Pricing and Mispricing of Accounting Fundamentals in the Time‐Series and in the Cross Section
Bibliographic record
Abstract
Abstract This study examines the extent to which parsimonious and general cross‐sectional valuation models, restricted to include only publicly available historical accounting information, explain share prices in the cross section, identify periods when market mispricing may be more pervasive, and also identify which shares within those cross sections are more likely to be mispriced. Our model simply includes historical book value, earnings, dividends, and growth, but it explains on average over 60 percent of the cross‐sectional variation in share prices in annual estimations across 1975–2011. We also examine the extent to which the residuals indicate mispricing. The quintile of stocks picked by our model as most likely underpriced outperform the quintile of stocks picked as most likely overpriced by an average of 9.9 percent over the following 12 months, after controlling for size. We also predict and find that value residuals are better predictors of future abnormal returns: (i) among firms that are not covered by analysts; (ii) among firms that face fewer accounting measurement challenges; and (iii) when we estimate value model parameters by industry/year. We also predict and find our approach works better in periods when the mapping of fundamentals into prices is weaker. This study contributes a novel and straightforward approach to map accounting fundamentals into share prices in order to identify mispricing in time‐series and in the cross section.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".