How Significant are the Differences in Financial Data Provided by Key Data Sources? A Comparison of XBRL, Compustat, Yahoo! Finance, and Google Finance
Bibliographic record
Abstract
ABSTRACT We compare the financial statement data (excluding footnotes) reported by 105 randomly selected firms in their 10-K filings with data contained in XBRL filings and data reported by three data aggregators/distributors: Compustat, Google Finance, and Yahoo! Finance. We find that 48 percent to 63.2 percent of the 10-K financial statement items available in XBRL filings are not available from the aggregators/distributors. However, aggregator/distributor-provided data contain many financial items that are not in the official 10-K or XBRL filings but could be useful to users. For items included both in XBRL and by aggregators/distributors, all but 0.01 percent of the XBRL data amounts agree with the 10-K filings, whereas 6.5 percent to 7.7 percent of the amounts provided by aggregators/distributors do not, depending on the aggregator/distributor. Most differences are material, and the differences in items used in bankruptcy prediction and earnings quality models result in significant differences in the model results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".