Most claimed statistical findings in cross-sectional return predictability are likely true
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arXiv
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866912717444481024 |
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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 |