Exploration of Earnings Management and Lexical Richness: A study of companies in North America
Bibliographic record
Abstract
This paper investigates how the lexical richness of a corporation’s annual financial reports connects with earnings management (EM) in all industry sectors (except industries with different accounting methods, such as Utilities and Financial services) and in North America only. In order to explore the above-mentioned relationship, this study introduces new variables – Type-token proportion (TTR). Utilizing the TTR and the square root of TTR (denoted as Unique Index in this paper) to quantify the lexical extravagance of the management discussion and analysis section of the annual report (MD&A), this paper anticipates and finds that firms probably going to have manipulated their earnings to beat the previous year’s benchmark and have MD&As with high-level writing proficiency (a kind of complexity). This is coherent with the conclusion of Lo et al.’s paper: good news is inherently easier to communicate, and suggests that words of choice help complicate disclosure and conceal bad news.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".