The Determinant of Capital Structure of SMEs in Malaysia: Evidence from Enterprise 50 (E50) SMEs
Bibliographic record
Abstract
Capital structure have implications in determining the ability and success of a firm, especially to small andmedium-sized enterprises (SMEs). This paper analyses the capital structure of SMEs in Malaysia focusing onEnterprise 50 (E50) SMEs. E50 is an annual awards program initiated by government and organized by SMECorporation & Deloitte Malaysia since 1997 to recognize the 50 best SME companies in Malaysia based on theirperformances and potential to succeed. The secondary data from Companies Commission of Malaysia has beencollected for the study. The study employed regression analysis on 334 companies, utilised the accounting datafor the five year period of 2005 to 2009. Capital structure is the Dependent Variable referring to debt ratio of thecompanies, decomposed into Long Term Debt ratio and Short term Debt ratio. The Independent Variables (IV)are age; size; tangibility; liquidity; profitability; growth and taxation. Two theories of capital structure haveguided this study i.e. the Trade-Off Theory and the Pecking Order Theory. The study found that size is importantif we decomposed the debt into longand short term. In addition, asset tangibility, liquidity and profitability arethe main capital structure determinants for SMEs. Age and growth are important for a long term, while taxationis not an important consideration in capital structure decision.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".