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Record W4416365752 · doi:10.1287/mnsc.2022.03251

Firms’ Stock Prices, Stock Returns, and Remaining Lifetime Earnings

2025· article· en· W4416365752 on OpenAlexaffabout
Sanjeev Bhojraj, Ashish Ochani, Shivaram Rajgopal

Bibliographic record

VenueManagement Science · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEarningsStock (firearms)Stock priceEarnings growthPublic informationGrowth stockStock trading

Abstract

fetched live from OpenAlex

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 .

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.226
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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