A Hybrid Fuzzy-SCOOT Algorithm to Optimize Possibilistic Mean Semi-absolute Deviation Model for Optimal Portfolio Selection

The uncertainty associated with the financial domain in modern portfolio selection problems can be overcome by using fuzzy set theory. The portfolio is modified in this paper using the possibility theory instead of the probability theory by formulating the risk return as fuzzy numbers, and we also take into account the V-shaped transaction costs. The possibilistic semi-absolute deviation portfolio selection technique is used to develop a portfolio selection framework by considering the investor demands and stock characteristics. The higher computational complexity associated with the possibilistic mean semi-absolute deviation portfolio model is reduced using the hybris salp swarm-based Coot algorithm (SCOOT). The main aim of the hybrid SCOOT algorithm is to reduce the risk and increase the expected return. The salp swarm algorithm is integrated with the coot algorithm to enhance the global search capability. The performance of the proposed approach is evaluated with the extensive experiments conducted on the Bombay Stock Exchange dataset. The results obtained show that the proposed methodology offers better performance when considering the transaction costs, and its performance is very high when compared to the conventional metaheuristic techniques.