genetic algorithm, liquidity, multi-objective optimization, one-versus-one support vector machines, radial basis functions, Applied mathematics. Quantitative methods, T57-57.97

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.