Abstract
AbstractIn capital markets, selecting best assets from the population in the portfolio is very essential practice for the investors. For risk aversive investors, minimization of risk is one of the most important concerns in portfolio selection. Therefore, risk attribution is very common practice to risk distribution in Risk-budgeted portfolio optimization. In this paper, a novel risk-budgeted portfolio selection strategy using stochastic fractal search (SFS) has been proposed with the aim of maximizing sharp ratio. Stochastic fractal search (SFS) is an evolutionary approach inspired by the growth process of the nature. The natural growth is modeled by using fractal search. To verify the effectiveness of proposed strategy, an experimental study has been conducted by comparing performance of proposed strategy with Genetic Algorithm by using dataset of the S&P BSE Sensex of Indian stock exchange. Study reveals the superior performance of the proposed strategy.KeywordsPortfolio selection problemRisk-budgeted portfolioPortfolio constraintsStochastic fractal searchEvolutionary algorithm
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