock in all robust data envelopment analysis (RDEA) models contain of RCCR-IO, RCCR-OO, RBCC-IO, RBCC-OO, RADD-constant returns to scale (CRS) and RADD-variable returns to scale (VRS) models
Summary
The portfolio selection and optimization problems are two of the main branches of studies in investment management. The goal of the current study is to propose a robust two-phase approach for portfolio construction problem by using data envelopment analysis and robust optimization approaches. The main advantages of the proposed approach in this study can be summarized as follows: (1) the presented approach can be applied in the presence of uncertain data, (2) computational complexity of portfolio optimization is decreased by the first phase in order to satisfy cardinality constraint, (3) conservatism levels of the investment process is increased using of twophases method and considering uncertainty, (4) all candidate stocks for investment are comprehensively assessed from different financial aspects and criteria by employing the MCDM approaches.
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