Financial Reports and Shareholdersâ Decision Making in Nigeria: Any Connectedness?
Bibliographic record
Abstract
The preeminent objective of this paper is to ascertain the impact of financial statements on shareholdersâ investment decision making in Nigeria. We employed the use of well-structured questionnaire to elicit the perception of shareholders regarding the importance of financial statements for investment decision making and also their discernment of the adequacy of the content of financial statements. The two hypotheses formulated in the course of the study, were tested by the use of ANOVA test and the Likelihood Ratio Test, and otherwise referred to as G-test or maximum likelihood statistical significance test. The results of the empirical tests show that Stockholders do possess the requisite technical and professional skills to analyze IFRs financial Statement. And that Stakeholders in financial reporting in Nigeria do rely on the Financial Information disclosed in financial statements for investment decision making. We recommended inter alia that stakeholders should in addition to the accounting figures in the financial statement, compute ratio, trend and common size analysis in order to secure deeper information. Secondly, investors should not be unaware of the possibility of the use of creative accounting techniques by directors, in painting a distorted picture of the state of health of the reporting entities. Additionally, investors should be mindful of the fact that financial statements are historical in nature. Since the past do not always paint a perfect picture of the present or future, investors should in addition to financial statements analysis, investigate the internal and external environment of the reporting entities before arriving at a final investment decision.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".