The Impact of Macroeconomic Factors on Indonesian Stock Market: Evidence from A VECM Analysis
Bibliographic record
Abstract
Objective: Macroeconomic variables continue to be a compelling subject for research, as numerous studies reveal inconsistent findings and a reliance on the fluctuations of capital markets and international financial markets. The objective of this research is to examine the impact of inflation, the interest rates set by Bank Indonesia, exchange rates, and economic growth on the fluctuations of the Composite Stock Price Index on the Indonesia Stock Exchange. Research Design & Methods: The research employs a quantitative methodology utilizing a Vector Autoregression (VAR) model. It implements the Vector Error Correction Model (VECM) technique with Eviews 10 analytical tools, utilizing secondary time series data derived from monthly intervals spanning from 2021 to 2024. Findings: In the long term, only the exchange rate has a significant impact on the IHSG. In the short term, there are substantial adjustment mechanisms leading towards long-term equilibrium, indicating that the model is dynamically stable. Partially, inflation has a significant positive effect on the IHSG. Meanwhile, the BI interest rate and the exchange rate have a significant negative impact, reflecting that an increase in interest rates and the depreciation of the rupiah suppress investment activity and the performance of the IHSG. GDP does not have a significant impact in the short term, suggesting that economic growth has not yet been fully reflected in the stock market. Implications & Recommendations: This finding emphasizes the need for adaptive monetary policy and effective financial policy coordination to respond to economic fluctuations, mitigate the negative impact of external shocks, and strengthen the resilience of the national financial system. Contribution & Value Added: This study provides the latest empirical evidence on the impact of GDP and monetary variables on the Indonesia Composite Index through a VECM approach and offers practical implications for investors and policymakers in maintaining the stability of capital markets and the financial system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".