Forecasting Health Sector Stock Prices using ARIMAX Method
Why this work is in the frame
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Bibliographic record
Abstract
In daily stock trading activities, stock prices can experience ups and downs. The rise and fall of stock prices occurs due to changes in supply and demand for these shares. The COVID-19 pandemic did not have a negative effect, instead it had a positive impact on stock prices in health companies. companies in the health sector experienced a fairly good profit of 10.46% in the fourth quarter of 2021. This fact made investors interested in buying shares in companies in the health sector in the hope of selling them when demand increased, resulting in doubled profits. Stock conditions continue to fluctuate every day, making investors need to pay attention and study the past data of the health sector company that will be selected before deciding to invest. Therefore, it is necessary to forecast stock prices in the health sector for the next several periods as a step in making investment decisions. The health sector companies that will be modeled are PT Kimia Farma (Persero) Tbk and PT Kalbe Farma Tbk. The method used in this study is the ARIMAX model. The test and analysis results show that based on the RMSE and MAPE values, the best model is ARIMAX(5,13) for PT Kalbe Farma Tbk shares with a MAPE value of 1% in in-sample data and 0.6% in out-sample data.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it