朴素分散投资何时有效及为何有效：一种简单的诊断策略
When and Why Naïve Diversification Works: A Simple Diagnostic Strategy
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中文总结 AI 辅助
研究朴素分散投资难题，提出基于预测误差协方差矩阵特征结构的简单条件及两阶段自适应策略，应用于美国股票溢价预测时能提升样本外预测表现，为投资组合构建提供时间范围依赖指导。
中文摘要 AI 辅助
我们用一个简单且可检验的条件解释了长期存在的朴素分散投资难题：当预测误差协方差矩阵具有均匀特征结构时，等权重是最小方差最优的。这个“黄金准则”驱动了一种两阶段自适应策略，该策略根据与该条件的经验距离动态混合朴素权重和优化权重。应用于美国股票溢价预测时，该方法在样本外预测准确性、效用和夏普比率方面实现了持续提升。多样性驱动的收缩在短时间范围内占主导，而优化权重在较长时间范围内重新占据优势，为投资组合构建提供了明确的时间范围依赖指导。
英文摘要
We explain the long-standing puzzle of naïve diversification with a simple, testable condition: equal weighting is minimum-variance optimal when the forecast-error covariance matrix has a uniform eigenstructure. This "Golden Criterion" drives a two-stage adaptive strategy that dynamically blends naive and optimized weights based on the empirical distance from this condition. Applied to U.S. equity premium forecasting, the method delivers consistent out-of-sample gains in forecast accuracy, utility, and Sharpe ratios. Diversity-driven shrinkage dominates at short horizons, while optimized weights regain their edge at longer horizons, offering clear horizon-dependent guidance for portfolio construction.
