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Autore principale: Chen, Andrew Y.
Natura: Preprint
Pubblicazione: 2022
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Accesso online:https://arxiv.org/abs/2206.15365
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author Chen, Andrew Y.
author_facet Chen, Andrew Y.
contents The false discovery rate (FDR) measures the share of false positives in a set of statistical tests. I develop simple and intuitive bounds on the FDR in cross-sectional predictability publications. The simplest bound requires just a few lines of math and finds $\text{FDR} \le 25\%$ based on summary statistics in eight out of nine previous studies. A more refined bound finds $\text{FDR} \le 9\%$. The FDR is small because randomly selecting accounting ratios produces statistically significant predictability far more often than would occur if there were no predictability. The bounds also reconcile the disparate FDR estimates in the literature.
format Preprint
id arxiv_https___arxiv_org_abs_2206_15365
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Most claimed statistical findings in cross-sectional return predictability are likely true
Chen, Andrew Y.
General Finance
The false discovery rate (FDR) measures the share of false positives in a set of statistical tests. I develop simple and intuitive bounds on the FDR in cross-sectional predictability publications. The simplest bound requires just a few lines of math and finds $\text{FDR} \le 25\%$ based on summary statistics in eight out of nine previous studies. A more refined bound finds $\text{FDR} \le 9\%$. The FDR is small because randomly selecting accounting ratios produces statistically significant predictability far more often than would occur if there were no predictability. The bounds also reconcile the disparate FDR estimates in the literature.
title Most claimed statistical findings in cross-sectional return predictability are likely true
topic General Finance
url https://arxiv.org/abs/2206.15365