The Relation between Market Values, Earnings Forecasts, and Reported Earnings*
Bibliographic record
Abstract
Abstract Recently, much of the research into the relation between market values and accounting numbers has used, or at least made reference to, the residual income model (RIM). Two basic types of empirical research have developed. The “historical” type explores the relation between market values and reported accounting numbers, often using the linear dynamics in Ohlson 1995 and Feltham and Ohlson 1995 and 1996. The “forecast” type explores the relation between market value and the present value of the book value of equity, a truncated sequence of residual income forecasts, and an estimate of the terminal value at the truncation date. The analysis in this paper integrates these two approaches. We expand the Feltham and Ohlson 1996 model by including one‐ and two‐period‐ahead residual income forecasts to infer “other” information regarding future revenues from past investments and future growth opportunities. This approach results in a model in which the difference between market value and book value of equity is a function of current residual income, one‐ and two‐period‐ahead residual income, current capital investment, and start‐of‐period operating assets. The existence of both persistence in revenues from current and prior investments and growth in future positive net present value investment opportunities leads us to hypothesize a negative coefficient on the one‐period‐ahead residual income forecast and a positive coefficient on the two‐period‐ahead residual income forecast. Our empirical results strongly support our hypotheses with respect to the forecast coefficients.
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How this classification was reachedexpand
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".