Zuzana Irsova Havrankova

This article provides concise, nontechnical, step‐by‐step guidelines on how to conduct a modern meta‐analysis, especially in social sciences. We treat publication bias, p‐ hacking, and systematic heterogeneity as phenomena meta‐analysts must always confront. To this end, we provide concrete methodological recommendations. Meta‐analysis methods have advanced notably over the last few years. Yet many meta‐analyses still rely on outdated approaches, some ignoring publication bias and systematic heterogeneity. While limitations persist, recently developed techniques allow robust inference even in the face of formidable problems in the underlying empirical literature. The purpose of this paper is to summarize the state of the art in a way accessible to aspiring meta‐analysts in any field. We also discuss how meta‐analysts can use advances in artificial intelligence to work more efficiently.

We examine whether estimates of hedge fund performance reported in prior empirical research are affected by publication bias. Using a sample of 1019 intercept terms from regressions of hedge fund returns on risk factors (the “alphas”) collected from 74 studies published between 2001 and 2021, we show that the selective publication of empirical results does not significantly contaminate inferences about hedge fund returns. Most of our monthly alpha estimates adjusted for the (small) bias fall within a relatively narrow range of 30–40 basis points, indicating positive abnormal returns of hedge funds: Hedge funds generate money for investors. Studies that explicitly control for potential biases in the underlying data (e.g., backfilling and survivorship biases) report lower but still positive alphas. Our results demonstrate that despite the prevalence of publication selection bias in many other research settings, publication may not be selective when there is no strong a priori theoretical prediction about the sign of the estimated coefficients.