On Comparing Cash Flow and Accrual Accounting Models for Use in Equity Valuation: A Response to Lundholm and O'Keefe (<i>CAR</i>, Summer 2001)*
Bibliographic record
Abstract
Abstract A claim is commonly made that cash flow and accrual accounting methods for valuing equities must always yield equivalent valuations. A recent paper by Lundholm and O'Keefe 2001, for example, claims that, because of this equivalence, there is nothing to be learned from empirical comparison of valuation models. So they dismiss recent research that has shown that accrual accounting residual income models and earnings capitalization models perform, over a range of conditions, better than cash flow or dividend discount models. This paper demonstrates, with examples, that the claim is misguided. Practice inevitably involves forecasting over finite, truncated horizons, and the accounting specified in a model — cash versus accrual accounting in particular — is pertinent to valuation with finite‐horizon forecasting. Indeed, the issue of choosing a valuation model is an issue of specifying pro forma accounting, and so, for finite‐horizon forecasts, one cannot be indifferent to the accounting.
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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 | 0.076 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".