Sharper Alpha


Traditional alpha-based tests face substantial noise when estimated on single stocks. Using diversified test portfolios help to reduce this noise, at the cost of inducing aggregation error and reducing cross-sectional variability. To address these shortcomings, we propose a more efficient statistic, a sharper alpha, that reduces estimation noise for single stocks. We find that, while sharper alphas estimated on portfolios are similar to traditional OLS alphas, they provide significant noise reduction at the stock-level and reveal cross-sectional patterns that were not visible before.

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