Two Stage Portfolio Selection and Optimization Model with the Hybrid Particle Swarm Optimization

Abstract

The selection criteria play an important role in the portfolio optimization using any ratio model. In this paper, the authors have considered the mean return as profit and variance of return as risk on the asset return as selection criteria, as the first stage to optimize the selected portfolio. Furthermore, the sharp ratio (SR) has been considered to be the optimization ratio model. In this regard, the historical data taken from Shanghai Stock Exchange (SSE) has been considered. A metaheuristic technique has been developed, with financial tool box available in MATLAB and the particle swarm optimization (PSO) algorithm. Hence, called as the hybrid particle swarm optimization (HPSO) or can also be called as financial tool box particle swarm optimization (FTB-PSO). In this model, the budgets as constraint, where as two different models i.e. with and without short sale, have been considered. The obtained results have been compared with the existing literature and the proposed technique is found to be optimum and better in terms of profit.

Solving constrained portfolio optimization model using stochastic fractal search approach

“…In this situation, many hybrid meta-heuristic methods are developed by combining the meta-heuristics and exact algorithms or other meta-heuristics. Here, some hybrid approaches have been also discussed to solve portfolio optimization problems (Tuba and Bacanin, 2014a, b; Faezy Razi and Shadloo, 2017; Zaheer et al , 2018; Salehpoor and Molla-Alizadeh-Zavardehi, 2019). This hybrid approach can be a combination of meta-heuristic approaches such as simulation annealing and evolutionary approach, ABC and PSO, GA and ACO, etc.…”

“…In 2014, Tuba and Bacanin proposed a hybrid algorithm for portfolio selection problems using ABC and FA meta-heuristic approaches with cardinality constrained mean-variance. Some recent work has also proposed a hybrid algorithm to solve the portfolio optimization problem (Faezy Razi and Shadloo, 2017; Zaheer et al , 2018; Salehpoor and Molla-Alizadeh-Zavardehi, 2019).…”

Portfolio selection model using teaching learning-based optimization approach

“…Some hybrid algorithms are also proposed by combining more than one algorithm to select optimum portfolios. Zaheer et al [23] developed a metaheuristic technique with a PSO algorithm named hybrid particle swarm optimization (PSO) to optimize the portfolio, which has to mean return and variance of return as selection criteria. Konstantinou et al [24] presented a hybrid optimization algorithm combining GA and sonarinspired optimization approach, and the performance of this algorithm is studied with its contemporary approaches.…”

Performance Evaluation of Genetic Algorithm & Fuzzy Logic for Portfolio Optimization

“…Saglam and Benson [12] presented the multi-period portfolio optimization problem in a mean-variance framework including diversification-by-sector constraints, buy-in-thresholds, transaction costs, and conditional value-at-risk. Zaheer [13] used the Shanghai Stock Exchange data to develop a hybrid PSO technique for portfolio optimization. He considered two different models with short sale and without short sale.…”