An Evolutionary Optimization Algorithm based Solution Approach for Portfolio Selection Problem

Mohammad Shahid, Mohd Shamim, ZUBAIR ASHRAF, Mohd Shamim Ansari


The portfolio selection problem is one of the most common problems which drawn the attention of experts of the field in recent decades. The mean variance portfolio optimization aims to minimize variance (risk) and maximize the expected return of the portfolio. In case of linear constraints, the problem can be solved by variants of Markowitz. There are many constraints need to be managed in portfolio selection to optimize the target parameters. These constraints have become so vital that conventional techniques are not good enough in giving solutions. Stochastic Fractal Search (SFS) is a strong population based meta-heuristic approach that has derived from evolutionary computation. In this paper, a novel portfolio selection model using Stochastic Fractal Search (SFS) based optimization approach has been proposed to maximize sharpe ratio. Stochastic fractal search (SFS) is an evolutionary approach and the natural growth process has been modeled using fractal theory. Performance evaluation has been conducted to determine the effectiveness of the model by making comparison with state of art models such as GA and SA with same objective and environment. The real datasets of the BSE Sensex of Indian stock exchange has been taken in the study. Study reveals the superior performance of the proposed method among its peers.


Portfolio Selection; Portfolio optimization; Evolutionary Algorithm; Stochastic Fractal Search; Sharpe Ratio



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