Incentives for accounting choices in Cash Flows Statements
Bibliographic record
Abstract
ABSTRACT This study sought to identify incentives that influence the accounting choices for classifying interest and dividends received or paid in Cash Flow Statements (CFSs), in the period from 2008 to 2014, in non-financial companies of the Brazilian capital market. The hypotheses refer to the effect of the choice of classification for interest and dividends over cash flow from operations (CFO), according to indebtedness, profitability, size, negative CFO, sector, and auditor. This article seeks to contribute by providing evidence on the accounting choices for classification in CFSs, considering the lack of consensus in the results of studies in the Brazilian capital market and helping to better understand these accounting choices and the incentives behind them. A correct understanding of the information in CFSs is fundamental for them to be useful to their users. The existence of accounting choices for classification in CFSs may directly affect this understanding and, consequently, their usefulness. The results help in better understanding the discretion contained in CFSs, enabling the correct use of their information. They can also generate evidence for regulatory bodies to rethink their accounting rules and for academia to direct future research. Two panel data models were developed, using a sample of 352 companies, 2,290 analyzed reports, and 3,764 data items. The results indicate that companies with a greater level of debt, profitability, and size make their accounting choices in order to report higher CFO in the CFS. The evidence obtained reinforces the international findings and adds new analyses in the Brazilian context, contributing to the development of accounting choice theory.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".