MétaCan
Menu
Back to cohort
Record W2128721600 · doi:10.5430/ijfr.v4n3p10

Earnings Management Prior to Initial Public Offerings and Its Effect on Firm Performance: International Evidence

2013· article· en· W2128721600 on OpenAlexvenueno aff
Arjan Premti

Bibliographic record

VenueInternational Journal of Financial Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccrualInitial public offeringEarnings managementAccountingEarningsMonetary economicsBusinessEconomicsDemographic economicsFinancial economics

Abstract

fetched live from OpenAlex

This paper investigates the degree of discretionary current accruals (DCA-1) prior to the initial public offerings (IPOs) of foreign firms in an attempt to study the two seemingly opposing views of Teoh, Welch, and Wong (1998) and Ball and Shivakumar (2008) in regards to pre-IPO earnings management. By analyzing a sample of 4962 IPOs from 28 countries, I find that, on average, IPO firms do not report significantly positive DCA-1. This result supports the view held by Ball and Shivakumar that IPO firms do not engage in earnings management and it is inconsistent with the earnings management hypothesis of Teoh et al. (1998). Furthermore, results support the criticism of Ball and Shivakumar (2008) that the use of discretionary accruals in the IPO year (DCA0) is a biased measure of earnings management. However, consistent with the hypothesis of Teoh et al. (1998), results show that firms with higher discretionary accruals (DCA-1 or DCA0) underperform in the long run. The negative relationship between the long-term performance and the level of DCA is robust to several measures of long-term performance (cumulative abnormal returns-CAR, buy-and-hold abnormal returns-BHAR, Fama-French 4-factor model-Alpha), to several time horizons (3 and 5 years), and holds even after controlling for several firm characteristics. Overall, the results show that although on average IPO firms don’t engage in earnings management, the ones that do, underperform in the long run.

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)0.003
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.335
Teacher spread0.291 · 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 designOther design
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

Citations11
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Financial ResearchSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207