Accounting Based Valuation Models: What Have We Learned?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present survey article formed the basis of a presentation by G. Richardson to the 8 July 2003 plenary session of the Accounting and Finance Association of Australia and New Zealand Conference in Brisbane, Australia. The present article reconciles the historical and forecasting branches in the published accounting literature. Prior survey articles have primarily focused either on the historical branch or the forecasting branch. While these approaches have yielded useful insights, they do not attempt to synthesize the link between the two branches of the published literature. An obvious link between the two branches is that the Ohlson model begins with the Residual Income Model as an initial assumption. We believe that there are other links that need further emphasis. In the process, we also review the empirical issues and the evidence within these two branches. We know of no paper to date that has surveyed the empirical evidence on both the historical and forecasting branches of the published literature. In particular, we draw inferences on the following question: on balance, what have we learned from nearly a decade of research on accounting based valuation models and its applications?
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it