The Value Relevance of Accounting Information: Evidence from Nigeria
Bibliographic record
Abstract
Value relevance of accounting information addresses the degree to which accounting information summarizes the information that is impounded in share prices. Therefore, the purpose of this paper is to contribute to the empirical literature on value relevance by examining the extent to which accounting information is associated with firm value, from an emerging market context. The paper uses the basic Ohlson (1995) model and the modification of the model that includes cash flow from operation, and dividends, to ascertain the value relevance of accounting information in Nigeria. The paper accommodates the documented relative inefficiency of the market by using stock price at three months and six months after year end as dependent variable. The study employs a pooled and panel data in the regression of share price and returns on accounting numbers. The ordinary least square (OLS) estimation and dynamic model estimation, with the Random and Fixed effects variants were used in the regression. We find that earnings, cash flow and dividends were statistically significantly associated with firm value but book value was related but not statistically significant. Based on these findings, it is suggested that the focus of investors should be on earnings, dividends and cash flows while less emphasis be placed on book values. Besides, the accounting information for investment purposes should be communicated to the investing public; and such information should be of high quality to avoid sub-optimal investment decisions by investors, with negative consequences for the overall economy
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".