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Record W4312141782 · doi:10.54097/hbem.v4i.3446

Research on the Investment Value of Three Companies on Industrial Sectors in the U.S. Capital Market

2022· article· en· W4312141782 on OpenAlexaboutno aff
Qinyuan Luo

Bibliographic record

VenueHighlights in Business Economics and Management · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessProfitability indexCorporationStock exchangeInvestment (military)BondFinanceManufacturingIndustrial organizationCommerceMarketing

Abstract

fetched live from OpenAlex

Stocks and bonds issued by industrial companies listed and traded on the stock exchange belong to industrial stocks. For example: electric power, steel, automobile, food, beverage, wine, textile, pharmaceutical, and other enterprises engaged in product manufacturing stocks, bonds and other securities. In the United States, industrial stocks make up a large proportion of the economy. In the process, investors can make a lot of profits. Despite more than a century of growth in such industries, there is still a lot of potential. Industrial stocks are among the areas with the longest shelf life in the United States and the world. This paper analyses the selected three companies in industrial sector the three aspects of risk, profitability and market ratio to predict basic trend in this area. Three companies are Canadian National Railway Company (CNI), Caterpillar Inc. (CAT), FedEx Corporation (FDX). The results show Canadian National Railway Company is less risky and FedEx Corporation is least profitable. The findings in this paper may benefit the different investors in financial markets on investment decisions.

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)0.011
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.169
GPT teacher head0.339
Teacher spread0.170 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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