Modelos de gestión de resultados: un estudio transnacional
Bibliographic record
Abstract
La información contable se concibe como el pilar fundamental para la toma de decisiones de los agentes de la empresa. Así pues, dicha información debe cumplir una serie de requisitos que aseguren la calidad de la misma, de tal forma que no introduzca sesgos. De este modo, en los últimos años ha adquirido gran relevancia el desarrollo de modelos y la implantación de medidas encaminadas a reducir los comportamientos oportunistas de los directivos. Así pues, a partir de los modelos desarrollados en la literatura para cuantificar la discrecionalidad contable, el objetivo de este trabajo es determinar si alguno de estos modelos ofrece mejores resultados en cuanto a la cuantificación de la gestión del resultado a partir de las pruebas de especificación y potencia. Para ello se ha utilizado una muestra de 33.410 observaciones correspondientes a empresas no financieras de Estados Unidos, Canadá, Reino Unido, Corea, Japón, Italia, Alemania, Francia, España, Canadá y Australia que han cotizado en mercados de valores a lo largo del periodo 2005-2009. Los resultados ponen de manifiesto la superioridad del modelo de Jones ajustado al ROA con respecto al modelo de Jones y al modelo de Jones modificado. Accounting information is conceived as one of the most important resources for decision making by company personnel. Thus, this information must meet certain requirements to ensure its quality and in such a way that does not introduce bias. Thus, in recent years it has become very important to develop models and implement measures to reduce the opportunistic behavior of managers. There are some models in the literature to detect discretionary accruals. Thus, the aim of this work is to determine whether any of these models is better than others by using specification and strength tests. The sample consisted of 33,410 observations of non-financial listed companies in the United States, United Kingdom, Korea, Japan, Italy, Germany, France, Spain, Canada and Australia over the period 2005-2009. Our results show the superiority of the Jones model adjusted to ROA as regards the Jones model and Jones modified model.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".