The Impact of Microeconomic Variables on Stock Return by Moderating of Money Supply
Bibliographic record
Abstract
The purpose of this study is to empirically investigate the effect of microeconomic variables on stock return with moderating role of money supply (MS). The selected microeconomic variables in this study are debt-to-equity ratio (DE), dividend per share (DPS), and quick ratio (QR). Firm size and book-to-market value are considered as controlling variables. The period of the study is from 2003 to 2012 and the sample population of this study is 300 companies listed on Kuala Lumpur Stock Exchange (KLSE). Secondary data were collected from DataStream International, financial annual reports, and the World Bank databank. Generalized least squares (GLS) technique was used to estimate the predictive regressions in form of multiple models of panel data sets. According to the findings, MS moderates the impact of DE and QR on stock return, but does not moderate the effect of DPS on stock return. Besides, MS moderates the impact of all selected predictors on stock return. The findings of this study further show that an increase in value of a firm’s debt relative to its equity would cause a decrease in the firm’s stock return. The results also indicate that firms with higher QR and DPS are likely to have a higher stock return. Overall, the findings of this research are consistent with Modigliani and Miller's capital structure theory, as well as Pecking Order and Bird In Hand theory. The findings of this study would be of interest to domestic and international investors, stockbrokers, board of directors, financial managers, and policy makers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".