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Record W4385506294 · doi:10.56028/aemr.6.1.609.2023

Stock Valuation and Analysis of Amazon, Alibaba, Baidu, and Alphabet

2023· article· en· W4385506294 on OpenAlexaff

Bibliographic record

VenueAdvances in Economics and Management Research · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicImpact of AI and Big Data on Business and Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntrinsic value (animal ethics)Valuation (finance)Financial economicsGrowth stockStock (firearms)BusinessEconomicsEarningsValue (mathematics)Cash flowInvestment valueEconometricsCashFinanceStock marketStatisticsMathematicsGeographyStock market bubble

Abstract

fetched live from OpenAlex

This comprehensive review paper provides an in-depth analysis of value investing, a prominent investment strategy that emphasizes the intrinsic value of stocks. The paper explores the relationship between intrinsic stock value, discounted cash flows, earnings per share (EPS), and investment returns. Additionally, it includes a sample analysis of the financial performance of Amazon, Alibaba, Baidu, and Alphabet from 2019 to 2021 to illustrate the application of value investing principles. The findings contribute to a deeper understanding of value investing strategies and their practical implications.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.233
GPT teacher head0.484
Teacher spread0.251 · 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
Published2023
Admission routes1
Has abstractyes

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