Detecting Creative Accounting in Businesses in Financial Distress
Bibliographic record
Abstract
This paper investigates whether the management of firms in financial distress applies creative accounting techniques in order to fine-tune the elements of their financial statements. For this purpose, the financial statements of 385 Greek bankrupt firms of the trade and manufacturing sectors for the period 2003 to 2014 were analyzed. The sample was divided into two sub periods; in the period before the financial crisis, that is from 2003 to 2008, and the period 2009 to 2014 during which the Greek economy was in crisis and recession. By applying factor analysis, five financial ratios were selected, which formed the independent variables in a Discriminant Analysis (MDA) and Logit models in order to find those firms which, while they were bankrupt they were classified in the last period of their operation as healthy (type I error).By selecting these firms (common to both models), their accounting data for the last two years before they went bankrupt have been investigated in order to determine whether they have been affected by the application of creative accounting methods. The results showed that the management of some of the selected firms applied creative accounting techniques during the last year of operation before their bankruptcy, which led to the manipulation and falsification of the published financial statements during the period before the financial crisis. However, this is not the case for the period 2009 to 2014, because the economic crisis affects the behavior of managers in applying creative accounting, which is owing to the changes in market rules.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".