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Selection versus diversification in noisy alpha environments

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  • Goto, Shingo
  • Yamada, Toru

Abstract

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.

Suggested Citation

  • Goto, Shingo & Yamada, Toru, 2026. "Selection versus diversification in noisy alpha environments," Journal of Banking & Finance, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:jbfina:v:189:y:2026:i:c:s0378426626001007
    DOI: 10.1016/j.jbankfin.2026.107726
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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