The Integration of International Capital Market from Indonesian Investors’ Perspective: Do Integration Still Give Diversification Benefit
Bibliographic record
Abstract
The objective of this study is to analyze whether, despite the international equity liberalization and growing world financial integration, Indonesian investors can be beneficial from international diversification. The study covers both emerging markets (Indonesia, Philippines, Malaysia, Thailand, Korea, China, and Taiwan) and developed markets (USA, UK, Japan, Singapore, and Australia) over the period of January 1st, 2007 to April 30st, 2017. It uses several state-of-the-art techniques: multivariate cointegration and vector error correction models (VECM) with the analysis of impulse response function (IRF) and forecast error variance decomposition (FEVD) to analyze the long-term level of integration and time-varying correlations with the Dynamic Conditional Correlation (DCC) aproach to analyze short term level of integration. The analysis provides the evidence of integration berween Indonesian market and international markets. The findings suggest that Indonesian investors have more chance to gain international diversification benefit from developed markets rather than emerging markets as the Indonesian market has low level of integration compared to developed markets.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".