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Record W4236421894 · doi:10.1092/7c61-9q4m-g7d2-64bk

The Relation between Market Values, Earnings Forecasts, and Reported Earnings

2002· article· en· W4236421894 on OpenAlexaffvenue
Joy Begley, Gerald A. Feltham

Bibliographic record

VenueContemporary Accounting Research · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEarningsRelation (database)EconomicsFinancial economicsBusinessEconometricsAccountingComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.090
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.155
GPT teacher head0.344
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2002
Admission routes2
Has abstractyes

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