MétaCan
Menu
Back to cohort
Record W4393979654 · doi:10.53555/sfs.v8i3.2433

Analyzing the Literature on Stock Returns

2022· article· en· W4393979654 on OpenAlexvenueno aff
Mr. Sunny Masand, Mr. Ashok Prem

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsnot available
Fundersnot available
KeywordsStock (firearms)Financial economicsEconomicsEconometricsHistoryArchaeology

Abstract

fetched live from OpenAlex

The primary goal of investment is to generate returns, which comprise dividends and capital appreciation from stocks. These returns are influenced by both systematic and unsystematic risks, encompassing macroeconomic variables and firm-specific factors, respectively. The study of stock returns has attracted considerable interest among research scholars over the past 15 years, spanning the period from 2000 to 2014 and encompassing 63 different journals. This analytical study conducts a content analysis of literature on stock returns, extracting information from 368 research papers. The findings reveal a significant volume of research conducted worldwide on stock returns during this period, yielding positive results. The analysis covers various factors such as predictability/forecasting of stock returns, volatility/variability of stock returns, and their relationship with inflation. These insights are anticipated to benefit stock exchanges, regulators, government agencies, and other stakeholders. Notably, predictability/forecasting, volatility/variability, and risk and liquidity aspects of stock returns have emerged as the primary focus areas for researchers over the past 15 years

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)0.095
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0950.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.007
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.389
GPT teacher head0.407
Teacher spread0.018 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries SciencesSame topicStock Market Forecasting MethodsFrench-language works237,207