The Effects of Corporate Financing Decisions on Firm Value in Bursa Malaysia
Bibliographic record
Abstract
The primary objective of shareholders and financial managers is generally stated to be the maximization of shareholders’ wealth by increasing the firm value. This research was undertaken to investigate the effect of corporate financing decisions on firm value . The research has been carried out using the panel data procedure for a sample of 256 firms from 9 sectors listed on Bursa Malaysia during the period 2000-2015. The study uses Tobin’s Q representing firm value for the dependent variable. The corporate financing was measured by leverage (short-term debt to total assets, long-term debt to total assets, total debt to total assets and total debt to total equity) and debt maturity (long-term debt to total debt). Short-term debt to total assets and long-term debt to total assets has a positive significant relationship to firm value. This finding is consistent with the view that leverage and dividends mitigate agency costs of free cash flow problems, therefore, increasing firm value. Total debt to total assets affects firm value negatively. This proves that although there are benefits of debts, there is also the cost of debts. The cost of debt financing arises from the increase in the probability of bankruptcy. Firm value does not depend on the length of debt maturity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".