Practical Portfolio Optimization with Metaheuristics:Pre-assignment Constraint and Margin Trading

ArXiv ID: 2503.15965 “View on arXiv”

Authors: Unknown

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.

Keywords: Portfolio Optimization, Genetic Algorithms, Particle Swarm Optimization, Risk-Adjusted Returns, Metaheuristics, Equities (Portfolio Management)

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats
  • Why: The paper uses mathematical formulations like the multi-objective optimization structure and Pareto optimality, but the empirical demonstration is an illustrative example without code, backtests, or heavy statistical metrics.
  flowchart TD
    A["Research Goal: Optimize Portfolio with Constraints"] --> B["Data & Parameters<br/>Asset Returns, Risk, Preferences"]
    B --> C["Pre-assignment Filtering<br/>Limit Search Space"]
    C --> D["Metaheuristic Optimization<br/>GA & PSO Algorithms"]
    D --> E["Compute Risk-Adjusted Returns<br/>& Rare Performance Ratio"]
    E --> F{"Compare against<br/>Traditional Benchmarks"}
    F --> G["Key Findings<br/>Superior Risk-Adjusted Returns<br/>& Enhanced Portfolio Performance"]