Firms’ Stock Prices, Stock Returns, and Remaining Lifetime Earnings
Bibliographic record
Abstract
We document the disconnect between earnings expectations as captured in stock prices and the ultimate realization of earnings over long periods. To do so, we compare firms’ stock prices on the first trading day and the beginning of each year to the realized earnings over their remaining lifetime (RLTEP ratio). We document that the RLTEP ratio, averaged over long periods and over 15,000 U.S. domestic public firms, approximates one, suggesting that expectations match actuals in the aggregate. However, most firms fail to deliver an RLTEP ratio greater than one. Acquisition prices are the largest contributors to the RLTEP ratio, with many surviving firms failing to generate enough earnings even after operating for between 15 and 45 years. The RLTEP ratio for survivors is positively associated with the future RLTEP ratio, future lifetime wealth creation, and future lifetime stock returns. Significant returns-based wealth creation by firms in the short term does not persist in the long term unless it is supported by fundamental wealth creation (high past RLTEP ratio). This paper was accepted by Eric So, accounting. Funding: Financial support from SC Johnson Graduate School of Management, Cornell University, Columbia Business School, Columbia University, Binghamton University School of Management, and Haskayne School of Business, University of Calgary is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03251 .
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".