Application of a Genetic Algorithm for Multi-Factor Portfolio Optimization Using Fundamental Indicators
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This paper presents an academic analysis of a computational framework developed to evaluate and optimize stock portfolios using a genetic algorithm(GA). The system processes multi-year financial fundamental data from multiple firms and uses investor preference vectors -represented as weighted factors- to rank stocks and construct portfolios over sequential market periods. The GA iteratively evolves these preference weights to maximize the final value of the portfolio.