When Financial Facts Change: Auditing Labels for Point-in-Time Financial Learning

Historical financial data are versioned observations rather than immutable rows: later annual reports can change values reported for an earlier period. This creates two hazards for machine learning—invalid revision-derived labels and predictors that silently contain future information. We construct accession-paired observations from the U.S. SEC's public as-filed XBRL data and test whether comparative changes or explicit restatement dimensions recover a regulatory error-correction construct. Across fiscal years 2011–2023, 31,688 of 354,650 matched core facts change, affecting 22.24% of 47,918 company-years. Explicit restatement dimensions cover only 6.17% of changed facts and are absent in the first five years, so their absence is not a stable negative label. We use the SEC's DocumentFinStmtErrorCorrectionFlag as a filing-level reference in two temporal cohorts whose protocols were frozen before label access. Any comparative change has precision 17.26%/12.68% and recall 57.43%/57.78%; the explicit-dimension proxy has precision 66.67%/49.21% and recall 39.60%/34.44% for fiscal years 2023/2024. Both proxies fail their frozen validity thresholds in both years. A retrospective fiscal-year benchmark is not a deployable timing test: its cohort conditions on future availability and its training labels can postdate test prediction times. We reconstruct first-filing cohorts of 3,961 and 4,158 companies without future eligibility filters, preserve unknown labels, and require training-label maturity at each prediction cutoff. The audit distinguishes comparative revision from regulatory error correction and identifies additional cohort and training-label timing requirements for financial ML. Complete score coverage is distinct from complete reference-label coverage. Recovering an earlier training cohort restores score coverage to all 4,158 test companies. On the 3,606 annotated cases, boosting achieves AUROC 0.591, while poor calibration and 552 unknown labels limit the prediction claim.