Influence of behavioural biases and capital structure determinants on capital structure and share price: Regression and path analyses for Indonesian publicly listed firms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".