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Record W1824874584 · doi:10.5430/ijfr.v6n4p1

Is Cash Flow per Share Paternalism by U.S. Accounting Rule Makers Warranted? An Empirical Study

2015· article· en· W1824874584 on OpenAlexvenueno aff
Ning Du, John E. McEnroe

Bibliographic record

VenueInternational Journal of Financial Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCash flowCash flow statementOperating cash flowFree cash flowAccountingFinancial statementBusinessActuarial scienceStatisticCash flow forecastingEconomicsAuditStatistics

Abstract

fetched live from OpenAlex

The Financial Accounting Standards Board (FASB) prohibits the reporting of cash flow per share (CFPS) information from the financial statements in fear of undermining the importance of the earning per-share metric. The objective of our research is to understand the validity of this argument by examining the effects of cash flow per share information on investors’ judgments. We conducted a two by one experiment where we manipulate cash flow per share as the absence or presence of the statistic in the financial statements. Eighty-eight MBA students assumed the role of investors and participated in this study. We find that the cash flow per share information affects investors’ reliance on cash flow information, but does not affect participants’ performance evaluation. Our results are in support of including CFPS in the income statement as it may lead investors to pay more attention to cash flow information.

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)0.014
metaresearch head score (Gemma)0.054
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 score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.074
GPT teacher head0.377
Teacher spread0.302 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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