Detecting Financial Misreporting with Real Production Activity: Evidence from an Electricity Consumption Analysis<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This study examines whether a real production activity measure, firm‐level electricity consumption growth, is useful in detecting firm financial misreporting. Identifying proxies for a firm's underlying financial performance that are not a function of the firm's accounting system is essential for detection of misreporting. We propose that the difference between revenue growth and electricity consumption growth (i.e., growth wedge (GW)) is a useful signal of financial misreporting. Using electricity consumption data for Korean firms from 2006 to 2014, we find that the GW is positively associated with discretionary revenues and accruals and the likelihood of financial misreporting as proxied by accounting restatements, qualified audit opinions, and regulatory enforcement actions. The GW provides incremental information over firm characteristics and earnings management signals examined by prior research. Our findings are robust to a battery of additional tests, including within‐firm and industry comparisons that do not require access to cross‐sectional firm‐level electricity data. Overall, our study documents new evidence on the role of a real production activity measure from an independent reporting entity in detecting financial misreporting. Our evidence speaks to the potential usefulness of real activity metrics in forensic economics.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".