| Abstract: |
Mining 29, 000 accounting ratios for t-statistics over 2.0 leads to
cross-sectional predictability similar to the peer review process. For both
methods, about 50% of predictability remains after the original sample
periods. Data mining generates other features of peer review including the
rise in returns as original sample periods end, the speed of post-sample
decay, and themes like investment, issuance, and accruals. Predictors
supported by peer-reviewed risk explanations underperform data mining.
Similarly, the relationship between modeling rigor and post-sample returns is
negative. Our results suggest peer review systematically mislabels mispricing
as risk, though only 18% of predictors are attributed to risk. |