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Record W2119750322 · doi:10.5267/j.msl.2013.05.026

A study on the effects of quarterly financial reports on systematic risk and return on assets: A case study of Tehran Stock Exchange

2013· article· en· W2119750322 on OpenAlexvenueno aff
Roozbeh Hedayat Mazhari

Bibliographic record

VenueManagement Science Letters · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsStock exchangeSystematic riskBusinessActuarial scienceEmpirical researchStock (firearms)FreedmanFinanceEconomicsAccountingStatistics

Abstract

fetched live from OpenAlex

Financial statements are considered as primary sources of information for most investors to make investment decisions. A crystal clear and comprehensive annual report helps many interested parties about the performance of any business unit. However, many rules and regulations ask management teams of organizations to provide quarterly financial results. In this paper, we perform an empirical investigation to study the effects of quarterly financial reports on three ratios including systematic risk, return on assets and firm size. The proposed study gathers the necessary data from 72 firms listed on Tehran Stock Exchange over the period of 2000-2006. The study determined the performances of these 72 firms before and after the releases of three quarterly reports and using Freedman test determined whether there were any meaningful differences between two groups of data or not. The results of Freedman test indicate that there were not any meaningful differences between stock performance and systematic risk before and after quarterly results. The survey also examines the relationship between systematic risk and size of firms using Pearson correlation test and the results indicate there were some meaningful differences size and systematic risk.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.157
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.216
Teacher spread0.201 · 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.

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
Published2013
Admission routes1
Has abstractyes

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