Evaluating the Relationship between Accounting Variables, Value-Based Management Variables, and Shareholder Returns: An Empirical Approach
Bibliographic record
Abstract
This study assessed the accounting-based variables and value-based management (VBM) variables that jointly affect firm value and performance. The study applied the causality test and variance decomposition to determine the variability of the variables, and further empirically employed fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) techniques to justify the results. Data covering 356 industries were purposively sampled to arrive at 61 companies spanning 2011–2020. Overall, the causality test found no relationship between economic value added and market value added but only found unidirectional causality from shareholder returns to MVA, EVA to shareholder returns, ROA to MVA, ROE to MVA, EVA to MVA, MVA to EVA, ROE to ROA, EVA to ROA, and EVA to ROE. A very strong bidirectional causality relationship was found between return on asset and shareholder return as a measure of company performance. Further results from the forecast error of the variance decomposition showed that shareholder returns are explained only by its own shock, contributing 45.38 percent in the long run, while the remaining variables, namely market value added, return on asset, return on equity, and economic value added, contribute about 35.96%, 14.06%, 4.08%, and 0.51%, respectively, to predicting the future values of shareholder return. This confirms the relationships between the variables from the short run to the long run. Additionally, results from the FMOL and DOL revealed that all accounting variables and VBM are good approaches for evaluating company performance as the empirical result from ROA, ROE, and EVA revealed positive and significant relationships. This confirms that a combination of both variables would produce a better evaluation as the accounting variables and VBM variables jointly relate to shareholder returns. This study serves as a guide to companies’ management and boards of directors in having better ways to evaluate company performance. Consequently, it is recommended that managers select combinations of accounting and VBM variables that suit their operations and jointly apply them in the performance evaluation of the company. This will be useful in providing both the relative and incremental performance information needed for diverse decision-making.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".