基于风险约束的智能投资组合优化
Intelligent Portfolio Optimization Based on Risk Constraints

智能投资组合优化旨在通过有效的资产配置来最大化投资回报，同时控制风险。本文研究了基于风险约束的智能投资组合优化策略，比较了遗传算法、模拟退火算法、粒子群算法和蚁群算法在实际市场数据中的应用。实验结果表明，蚁群算法在处理复杂投资组合优化问题时表现出色，尤其在高波动市场中。基于不同市场条件和投资者需求的比较分析，本文提供了优化选择建议，以提升投资组合管理效率。这一研究不仅丰富了智能投资组合优化的理论基础，还为实际投资决策提供了实用的指导。
Intelligent portfolio optimization aims to maximize investment returns through effective asset allocation while controlling risks. This paper investigates risk-constrained intelligent portfolio optimization strategies, comparing the applications of genetic algorithms, simulated annealing algorithms, particle swarm algorithms, and ant colony algorithms using actual market data. Experimental results demonstrate that the ant colony algorithm excels in handling complex portfolio optimization problems, particularly in high-volatility markets. Based on a comparative analysis of different market conditions and investor needs, this paper provides optimization recommendations to enhance portfolio management efficiency. This research not only enriches the theoretical foundation of intelligent portfolio optimization but also offers practical guidance for actual investment decision-making.