Abstract
We study statistical significance of 93 fundamental anomalies published in academic journals in a multiple hypothesis setting. We generate a universe of 1,499 data-mined fundamental strategies in order to overcome a problem of not being able to observe strategies that were tried but not published. The multiple hypothesis tests reveal that the number of significant anomalies heavily depends on the precise specification of the tests. We show that the adjustment of standard errors on portfolio returns for heteroskedasticity and autocorrelation is of first order importance and t-statistics on the portfolio returns do not have critical values of the normal distribution.
Published Version
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