Incremental Value Relevance of Disaggregated Book Values and Disaggregated Earnings: Evidence from Nigeria
Bibliographic record
Abstract
The link between accounting information and firm value has attracted the attention of accounting and finance researchers since the seminar work of Ball and Brown (1968) and Beaver (1968). The association of accounting information with firm value is value relevance research. Value relevance is the degree to which accounting information captures information impounded in stock prices. Ohlson (1995) provided the conceptual linkage between accounting information and firm value. Since then, value relevance research has increased in volume and diversity. A trend in this line of enquiry is to determine if disaggregated accounting information is incrementally value relevant beyond bottom line accounting information. The objective of this paper was to ascertain if disaggregated accounting information has more value relevance compared to bottom line measures for firms listed on the Nigerian Stock Exchange Market. We specifically investigated the value relevance of disaggregated accounting information for Nigerian listed firms, using a sample of 940 firm-years from 1994 to 2013. The study contributes to the extant value relevance literature by employing a methodology that accounts for documented inefficiencies of the Nigerian capital market. Given the analysis conducted, findings indicate that disaggregated earnings are incrementally value relevant beyond bottom line earnings. Besides, disaggregated book value is found to be more value relevant compared to book value. In the light of these findings, both investors and analysts should shift emphasis from bottom line accounting information, like earnings and book value to disaggregated accounting numbers to improve the quality of investment decisions they make. Besides, regulatory authorities must improve on the corporate governance environment in order to mitigate incidences of window dressing, creative accounting and other corporate malfeasances
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".