🤖 AI Summary
This paper addresses portfolio optimization under investor preferences and margin trading mechanisms. Methodologically, it proposes a constraint-guided metaheuristic framework that: (1) explicitly incorporates pre-allocation constraints to encode investor preferences and reduce the feasible solution space; (2) endogenizes margin trading costs and leverage risk into a dynamic return model; and (3) replaces the conventional Sharpe ratio with the Rare Performance Ratio (RPR) to enhance robustness and practicality in risk-adjusted performance evaluation. The framework innovatively integrates pre-allocation constraints and leverage-aware strategies within standard metaheuristics—including genetic algorithms and particle swarm optimization—enabling synergistic constraint handling and search guidance. Empirical results across realistic market settings demonstrate statistically significant outperformance over traditional benchmarks, with an average 18.7% improvement in risk-adjusted returns. This validates both the methodological efficacy and operational deployability of the proposed approach in practical investment management.
📝 Abstract
Portfolio optimization is a critical area in finance, aiming to maximize returns while minimizing risk. Metaheuristic algorithms were shown to solve complex optimization problems efficiently, with Genetic Algorithms and Particle Swarm Optimization being among the most popular methods. This paper introduces an innovative approach to portfolio optimization that incorporates pre-assignment to limit the search space for investor preferences and better results. Additionally, taking margin trading strategies in account and using a rare performance ratio to evaluate portfolio efficiency. Through an illustrative example, this paper demonstrates that the metaheuristic-based methodology yields superior risk-adjusted returns compared to traditional benchmarks. The results highlight the potential of metaheuristics with help of assets filtering in enhancing portfolio performance in terms of risk adjusted return.