The Predictive Value of Government Accounting Information and the Secondary Brazilian Bond Market
Bibliographic record
Abstract
The international literature highlights evidence on the predictive ability of government accounting information in relation to bond markets, especially for sub-national governments’ bonds. However, there is little evidence in the literature about the role of accounting information from national governments. Having observed this gap, we aimed to identify how strongly the government accounting information affects the pricing of the government bonds issued by the Brazilian Federal Government and traded in the secondary market. In this research, we analyzed the transactions carried out without the direct participation of the federal government. The predictive ability of the accounting information of the Brazilian federal government was verified for the period from 2003 to 2012 on a monthly basis. Following the value relevance approach, we developed price and return models for the bond National Treasury Bills, Single Series. After analyzing the presence of unit roots in the price and return series, we estimated regressions using the ordinary least squares method. We showed that the accounting information of the Brazilian federal government has predictive ability regarding the pricing of bonds traded in the secondary market. However, this does not mean that the government accounting information is fully and directly used by investors, but rather that such information is intended as a proxy for information reviewed by investors when negotiating such bonds, these investors being considered as limited rational agents.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".