Financial Hegemony, Diversification Strategies and the Firm Value of Top 30 FTSE Companies in Malaysia
Bibliographic record
Abstract
This study investigates the relationships between financial hegemony groups, global diversification strategies and firm value of the Malaysia’s 30 largest companies listed in FTSE Bursa Malaysia Index Series during 2009 to 2012 period. We chose Malaysia as an ideal setting because the findings contribute to the phenomenon of the diversification–performance relationship in the Southeast Asian countries. We apply hegemony stability theory to explain the importance of financial hegemony groups in deciding international locations for operations. By using panel data analysis, we find that financial hegemony groups are significantly important in international location decisions. Results reveal that the stability of financial hegemony in BRICS and G7 groups enhances the financial value of the Malaysia’s 30 largest companies, whereas the stability of financial hegemony in ASEAN groups is able to enhance the non-financial value of the firms. Overall, this paper suggests that in order to diversify globally, it is necessarily for the manager in the guest country to evaluate and fully understand the host country’s geopolitical situation and its financial stability.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".