Leverage Effect and Switching of Market Efficiency Post Goods and Services Tax (GST) Imposition
Bibliographic record
Abstract
This paper investigates the leverage effect and switching of market efficiency after the GST imposition on fee-based financial services in Bursa Malaysia and Australian Securities Exchange (ASX). The sample in this paper comprises of public listed companies for the period of one year before and after the GST imposition. GJR-GARCH is employed to evaluate the asymmetry response that is associated with the negative news shocks. To assess the effect of transactional efficiency on the informational efficiency and the structural change of time-varying volatility, SGARCH is adopted. This research reveals the presence of leverage effect in developing and developed market. The GST imposition on fee-based financial services significantly reduces the informational efficiency in Bursa Malaysia, but not in ASX. To boost the tax revenues generated from the financial sector, the policymakers in the developed markets (similar to ASX) should contemplate imposing GST on the fee-based financial services without affecting the stability of the stock market. The investors in thin markets (such as Bursa Malaysia) could forecast the stock returns of the thin market upon GST imposition on fee-based financial services.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".