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Record W4292566169 · doi:10.5267/j.msl.2022.5.003

Influence of behavioural biases and capital structure determinants on capital structure and share price: Regression and path analyses for Indonesian publicly listed firms

2022· article· en· W4292566169 on OpenAlexvenueno aff
David Rimbo Lim, Hendrawan Supratikno, Gracia Shinta S. Ugut, Edison Hulu

Bibliographic record

VenueManagement Science Letters · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCapital structureOverconfidence effectEconomicsEconometricsPanel dataPath analysis (statistics)Financial economicsMonetary economicsDebtPsychologyFinanceStatisticsSocial psychology

Abstract

fetched live from OpenAlex

The relationship between behavioural characteristics (both rational and irrational measures) and capital structure determinants has been empirically validated. This study examines the influence of the behavioural traits of overconfidence and optimism on capital structure determinations by IDX-listed public Indonesian firms’ (Tbks) management. This is statistically tested via a comprehensive hypothesis modelling construct that includes empirically validated capital structure determinants (market timing, profitability, tangibility, size and their impacts on stock price). Panel regression PLS and path analysis were performed on stock price data and financial metrics extracted from the 2013–2020 financial statements of 55 Tbks from the LQ-45 and Kompas-100 stock indices. This study found that Optimism, Market Timing and Adjusted Debt on Market Timing are not determinants of capital structure for Tbks, while Overconfidence and the control variables Firm Profitability, Firm’s Asset Tangibility and Firm Size were statistically validated as capital structure determinants. Overconfidence (as a behavioural bias) is observed to have significant negative influence on management’s capital structure determinations, while Optimism has insignificant positive influence. The less aggressive leveraged models adopted by the sampled Tbks may indicate that implemented good principles of corporate governance have played a role in preventing capital structure determinations skewed by managements’ behavioural biases or psychological tendencies.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.001
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 head0.032
GPT teacher head0.259
Teacher spread0.227 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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