Multi-objective portfolio optimization using real coded genetic algorithm based support vector machines

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Investors need to grasp how liquidity affects both risk and return in order to optimize their portfolio performance. There are three classes of stocks that accommodate those criteria: Liquid, high-yield, and less-risky. Classifying stocks help investors build portfolios that align with their risk profiles and investment goals, in which the model was constructed using the one-versus-one support vector machines method with a radial basis function kernel. This model was trained using a combination of the Kompas100 index and the Indonesian industrial sectors stocks data. Single optimal portfolios were created using the real coded genetic algorithm based on different sets of objectives: Maximizing short-term and long-term returns, maximizing liquidity, and minimizing risk. In conclusion, portfolios with a balance on all these four investment objectives yielded better results compared to those focused on partial objectives. Furthermore, our proposed method for selecting portfolios of top-performing stocks across all criteria outperformed the approach of choosing top stocks based on a single criterion.

Language:
English
Published:
Iranian Journal of Numerical Analysis and Optimization, Volume:15 Issue: 2, Spring 2025
Pages:
600 to 624
https://www.magiran.com/p2864293