Bibliographic record
Abstract
Capital structure is still a puzzle among finance scholars. Purpose of this study is to review various capital structure theories that have been proposed in the finance literature to provide clarification for the firms’ capital structure decision. Starting from the capital structure irrelevance theory of Modigliani and Miller (1958) this review examine the several theories that have been put forward to explain the capital structure.Three major theories emerged over the years following the assumption of the perfect capital market of capital structure irrelevance model. Trade off theory assumes that firms have one optimal debt ratio and firm trade off the benefit and cost of debt and equity financing. Pecking order theory (Myers, 1984, Myers and Majluf, 1984) assumes that firms follow a financing hierarchy whereby minimize the problem of information asymmetry. But neither of these two theories provide a complete description why some firms prefer debt and others prefer equity finance under different circumstances.Another theory of capital structure has introduced recently by, Baker and Wurgler (2002), market timing theory, which explains the current capital structure as the cumulative outcome of past attempts to time the equity market. Market timing issuing behaviour has been well established empirically by others already, but Baker and Wurgler (2002) show that the influence of market timing on capital structure is regular and continuous. So the predictions of these theories sometimes acted in a contradictory manner and Myers (1984) 32 years old question “How do firms choose their capital structure?” still remains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".