Bibliographic record
Abstract
Stakeholders are increasingly insistent that companies increase firm value. The problem is that stakeholders of financial services firms are unable to accurately determine firm value. The purpose of this correlational study was to examine the accuracy of 4 valuation models in predicting the market value of equity of commercial finance companies. Study participating companies were 8 listed U.S. or Canadian commercial finance companies. The theoretical constructs of the study included the accuracy of valuation models, modern portfolio theory, and the correlation of book value of equity to market value of equity. Financial information on participating companies obtained from public filings were input data in 4 valuation models. Multiple regression analysis of valuation model results and book value of equity (the predictor variables) were used to determine the accuracy of the models in predicting the market value of equity (response variable). The findings of the study showed that all 4 valuation models in combination with the book value of equity were statistically significant predictors of the market value of equity of the participating companies at the p < .05 level. However, the dividend discount model (DDM) and residual income model (RIM) were statistically more accurate without the combination of book value of equity (p = .000 and p = .000, respectively) than the discounted cash flow and risk-adjusted discounted cash flow valuation models (p = .371 and p = .904, respectively). The results of this study contribute to positive social change by providing business leaders an ability to measure the effectiveness of their actions in creating firm value. Corporate social responsibility activities correlate to value creation for firms that engage in promoting employee welfare and other stakeholder welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".