Selection versus diversification in noisy alpha environments
研究了在众多收益预测信号存在时，信号选择与分散化之间的权衡，发现分散化策略优于严格基于统计显著性的信号选择。
We study the trade-off between signal selection and diversification in asset pricing when many return predictors are available. Using the data-mining framework of Yan and Zheng (2017), we form long–short portfolios from financial ratio signals and evaluate performance relative to the CAPM and the Fama–French six-factor model. Although null signals are prevalent, portfolio performance is largely insensitive to their inclusion. Portfolios restricted to the most statistically significant signals underperform more diversified strategies. Out-of-sample information ratios are highest at p -value thresholds between 5% and 10%, well above levels typically advocated for false-discovery-controlled inference. The results indicate that diversification is more effective than strict inference-oriented signal selection for portfolio construction.