Do the Financial Statements of Listed Companies on the Ljubljana Stock Exchange Pass the Benford’s Law Test?
Bibliographic record
Abstract
Currently, we need to think about the risks in using the financial statements. Abroad, for a long time, in the detection of irregularities in the financial statements, Benford's law test has been used, which is a very simple, objective and efficient digital analysis that can help identify controversial areas. Since, in Slovenia, its use is still unknown and in practice, and it is rarely used, we checked whether the financial statements of Slovenian companies listed on the Ljubljana Stock Exchange pass the Benford’s law test. Our study is original, as no one has ever tested the company's financial statements on the Ljubljana Stock Exchange with this test. We found that the tested data very well matched the theoretical distribution according to Benford's law. If the deviation of the analysed data from the theoretical distribution is very large, this does not mean that this is a possible fraud in the used financial data. Benford's law helps us identify the controversial areas that require our attention and the decision on how to proceed with the audit or possible investigation of accounting data.
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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 | 0.012 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".