Fair value accounting and reliability of accounting information of listed firms in Nigeria
Bibliographic record
Abstract
This study examined the association between fair value accounting and reliability of accounting information. The study adopted survey research along with quantitative methods. Users of the accounting information represented by corporate investment analysts and corporate portfolio managers were the respondents for the purpose of this study. The population size was one hundred and sixty-one (161) users of accounting information decomposed into one hundred (100) corporate investment analysts and sixty-one (61) corporate portfolio managers. The primary source of data was employed with the structured questionnaire as an instrument used to collect the data. Data was collected through the administration of 161 copies of the questionnaire to both corporate investment analysts and corporate portfolio managers. One hypothesis was formulated and was tested using the Pearson product moment correlation technique at a significant level of 5% and 10% while the Statistical Package for Social Science (SPSS) was engaged to analyze the data. Findings revealed a significant association between fair value accounting and reliability of accounting information of the firms in Nigeria. Hence, the study recommended that adequate and regular training programs and conferences on fair value accounting application have to be organized. This is because most of the employees of the companies in Nigeria did not understand how to use fair value in an inactive market, appropriately. Thus, it is of great importance that they were trained to understand different valuations and estimation techniques of fair value; how and when to apply them in the measurement of assets and liabilities in the financial statement.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".