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Record W6930279845 · doi:10.5281/zenodo.10892753

Application of Market Timing Theory: Evidence from Canadian Firms

2024· article· en· W6930279845 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicReliability and Agreement in Measurement
Canadian institutionsnot available
Fundersnot available
KeywordsMarket timingCapital structureCorporate financeLeverage (statistics)Equity (law)Valuation (finance)Capital marketStock marketVolatility (finance)External financing

Abstract

fetched live from OpenAlex

Internal and external finance are the two primary forms of funding for businesses. Internal financing is derived from retained profits, while external financing may come through borrowing money or the issuance of stocks. Businesses utilize it constantly to grow and stay alive, so the choices they make about finance are crucial. Market timing is vital for determining the appropriate financial structure for a company's success because volatility in market valuation greatly affects the capital structure. Capital structure requires a decision-making tactic that is an art to tackle complex situations. Modigliani and Miller started this ground-breaking study on capital structure in the field of corporate finance in 1958. After that, several theories were developed, but one of those theories was the market timing theory of capital structure, which explains that firms issue new stock when their share price is overvalued and repurchase shares when their share price is undervalued. These price fluctuations of equity will affect corporate financing decisions and ultimately corporate capital structures. The goal of this study is to test the applicability of market timing theory in the context of Canadian firms; thus, the data have been collected from the FINVIZ Stock Screener for the period (2022) and analyzed by a generalized linear model technique through the EViews 13. The research concludes that the market-to-book ratio has a statistically significant negative effect on market leverage as well as book leverage.

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)0.016
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0050.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0390.000

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 head0.512
GPT teacher head0.598
Teacher spread0.086 · 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.

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
Published2024
Admission routes1
Has abstractyes

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