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The Functions, Pricing Determinants, and Macroeconomic Influence of Stocks

2025· article· W4416123135 on OpenAlexaff
Bangqi Xu

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMarket liquidityOrder (exchange)Equity (law)Stock (firearms)Financial intermediaryCapital marketIntermediationInterest rateStock market

Abstract

fetched live from OpenAlex

Against the backdrop of expanding retail participation, rising passive ownership, and evolving market microstructure, understanding the links between trading, pricing, and the real economy has renewed importance. This review examines how equity markets function, what determines stock prices, and how those prices shape macroeconomic outcomes. The study’s theme is integrative: it synthesizes theory and evidence on four core market functions (price discovery, risk sharing, capital formation and governance, and liquidity provision), organizes pricing determinants across present-value logic, risk-based factor models, and demand- or constraint-driven mechanisms, and traces macroeconomic transmission through household wealth, firm investment, credit conditions, and monetary policy. This study connects canonical models with recent empirical findings and institutional observations, highlighting points of agreement and contention. The significance of this study lies in providing an integrated framework for researchers and policymakers: it clarifies when prices primarily reflect cash-flow news versus pressures driven by discount rates or order flows; it highlights how ownership composition and intermediation capacity shape price impacts; and it identifies practical implications for estimating the cost of capital, designing markets, and monitoring financial stability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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