Analysis of the Impact of Portfolio Optimization Algorithms on Fund Performance
DOI:
https://doi.org/10.62051/ijgem.v6n3.26Keywords:
Portfolio optimization, Fund performance, Machine learning, Risk-adjusted returns, Algorithmic reflexivityAbstract
This study explores the impact mechanism of portfolio optimization algorithms on fund performance. Based on theoretical analysis and empirical research, it is found that optimization algorithms significantly affect fund performance in three paths: return enhancement, risk control and cost management, but the effects are moderated by the market environment and algorithm type. Machine learning and hybrid optimization strategies exhibit better risk-adjusted returns, while traditional algorithms maintain value in specific market environments. The study reveals that optimization algorithms face three major challenges: model risk, market contrariness, and regulatory compliance, and proposes coping strategies based on multi-scenario testing and human-machine collaboration to provide theoretical guidance for the practical application of optimization algorithms.
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