标题： 基于等相关性投资组合策略的新型风险度量用于投资组合优化 显示英文标题

标题： Novel Risk Measures for Portfolio Optimization Using Equal-Correlation Portfolio Strategy

摘要： 组合优化长期以来一直由基于协方差的策略主导，例如马科维茨均值-方差框架。 然而，这些方法往往无法确保资产之间的风险结构平衡，导致集中在少数证券上。 在本文中，我们引入了基于等相关性组合策略的新风险度量，旨在构建每个资产与整体组合收益保持相等相关性的组合。 我们制定了一个数学优化框架，明确控制组合范围内的相关性，同时保持理想的风险与回报权衡。 所提出的模型通过历史股票市场数据进行了实证验证。 我们的研究结果表明，通过这种方法构建的组合在各种市场条件下表现出更好的风险分散和更稳定的回报。 该方法为传统的多样化技术提供了一个有吸引力的替代方案，并对机构投资者、资产管理者和量化交易策略具有实际意义。 显示英文摘要

摘要： Portfolio optimization has long been dominated by covariance-based strategies, such as the Markowitz Mean-Variance framework. However, these approaches often fail to ensure a balanced risk structure across assets, leading to concentration in a few securities. In this paper, we introduce novel risk measures grounded in the equal-correlation portfolio strategy, aiming to construct portfolios where each asset maintains an equal correlation with the overall portfolio return. We formulate a mathematical optimization framework that explicitly controls portfolio-wide correlation while preserving desirable risk-return trade-offs. The proposed models are empirically validated using historical stock market data. Our findings show that portfolios constructed via this approach demonstrate superior risk diversification and more stable returns under diverse market conditions. This methodology offers a compelling alternative to conventional diversification techniques and holds practical relevance for institutional investors, asset managers, and quantitative trading strategies.