This article evaluates the usefulness of high-frequency data in optimal portfolio choice. The authors use a comprehensive list of major stock indexes and different frequencies of observations. Furthermore, they consider the impact of economic cycles, microstructure noise, and seasonality on performance. Their results show the ability of high-frequency data–based strategies to beat both monthly and daily based strategies and the benchmark equally weighted portfolio, even in presence of transaction costs. The authors also find that the outperformance arises from the reduction in the estimation error of the covariance matrix, which offsets the increase in transaction costs. TOPICS: Performance measurement, portfolio construction, portfolio theory Key Findings • The authors evaluate the usefulness of high-frequency data in optimal portfolio choice. • They employ several stock indexes and different frequencies of observations. • The authors consider the impact of economic cycles, microstructure noise, and intraday seasonality on performance.

中文翻译：

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最佳方式击败朴素的多元化：使用高频数据进行资产分配

本文评估了高频数据在最佳投资组合选择中的有用性。作者使用了主要股票指数和不同观察频率的综合列表。此外，他们还考虑了经济周期，微观结构噪声和季节性对性能的影响。他们的结果表明，即使在存在交易成本的情况下，基于高频数据的策略也能击败基于月度和每日策略以及基准均等加权投资组合。作者还发现，出色的表现是由于协方差矩阵的估计误差减少而造成的，这抵消了交易成本的增加。主题：绩效衡量，投资组合构建，投资组合理论主要发现•作者评估了高频数据在最优投资组合选择中的有用性。•他们使用几种股票指数和不同的观察频率。•作者考虑了经济周期，微观结构噪声和日内季节性对绩效的影响。