The interdependence of Indonesia stock market against the \nprice volatility of G-20 countries’ stock market
Bibliographic record
Abstract
The issue of international financial system integration has appeared in many countries in the world. This is characterized by the closely linked together between financial markets in the global economy, regional, and neighbouring countries. It is a challenge to identify and determine the correlation of the Indonesia stock market with the other member of G- 20 countries where the major and emerging market are in it. The study aims to identify the correlation of Indonesia stock market and other member countries of G-20, as well as to identify either the volatility of other member countries in G-20 can lead the Indonesia \nstock market in the same movement in both short-term and long-term, or Indonesia tends to be more independent. The study utilizes the Autoregressive Distributed lag (ARDL) bound test to meet the objective of the study. This study uses Indonesia stock market as the dependent variable and other G-20 countries‟ stock market as the independent \nvariable. The data is taken on a monthly average from January 2015 to June 2020. The finding from this study shows that Indonesia stock market is cointegrated with the other member counties in G-20 in both short-term and long-term. The regression result shows in the short-term can be found that Australia, Germany, European Union, United \nKingdom, and the United Stated stock markets have a positive significant correlation. Then, Canada, China, France, Japan, South Africa stock market have a negative significant correlation. Meanwhile in the long-term can be found that Australia, Canada, Japan, Mexico, Russia, and the European Union have a negative significant correlation with the Indonesia stock market. Then, United States, United Kingdom, Turkey, South Africa, Saudi Arabia, and Germany have a positive significant correlation with Indonesia stock market. The degree of Indonesia open international trade with other G-20 member \ncountries, stock market size comparison to other countries' stock market size, and economic performance becomes to have a vital role in the degree of cointegration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".